Awareness of safety aspects Essay

Safety aspects, in relation to environment and equipment, include checking the area you are about to do your activity in, i.e. if you’re are going to play football, check there is no dangerous items such as large stones or glass on the pitch and to also check the equipment you are going to use to do your activity, to make sure it is all safe and provides proper protection, for example if you going to play football, check your football and goal posts if they are all in good condition, and ware adequate protection, for the game such as shin pads. We need to warm up for several reasons. One is to prepare mentally for the exercise we are about to do and to get our minds in the correct state to carry out the necessary physical exercises. The other is to warm up physically, to stretch the muscles so they are not all tensed up when we go to do physical activity. This makes our bodies a lot more flexible and a lot more prepared for a game situation. It is necessary to warm up the different muscle groups. For example, warming up your biceps and triceps by reaching for your toes and also warming up your hamstrings by doing squat thrusts to warm up your muscles. You should aim to warm up different muscles at different intervals, so as not to warm up your upper-body muscles all in one go whilst leaving out your lower-body muscles. First, for my warm up we will jog around the gym doing around 5 laps and while jogging around the gym I will be shouting out things i.e. left hand down, right hand down, get down and do 10 press ups and so on. After we have stretched off properly I will play a game called bull dog this game is really easy to play and enjoyable as well so how you play is two people (bull dogs) have to stand in the centre of the gym and shout out one of the persons name and what the person does is shouts out proper loud bull dogs so every one has to go from one end of the gym to the other without getting caught and if you do get court your a bull dog as well so this means that the last man standing wins. This game is good for warming you up because it includes a lot of running in the game. There are three phases to a warm up and they are  Pulse raiser: The aim of the pulse raiser is to elevate the heart rate and respiratory rate by doing light physical activity. This increases the blood flow and helps with the transportation of oxygen and nutrients to the working muscles. This also helps to increase the muscle temperature, allowing for a more effective static stretch. Stretches: Stretching is one of the most critical parts of the Warm Up and of my performance. A more flexible muscle is a stronger and healthier muscle. A stronger and healthier muscle responds better to exercise and activities and helps prevent athlete injury. Before sprinting I stretch for up to 7-12minutes.Bend both knees and put the soles of your feet on the floor to begin.  Leaving one leg bent, straighten the other leg and hold either, behind the thigh or if you are flexible behind the calf. Ensure the flats of your feet remain in contact with the floor at all times  For that additional stretch gently push the flat of the foot out in front of you into the floor.  Hold this stretch for 15 – 30 seconds and repeat on the opposite side. Gluteus Stretch Performed in a supine position. Lie flat out and bend your knees, placing the sole of your feet on the floor. Take leg (a) and place one foot on the knee of the opposite leg (b). Reach through the gap and around the outside of the leg (b), which is bent and on the floor, pull the leg in as far as, is comfortable. Feel the stretch in the buttocks of leg  Hold this stretch for 15 – 30 seconds  Gluteal stretch  Sit upright with your legs stretched out in front of you. Bend one leg (a) and place it over the top of the straight leg (b). Put the sole of the foot of leg (a) on the floor and hug the knee towards the chest. Feel the stretch in the buttocks on the side of leg. Hold this stretch for 15 – 30 seconds. A quadriceps stretch- flat on the floor  Lie face down, with your body in a straight line. Bend one leg aiming to put your heel on your buttocks.  Take hold of the ankle and relax in this position. To increase the stretches push your hips into the floor  Hold this stretch for 15 – 30 seconds  quadriceps stretch performed in a standing position  Ensure your supporting leg is slightly bent and use a wall for support if necessary. Bend your knee and take hold of the ankle of the leg, gently pull the ankle towards your buttocks. Keep your knees together and your head in line with your spine whilst performing this exercise.

Perkin Warbeck Essay

Explain why Perkin Warbeck remained a threat to the security of Henry VII for so many years. Perkin Warbeck was the second Yorkist pretender to the English throne after Lambert Simnel was defeated at the Battle of Stoke in 1487. Between the years 1491-99, he posed a significantly destabilising threat to Henry VII in dynastic terms, impersonating Richard of York, the younger son of Edward IV. The successes of Warbeck, which led him to remain a threat to the King’s security, may be largely categorised into the support from foreign powers during this period and discontent existing in England due to Henry’s methods of ruling with relation to sustaining an authoritative and respected status both within his own country and abroad. Whilst it is possible to challenge such a concept of a threat, Warbeck was eventually executed in November 1499, demonstrating in a modern context that the King’s perception of this pretender was one of great concern for his position. Steven Gunn has suggested that ‘Henry was a usurper and carried with him the problems of this title’. Upon taking the English throne, it was inevitable that Henry would face opposition to his rule, particularly in consideration of his lack of familiarity with English politics and those supporters of the Yorkist claim. With the knowledge that Richard III intended John de la Pole to inherit the crown and having already fought at the Battle of Stoke to secure his position, it would seem almost equally likely that Henry would be a paranoid character by the time that Perkin Warbeck came to attention, and he could not predetermine the reactions of the De la Pole family, who were largely responsible for the pretenders. It is significant not only that Warbeck was the second embodiment of discontent within Henry’s reign, but also that political unsettlement had been apparent under his predecessor – also a usurper. Henry is likely to have feared reaching the same fate as Richard III, whose reign had been plagued with suspicions surrounding the Princes in the Tower controversy and indeed, the dating of his coronation to appear three days before his victory at Bosworth suggests paranoia existing even before his rule. Thus Warbeck is best viewed in one sense as a non-military threat, being an encouragement of this paranoia through the exploitation of Henry’s potentially weak position. He reminded the King of the existence of dissatisfaction within his own country and later among foreign powers. The princes had never been seen again and were presumed murdered by their uncle, Richard III. In this way, there was an immediate simplicity for Warbeck to exploit the lack of clarification as to what had happened to them. Whereas Lambert Simnel’s claim was flawed in that Henry was able to parade the true Earl of Warwick through London during the crisis, Warbeck was imitating Edward IV’s younger son, Richard of York. Hence this crisis was much more serious, for Henry was unable to prove the existence of the true Richard and if Warbeck truly was the prince he impersonated, his claim to the English throne would have been superior to that of Henry. The threat of Warbeck was enlarged by the context in which it occurred – those behind the pretender were able to follow a potentially fatal method by which Henry’s position could be destabilised. In order to ascertain why any pretender to the throne would become a prolonged threat, the nature of the monarch’s rule must be evaluated. The view of Steven Gunn could be expanded upon to maintain that Henry’s rule naturally had characteristics of that of a usurper: unlike a hereditary monarch, he required more stringent methods of discipline to enforce his position as a ruler. In addition to passing attainders against those involved with the pretenders (Edmund de la Pole was fined à ¯Ã‚ ¿Ã‚ ½5000 to inherit some of his brother’s lands and never inherited the dukedom of Suffolk for his involvement with Simnel) Empson and Dudley greatly assisted in coordinating the increased use of bonds and recognisances against subjects. Then in 1497, Perkin Warbeck’s forces rallied the rebels during the second Cornish Rising in protest against raised taxes to support the invasion of Scotland and together, they nearly took the city of Exeter. In this way, it can be observed that Warbeck becoming a threat was in a paradoxical sense – Henry needed to use more powerful methods of rule to secure his own position and dynasty than an ordinary monarch due to his status, though the discontent which such methods brought about led to resentful individuals turning to the diversion of Warbeck. Threats in military terms from Perkin Warbeck during this period may be largely attributed to foreign support. Indeed, Warbeck potentially had concern also from nobility such as Sir William Stanley, as Margaret of Burgundy, sister of Edward IV, proclaimed him as her long-lost nephew. The role of France was crucial in causing Warbeck to remain a threat to the security of Henry’s position. In 1491, he moved to the French court and was treated similarly to a prince by Charles VIII, which illustrated to Henry a possibility of an invasion – the French had funded his own invasion of England in 1485 to overthrow Richard III, and the case could potentially have been that this claimant was genuine. The threat was heightened by Charles’ discontent in 1492 after Henry’s assistance of the Duchess of Brittany in an attempt to retain her independence from France, whilst the 1489 Treaty of Medina del Campo with Spain led the French to encourage James IV of Scotland to invade England. It was Henry’s invasion of France in 1492 which appeared to be a very drastic action with regard to this pretender – it was clear that with foreign military backing, his position could be significantly threatened. However, in this respect, it must be noted that the threat was not Warbeck as an individual, but rather the concept of the proceedings and those supporting him. He became a focus for French discontent with Henry VII in the same way that he had been a Yorkist focus in England. A prolonged threat from any pretender to any throne can always be attributed to discontent with a monarch’s rule. Conformingly, Perkin Warbeck was often able to exploit this to enlist support and increase opposition to the King. It is clear that the level of foreign support given to the pretender ultimately was the greatest contributing factor in threatening Henry’s security due to the possibility of a successful invasion, though the paranoia and the methods of rule enlisted to secure his dynasty were equal factors in causing Warbeck to remain a threat. In contrast, it is very significant that Warbeck was never a primary threat as an individual, but merely a strong secondary threat behind the foreign powers and nobility that supported him – it was the concept that destabilised the position of Henry VII.

