Optimization for entransy dissipation minimization in heat exchanger

A common of two-fluid flow heat exchanger, in which the heat transfer between highand low-temperature sides obeys Newton's law [q∝Δ(T)], is studied in this paper. By taking entransy dissipation minimization as optimization objective, the optimum parameter distributions in the heat exchanger are derived by using optimal control theory under the condition of fixed heat load. The condition corresponding to the minimum entransy dissipation is that corresponding to a constant heat flux density. Three kinds of heat exchangers, including parallel flow, condensing flow and counter-flow, are considered, and the results show that only the counter-flow heat exchanger can realize the entransy dissipation minimization in the heat transfer process. The obtained results for entransy dissipation minimization are also compared with those obtained for entropy generation minimization by numerical examples.

Entransy dissipation minimization for a class of one-way isothermal mass transfer processes

A class of one-way isothermal mass transfer processes with Fick’s diffusive mass transfer law[g ∝Δ(c)]is investigated in this paper.Based on the definition of the mass entransy,the entransy dissipation function which reflects the irreversibility of mass transfer ability loss is derived.The optimal concentration allocations of the key components corresponding to the highand low-concentration sides for the minimum entransy dissipation of the mass transfer process are obtained by applying opti- mal control theory and compared with the strategies of the minimum entropy generation,constant mass transfer flux(constant concentration difference),and constant concentration ratio(constant chemical potential difference).The results are as follows. For the optimal mass transfer strategy of the minimum entransy dissipation,the product of the square of the key component concentration difference between the high-and the low-concentration sides and the inert component concentration at the low-concentration side is a constant,while for that of the minimum entropy generation,the ratio of the square of the key com-ponent concentration difference between the high-and the low-concentration sides to the key component concentration at the low-concentration side is a constant;when the mass transfer process is not involved in energy conversion process,the optimi-zation principle should be the minimum entransy dissipation;the mass transfer strategy of constant concentration difference is superior to that of constant concentration ratio.The results obtained in this paper can provide some theoretical guidelines for optimal designs and operations of practical mass transfer processes.

Hamilton-Jacobi-Bellman equations and dynamic programming for power-optimization of a multistage heat engine system with generalized convective heat transfer law

A multistage endoreversible Carnot heat engine system operating between a finite thermal capacity high-temperature fluid reservoir and an infinite thermal capacity low-temperature environment with generalized convective heat transfer law [q∝(ΔT) m ] is investigated in this paper.Optimal control theory is applied to derive the continuous Hamilton-Jacobi-Bellman (HJB) equations,which determine the optimal fluid temperature configurations for maximum power output under the conditions of fixed initial time and fixed initial temperature of the driving fluid.Based on the universal optimization results,the analytical solution for the Newtonian heat transfer law (m=1) is also obtained.Since there are no analytical solutions for the other heat transfer laws (m≠1),the continuous HJB equations are discretized and dynamic programming algorithm is performed to obtain the complete numerical solutions of the optimization problem.The relationships among the maximum power output of the system,the process period and the fluid temperature are discussed in detail.The results obtained provide some theoretical guidelines for the optimal design and operation of practical energy conversion systems.