A new synthetic metamodel methodology for liquid‐propellant engine's cooling system optimization

The present paper strives for optimization of the cooling system of a liquid‐propellant engine (LPE). To this end, the new synthetic metamodel methodology utilizing the design of experiment method and the response surface method was developed and implemented as two effective means of designing, analyzing, and optimizing. The input variables, constraints, objective functions, and their surfaces were identified. Hence, the design and development strategy of combustion chamber and nozzle was clarified, and 64 different experiments were carried out on the RD‐161 propulsion system, of which 47 experiments were approved and compatible with the problem constraints. This engine used all three modes of cooling: the radiation cooling, the regenerative cooling, and the film cooling. The response surface curves were drawn and the related objective function equations were obtained. The analysis of variance results indicate that the developed synthetic model is capable to predict the responses adequately within the limits of input parameters. The three‐dimensional response surface curves and contour plots have been developed to find out the combined effects of input parameters on responses. In addition, the precision of the models was assessed and the output was interpreted and analyzed, which showed high accuracy. Therefore, the desirability function analysis has been applied to LPE's cooling system for multiobjective optimization to maximize the total heat transfer and minimize the cooling system pressure loss simultaneously. Finally, confirmatory tests have been conducted with the optimum parametric conditions to validate the optimization techniques. In conclusion, this methodology optimizes the LPE's cooling system, a 2% increase in the total heat transfer, and a 38% decrease in the pressure loss of the cooling system. These values are considerably large for the LPE design.     

Hydrogen supply chain optimization for deployment scenarios in the Midi-Pyrénées region,France

Several roadmaps and international projects are interested in the development of the hydrogen economy for the transportation system. Yet, the development of a hydrogen economy suffers from a lack of infrastructure to store and supply H₂ fuel to the refuelling stations, while at the same time, hydrogen can be just seen as one alternative among others to compete with the current fossil fuels. To determine if hydrogen is a competitive option, many scenarios must be assessed considering not only the cost as the target to determine the feasibility but, also environmental and safety objectives. This work is focused on the design of a hydrogen supply chain for deployment scenarios in the Midi-Pyrénées region in France based on multi-objective optimization. Specific constraints related to the energy sources have been integrated and a multi-period long-term problem is examined (2020–2050). Two solution strategies will be implemented to solve this multi-period problem: a global optimization through ε-constraint method and a sequential optimization through lexicographic and ε-constraint methods. The consideration of different geographical scales and the impact of the initiation step in the development of a sustainable supply chain have been highlighted.     

Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models

The definition of an efficient optimization methodology for internal combustion engine design using 1D fluid dynamic simulation models is presented. This work aims at discussing the fundamental numerical and fluid dynamic aspects which can lead to the definition of a best practice technique, depending on the complexity of the problem to be dealt with, on the number of design parameters, objective variables and constrains. For these reasons, both single-and multi-objective problems will be addressed, where the former are still of relevant interest (i.e. optimization of engine performances), while the latter have a much wider range of applications and are often characterized by conflicting objectives.     

Multi-objective optimization of annular fin array with B-spline curve based fin profiles

The design of annular fin array with variable thickness fin profiles defined by B-spline curves is studied as a multi-objective optimization problem for simultaneously maximizing heat transfer rate and minimizing thermal stress. Maximization of surface e?ciency and augmentation factor as well as minimization of fin volume are considered as additional objective functions for further assessment of fin array performance. Evaluating the objective values through hybrid spline difference method, different cases are investigated by solving the optimization model by non-dominated sorting genetic algorithm II. The proposed scheme should aid designers in selecting compromise optimal solutions for practical problems.     

Multi-objective optimization for GPU3 Stirling engine by combining multi-objective algorithms