Summer Sport Camp at State University

14th MANCO Linear Programming Approach for Irrigation Scheduling – A case Study H. MD. AZAMATHULLA, Senior Lecturer, River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia; email: [email  protected] usm. my, [email  protected] com (author for correspondence) AMINUDDIN AB GHANI, Professor, REDAC, Universiti Sains Malaysia, email: [email  protected] usm. my NOR AZAZI ZAKARIA, Professor, REDAC, Universiti Sains Malaysia, email: [email  protected] usm. my CHANG CHUN KIAT, Science Officer, REDAC, Universiti Sains Malaysia, email: [email  protected] sm. my Abstract There is an increasing awareness among irrigation planners and engineers to design and operate reservoir systems for maximum efficiency to maximize their benefits. Accordingly, significant work has been done on reservoir operation for known total irrigation demand and on the optimal allocation of water available to c rops at the farm level. Very few studies have been conducted to derive optimal reservoir operation policies integrating the reservoir operation with the on-farm utilisation of water by the various crops.This present paper deals with the development of model — Linear Programming (LP) — to be applied to real-time reservoir operation in an existing Chiller reservoir system in Madhya Pradesh, India. Keywords: Cropping pattern, Water resource management, Irrigation management, Optimization 1. Introduction In most developing countries, a huge share of the limited budget goes to creating facilities for irrigation. Construction of reservoirs requires very high investment and also causes socioeconomic and environmental issues.Water in the reservoir has multiple claimants and needs to be optimally utilized to generate maximum benefits through proper operation, which must remain consistent despite uncertain future inflows and demands. According to the World Commission on Dams, ma ny large storage projects worldwide are failing to produce the anticipated benefits (Labadie, 2004). Similarly, small storage projects made for local areas in developing countries, like India, are also failing to meet expectations.The main cause identified at various levels of discussion, as reported by Labadie (2004), is inadequate consideration of the more mundane operation and maintenance issues once the project is completed. For existing reservoirs, optimum operation is critical, since all the expected benefits are based on timely water releases to meet the stipulated demand. Real-time operation of a reservoir requires making relatively quick decisions regarding releases based on short-term information. Decisions are dependant on the storage in the reservoir and information available in the form of forecast hydrologic and meteorological parameters.This is especially important during floods and power generation, where the system has to respond to changes very quickly and may need to adapt rapidly (Mohan et al. 1991). For reservoir systems operated for irrigation scheduling, real-time operation is not very common because of longer decision steps. Traditionally, the reservoirs meant for irrigation purposes are operated on heuristics and certain rules derived from previous experiences. This defies the concept of water-management; much of the water is lost, which in turn leads to loss of revenue.In the early 1960s, mathematical programming techniques became popular for reservoir planning and operation; pertinent literature is available. An excellent review of the topic is given by Yeh (1985), followed by Labadie (2004) and Wurbs (1993). Along with simulation studies, Linear Programming (LP), Dynamic Programming (DP) and Non Linear Programming (NLP) are the most popular modelling techniques. A comparative study on the applicability and computational difficulties of these models is presented by Mujumdar and Narulkar (1993).Many of the aforementioned techniques ha ve been implemented in realistic scenarios, and many reservoir systems worldwide are operated based on the decision rules generated from these techniques. However, there exists a gap between theory and practice, and full implementation has not been achieved yet (Labadie, 2004). 1 14 & 15 February 2009 Kuching, Sarawak The basic difficulty a reservoir manager faces is to take a real-time optimum decision regarding releases according to the future demand and inflow. This leads to the problem of optimization of the stochastic domain.Two approaches of stochastic optimization are practised: i) Explicit Stochastic Optimization (ESO), which works on probabilistic descriptors of random inputs directly and ii) Implicit Stochastic Optimization (ISO), which is based on historical, generated or forecasted values of the inputs through the use of Time Series Analysis or other Probabilistic approaches. The ESO approach has computational difficulties; ISO methods are simple, but require an addition al forecasting model for real time operation. In the case of irrigation reservoirs, decision making at the reservoir level depends upon the water demand arising at the field level.In order to operate the reservoir in the best possible way, it becomes imperative to understand the processes occurring in the crop-soil-water-atmosphere system. This helps not only in the estimation of accurate demands, but also ensures optimum utilisation of water. If the processes at the field level are also modelled properly and integrated with the reservoir level model, the goal of water management can be achieved in the best possible way. Dudley et al. (1971) pioneered the integration of the systems in the determination of optimal irrigation timing under limited water supply using a Stochastic DP model.Dudley and his associates then improved the model (Dudley and Burt, 1973; Dudley, 1988; Dudley and Musgrave, 1993). Vedula and Mujumdar (1992, 1993) and Vedula and Nagesh Kumar (1996) have also contrib uted to this area. Their approach was to derive a steady state reservoir operation policy while maximizing the annual crop yield. DP-SDP and LP-SDP were used in the modelling. However, for real-time reservoir operation, Vedula and Nagesh Kumar (1996) stressed the need to forecast inflows and rainfall in the current season to implement the steady state operation policy.As a result, the ESO model has to be supplemented with an ISO model to get a policy for the current period. As an extension to the work of Vedula and Mujumdar (1992), a significant contribution to the real-time reservoir approach was presented by Mujumdar and Ramesh (1997). They addressed the issue of short term real-time reservoir operation by forecasting the inflow for the current period, a crop production state variable and a soil moisture state variable. Their work was based on SDP, but had all the limitations of SDP regarding the curse of dimensionality.Against this background, a model for the derivation of real-t ime optimal operating policy for a reservoir under a multiple crop scenario is proposed in the present study. The primary issue is that the reservoir gets inflows during the wet season (monsoon season) and is operated for irrigation in the dry season (non-monsoon season). The reservoir storage and the soil moisture level are considered to be the principal state variables, and the irrigation depths are the decision variables.An optimal allocation model is embedded in the integrated model to evaluate the irrigation water depth supplied to different crops whenever a competition for water exists amongst various crops. The model also serves as an irrigation-scheduling model because it specifies the amount of irrigation for any given fortnight. The impact on crop yield due to water deficits and the effect of soil moisture dynamics on crop water requirements are taken into account. Moreover, a root growth model is adopted to consider the effects of varying root depths on moisture transfer. The only stochastic element in the season is the evapotranspiration. The handling of stochasticity has been accomplished through dependability based forecasting in an ISO model. The rest of the variables, such as soil moisture status and the reservoir storage status, at the beginning of any period are considered to be state variables. The basic formulation is based on a LP model and is later transformed into a GA framework. 