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1 Thermodynamic optimization of Organic Rankine Cycles for low and ultra low grade waste heat recovery applications: influence of the working fluid on the ORC net power output GIOVANNA CAVAZZINI SERENA BARI GUIDO ARDIZZON GIORGIO PAVESI Department of Industrial Engineering University of Padova Via Venezia Padova ITALY Abstract: - The present work is focused on the thermodynamic optimization of Organic Rankine Cycles (ORCs) for power generation from low and ultra low grade waste heat recovery. The paper is aimed at providing a preference selection order of working fluids for applications characterized by low and ultra low heat source temperatures (from 80 C to 150 C). Among the commonly available working fluids, a selection based on environmental and technical criteria was carried out resulting in a list of 12 working fluids: R245fa, R245ca, R1234yf, R134a, R227, R236fa, RC318, Isobutane, Butane, Isopentane, Pentane. A model of a simple ORC cycle was developed and optimized by means of a recent evolution of the Particle Swarm Optimization (PSO) algorithm. The evaporation pressure and the approach and pinch point temperature differences have been chosen as decisional variables. Two refrigerants (R1234yf and R134a) were able to maintain good performance in the whole considered range of temperature, whereas due to their thermos-fluid-dynamic properties the hydrocarbons always remained near the bottom apart from the temperature. However, for heat source temperatures lower than 100 C, the difference between the most and the less performing fluid in terms of net power output and system efficiency significantly reduces, demonstrating that for ultra low waste heat recovery applications the net power output resulted not to be a really effective selective criteria and should be combined with other environmental and/or technical criteria (minimum costs, minimum environmental impact, maximum heat exchange efficiency ). Key-Words: waste heat, ORC, optimization, simulation model, PSO, working fluid 1 Introduction The interest for low grade heat recovery has been growing for the last ten years because of the increasing concern over energy shortage and global warming. Since conventional steam power cycles cannot give a good performance to recover low grade waste heat, new solutions have been proposed: among them the Organic Rankine Cycle (ORC) system seems to be the most promising process and it is the most widely studied. This system includes the same components as in a conventional steam power plant (a vapour generator, an expansion device, a condenser and a pump), with the exception that the working fluid is an organic substance, characterized by a low ebullition temperature allowing for smaller evaporating temperatures. Previous papers ( [1], [2], [3], [4]) are focused on the screening of fluids following basic screening criteria such as the slope of the saturation vapour curve, the critical point position and other thermodynamic properties. Qiu [5] proposed a selection method for the working fluid for the medium-to-low temperature ORC considering only thermo-physical characteristics of the organic refrigerants without taking in account any cycle parameters. Chen et al. [6] analysed the selection criteria of potential working fluids and the influence of fluid properties on cycle performance. Other papers performed optimization analysis ( [7], [8], [9]) or system simulations ( [10], [11], [12]) considering a selected number of fluids. Dai et al. [13], conducted a parametric optimization by means of genetic algorithm using as objective function the exergetic efficiency. The heating source considered was an exhaust gas stream at 145 C and among the fluids taken in account R2236ea showed the higher efficiency under the same given waste heat condition. Wang et al. [14] used a genetic optimization algorithm to investigate the expander inlet pressure and temperature, approach-point and pinch-point influence on the power output and the total thermal ISBN:

2 exchange area. The usual target for the system optimization works are the power output ( [15]), the thermal or global efficiency of the cycle ( [14], [16], [17],), the total exergy destruction, the heat exchangers area ( [18],) or others parameters such as the size of the expansion device ( [19]) or the overall cost for the entire system ( [20], [21]). In this paper a list of nearly 50 fluids was preliminary taken in account and was screened applying basic criteria such as the slope of the saturation curve, the Ozone Depleting Potential (ODP) and the eventual phasing out date of the organic fluid. Then an optimization of the ORC parameters aimed at maximizing the net power output, was carried out by means of a PSO-based algorithm on a selection of working fluids at different temperatures of the heat source in order to find a preference selection order for ultra low grade waste heat recovery applications. 2 The model A basic ORC to recover energy from a low temperature heat source, consisting of a pump, evaporator expander and condenser, was modeled in Matlab/Simulink environment (Fig. 1). The following energy balances were used to model the different components: PUMP: η pump = h 2is h 1 h 2 h 1 (1) P pump = ṁ wf (h 2 h 1 ) (2) EVAPORATOR: ṁ s cp s (Ts in Ts out ) = ṁ wf (h 3 h 2 ) = = Q ev (3) ṁ s cp s (Ts in Ts r ) = ṁ wf (h 3 h F ) (4) EXPANDER: η exp = h 3 h 4 h 3 h 4is (5) P exp = ṁ wf (h 3 h 4 ) (6) CONDENSER: ṁ p cp p (Tp out Tp in ) = ṁ wf (h 4 h 1 ) = = Q cond (7) ṁ p cp p (Tp r Tp in ) = ṁ wf (h G h 1 ) (8) The pump and expander efficiencies were set equal to 0.8. The net power output of the cycle is hence: P net = P turb P pump (9) As regards the cycle efficiency, two performance indexes were calculated. The first one is the thermal efficiency, defined as the ratio of the net power gained over the thermal power absorbed during the evaporation process: η th = P net Q ev (10) This index does not take into account the heatexchange efficiency, which is considered in the system efficiency, defined as the product of the thermal efficiency by the heat exchange efficiency: η syst = η th χ = P net Q ev Q ev Q hs = P net Q hs (11) where Q hs is determined as follows: Q hs = ṁ s cp s (Ts in T 0 ) Figure 1 T-s diagram of a typical simple ORC For the heat source, a water stream entering in the evaporator at different temperatures (Ts in= C) with a mass flow rate of 5 kg/s (ṁ s) was considered. The cooling fluid in the condenser was assumed to be water, with an inlet temperature of 10 C (Tp in). At the condenser exit, the fully saturated liquid working fluid is pumped to the evaporator (process 1-2 in fig. 1) where it absorbs heat at a relatively high pressure (2-3). Then it expands in the expander producing useful shaft work (3-4) and rejects heat at a lower pressure (4-1) in the condenser. 3 The ASD-PSO algorithm The standard Particle Swarm Optimizer (PSO) is a relatively recent heuristic population-based search method based on the social behaviour of a swarm of insects or a flock of birds where a continuous exchange of information allows each component to move towards promising regions of the search space by exploiting both its own knowledge and the knowledge of the swarm as a whole. In the context of a multivariable optimization, the swarm is characterized by a specified number of particles (where particle denotes a bird or an insect), each one characterized by a position and a velocity. The particles, initially located at random locations, wander around in the search space with a ISBN:

3 Figure 2 - Distribution of the acceleration coefficients and of the inertia weight for the i-th particle as a j function of the particle distance d j,i from the global best solution: (a) C 1; (b) C 2; (c). d max is the maximum distance reached by the swarm in the j-th dimension target (for example the identification of the food source). When a particle locates a good position, it instantaneously communicates this information to the swarm that does not directly converge to that position. Each particle maintains its independent thinking and adjusts its position and velocity on the basis of the swarm information on good positions, but also of its own best position, gradually going to the target after some iterations. As regards the PSO algorithms, the mathematical problem is to find the n-dimensional vector X={x 1,...x n} that minimizes the objective function f(x), i.e. the target of the swarm random search (where n is the number of variable to be optimized). The search space is defined by the lower X(l) and upper X(u) bounds on X: X(l) X X(u) The procedure of the PSO can be summarized through the following steps: 1. Definition of the swarm size: the number of particles np of the swarm should be assumed: X 1, X np. If n is the number of variables to optimize, Fan et al. suggests, as a criterion, assuming a number of particles equal to 2n [22]. 2. Initialization of the swarm: a random location for each particle X i(0) (i=1, np) is generated in the search space. All the particles may be assumed initially to be at rest (iteration 1). 