Stirling engine has become preferable for high attention towards the use of alternate renewable energy resources like biomass and solar energy. Stirling engine is the main component of dish Stirling system in thermal power generation sector. Stirling engine is an externally heating engine, which theoretical efficiency is as high as Carnot cycle's, but actual ones are always far below compared with the Carnot efficiency. A number of studies have been done on multi-objective optimization to improve the design of Stirling engine. In the current study, a multi-objective optimization method, which is a combination of multiple optimization algorithms including differential evolution, genetic algorithm and adaptive simulated annealing, was proposed. This method is an attempt to generalize and improve the robustness and diversity with above three kinds of population based meta-heuristic optimization techniques. The analogous interpreter was linked and interchanged to find the best global optimal solution for Stirling engine performance optimization. It decreases the chance of convergence at a local minimum by powering from the fact that these three algorithms run parallel and members from each population and technique are swapped. The optimization considers five decision variables, including engine frequency, mean effective pressure, temperature of heating source, number of wires in regenerator matrix, and the wire diameter of regenerator, as multiple objectives. The Pareto optimal frontier was obtained and a final optimal solution was also selected by using various multi-criteria decision making methods including techniques for Order of Preference by Similarity to Ideal Solution and Simple Additive Weighting. The multi-objective optimization indicated a way for GPU-3 Stirling engine to obtain an output power of more than 3 kW and an increase by 5% in thermal efficiency with significant decrease in power loss due to flow resistance.     

A hybrid multi-level optimization approach for the dynamic synthesis/design and operation/control under uncertainty of a fuel cell system

During system development, large-scale, complex energy systems require multi-disciplinary efforts to achieve system quality, cost, and performance goals. As systems become larger and more complex, the number of possible system configurations and technologies, which meet the designer’s objectives optimally, increases greatly. In addition, both transient and environmental effects may need to be taken into account. Thus, the difficulty of developing the system via the formulation of a single optimization problem in which the optimal synthesis/design and operation/control of the system are achieved simultaneously is great and rather problematic. This difficulty is further heightened with the introduction of uncertainty analysis, which transforms the problem from a purely deterministic one into a probabilistic one. Uncertainties, system complexity and nonlinearity, and large numbers of decision variables quickly render the single optimization problem unsolvable by conventional, single-level, optimization strategies.To address these difficulties, the strategy adopted here combines a dynamic physical decomposition technique for large-scale optimization with a response sensitivity analysis method for quantifying system response uncertainties to given uncertainty sources. The feasibility of such a hybrid approach is established by applying it to the synthesis/design and operation/control of a 5 kW proton exchange membrane (PEM) fuel cell system.     

Stress constrained thermo-elastic topology optimization based on stabilizing control schemes

Abstract

This article proposes two effective stabilizing control schemes for addressing the stress constrained thermo-elastic topology optimization in a non-uniform temperature field. Based on the density interpolation scheme, two linear elastic equations for coupling a thermo-elastic problem are considered. For comparison, different topology problem formulations for minimizing compliance or volume subject to stress constraints are solved. By virtue of a stabilization transform method, two stabilizing control schemes combined with the grouped aggregation method are developed to handle the challenging difficulties stemming from the local nature of highly nonlinear stress constraints. Moreover, the adjoint method is adopted to perform the sensitivity analysis. The design variables are updated by utilizing the method of moving asymptotes. The results of several typical numerical examples verify the validity of the proposed methodology, including the present stabilizing control schemes which can be employed to obtain clear topological design and fast convergence rate for thermo-elastic coupling problems. Meanwhile, compliance minimization design with stress constraints is appropriate to achieve balance between stress level and stiffness.     

Thermodynamic optimization of geometry: T- and Y-shaped constructs of fluid streams

This paper presents a series of examples in which the global performance of flow systems is optimized subject to global constraints. The flow systems are assemblies of ducts, channels and streams shaped as Ts, Ys and crosses. In pure fluid flow, thermodynamic performance maximization is achieved by minimizing the overall flow resistance encountered over a finite-size territory. In the case of more complex objectives such as the distribution of a stream of hot water over a territory, performance maximization requires the minimization of flow resistance and the leakage of heat from the entire network. Taken together, these examples show that the geometric structure of the flow system springs out of the principle of global performance maximization subject to global constraints. Every geometric detail of the optimized flow structure is deduced from principle. The optimized structure (design, architecture) is robust with respect to changes in some of the parameters of the system. The paper shows how the geometric optimization method can be extended to other fields, e.g., urban hydraulics and, in the future, exergy analysis and thermoeconomics.     