2. The Model Formulation and Concept The real-time operation model proposed in the present study integrates the reservoir level and a field level decision (Figure 3).It considers the soil-moisture status and the reservoir storage as the state variables and the applied irrigation depths as decision variables. The formulation is based on the conceptual model for soil moisture accounting and the reservoir storage continuity relationships. A major emphasis is laid on maintaining soil moisture in a state such that the evapotranspiration from the crops takes place at a rate that achieves better results in the form of increased yields from the crops. To assess the timing of irrigation water application, the soil moisture status of the crop is an important parameter.Whenever the soil moisture status approaches a critical limit, irrigation is applied. Thus, the soil moisture status is monitored either by physical measurement or through soil moisture models. Soil moisture models are more popular since they do not require a lot of instrumentation to be installed in the field. Soil moisture models can be formulated either by a physical approach (Fedders et al. , 1978) or a conceptual approach (Rao, 1987). The conceptual approach has been used by Rao et al. (1988), Rao et al. (1990) and 2 14th MANCO Hajilal et al. (1998) for the problem of irrigation scheduling.Vedula and Mujumdar (1992) utilised the conceptual model in their study. The same concept is adopted in the present study. Figure 3 Flow chart of real-time operation of reservoir 3 14 & 15 Februa ry 2009 Kuching, Sarawak 3. The Conceptual Model In the conceptual model for the Crop-Soil-Water-Atmosphere (CSWA) system, the basic assumption is that the soil acts as a reservoir, the main inputs to the reservoir are rainfall irrigation, and the main outputs are evapotranspiration, percolation and drainage. The extent of the reservoir is considered to be up to the effective root zone at the particular time.The soil water reservoir is governed by a continuity equation: ? ik +1 ED ik +1 ? ? ik ED ik ? IRR ik + AET i k = RF k (1) The conceptual model stated by Eq. 1 is used to compute the irrigation to be applied for the LP model with area as a decision variable. The following parameters are important for the conceptual model. Figure 1 shows the sketch for the conceptual reservoir. In the context of the conceptual model two parameters are important: IRRk RFk AETk EDk ?k Figure 1 Conceptual model Variation of Evapotranspiration with the Available Soil Moisture Evapotranspiration as a function of the available soil moisture is expressed as: kAETi k = PETi k if aai ? Zww (2) or AETi k = k aai PETi k Zww where AETi k (3) is the actual evapotranspiration that has occurred from crop i in fortnight k (mm), PETi k is the potential evapotranspiration in a particular geographical location (mm), Zww is the critical available moisture limit (mm/cm) = (Zf? Zw) d, Zf is the field capacity for the soil (mm/cm), Zw is the permanent wilting k point for the soil (mm/cm), d is the depletion factor and assumed to be 0. 5 in the present study, and a ai is the average available soil moisture over a fortnight (mm/cm). The average available soil moisture over a fortnight is given by ik + aik +1 a= 2. 0 k ai where otherwise aik = ? ik ? Zw if aik < Zww aik = Zww k +1 A similar expression can be used for ai . 4 14th MANCO Root Zone Depth Growth The root depth data in relation to the time stages are prepared according to the Linear Root Growth Model (adopted by Narulkar, 1995). The model assumes that maximum root depth is achieved at the start of the yield formation stage. It remains at the maximum depth until the maturity stage. A minimum depth of 15 cm is considered in the first fortnight to account for the conditions of bare soil and an area with sparse crops.The root depth model is shown in Figure 2. Life span of group Growth stages of group V F G Root Depth Max. Depth Figure 2 Root Depth growth model Relative Yield Ratio The yield of a crop is affected by water deficits and the rate of evapotranspiration. The rate of evapotranspiration tends to decrease depending on the available moisture content. There are many methods to model the phenomenon. However, the model used in the present study is the most commonly-adopted model. The relative yields are computed on the basis of the expression given by Doorenbos and Kassam (1979) YaiAETi k ? k? = 1 ? Ky ? 1 ? ? PET k ? ? Ymi i? ? (4) Equation (4) gives a yield ratio for a single period only. However, the aggregate ef fect of moisture deficits over all fortnights of crop growth is also evaluated. The final yield ratios computed for the crop during various time periods of a season is computed by a multiplicative model (Rao et al. , 1990). The determination of the yield ratio is very important since they reflect the operation policy for an irrigation system. The expression is given by ? AETi k Yai ncr ? = ? ?1 ? Ky k ? 1 ? ? PET k ? Ymi i =1 ? i ? (5)Water Requirements of the Crops The model derived for an optimal crop pattern uses predetermined irrigation demands. On the basis of this, the optimisation model selects an appropriate area for an individual crop. The irrigation demands are determined using the conceptual model stated in Eq. 1. The irrigation requirements may be calculated by substituting a value of critical soil moisture content instead of soil moisture in either of the fortnights k and k+1 and replacing the values of actual evapotranspiration by potential evapotranspiration and re arranging the terms of Eq. : ( ) IRRik = ? cr EDik +1 ? EDik + PETi k (6) 5 14 & 15 February 2009 Kuching, Sarawak where ? cr is the critical soil moisture content below which the actual evapotranspiration may fall below the potential rate. 4. Integrated LP Formulation In the objective function, the weighted sum of all the actual evapotranspiration values is maximised. The weights are assigned according to the yield response factors for individual crops in individual periods. The objective is to maximise the actual evpotranspiration rate to minimise the deficits in the yields.The available soil moisture in any time period in the objective function is indirectly maximised: ncr np ? a k + aik +1 ? Ky k MaxZ = ? ? ? i ? 2. 0 ? Zww i =1 k =1 ? (7) subject to the following constraints: 1. Soil moisture continuity ? aik + aik +1 ? PET = RF k ? 2. 0 ? Zww ? ? ik +1 EDik +1 ? ? ik EDik ? IRRik + ? (8) ? ik +1 ? aik +1 ? bik +1 = ZW (9) where with physical bounds ? ik +1 ? 4. 0 a 2. k +1 i ( 10) ? 0. 9 (11) Reservoir continuity ncr A k S k +1 ? B k S k + ? i =1 S k +1 ? 31. 1 5. IRRik * AREAik = ? ID ? Ao RE k Eff (Maximum Reservoir Capacity M m3) (12) (13) Crop Simulation ModelThe optimisation model presented above yields some irrigation depth values that are based on forecasted values for the reference evapotranspiration. This reference evapotranspiration, in turn, is based on a dependability model. However, the actual evapotranspiration value differs from these values, and thus, before going into the next fortnight, the soil moisture status must be updated with the applied irrigation and actual climatic factors. The formulation for crop simulation is as follows: First compute the final soil moisture with the following relation ? ik = (? ik +1 EDik +1 + IRRik ?Fkcik APET k + ARF k ) / EDik If (14) ? ik +1 < 3. 1 ?k ? Fkcik +1 APET k +1 Fkcik +1 APET k +1 ZW + ARF k +1 ? ? i EDik + IRRik +1 ? + ? 2. 0 2. 0 ? EDik +1? ik +1 = ? k +1 k +1 Fkci APET EDik +1 2. 0 ( ) (15) or 6 14th MANCO ? ? ik = ? ik ? 1 ? EDik ? 1 ? ? Fkcik APET ? Fkcik APET Fkcik APET + Zw + ARF k + IRRik ? ? EDik ? 2 . 0 2 . 0 2 . 0 ? (16) or ? k ? 1 ? k ? 1 Fkcik APET ? Fkcik APET Fkcik APET ? k k ? ? = i ? EDi ? Zw? ? ? EDi ? ? + IRRi + ? ? 2. 0 2. 0 2. 0 ? ? ? ? k i (17) The computed soil moisture status of the crops is used in the next fortnight to compute the demand. . Stochastic Analysis of Evapotranspiration It was previously stated that the data regarding the climatic factors is uncertain in nature and the determination of these factors beforehand is impossible. However, there is a general trend to assume the expected values for these factors and carry out the operation. The concept does not give a clear picture of the actual scenario and the appropriate weights for the individual growth stage of the crops are not assigned. The present study proposes a different method of forecasting the expected values for the climatic factors.The method of analysis starts with the co mputations of dependability values of reference evapotranspiration factors from the available data. The dependability of realisation of any stochastic variable is defined as the probability of equalling or exceeding that variable with a particular value. Mathematically, P(x ? X ) (18) where P (. ) is the probability and x is the variable under consideration and X is a stipulated value of the variable. A traditional method of estimation of the dependability value is the use of standard frequency formulae (e. . Wiebull’s formula or Hazen’s formula). In the present study, a detailed probability analysis for the data is performed. The data is fitted to a standard probability distribution and the best fitting distribution is tested through the Kolmogorov Smirnov Test (Haan, 1977). Once the values corresponding to different dependabilities are evaluated, dependability values for reference evapotranspiration are assumed to be different in different growth stages. The analysis is performed on the basis of the yield response factor.A high yield response factor signifies greater sensitivity towards the deficits, and thus, a higher level of dependability is assumed for the evapotranspiration data and a lower level of dependability is assumed for the rainfall data. This will ensure a higher value of irrigation required for the crop in the sensitive period. As a result, the crop will be safeguarded against any poor moisture content conditions. 