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Design Optimization of Ocean Thermal Energy Conversion(OTEC)Considering the Off-Design Condition

Journal of Thermal Science 2023年第6期32卷 2126-2143页

作者： FAN Chengcheng CHEN Yongping School of Energy and Environment Southeast UniversityNanjing210096China

The comprehensive optimization of thermodynamic and economic performances is significant for the engineering application of ocean thermal energy conversion(OTEC).Motivated by this,this paper develops a thermo-economic OTEC model and conducts a sensitivity analysis of the OTEC system concerning its thermodynamic and economic performances.Specifically,the impact of warm-seawater temperature and cold-seawater pumping depth on the net thermal efficiency and the total investment cost are investigated.The results indicate that,an increase in warm-seawater temperature and cold-seawater pumping depth can improve the net thermal efficiency and a higher installed capacity is beneficial to the system economics.Building on these,a design optimization method with considering the on-design and off-design conditions is proposed in this paper,and the dynamic variation of warm-seawater temperature are considered in this method.In multi-objective optimization procedure,with the objective functions being the average net thermal efficiency and unit power cost within the operational cycle,the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) is employed to maximize the net thermal efficiency and minimize the unit power investment cost,resulting in the Pareto front.The net thermal efficiencies of OTEC systems using ammonia and R245fa as working fluids are 4.13% and 3.8%,respectively.This represents an improvement of 19.4% and 57.0%,respectively,compared to traditional optimization methods that do not account for off-design conditions.

关键词： ocean thermal energy conversion sensitivity analysis design optimization off-design scenario approach

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A Theory of the Risk for Empirical CVaR with Application to Portfolio Selection

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Journal of Systems Science & Complexity 2021年第5期34卷 1879-1894页

作者： ARICI Giorgio CAMPI Marco C. CARE Algo DALAI Marco RAMPONI Federico A. Department of Information Engineering University of Bresciavia Branze 3825123 BresciaItaly.

When decisions are based on empirical observations,a trade-off arises between flexibility of the decision and ability to generalize to new situations.In this paper,we focus on decisions that are obtained by the empirical minimization of the Conditional Value-at-Risk(CVa R)and argue that in CVa R the trade-off between flexibility and generalization can be understood on the ground of theoretical results under very general assumptions on the system that generates the observations.The results have implications on topics related to order and structure selection in various applications where the CVa R risk-measure is used.A study on a portfolio optimization problem with real data demonstrates our results.

关键词： Distribution-free results empirical CVa R generalization order selection risk scenario approach

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Probability-constrained optimal control of combustion engines

Probability-constrained optimal control of combustion engine...

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第37届中国控制会议

作者： Xun Shen Tielong Shen Department of Engineering and Applied Sciences Sophia University

This paper summarizes the framework of applying probability-constrained optimal control for combustion engines. The probabilistic constrained control problem is formulated in engine, after which, both the data driven-based methodology and explicit model-based are briefly reviewed to address the problem. Two study cases are given as the explanation for the applications of probability-constrained optimal control in engine system. The first study case is knock probability constrained thermal efficiency optimization. An iterative learning-based control strategy is proposed to solve the problem by combining extremum seeking method and likelihood-based method. On the other hand, scenario approach is applied to solve torque tracking control with combustion phase probabilistic threshold.

关键词： Probability-constrained optimal control combustion engines data driven scenario approach

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