3. Velocity update: each particle wanders around in the search space updating at each iteration t its own position X i(t) and velocity V i(t) on the basis of its own past flight experience (the historical own best position of X i(t): P best,i) and on that of the swarm (the historical best position encountered by any of the particles: G best). The particle velocity is updated as follows: V i (t) = ω V i (t 1)+ +C 1 r 1 [P best,i X i (t 1)]+ +C 2 r 2 [G best X i (t 1)] (i = 1,2,, np) where C 1 and C 2 are positive constants representing the individual and group learning rates respectively, is an inertia weight, and r 1 and r 2 are two independent sequences of random numbers generated in the range 0 and 1, used to avoid entrapment on local minima and to allow the divergence of a small percentage of particles for a wider exploration of the search space. As observed, the particle updates its velocity on the basis of three components: the previous velocity moderated by the inertia weight, the tendency of the particle to move toward the previously discovered best position, and the tendency to move towards the best position discovered by the swarm. ISBN:

4 Figure 3 - A simplified flow chart of the ASD-PSO procedure 4. Position update: the particle position at the iteration t is updated as follows: X i (t) = X i (t 1) + V i (t) t (i = 1,2,, np) 5. Convergence check: the convergence is achieved when the positions of all particles converge to the same set of values. In the standard PSO algorithm, the weight of the cognitive experience (C 1) of a particle is the same as that of the social experience (C 2) acquired by the swarm to balance the attraction toward the global best solution (gbest) with the wandering aptitude of each particle in the search space. Kennedy and Eberhart [23] suggested C 1 = C 2 = 2, whereas the inertia weight is often reduced linearly from 0.9 to 0.4 during a run [24]. Such a compromise has generally proved to be useful in avoiding premature convergence in a local optimum, even though it is very hard to prevent early convergence when solving multimodal problems. In the adaptive PSO strategy [25], adopted in this analysis, a different concept of cooperation among the particles of the swarm has been developed. The particles are diversified depending on their relative position compared to the G best, which roughly represents the center of the swarm. During the swarm random search, the particles on the external periphery of the swarm are mainly involved in enhancing the exploration ability of the swarm, while the particles closer to the global best solution discovered so far are mainly involved in local search refinement of the current best solution, that is in the exploitation of the current promising search space. To do this, both the inertia weight and the acceleration coefficient C 1 and C 2 properly vary as a function of the distance d i,j of the particle i (i=1, 2,, np) in each spatial dimension j (j=1, 2,, n - number of variables to optimize) from the global best solution (Fig. 2). A simplified scheme of the ASD-PSO procedure is reported in Fig. 3. More details about the ASD-PSO ISBN:

5 algorithm can be found in [25]. In the optimization procedure, the ASD-PSO was set as follows: np=20; C 1max=3, C 2max=3, C 2min=0.5, min=0.4 and max=0.9. In the optimization model, the following parameters have been chosen as independent variables: The evaporating pressure (p ev) The temperature difference at the pinch point in both the heat exchangers: (ΔT pp ) cond = T G T pr (12) (ΔT pp ) evap = T sr T F (13) The approach point temperature difference in both the heat exchangers: (ΔT ap ) evap = Ts in T 3 (14) (ΔT ap ) cond = T 1 Tp in (15) As regards the search bounds, the temperature differences at the pinch points (ΔT pp) were fixed to vary between 5 and 25 C, whereas those at the approach point between 10 C and 25 C. The input parameters were optimized in order to achieve the maximum net power output P net for each considered working fluid. Since the aim of the ASD- PSO algorithm is to search for a minimum, the following objective function was built: a = P ref P net P ref (16) where P ref is the net power output obtained by an ideal Carnot cycle operating between the same heat and sink temperatures (Ts in= C; Tp in=10 ): P ref = Q C η C = = ṁ s cp s (Ts in Tp in ) (1 Tp in Ts in ) (17) 4 Results Figs. 4 and 5 report net power output and system efficiency obtained for different working fluids depending on the temperature of the heat source. As it can be seen, the influence of the working