7. LP Model Formulation for Optimal Cropping Pattern At the start of each dry season, depending on the storage volume in the reservoir, the crop pattern must be determined.To evaluate the crop pattern, another LP model is used. In this model, irrigation depths are calculated from Eq. (6). The formulation is as follows: The objective function is MaxZ = C1 X1+ C2 X2+ C3 X3 (19) which is subject to the following constraints: 1. Total available area X1+X2+X3? A (20) where X1, X2, and X3 are the decision variables related to the area of individual crops;C1, C2, and C3 are the cost coefficient for each crop in Indian Rupees (1 US $ = 50 INR); and A is the maximum area available for irrigation. 2.Area of each individual crop: 7 14 & 15 February 2009 Kuching, Sarawak The area under each crop is required to be constrained; thus, there are lower and upper bounds on the area under each crop. The lower bounds indicate the minimum area that can be allocated to a crop, while the upper bound indicates the maximum. In the present study, the lower bounds were defined for all the crops except cash crops, while the upper bounds were defined considering the present cropping pattern. The constraints can be expressed as Li? Xi? Mi (21) here Li corresponds to the lower bound of the area for the ith crop and Mi corresponds to the upper bound on the area of the ith crop. 8. Model Application The developed models were applied to the Chiller reservoir system in Madhya Pradesh, India (Latitude 23o23’ N and Longitude 7 6o18’ E). In the central part of India, many reservoir projects have been constructed for irrigation, but no irrigation is available from these reservoirs during the monsoon period (from June to September). The area receives about 90 to 95 % of its rainfall during the Monsoon season. The rainfall then becomes runoff to the reservoirs.These reservoirs are designed to contain the runoff in the monsoon season, but there is no runoff during non-monsoon months. The present formulations are specially suited for these types of reservoirs. Non-monsoon rainfall is rare and provides little runoff. A systematic data base was prepared for the various physical features of the reservoirs, including the meteorological and hydrological data such as evapotransiration, details of crops in the command area, details of net returns from individual crops and soil properties collected from the College of Agriculture, Indore, India. . Results and Discussion Optimum Crop Pattern A separate computer p rogram was run before the real time operation program to determine the optimum crop pattern for all possible storage values. The results of the optimum crop pattern are stated in Table 1. The results indicate that from a storage level of 31. 10 M m3 to a storage level of 26. 06 M m3, the cropping pattern is same as the one that has been adopted in the project formulation. However, below a storage level of 26. 06 M m3, the crop pattern changes suddenly, and wheat (ordinary) is not recommended by the model.The area of wheat (hybrid) also gets reduced when the rainfall storage is below this level. However, the area for Gram is full, up to a storage level of 15. 83 M m3. The change in cropping pattern indicates that efficient water usage is maintained. Table 1 Optimum Cropping Pattern for Different Live Storage Values Area (ha) for different crops Live storage (M m3) Wheat (ordinary) Gram Wheat (hybrid) 4. 3230 342. 910 120. 00 8. 2379 427. 580 500. 00 12. 3246 15. 8632 20. 7581 26. 098 6 28. 8610 30. 1250 31. 1000 300. 0 300. 0 300. 0 300. 0 1084. 015 1100. 000 1100. 00 1100. 000 1100. 000 1100. 000 1100. 000 500. 00 855. 00 1434. 00 1700. 00 1700. 00 1700. 00 1700. 00 Results from Real-Time Operation Model The real-time operation model gives an optimal operating policy for the available storage in the present fortnight considering the future. The model also yields the values of irrigation to be applied to individual crops in the fields. In the wake of deficient water supplies, the model distributes the available water over the time for different crops optimally. The sample results of the present model are stated in Table 2.The available moisture to the crops is not affected, and generally the soil remains at the upper limit of the available soil-moisture. This 8 14th MANCO is because the crop pattern is predicted according to the availability of the storage in the reservoir. The results are indicative of successful application of the real-time operation strategy proposed in the present work. Table 2 Sample Results Showing the Soil Moisture, Available Soil Moisture, Storage, and Irrigation to be applied for Different Crops for a Real-Time Reservoir Operation Model (LP) Live Storage in the Reservoir 31. 1 M m3 FORTNIGHTPARAMETER 1 2 3 4 5 6 7 8 9 10 11 Reservoir Storage (M m3 ) 29. 28 28. 17 26. 30 22. 22 Crop 1) Soil Moisture (mm/cm) 3. 76 3. 89 3. 84 3. 07 2) Available soil Moisture 0. 9 0. 9 0. 9 0. 87 (mm/cm) 3) Applied Irrigation (mm) 53. 62 90. 63 92. 87 36. 04 Crop 1) Soil Moisture (mm/cm 3. 90 3. 07 3. 28 3. 15 2) Available soil Moisture 0. 9 0. 87 0. 9 0. 9 (mm/cm) 3) Applied Irrigation (mm) 68. 76 22. 27 60. 67 41. 59 Crop 1) Soil Moisture (mm/cm — – 4. 00 2) Available soil Moisture —0. 9 (mm/cm) 3) Applied Irrigation (mm) — – 94. 21 19. 68 14. 64 10. 87 Wheat (ordinary) 3. 54 3. 30 3. 22 0. 9 . 9 0. 9 5. 62 4. 24 3. 63 3. 60 3. 17 0. 9 4. 0 0. 9 – -. — — 163. 9 8. 44 23. 02 GR AM 3. 28 3. 66 0. 9 0. 9 19. 94 102. 6 — — 3. 23 0. 9 3. 47 0. 9 — — 37. 64 53. 15 Wheat (hybrid) 3. 06 3. 48 3. 32 0. 86 0. 9 0. 9 0. 00 33. 17 — — 3. 28 0. 9 3. 38 0. 9 3. 18 0. 9 3. 19 0. 9 37. 19 162. 9 0. 00 36. 09 0. 0 3. 4 0. 9 26. 96 127. 9 78. 89 Relative Yield Ratios Relative yield ratios computed for different crops at different live storage values are shown in Table 3. The relative yield ratios for all the crops become one if live storage in the reservoir is equal to or greater than 28. 9 M m3. The GA model is found to be better for application in real world operation of the reservoir. Table 3 Relative Yield Ratio for Different Live Storage Values Computed With a Real-Time Reservoir Operation Model Relative yield ratio for Live different crops storage LP (M m3 ) Wheat Gram Wheat (hybrid) (ordinary) 4. 3230 0. 9677 1. 000 8. 2362 0. 9083 1. 000 12. 3246 0. 9576 1. 000 – 0. 989 1. 000 20. 7581 26. 0986 1. 000 0. 987 0. 987 0 . 911 0. 952 28. 8610 1. 000 0. 987 1. 000 30. 1250 31. 1000 10. – 15. 8632 1. 000 1. 000 1. 000 1. 000 1. 000 1. 000 ConclusionA real-time model using an integrated Linear Programming Model for a reservoir system meant for irrigation has been developed in the present study to obtain an optimal reservoir operating policy that incorporates field level decisions, while also deciding the appropriate time and amount of water to release from the reservoir. 9 14 & 15 February 2009 Kuching, Sarawak From the analysis, the following conclusions can be drawn: The developed model can be successfully applied to irrigation supporting reservoir systems. Furthermore, the models ensure an optimum reservoir release over different time periods.In addition, they also ensure optimum allocation of the available water over the different crops in the fields. While allocating the water to different crops in the fields, the model takes into account the critical growth stages of the crops and allocate s sufficient water to each crop to safeguard it against any ill effects of water deficits. The optimum crop pattern model used in the study will only allow productive irrigation, so the amount of wasted water is reduced. Acknowledgements The authors would like to express sincere thanks to Universiti Sains Malaysia for the financial support of this work.Nomenclature AETi k k Actual evapotranspiration in period k from crop i (mm) APET ARFk Ak and BK Ao d Actually occurring potential evapotranspiration in period k (mm) Actual rainfall value in the fortnight k Constants relating the storage to reservoir evaporation Area of spread at dead storage level Depletion factor EDik Effective root zone depth of a crop i in period k (cm) k +1 i ED Effective root zone depth of a crop i in period k+1 (cm) Eff Fkcik ID Overall efficiency Crop evapotranspiration coefficient Industrial supply from the reservoir (mandatory release) IRRikIrrigation applied to crop i in stage k (mm) k Ky Yield response fa ctors for a crop i in period k PETi k RE RF k Potential evapotranspiration in a particular geographical location (mm) Rate of evaporation in fortnight k k Sk Sk+1 Zf Zw Zww Rainfall in period k (mm) Reservoir storage at the beginning of period k Reservoir storage at the end of period k Field capacity for the soil (mm/cm) Permanent wilting point for the soil (mm/cm) Critical available moisture limit (mm/cm) ? ik ? ik +1 Final soil moisture in a particular time stage k for a particular crop i (mm/cm) Yai Ymi Actual crop yield Maximum crop yieldInitial soil moisture in the time stage k in for a crop i (mm/cm) 10 14th MANCO References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. Doorenbos, J. , and Kassam, A. H. (1979). Yield Response to Water. Irrigation and Drainage Paper, 33, FAO, Rome. Dudley, N. J. , Howell, D. T. , and Musgrave, W. F. (1971). Optimal intraseasonal irrigation water allocation. Water Resour Res. , 7(4), 770-7 88. Dudley, N. J. and Burt O. R (1973). Stochastic reservoir Management and system design for irrigation, Water Resources Res. 9(3), 507-522. Duldley, N J. 1988). A single decision-maker approach to irrigation reservoir and farm management decision making, Water Resources Res. , 24(5) 633-640. Dudley, N. J. and Musgrave, W. F. (1993). Economics of water allocation under certain conditions. In Biswas, A. K. ; et al. , ed. Water for sustainable development in the twenty-first century. Oxford University Press, Delhi. Fedders, R. A. , Kowalic, P. S. and Zarandy, H. , (1978). Simulation of field water use and crop yield. Centre for Agricultural Publishing and Documentation, Wganingen. Haan, C T. (1977). Statistics methods in hydrology, Iowa State Press, Iowa.Hajilala, M. S. , Rao, N. H and Sarma, P. B . S. (1998). Real time operation of reservoir based canal irrigation systems, Agricultural Water Management, 38, 103-122. Holland, J. H. , (1975). Adaptation in natural and artificial syste ms, University of Michigan Press, Cambridge Mass. Labadie, J. W. (2004). Optimal operation of multi reservoir systems: State-of-the-art review, J. Water Resour. Plan. Manage. 130(2), 93–111. Mohan, S. , Raman, S. , and Premganesh, G. , (1991). Real-time reservoir operation. IWRS, 11(1), pp. 35-37. Mujumdar, P. P. , and Sandeep Narulkar. , (1993).Optimisation models for multireservoir planning and operation†, Hydrology Review, Vol. VIII, No. 1, pp. 29-52. (Pub: Indian National Committee on Hydrology, Roorkee, India) Mujumdar, P. P. and Ramesh, T S. V. , (1997). Real time reservoir operation for irrigation, J. Water resources research, Vol. 33, No 5, 1157-1164. Narulkar, S. M. (1995). Optimum real-time operation of multi reservoir systems for Irrigation scheduling. Ph. D Thesis submitted at I. I. T. , Bombay, India Oliveira, R. and Loucks, D. P. , (1997). Operating rules for multi reservoir system, Water Resources Research 33(4), 839–852. Rao, N. H. , (1987).Field test for a simple soil-water balance model for irrigated areas. J. of Hydrology, 91, 179-186. Rao, N. H. , Sarma, P. B. S. and Chander, S. (1988). Irrigation scheduling under a limited water supply. Agri. Water Management, 15, 165-175. Rao, N. H. , Sarama, P. B. S. , and Chander, S. (1990). optimal multicrop allocations of seasonal and interseasonal irrigation water. Water Resour. Res. , 26(4), 551-559. Reddy, J. M. and Nagesh Kumar, D. (2006). Optimal reservoir operation using multi-objective evolutionary algorithm, Water Resources Management, Springer, 20, No. 6, 861-878. Reddy, J. M. and Nagesh Kumar, D. (2007).Optimal reservoir operation for irrigation of multiple crops using elitist-mutated particle swarm optimization, Hydrological Sciences Journal, IAHS Press, UK, Vol. 52, No. 4, 686-701, Sharif, M. and Wardlaw, R. (2000). Multireservoir System Optimization Using genetic algorithms Case Study. J. Comp. in Civ. Engrg. ASCE, 14(4), 255–263. Shie-Yui L. , Al-Fayyaz T. A. , and Sai L. K. (2004). Application of evolutionary algorithms in reservoir operations, Journal of the Institution of Engineers, Singapore, 44(1), 39-54. Vedula, S. and Mujumdar, P. P. (1992). Optimal Reservoir Operation for Irrigation of Multiple Crops. Water Resour. Res. 28(1), 1-9. Vedula, S. and Mujumdar, P. P. (1993). Modelling for Reservoir Operation for Irrigation. Proceedings of Intl Conf. on Environmentally Sound Water Resources Utilisation, Bangkok. Vedula, S. and Nagesh Kumar, D. (1996). An integrated model for optimal reservoir operation for irrigation of multiple crops, Water Resources Research, American Geophysical Union, 32, (4), 1101-1108. Wurbs, R. A. (1993). Reservoir system simulation and optimization models. J. Water Resource Manage. ASCE 119 (4), 455–472. Yeh, W. W. G. (1985). Reservoir management and operation models: A State of the Art Review, Water Resour. Res. 21(1), 1797-1818. 11

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Implement a marketing solution (case study) Case Study

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Healthy Eating is Healthy Living Essay Example for Free

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Pressure Distribution around Circular Cylinder Lab Report

Pressure Distribution around Circular Cylinder Lab Report The aims of the investigation is to measure the pressure distribution on the surface of a smooth cylinder placed with its axis perpendicular to the flow and to compare it with the distribution predicted for frictionless flow, and to calculate the drag coefficient of the cylinder. In the investigation being carried out, a cylinder in a closed circuit wind tunnel will be experimented upon to gather the pressure distribution acting on it at different speeds. When the cylinder is standing vertically to the incoming flow in a wind-tunnel, two experiments will be carried out for the same cylinder, one with smooth (laminar) flow and the other with turbulent flow. The experimental pressure distribution obtained from each experiment will be compared with the theoretical distribution predicted for frictionless flow. The drag coefficient for the cylinder will be calculated together with the tunnel calibration constant for both tests. The smooth cylinder has got 12 pressure tappings at angular intervals of 30 ° on its surface; it is also placed with its axis vertical on a turntable on the floor. These tappings are connected to multi-tube (methylated spirits) manometer, which is inclined at an angle of 30 ° to the horizontal. The multi-tube manometer has got a total of 34 tubes, out of which the first 12 are directly connected to the pressure tappings on the cylinder, so that tube 1 is connected to pressure tapping 1 and so on. Pressure tapping 1 is facing the oncoming flow when the angular position indicator is set at 0 °. Since pressure tapping 1 is connected to tube 1 in the multi-tube manometer, the head pressure shown on tube 1 will represent the stagnation pressure. Tube 34 in the multi-tube manometer is connected to the upstream part of the wind-tunnel. The Betz manometer is used to change the incoming flow velocity at the upstream section. Since both the Betz manometer and tube 34 (in the multi-tube m anometer) are connected to the upstream section, both will show the same equivalent reading for pressure but in different units. Background theory: Laminar flow is defined when a fluid flows in parallel layers, with no disruption between the layers. In comparison to this Turbulent flow has a much more disorganized pattern, it is characterized by mixing of the fluid by eddies of varying size within the flow. The Reynolds number (Re), gives the measure for laminar and turbulent flows. Laminar flow takes place when Reynolds number is lower than 104, and for Turbulent flow the Re must be greater than 3ÃÆ'-105. Reynolds number has got no units since it is just a ratio. There are many diverse types of equations for deriving the Reynolds number of an actual shape. Fig. A shows the different types of flow patterns at various angles. The appropriate equation for the cylinders Reynolds number can be acquired from: Re = [eq. 1] Where: d = Diameter of cylinder V蠁 ¾ = Velocity of fluid upstream v = Kinematic viscosity of air (1.46ÃÆ'-10-5 m2s-1) From the equation both values of d and v stay constant for both experiments, therefore the change in Reynolds number depends directly on the upstream velocity of the wind-tunnel. From the Bernoullis equation the relationship between fluid pressure and velocity can be established [eq.2] Where: P = pressure of the fluid Z = Height à ¯Ã‚ Ã‚ ²air = air density à ¯Ã‚ Ã‚ ² = The density of the fluid G = Gravity V= the velocity of the fluid The height in remains constant therefore the flow in the wind tunnel has an equation. [eq.3] By dividing both sides with à ¯Ã‚ Ã‚ ² and taking measurements from the point where the flow of velocity stops, (the stagnation point where V2 = 0). This is for the reason that at the stagnation point on the surface of the tube that is perpendicular to the flow to it, therefore the dynamic pressure is given by: [eq. 4] The pressure is measured using the manometer, and then therefore the pressure at the tapping must be the same as the pressure head at à ¯Ã‚ Ã‚ ²gH. Then the stagnation head and static head measured on the multi-tube manometer Inclined at a specific angle are given by: [eq.5] [eq.6] So when using these two equations the pressure differences found using them become. [eq.7] The dynamic pressure upstream of the cylinder is gained from: [eq.8] In the equation above k = the tunnel calibration constant. The pressure change across the wind tunnel contraction is measured using two different instruments; the Betz manometer and the Multi-tube manometer (tube no. 34). In an ideal system where there are no losses in energy. In procedures with energy losses, the ratio value is below 1. The relation is known as the tunnel calibration constant (k). is the pressure variation across the contraction as displayed by the reading on the Betz micro-manometer. The tunnel calibration constant can be attained directly from: [eq.9] Differences between the pressure at angles from the front of the stagnation point and the free steam pressure Pà ¯Ã¢â‚¬Å¡Ã‚ µ is gained from the following equation: [eq.10] hà ¯Ã‚ Ã¢â‚¬   is a reading on tube 1 when pressure tapping number 1 is at an angle from the front of the stagnation point. Therefore the pressure coefficient at an angle can be defined: [eq.11] To find the pressure coefficient based on frictionless flow by using: [eq.12] The equation above is applied when plotting a graph, of variation of cÃŽÂ ¦ vs. ÃŽÂ ¦, by replacing values of angles into the equation above and then finding out the subsequent values for pressure coefficient (cÃŽÂ ¦). The consequent graph drawn from the derived equation will only correspond to a theoretical relationship, where the flow is believed to be frictionless. In the illustration below, the pressure in the wake region is less than pressure upstream; this causes drag, mainly due to flow separation behind the body. The streamline pattern and the pressure distribution are not balanced and a wake of slow-moving air is produced behind the cylinder. Fig. B shows flow separation taking place behind the cylinder The drag force, due to the pressure forces on the cylinder can be derived from: [eq.13] As the term integrates to zero, the drag coefficient can be simplified to: [eq.14] Apparatus The cylinder being experimented on is placed in the wind tunnel. The part that will be under testing will be of size of 1.000m X 0.760m. The wind tunnel will have a contraction ratio of 5.6 To connect the pressure tappings from upstream and downstream of the tunnel contraction to a Betz micro-manometer (mmH2O). The velocity of air in the test section is to be fluctuated by adjusting the fan speeds on a controller. Thirdly a smooth circular cylinder with diameter 114.3 mm to be placed with its axis vertical, on a turntable on the ground of the test section area. It can be seen in the cylinder where halfway along there are pressure tappings at angular intervals of 30à ¯Ã¢â‚¬Å¡Ã‚ ° on its surface, near to the tappings are marked numbers from 1 to 12, these are connected to the upper ends of 1 to 12 tubes on a manometer. This manometer is to be a multi-tube methylated spirits manometer. The pressure upstream of the cylinder is sensed by a taping on the tunnel wall and is connected to one of the tubes. In this experiment to be tube number 34. The remaining tubes 13 to 33 are open to the atmosphere. The level of turbulence has to be changed, so in this test section it is small however to be increased by the insertion of a grid. This grid as an array of circular rods upstream of the test section. Lastly as the cylinder is to be placed on the turntable that is to be rotated. The angular position of pressure tapping number 1 is indicated on a digital counter in degrees and in tenths of degree. Method: To be able to do this experiment the cylinder to be already oriented so that the pressure from tapping 1 is facing the oncoming flow and the angular position indicator will have to be set at 0à ¯Ã¢â‚¬Å¡Ã‚ °. As we know that the pressure P1 tapping is the stagnation pressures and exceeds the pressure Infinity upstream of the cylinder by an amount. The experiment firstly to test laminar flow The laminar flow of the velocity of the wind tunnel will be increased gradually until the Betz manometer reads 15mH2O. For this velocity to remain constant adjustments are made. From the multi-tube manometer, to take readings of fluid heights to show a general idea of the pressure distribution. The fluid height to be noted from the tube which is connected to the tunnel wall upstream of the cylinder. To measure the fluid height in tube 1 is measured then the table is to be turned in intervals of 10 degrees, this is to be repeated for every 10 degrees until it has fully rotated around 360 degrees. The experiment is also to test in a turbulent flow To have a grid with an array of squares inserted in to the wind tunnel, where the air flow and the velocity increases until the Betz manometer reads 35mmH2O, as the air becomes turbulent. This whole procedure to be repeated. Fig. C shows the manometer tube readings at a zero angle for smooth flow. Fig. D shows the manometer tube readings at a zero angle for turbulent flow. The readings of the multi-tube manometer were taken before starting to rotate the cylinder (at zero angle). This preliminary data collected is presented visually to show the shape of the pressure distribution around the cylinder. Tube 33 is open to air; hence it shows the atmospheric pressure. And tube 34 as mentioned earlier, shows the head pressure of the upstream section of the wind-tunnel. Results Raw Results: The scale of the manometer used was in inch. Therefore, the results obtained have to be changed to metres. This is done as follows: 1 inch = 0.0254 metres The heights of the fluids have to be multiplied by 0.0254 to change to metres. Smooth flow: ÃŽÂ ± = 30 ° h蠁 ¾ = 11 inch = 11 x 0.0254 = 0.2794m Turbulent flow: ÃŽÂ ± = 30 ° h蠁 ¾ = 10.4 inch = 10.4 x 0.0254 = 0.26416m The pressure coefficient, cà Ã¢â‚¬ ¢, at an angle can be found by using eq 8. The calculations to find cà Ã¢â‚¬ ¢ will be the same for both laminar and turbulent flows. The only difference would be that the value of h蠁 ¾ would be different in each case. The value for h1 is the value obtained when the cylinder is at 0 °. The calculations to find the pressure coefficient for the laminar flow at ÃŽÂ ¦ = 0 ° is shown below: = 1 Calculated Results The following data shows values, which will be used to determine the pressure coefficient, this will be calculated using eq. 11. Smooth flow Transonic Flow Frictionless flow The values for the pressure coefficients will be the same for both laminar and turbulent flows since cà Ã¢â‚¬ ¢ only depends on the angle ÃŽÂ ¦. The pressure coefficient for a frictionless flow is found using eq. 12. Theoretical graph: The graph represents in a theoretical manner in which the experimental values should be able to compare to, whereby the air flowing in the graph, shows constant change at regular angle intervals. Also all peaks and troughs on the graph show relevant pressure coefficients. The graphs illustrate the pressure coefficient variation with changes in angle. The above graph shows the variation of cÃŽÂ ¦ vs. ÃŽÂ ¦ in laminar flow. This is then compared to the cÃŽÂ ¦ vs. ÃŽÂ ¦ in turbulent flow. Calculations to find the drag coefficient, CD: The value for the drag coefficient depends on the value of cÃŽÂ ¦ cosÃŽÂ ¦, this relation can also be noticed in eq. 14. Laminar flow Discussion: Throughout this experiment several factors were found out these include: The pressure distribution in the system Drag of the cylinder Drag coefficient Reynolds number Errors in the experiment which may have caused anomalies Looking at the graphs it can be seen that the pressure distribution in the system as in both lamina and turbulent flow also in parts off the graph it shows steady correlation, between angles 900 and 3100 in laminar flow. Angles 1300 and 2300 in turbulent flow. The drag on the cylinder in turbulent and lamina conditions show through the results and graphs shown. As there is more drag when there is turbulent flow than lamina, however this easy to understand as, in lamina flow the eddies produced have a small wake so therefore it does not have a large pressure so do not increase drag. From the graphs it can be seen that the pressure coefficient in lamina flow at 90à ¯Ã¢â‚¬Å¡Ã‚ ° is greater than the pressure coefficient at turbulent flow. However in the turbulent flow the motion reduce the pressure and so increases the drag. The drag coefficient can be found by looking at the results and graphs, which both show that it is less in turbulent flow as the separation point occurs after 90à ¯Ã¢â‚¬Å¡Ã‚ ° resulting in less eddies so less wake and therefore a high pressure with a end of low drag coefficient. Whereas in lamina flow it is greater than in turbulent. This may be due to the fact that the separation point occurs before 90à ¯Ã¢â‚¬Å¡Ã‚ °, this has a resulting effect of more eddies which induce wakes and low pressure, the end result of this is a high drag coefficient. Separation point is where the angle flows become steady. The separation point occurs when the velocity of the fluid is reducing, in which the pressure flows induce a positive pressure gradient. Then once the separation has passed the boundary layers bend over and flow in the opposite direction. The pressure remains constant after the separation point because eddies are transferred to another energy. The separation point at lamina flow is at 90à ¯Ã¢â‚¬Å¡Ã‚ ° than for turbulent which is after 90à ¯Ã¢â‚¬Å¡Ã‚ ° as the pressure gradient is greater in laminar flow, which means that the greater the pressure gradient the earlier the separation. The reason why Reynolds number is greater in turbulent flow than lamina , as the main reason for this is that less pressure and drag coffeicnet and more drag is acting on to the cylinder. Conclusion: In this experiment a cylinder was used to find laminar and turbulent flow around it, the main objective was to see if the drag and flow increased or decreased, this was achieved and so this was shown that they increased in turbulent conditions and decreased in laminar conditions. Also in a turbulent condition the separation will increase to 90 degrees and the Reynolds number also increases. Discussion Figure G H shows the head pressure distribution around the cylinder. As it can be seen from the laminar head pressure distribution (figure G), the pressure between tubes 3-11 (i.e. angle 90 ° up to 330 °) is approximately the same. This shows that the pressure is more or less the same around the cylinder (seen from top view), except from its front point facing the oncoming flow, where the pressure is equal to the stagnation pressure. In the case of turbulent flow (figure H), the head pressure distribution pattern is somewhat different. The head pressure values drop between tube no. 5 to 9, which is the rear part of the cylinder. This proofs that a low-pressure region exists at the rear of the cylinder in turbulent flow. This region of low-pressure is referring to the wake region. The pressure distribution is also clearly symmetrical about tubes 6 and 7, which proofs that the pressure distribution on one (horizontal) side of the cylinder is the similar to the one on the opposite side of the cylinder. The percentage error for the coefficient of drag (CD) value in experiment 1 was calculated to be around 2.5%. This is a relative small percentage error, which shows that the experimental errors involved in experiment 1 were not significant. However, the percentage error for the CD value in experiment 2 was a massive 56%, which clearly shows that the experimental errors involved in experiment 2 did play a significant role. The error due to parallax is one of those errors. The parallax error is human reading error, where the eye needs to be exactly in line with the reading to be measured. In both experiments, the multi-tube manometer was at an inconvenient position (on the floor). And furthermore the whole manometer system was slanting at an angle of 30 ° to the horizontal. Both of these factors made it difficult to get the eye level exactly in line with the reading to be measured from the multi-tube manometer. This might have caused inaccuracy in the readings. The trapezium rule was used to determine the area under the graph of cÃŽÂ ¦cosÃŽÂ ¦, which was used to calculate the coefficient of drag. Since the graph had regions of both negative and positive areas, the trapezium rule had to be applied separately for each section of the graph. The whole graph was divided into strips, each with a width of à Ã¢â€š ¬/18 (10 degrees in radians). When the graph of cÃŽÂ ¦cosÃŽÂ ¦ goes from a positive region into a negative one, the experimental data in some cases does not reach exactly zero before the data switches signs. This results in some minor areas of the graph being neglected. This would not significantly affect the total area found from the trapezium rule, since the areas neglected are relatively small. But since some areas of the graph are ignored, the total area found would not be the exact area under the graph. This will result in an error in the final values of the coefficient drag. The tunnel calibration constant for laminar flow (k1) was found to be 1.0959. Clearly this value cannot be accurate because the ratio of the two pressures must be equal to 1 or below, since both are representing the pressure across the same points. The unexpected high value for k1 must have been a result of experimental error. Since most terms are constant in the equation used to find k, the only factors that could have contributed to the error must have been the values of h蠁 ¾ h1, which were obtained from the multi-tube manometer. While taking the readings from the multi-tube manometer, the fluid (methylated spirit) in the tubes was fluctuating. Some of the fluctuations were as large as +/- 0.5 inch. For this reason, many of the readings obtained might have been greatly inaccurate, which eventually could have lead to a significant error in the final values for the coefficient of drag in both experiments, as well as the value of k for laminar flow. The tunnel calibration constant for turbulent flow (k2) was found to be 0.7632. This value clearly indicates that energy losses did take place since the value is well below 1. Energy losses may have occurred in 2 main forms; as heat and sound energy produced by friction and collisions of air molecules with especially the grid system at the upstream. Heat energy (and some sound energy) is also produced due to friction of the inner wall of the wind tunnel. The flowing air must do work to overcome this friction, and therefore some kinetic energy of the flowing air is lost as heat. To get turbulent flow, the flow rate was increased and a grid system was introduced. With the introduction of a grid system at the upstream in experiment 2, more collisions of air molecules took place, hence resulting in increased loudness of flow. This increase in loudness (due to the increase in collisions and friction) resulted in some kinetic energy of the flow being converted into heat and more noticeably sound energy, and therefore some of the initial kinetic energy of the flow was lost. Energy in form of heat is also lost due to formation of turbulent eddies. The formation of eddies takes place in turbulent flow. All these energy loss factors mentioned earlier might explain why the k-value for turbulent flow was less than the k-value of laminar flow. In figure I, three graphs were plotted for the variation of pressure coefficient with angle. Each of the graphs was representing data for a unique condition. In the case of the theoretical data graph (green coloured), which represents the condition of frictionless flow, shows that at zero angle the pressure is a maximum (stagnation pressure). Moreover at an angle of 180 ° (rear of cylinder), the pressure once again reaches a maximum value. This relationship clearly indicates that the pressure distribution pattern would have been exactly symmetrical around a vertical axis at the centre of the cylinder. However, in the case of both laminar and turbulent flow the lines if symmetry is not vertical but horizontal about the centre of the cylinder. By looking at the graphs, all graphs show a maximum pressure coefficient at an angle of zero (equivalent to 360 °), which shows that whatever flow condition is imposed a maximum pressure will still remain at the stagnation point, which is the point where the fluid is brought to a stop. For laminar flow, the pressure coefficient remains more or less constant after an angle of approximately 75 °. This point is referred as the angle of separation, where the flow starts to separate from the cylinders surface. This value of 75 ° is very close to the value of 82 ° given for angle of separation for laminar flow in figure E. In the case for turbulent flow, the pressure coefficient stays more or less constant after an angle of approximately 130 ° (i.e. angle of separation). Once again, even this value for angle of separation is very similar to the value of 120 ° given for turbulent flow in figure E. One of the main reasons why there is a difference in shape between the theoretical graph and the experimental graphs for pressure coefficient is due to the fact that the assumption of air being a frictionless flow is invalid, since air is a viscous fluid. Overall the characteristics of the pressure coefficient graphs can be said to be an accurate presentation of actual data, since the three pressure coefficient graphs (between angle 0 to 180 °) are highly identical in terms of both the shape and scale of the pressure coefficient graphs shown in figure E. Conclusion The value of tunnel calibration constant (k) for experiment 1 was found to be inaccurate (due to experimental errors) since the value is not expected to exceed 1. However, the value of k for experiment 1 shows that very negligible energy losses take place across the contraction of the wind tunnel under laminar flow. The value of k was significantly lower for experiment 2; this clearly shows that the energy losses that take place across the contraction of the wind tunnel are significant. The main reason for significant amount of energy losses is due to the introduction of a grid system at the upstream in experiment 2. The grid system dramatically increases the effects of friction to the oncoming flow. For this reason, some of the initial kinetic energy of the airflow is lost as heat and sound energy. The obtained graphs for variation of pressure coefficient with angle around the cylinder perimeter can be said to be fairly accurate, since both the shape and scale of them is extremely identical to the graphs representing the same data in figure D. For experiment 1 the coefficient of drag value had a percentage error of only 2.5%, which shows that the experimental errors did not have a significant impact on the final result. However, in the case of experiment 2, the percentage error for the coefficient of drag was a massive 56%, clearly the experimental error did significantly affect the final result in experiment 2. In future improvements, the percentage error of the CD value in experiment 2 could be further decreased by reducing experimental errors mentioned in the discussion section.