Multi-objective design optimization of a natural gas-combined cycle with carbon dioxide capture in a life cycle perspective

The use of multi-objective optimization techniques is attractive to incorporate environmental objectives into the design of energy conversion systems. A method to locally optimize a given process while considering its global environmental impact by using life cycle assessment (LCA) to account for avoidable and unavoidable off-site emissions for each independent material input is presented. It is applied to study the integration of a CO₂-capture process using monoethanolamine in a natural gas-combined cycle power plant, simultaneously optimizing column dimensions, heat exchange, and absorbent flow configuration with respect to two objectives: the levelized cost of electricity and its life cycle global-warming potential. The model combines a process flow-sheeting model and a separate process-integration model. After optimization using an evolutionary algorithm, the results showed that widening the absorber and generating near-atmospheric pressure steam are cost-effective options but that increasing stripper complexity is less so. With $7.80/GJ natural gas and $20/ton CO₂ handling, the minimum on-site CO₂ abatement cost reaches $62.43/ton on a life cycle basis, achieved with a capture rate of over 90%. Of this, $2.13/ton is related to off-site emissions – a specific advantage of LCA that could help industries and governments anticipate the actual future costs of CO₂ capture.     

Multi-objective optimization for efficient modeling and improvement of the high temperature PEM fuel cell based Micro-CHP system

Fuel cells due to different useful features such as high efficiency, low pollution, noiselessness, lack of moving parts, variety of fuels used and wide range of capacity of these sources can be the main reasons for their tendency to use them in different applications. In this study, the application of a high temperature proton exchange membrane fuel cell (HT-PEMFC) in a combined heat and power (CHP) plant has been analyzed. This study presents a multi-objective optimization method to provide an optimal design parameters for the HT-PEMFC based micro-CHP during a 14,000 h lifetime by considering the effect of degradation. The purpose is to optimize the net electrical efficiency and the electrical power generation. For the optimization process, different design parameters including auxiliary to process fuel ratio, anodic stoichiometric ratio, steam to carbon ratio, and fuel partialization level have been employed. For optimization, A new technique based on Tent mapping and Lévy flight mechanism, called improved collective animal behavior (ICAB) algorithm has been employed to solve the algorithm premature convergence shortcoming. Experimental results of the proposed method has been applied to the data of a practical plant (Sidera30) for analyzing the efficiency of the proposed ICAB based system, it is compared with normal condition and another genetic algorithm based method for this purpose. Final results showed that the difference between the maximum electrical power production under normal condition and ICAB based condition changes from 2.5 kW when it starts and reaches to its maximum value, 3.0 kW, after 14,000 h lifetime. It is also concluded that the cumulative average for the normal and the ICAB based algorithm are 24.01 kW and 27.04 kW, respectively which showed about 3.03 kW cumulative differences.     

A multi-objective fuzzy optimization for optimum dimensions design of a convective spine

A systematic multi-objective fuzzy optimization procedure to design the optimum dimensions of a conical convective spine is presented in this paper. Conflicting fuzzy design objectives, such as the weight and the length of spine as well as ‘soft’ constraints, are considered simultaneously in this study. The conical convective spine design based on the proposed procedure leads to a practical spine shape for production. The proposed design procedure cal also is applied to other design problems with complicated constraints.     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Assessment of mono and multi-objective optimization to design a hydrogen supply chain

This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness. The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multi-objective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an ?-constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type.     

Multi-objective optimization of a trigeneration plant

A multi-objective optimization method was developed for the design of trigeneration plants. The optimization is carried out on technical, economical, energetic and environmental performance indicators in a multi-objective optimization framework. Both construction (equipment sizes) and discrete operational (pricing tariff schemes and operational strategy) variables were optimized based on realistic conditions. The problem is solved using a multi-objective evolutionary algorithm. An example of a trigeneration system in a 300 bed hospital was studied in detail in order to demonstrate the design procedure, the economic and energetic performance of the plant, as well as the effectiveness of the proposed approach even under fluctuating energy prices.     

A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch

This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors.     

A dual objective global optimization algorithm based on adaptive weighted hybrid surrogate model for the hydrogen fuel utilization in hydrogen fuel cell vehicle

Current engineering optimizations mainly use surrogate models (SMs) to approximate complex black-box problems. However, SMs with different approximate characteristics may make the designers unable to accurately judge which type of SMs is more suitable for the actual optimization design. A reasonable combination of different SMs might be one of the solutions. To this end, a global optimization algorithm based on an adaptive weighted hybrid surrogate model (GOA-AWHS) is proposed. In each iteration, a hybrid model based on kriging and RBF is first constructed by adaptively selecting weight coefficients. Next, two objectives consisting of predicted objective, root mean square error and distance parameters are optimized to generate the Pareto frontier. Finally, further selection of data points on the Pareto front yields multiple promising optimal solutions. A series of standard numerical functions and hydrogen fuel utilization in hydrogen fuel cell vehicles are tested to demonstrate the effectiveness and robustness of the GOA-AWHS method.     

Portfolio optimization using Mixture Design of Experiments: Scheduling trades within electricity markets

Deregulation of the electricity sector has given rise to several approaches to defining optimal portfolios of energy contracts. Financial tools - requiring substantial adjustments - are usually used to determine risk and return. This article presents a novel approach to adjusting the conditional value at risk (CVaR) metric to the mix of contracts on the energy markets; the approach uses Mixture Design of Experiments (MDE). In this kind of experimental strategy, the design factors are treated as proportions in a mixture system considered quite adequate for treating portfolios in general. Instead of using traditional linear programming, the concept of desirability function is here used to combine the multi-response, nonlinear objective functions for mean with the variance of a specific portfolio obtained through MDE. The maximization of the desirability function is implied in the portfolio optimization, generating an efficient recruitment frontier. This approach offers three main contributions: it includes risk aversion in the optimization routine, it assesses interaction between contracts, and it lessens the computational effort required to solve the constrained nonlinear optimization problem. A case study based on the Brazilian energy market is used to illustrate the proposal. The numerical results verify the proposal's adequacy.     

Multi-objective optimization and exergoeconomic analysis of a multi-generation system based on biogas-steam reforming

In the current work, a new design of a multi-generation integrated energy system powered by biogas energy is proposed, assessed, and optimized. To scrutinize the workability of the offered system, energy, exergy, exergo-economic, and economic investigations have been applied as robust tools to the evaluation of the system. Moreover, to boost the rate of hydrogen production rate, the steam reforming method and purification process are integrated into the systems. It is found that the designed multi-generation integrated energy system is able to generate 108.7 kW, 888.7 kW, and 703.3 kg/h, power, cooling load, and hydrogen, sequentially. Besides, it is determined that the energy and exergy efficiencies of the system are about 31.51% and 31.14%, sequentially. Furthermore, a comprehensive parametric evaluation is employed to appraise the influences of key variables on the operation of the system and relying on its achieved outcomes, two different optimization styles are established. From the optimization outcomes, it is remarked that in the multi-objective optimization mode, a TCOP of 16.23 S/GJ and a net power of 158.21 KW, are achievable.     

Multi-objective optimization of an experimental integrated thermochemical cycle of hydrogen production with an artificial neural network

In this study, an experimental lab-scale copper-chlorine (Cu–Cl) cycle of hydrogen production is examined and optimized in terms of exergy efficiency and operational costs of produced hydrogen. The integrated process is modeled and simulated in Aspen Plus incorporating the reaction kinetic parameters with a sensitivity analysis of a range of operating conditions. An artificial neural network (ANN) method with machine learning is used to generate a mathematical function that is optimized based on a multi-objective genetic algorithm (MOGA) method. A sensitivity analysis of variations of each design parameter for both the objective functions and the effectiveness of exergy performance relative to operational costs of produced hydrogen is demonstrated. The sensitivity analysis and optimization results are presented and discussed.     

Multi-objective optimization design of condenser in an organic Rankine cycle for low grade waste heat recovery using evolutionary algorithm

The optimum design of a condenser is significant in an organic Rankine cycle to achieve higher waste heat utilization efficiency. Based on the mathematical model of a condenser using plate heat exchanger (PHE), some key geometric parameters on the total heat transfer surface area and pressure drop of the condenser are examined. In order to obtain geometric parameters of a plate heat exchanger, a multi-objective optimization of the condenser in organic Rankine cycle is conducted to achieve the optimal geometry design. The total heat transfer surface area and pressure drop are selected as two objective functions to minimize both total heat transfer surface area and pressure drop under the constant heat transfer rate and LMTD conditions. The plate width, plate length and plant distance are selected as the decision variables. Non-dominated sorting generic algorithm-II (NSGA-II) which is an effective multi-objective optimization method is employed to solve this multi-objective optimization design of PHE. The results show that an increase in channel distance or plate width increases the total heat transfer surface area and decreases pressure drop in the condenser. It is noted that the plate length of PHE has a positive effect on the optimization design of PHE. By multi-objective optimization design of the PHE, a Pareto optimal point curve is obtained, which shows that a decrease in total heat transfer surface area of a condenser can increase the pressure drop through the condenser.