fluid on the resulting ORC performance in terms of net power output and system efficiency becomes smaller as the temperature of the heat source reduces. The percentage difference in terms of net power output between the most performing working fluid and the less performing one reduces from 24.88% to 8% for a heat source temperature of 100 C and to 4.32% for a temperature of 80 C. It is also interesting to notice that the ranking of the working fluids is not significantly modified by the temperature decrease. R1234yf and R227ea are able to maintain good performance in the whole considered range of temperature. R134a and R236fa slightly reduces their performance as the temperature decrease, whereas R245ca, Butane, Pentane and Isopentane remain near the bottom apart from the temperature. These results are mainly affected by the different values of heat recovery efficiency characterizing the considered fluids (fig. 6). For ultra low heat source temperatures (T 100 C), despite of thermal efficiency values th almost equivalent (fig. 7), the heat recovery efficiency (fig. 6) presents more remarkable differences between the working fluids, significantly affecting their final ranking. For example, for a heat source temperature of 100 C, the thermal efficiencies are included between 7.98% and 8.34%, whereas the heat recovery efficiency varies of about 4% between the less and the most performing fluid. Fluid P net [kw] th syst R1234yf % 60.37% 5.03% R227ea % 60.65% 4.96% R134a % 59.22% 4.84% R236fa % 59.22% 4.84% RC % 60.55% 4.83% R236ea % 58.45% 4.78% Isobutane % 57.87% 4.77% R245fa % 57.56% 4.76% Butane % 57.60% 4.73% R245ca % 57.12% 4.72% Isopentane % 56.68% 4.66% Pentane % 56.49% 4.66% Table 1 - Performance in terms of net power output and efficiencies of the difference working fluids at 100 C For heat source temperatures greater than 100 C, this behaviour is generally confirmed (Table 2). Near the top position, it is possible to find working fluids characterized by the greatest values of heat recovery efficiency with two exceptions represented by R134a and R236fa due to the widest range of thermal efficiency values. R134fa is able to achieve a very good value of heat recovery efficiency combined (76.19%) with good value of thermal efficiency (11.90%), gaining the top of the list, whereas R236fa makes up for the small value of heat recovery efficiency (65.27%) with the greatest value of thermal efficiency (13.32%). The reason of these different strategies adopted by the optimization algorithm to optimize the ORC parameters, are related to the different thermo-fluiddynamic characteristics of the considered working ISBN:

6 Figure 4 Net power output achieved by the different working fluids as a function of the heat source temperature Figure 5 ORC system efficiency sist (eq. 11) achieved by the different working fluids as a function of the heat source temperature fluids and can be understood analyzing the influence of the evaporation pressure on the resulting ORC cycle (figures 8-10). ISBN:

7 Figure 6 Heat exchange recovery achieved by the different working fluids as a function of the heat source temperature Figure 8 Influence of the evaporation pressure on the enthalpy drop at the expander for a heat source temperature of 100 C for R1234yf, R245fa, RC318 Figure 7 Thermal efficiency th achieved by the different working fluids as a function of the heat source temperature Fluid Pnet [kw] th syst R134a % 76.19% 9.07% R1234yf % 81.81% 8.98% R227ea % 81.58% 8.92% R236fa % 65.27% 8.69% RC % 75.73% 8.61% Isobutane % 64.15% 8.08% R236ea % 65.61% 8.04% R245fa % 64.47% 7.99% Butane % 63.53% 7.88% R245ca % 63.83% 7.84% Pentane % 62.82% 7.61% Isopentane % 63.04% 7.60% Table 2 - Performance in terms of net power output and efficiencies of the difference working fluids at 150 C Figure 9 Influence of the evaporation pressure on the vapour mass flow rate for a heat source temperature of 100 C for R1234yf, R245fa, RC318 An increase of the evaporation pressure causes two different events, having conflicting effects on the net power output. Greater evaporation pressure values determine an increase of the enthalpy drop at the expander (figure 8) with a consequent increase of the net power output. However, the heat absorbed during the evaporation process decreases with increasing evaporation pressure, resulting in a smaller vapour mass flow rate produced in the evaporator (fig. 9) and hence in a reduction of the net power output of the ISBN:

8 thermodynamic cycle (fig. 10). R1234yf R227ea R236fa RC Isobutane R236ea R245fa Butane R245ca Pentane Isopentane Table 3 Vapour mass flow rate and enthalpy drop of the difference working fluids at 150 C Figure 10 Influence of the evaporation pressure on the net power output for a heat source temperature of 100 C for R1234yf, R245fa, RC318 As a consequence, the net power output of the cycle increases as long as the increase in enthalpy drop prevails against the decrease of vapour mass flow rate. It reaches a maximum value when the two conflicting events are balanced and then starts to decrease due to the greater influence of the vapour mass flow rate reduction. Depending on the thermo-fluid-dynamic properties of the working fluid, the optimal condition is located on a greater or smaller value of evaporation pressure, hence favouring the thermal efficiency of the ORC cycle or the heat recovery efficiency respectively. For example, the greater values of latent heat characterizing the hydrocarbons reduces the influence of the vapour mass flow rate on the net power output, determining an optimal condition with greater values of thermal efficiency (greater entropy drops) and smaller values of heat recovery efficiency (smaller vapour mass flow rates) than the refrigerants (Table 3). These thermo-fluid-dynamic characteristics penalize the hydrocarbons in comparison with the refrigerants for low heat source temperatures (100 C <T<150 C). However, for ultra low heat source temperatures (T<100 C), also the heat recovery efficiency of the refrigerants decreases and this reduces the difference between the most and the less performing fluids in terms of net power output (4.32%) and system efficiency (0.15%). Fluid ṁ wf h 3-h 4 [kg/s] [kj/kg] R134a Conclusions A thermodynamic optimization of Organic Rankine Cycles (ORCs) for power generation from low and ultra low grade waste heat recovery was carried out. The analysis was aimed at providing a preference selection order of working fluids for applications characterized by low and ultra low heat source temperatures (from 80 C to 150 C). Among the commonly available working fluids, a selection based on environmental and technical criteria was carried out resulting in a list of 12 working fluids: R245fa, R245ca, R1234yf, R134a, R227, R236fa, RC318, Isobutane, Butane, Isopentane, Pentane. A model of a simple ORC cycle was developed and optimized by means of a recent evolution of the Particle Swarm Optimization (PSO) algorithm. The evaporation pressure and the approach and pinch point temperature differences were chosen as decisional variables. The influence of the working fluid on the resulting ORC performance in terms of net power output and system efficiency became smaller as the temperature of the heat source reduced. The ranking of the working fluids resulted not to be significantly modified by the temperature decrease. R1234yf, R227ea were able to maintain good performance in the whole considered range of temperature. R134a and R236fa slightly reduced their performance as the temperature decrease, whereas R245ca, Butane, Pentane and Isopentane remained near the bottom apart from the temperature. These results were mainly affected by the different values of heat recovery efficiency characterizing the considered fluids. In particular it was demonstrated that hydrocarbons, due to their great values of latent heat, did not reach significant values of heat exchange efficiency and were not able to made up for it by significantly ISBN:

9 increasing the thermal efficiency. However, for heat source temperatures lower than 100 C, also the heat recovery efficiency of the refrigerants decreased and this reduced the difference between the most and the less performing fluids in terms of net power output (4.32%) and system efficiency (0.15%). Therefore, even though some refrigerants and in particular R1234yf showed better performance than the other in low grade waste heat recovery applications, the maximum net power output could not be an exhaustive selection criterium for ultra low heat source temperatures but further considerations on the environmental impact and on the cost of the ORC components should be added. References [1] D. Wang, X. Ling, H. Peng, L. Liu e L. Tao, Efficiency and optimal performance evaluation of organic Rankine cycle for low grade waste heat power generation, Energy, Vol. 50, 2013, pp [2] E. Wang, H. Zhang, B. Fan, M. Ouyang, Y. Zhao e Q. Mu, Study of working fluid selection of organic Rankine cycle (ORC) for engine waste heat recovery, Energy, Vol. 36, 2011, pp [3] M. Chys, M. v. d. Broek, B. Vanslambrouck e M. D. Paepe, Potential of