Oxfam Marketing Analysis

Oxfam Marketing Analysis The charitable organisation chosen in this assignment is Oxfam international organisation. Oxfam standing for Oxford Committee for Famine Relief was founded in Britain in 1942 by a group who ran campaigns for food supplies to send for starving women and children in .. (Oxfam, 2012) In 1955, the Oxfam international charitable organisation was formed by a group of 17 independent non- government organisations in 94 countries with the aim to reduce the poverty and injustice. This assignment will discuss various activities of what Oxfam does and how this organisation raise money including fundraising campaign, the awareness programs, organizing inspirational events, etc. running shops which Oxfam has been doing to get money and implement 2 important objectives of the organisations including fighting poverty and fighting for right. Also, this assignment will explain various market and environmental factors which affect the organisation present and future direction. These factors include micro environment and macro environment. In an organization, marketing department is often considered as one of primely important departments to contribute to an organisations success. So, the roles and duties of marketing staffs cannot be denied to success of the department and the organization. This assignment will also discuss about the role play by the marketing staffs of The Oxfam international charitable organization. ANALYSIS The organisations activities Oxfams objectives is fighting poverty and for human rights, in more specific term, injustice in all over the world. The organization has worked world-wide to enable people to act for their rights and manage for their own lives. They believe that everyone has the right to access the essentials of life such as food, clean water, shelter, healthcare, sanitation, education, and livelihood to support themselves, their families and the society. Also, everyone has the right to live free from injustice such as gender injustice, oppression, human violence. So, how does Oxfam accomplish its aims ? The organization has been run many activities including running fundraising campaign, entertainment event, to get money from the donors as well as run many shops selling donated items from donors or products from companies. They will use all the money to support poor and needy people and victims of injustice, so that, these people can eliminate their difficulties and have better life. Firstly, Oxfam believes that poverty is not inevitable , however, it is a real challenge to overcome (Oxfam). To tackle this challenge, Oxfam has been focusing on 2 important areas which are economic justice and Essential services. With economic justice, Oxfam Charitable organization aim is that more men and women will realize their right to secure and durable livelihoods (OXFAM INTERNATIONAL STRATEGIC PLAN 2007-2012). To achieve this aim, Oxfam charitable organization works together with many allies and partners in all over the world to accomplish 3 things which are making agriculture work, achieving fairer trade rules, and decreasing the impacts of climate change. They make more agriculture work for poor farmers and labors living in susceptible circumstances to reduce the economic differences among people. They also work to make sure farmers in poor countries have fair trade in world trade. By this activity, farmers in poor countries can access to the big fair trade zone such as EU agricultural markets. For example, Oxfam urges EU to allow Arab countries to fully access to EU agricultural markets while keeping a certain level of protection to their rural development needs (Euro-Med: Seeds of a raw deal?). Oxfam, in addition, supports vulnerable people, especially in agriculture, to adapt climate change. With essential services, the target of Oxfam is that people in poor condition, especially women and girls realize their rights to accessible and affordable essentials of life such as food , clean water, shelter, healthcare, sanitation, edu cation. This activity of Oxfam is focusing on demanding national governments to perform their responsibilities as well as their commitments in delivery of these essentials, supporting civil society organizations to push these governments to take their responsibilities in supplying these essentials, and making sure that rich countries and international associations will donate more funds as well as commit their promise on aid and debt reduction. Secondly, besides fighting poverty, Oxfam is also fighting injustice, especially gender injustice. Oxfam believes that Poverty often happen when peoples basic rights are violated. So that, fighting injustice is one of many ways to fight poverty (Oxfam, 2012). In more specific area, Oxfam fight gender injustice. The goal of Oxfam in this activity is to help more women attain power in their lives, live their lives without violence, and have the right to make decisions. This activity aims to change women attitudes, thinking and beliefs about relation of men and women in order to reduce the rate of violence to women as well as eliminate the gender differences in the work place . This activity supports women to engage more activities, access to leadership at all levels and have the rights to make decisions, so that, they can gain their power over and manage their own lives. Oxfam is known as one of the top charitable organizations in UK as well as around the world. Like other charitable organizations, Oxfam have to do fundraising to get money for activities that they are doing for people and it has to comply to UK legal principles and international laws and regulations for charitable organizations as well. There are many activities which Oxfam have been conducting to raise funds for the organization. There are 2 types of activities that the organization, specifically members of the organization, perform to raise money, and they are direct and indirect activities. First of all, indirect activities of a charitable organization would include activities such as †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ Influences of Market and Environment Factors to the Organization. In the 21st century, Profit organizations and Non-for-profit or charitable organizations have to face many challenges. The rapid changes of the economy environment in national and international sphere require these organizations re-formulate and implement new strategies, objectives to meet new challenges. Together with formulate the strategies, objective, the organizations themselves need to make changes to enjoy the competitive advantage from other competitors. As a charitable organization, Oxfam has to face with some changes and challenges that they never faced in the past which are climate change and its consequences, famines and crises of the prices, global financial crises, limitation of energy, proliferation of weapons, urbanization, shortages of natural resources, etc (Oxfam, 2012) . http://www.oxfam.org/en/about/what/purpose-and-beliefs To adapt these changes, Oxfam has to figure out what market and environment factors and forces may influence on their strategies, objective and future direction. There forces can be classified into 2 types which are micro environment and macro environment (Jobber, 2001). The micro environment consisting of factors such as suppliers, customers, distributions and competitors may directly influence the organisations ability to manage to supply their products and services to their selected markets. The macro environment includes various broader forces which not only affect the organization itself but also all factors in the micro environment. These factors in macro environment consist 6 key groups under demographic environment, economic environment, technological, political-legal, and social-cultural environment ( Kotler, †¦). While the micro environment factors can be managed and controlled by the organisation, the macro environment factors are uncontrollable. (Kotler, ). There are 2 types of approaches which are undertaken by Oxfam to analysis the micro or internal environment and macro or external environment market environment. They are PEST analysis and SWOT analysis. PEST ANALYSIS ; SWOT ANALYSIS To analyse the macro environment, it is helpful to indentify all the factors which may affect the organisations demand level, supply level, and cost (Kotter and Schlesinger, 1991). kotler (1998) claims that PEST analysis is a tool which is very helpful for an organization to understand their position in the market, the stage of their business like growth or decline. Together with SWOT analysis, an organization can identify their strengths, weakness, potential opportunities or threads. So that, the organization can develop plan, strategy and direction for operation properly. The marketing of the charitable organization is quite different from marketing of any other profit organizations. While the profit organization can earn the profits by their business by hiring and paying their staffs to achieve the target, the charitable organization keeps their jobs going by getting the donations from donors and supports from volunteers. The Oxfam charitable organization run their all activities not for profit maximization, but more on collecting the money which they get and helps people who are in poverty and need help such as poor farmers, children, women, illiterate, etc. Like other profit and not-for-profit organization, Oxfam charitable organization is affected by market and environment forces. Firstly, economic factor may be the first factor which affects the Oxfams income in particular period obviously because the salary and income of common donors may be affected by the economic conditions such as inflation, high rate of unemployment. The changes of economic condition also affect the selling of products of many of Oxfams shops (Oxfam, 2004?) . Secondly, Oxfam charitable organization is affected by the legal-political environment which varies from country to country. As a international organization, Oxfam affiliates are all over the world. All their activities have to comply with each countrys laws, regulations, legal framework to avoid the illegal form in collecting donations which could affect their image in the future. For example, in UK, all the activities and operations of charitable organizations have to follow the regulations of   the Charities Act, 2006 () which requires the organization keep the annual records, prepare annual account and publish to public on request. So, the Oxfam organization in UK has to follow this regulation strictly in keeping all record about the income, expenditure from fundraising campaign as well as shop selling activities. PHILOSOPHIES Known as one of significant charitable firms in the world, Oxfams marketing concept can be considered as Societal marketing concept. The firm with societial marketing concept manages their marketing strategy in the way that emphasizes on determining and satisfying the needs, wants and interests of target market more efficiently and effectively than competitor. of poor people and people living in poverty and SEGMENTATION; TARGETING; POSITIONING When an organization knows about their target audience, they know everything and it is much easier for them to able to communicate with their target audience. There are many factors that an organization should know about target audience, which can be age, gender, income, types of products they use, programs they watch, etc. The target market of Oxfam charitable organization is the people in poor condition and needs assistances from all areas, regions, religions, etc in all over the world. The target market of the organization also emphasizes on people who are living in condition of injustice, specially women and girls. http://www.oxfamtrailwalker.org.nz/about/oxfam http://www.oxfam.org/en/about/what/purpose-and-beliefs http://www.oxfam.org/en/about/why http://www.oxfam.org/en/about/accountability/strategic-plan