zeotropic mixtures as working fluids in organic Rankine cycles, Energy, Vol. 44, 2012, pp [4] C. Andersen, J. Bruno, Rapid Screening of Fluids for Chemical Stability in Organic Rankine Cycle Applications, Ind. Eng. Chem. Res., 2005, pp [5] G. Qiu, Selection of working fluids for micro- CHP systems with ORC, Renewable Energy, Vol. 48, 2012, pp [6] H. Chen, D. Y. Goswami, E. Stefanakos, A review of thermodynamic cycles and working fluids for the conversion of low-grade heat, Renewable and Sustainable Energy Reviews, Vol. 14, 2010, pp [7] H. Hettiarachchia, M. Golubovica, W. M. Worek and Y. Ikegamib, Optimum design criteria for an Organic Rankine cycle using lowtemperature geothermal heat sources, Energy, Vol. 32, 2007, pp [8] S. Quoilin, S. Declaye, B. F. Tchanche, V. Lemort, Thermo-economic optimization of waste heat recovery Organic Rankine Cycles, Applied Thermal Engineering, Vol. 31, 2011, pp , 2011 [9] E. Wang, H. Zhang, B. Fan e Y. Wu, Optimized performances comparison of organic Rankine cycles for low grade waste heat recovery, Journal of Mechanical Science and Technology, Vol. 26, No. 8, 2012, pp [10] V. Lemort, S. Quoilin, Designing scroll expanders for use in heat recovery Rankine cycles, Institution of Mechanical Engineers - International Conference on Compressors and their Systems, 7-9 September 2009, London, UK, pp [11] T. Yamamoto, T. Furuhata, N. Arai, K. Mori, Design and testing of the organic Rankine cycle, Energy, Vol. 26, 2001, pp [12] S. Quoilin, R. Aumann, A. Grill, A. Schuster, V. Lemort, H. Spliethoff, Dynamic modeling and optimal control strategy of waste heat recovery Organic Rankine Cycles, Applied Energy, Vol. 88, 2011, pp [13] Y. Dai, J. Wang, L. Gao, Parametric optimization and comparative study of organic Rankine cycle (ORC) for low grade waste heat recovery, Energy Conversion and Management, Vol. 50, 2009, pp [14] J. Wang, Z. Yan, M. Wang, S. Maa, Y. Dai, Thermodynamic analysis and optimization of an (organic Rankine cycle) ORC using low grade heat source, Energy, Vol. 49, 2013, pp [15] A. Lakew, O. Bolland, Working fluids for low-temperature heat source, Applied Thermal Engineering, Vol. 30, 2010, pp [16] V. Lemort, S. Declaye, S. Quoilin, Design and Experimental Investigation of a Small Scale Organic Rankine Cycle Using a Scroll Expander, Proceedings of the 13 th International Refrigeration and Air Conditioning Conference, Paper No. 1153, July 2010, Purdue, Indiana, USA. [17] S. Quoilin, S. Declaye, B. F. Tchanche, V. Lemort, Thermo-economic optimization of waste heat recovery Organic Rankine Cycles, Applied Thermal Engineering, Vol. 31, 2011, pp [18] Z. Wang, N. Zhou, J. Guo, X. Wang, Fluid selection and parametric optimization of organic Rankine cycle using low temperature waste heat, Energy, Vol. 40, 2012, pp [19] M. Khennich, N. Galanis, Optimal Design of ORC Systems with a Low-Temperature Heat Source, Entropy, Vol. 14, 2012, pp [20] T. Guo, H. Wang, S. Zhang, Fluids and parameters optimization for a novel cogeneration system driven by low-temperature geothermal sources, Energy, Vol. 36, 2011, pp [21] J. Wang, Z. Yan, M. Wang, M. Li, Y. Dai, Multi-objective optimization of an organic ISBN:

10 Rankine cycle (ORC) for low grade waste heat recovery using evolutionary algorithm, Energy Conversion and Management, Vol. 71, 2013, pp [22] S.-K. S. Fan, Y.-C. Liang, E. Zahara, A genetic algorithm and a particle swarm optimizer hybridized with Nelder-Mead simplex search. Computers & Industrial Engineering, Vol. 50, 2006, pp [23] J. Kennedy, R.C. Eberhart, Particle Swarm Optimization. Proceeding of the IEEE Int Conf on Neural Networks, Perth, Australia, 1995, pp [24] Y. Shi, R.C. Eberhart, Empirical study of particle swarm optimization. Proceeding of the IEEE Int Congress on Evolutionary Computation, Washington, DC, 1999, pp [25] G. Ardizzon, G. Cavazzini, G. Pavesi, Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Information Sciences, Vol. 299, 2015, pp pump Pump ref Reference s Heat source fluid syst System th Thermal tot Total turb Turbine/expander device v Vaporization wf Working fluid 0 Environment Condition Nomenclature a Objective function (-) A Area (m2) cp Heat Capacity at constant pressure (kj/kg C) h Enthalpy (kj/kg) ṁ Mass flow rate (kg/s) p Pressure (kpa) P Power (kw) Q Heat Power (kw) T Temperature ( C) Greek letters Δ Difference χ Heat recovery efficiency (%) η Efficiency (%) Superscripts and subscripts a Approach Point av Available C Carnot cycle cond Condenser ev Evaporator hs Heat Source in Inlet is Isentropic out Outlet p Cooling fluid pp Pinch Point ISBN: