Triple-objective Optimization Research Articles

Optimizing compressed air energy storage with organic Rankine cycle and ejector refrigeration for sustainable power and cooling provision

In the pursuit of sustainable energy systems, integrating storage technologies is crucial. Compressed air energy storage (CAES) emerges as a significant option for ensuring reliable power supply during peak hours. This study focuses on a configuration combining a CAES unit with two integrated organic Rankine cycle and ejector refrigeration units (ORCERC). The primary objective is to ensure adequate power supply during peak hours in the discharge period, while also providing cooling capacity to enhance system flexibility and efficiency. The proposed system is evaluated from thermodynamic, exergoeconomic, and exergoenvironmental perspectives to determine optimal operating conditions for the CAES unit. A fluid selection process identified a suitable zeotropic mixture, R141b/Hexane, as the working fluid for both ORCERCs. The results demonstrate a net power production of 20,749.91 kW and a cooling load of 1448.23 kW, with cost and exergoenvironmental impact rates of $1193.59/h and 143.42 Pt/h, respectively. Additionally, the configuration achieves an exergy round-trip efficiency of 65.85 % and a payback period of 2.88 years. Increasing the HTES temperature from 1150 to 1250 K improved both the round-trip efficiency (RTE) and exergetic round-trip efficiency (ERTE) from 52.22 % to 53.39 % and 62.98 %–63.71 %, respectively, while reducing the product cost and exergoenvironmental impact rate from $1128 to $1117/h and 134.35 to 127.93 Pt/h, respectively. Three triple-objective optimization scenarios were considered, yielding diverse outcomes, each contributing unique insights into the system's performance and potential improvements.

AI-assisted Optimal Energy Conversion for Cost-Effective and Sustainable Power Production from Biomass-Fueled SOFC Equipped with Hydrogen Production/Injection

This study introduces a novel energy conversion and management framework to reduce carbon emissions in the energy sector and expedite the global shift towards sustainable practices. The system is driven by biomass-based solid oxide fuel cells for efficient power generation. Central to this approach lies the integration of additional hydrogen injection provided by a thermally-driven vanadium chloride cycle, aiming to enhance the quality of the syngas entering the fuel cells. The system is also combined with a super-critical CO2 cycle that generates power by passively enhancing performance through flue gas condensation. The proposed model's feasibility is evaluated in depth, techno-economically, considering thermodynamics and specific cost theories. As part of artificial intelligence, a neural network model is coupled with the genetic algorithm to determine the best operating status while minimizing computation time. According to the results, the suggested new integration results in higher efficiency and lower cost than a similar system without hydrogen injection. The results further show that the triple-objective optimization achieves output power, second-law efficiency, and overall system cost of 3425kW, 48.5%, and 2.3M$/year, respectively. Eventually, the gasifier is the main contributor to the highest level of exergy destruction, and fuel utilization and current density are the most important parameters in modeling.

Triple-objective optimization using ANN+NSGA-II for an innovative biomass gasification-heat recovery process, producing electricity, coolant, and liquefied hydrogen

A novel thermal integration approach is introduced for a biomass-driven gas turbine power plant that generates electricity, coolant, and liquefied hydrogen. The designed scheme encompassed an organic flash cycle, a bi-evaporator ejector refrigeration unit, a high-temperature water electrolyzer for hydrogen production, a multi-effect desalination cycle for supplying water for electrolysis process, and a Claude cycle for hydrogen gas liquefaction. The designed system's importance comes back to using biomass feedstock as the input fuel and utilizing the hydrogen liquefaction method. In addition to the possibility of deploying the system in remote areas, it provides the opportunity for hydrogen storage in a smaller volume for more accessible storage and transportation. On the other hand, using a comparative method for selecting an environmentally friendly fluid for the heat recovery subsystem is another crucial aspect of the present study from the environmental aspect. It is found that R161 is the appropriate choice among the seven studied working fluids. Subsequently, a comprehensive evaluation of the entire system's thermodynamic and environmental aspects is performed using an intelligent process. By considering energy and exergy efficiencies along with CO2 emissions as the objective functions, a thorough sensitivity analysis and a triple-objective optimization are carried out. Hence, artificial neural networks for the objectives are developed and integrated into the NSGA-II optimization method. Employing LINMAP decision-making, the values of objective are attained, exhibiting 39.6 % for energy efficiency, 36.1 % for exergy efficiency, and 631.7 kg/MWh for CO2 emissions. Considering the optimum solution, the proposed system is capable of producing electricity, cooling, and liquefied hydrogen with capacities of 4526 kW, 1875 kW, and 21.22 m3/day, respectively. Additionally, the scenario yields an exergoenvironmental index of 0.579 and an exergetic stability index of 0.61. and a liquefied hydrogen generation rate of 21.22 m3/day.

Developing and optimizing novel Cr3+-activated inorganic NIR phosphors by combining triple-objective optimization and crystal field engineering

A novel approach combining TOO and CFE for new NIR phosphors development was proposed. Under the guidance of this strategy, LaGa0.5Sb1.5O6:Cr3+ with wavelengths longer than 850 nm and balanced performance was discovered.

Operational Scheduling of Household Appliances by Using Triple-Objective Optimization Algorithm Integrated with Multi-Criteria Decision Making

Load scheduling is a key factor in demand side management (DSM), which manages available generation capacity with regard to the required demand. In this paper, a triple-objective load scheduling optimization problem (LSOP) is formulated for achieving optimal cost and peak demand as well as minimum customer inconvenience. A Henry gas solubility optimization (HGSO) algorithm that is based on multi-objective is used for solving LSOP. The proposed HGSO offers a set of compromise solutions that represent the tradeoff between the three objectives of the formulated problem. A set of all compromise solutions from the dominant Pareto front is achieved first, and then ranked by using MCDM so as to optimally sort these solutions. An entropy weighting method (EWM) is then used for computing the weights of various criteria that dominate the LSOP and is provided as a technique for ordering preferences by similarity to achieve the ideal solution (TOPSIS) so as to rank the sorted solutions. Two types of end-users are considered so as to show the effectiveness of the proposed LSOP: non-cooperative and cooperative users. The results of the proposed load scheduling method show the significance of the proposed method for both the cooperative and non-cooperative end-users. The proposed method achieves a cost of energy of R50.62 as a total cost of energy consumed by four non-cooperative end-users. The cost of energy for the cooperative end-users is found to be R47.39. Thus, saving in the energy cost unit is found to be around 5.5% by using the proposed method; moreover, the peak demand value is reduced by 9.7% when non-cooperative end-users becomes cooperative.

Chemistry-informed multi-objective mix design optimization of self-compacting concrete incorporating recycled aggregates

One of the primary objectives of modern concrete advancements is to strike a delicate balance among factors such as improved mechanical performance, appropriate economic viability, and reduced carbon emissions. Given this backdrop, self-compacting concrete incorporating recycled aggregates (referred to herein as RASCC) has gained attention in the quest for sustainable construction materials. Nevertheless, concurrently attaining the above objectives is not an easy feat for this particular type of concrete. In this study, a machine learning model based on the XGBoost algorithm was developed using 368 sample data points to predict the compressive strength of RASCC. To enhance the model’s accuracy, the chemical composition of RASCC’s binding materials, rather than the quantities of constituent materials, was chosen as part of input parameters, giving rise to the term “chemistry-informed” for this model. The model’s interpretability was comprehensively examined using the SHAP library. Then, in conjunction with the explainable machine learning model, the NSGA-II algorithm was leveraged to establish a RASCC auxiliary design system, enabling triple-objective optimization (i.e., strength, cost, and carbon emissions). The findings indicated that the XGBoost-based model achieved superior accuracy in predicting RASCC’s strength as compared to an existing neural network-based model. Additionally, using compound contents as inputs imbued the model with chemical significance, thus further enhancing its accuracy and interpretability. In conclusion, this study presents a plausible and beneficial tool for the efficient, cost-effective, and low-carbon design of RASCC.

The γ/γ′ microstructure in CoNiAlCr-based superalloys using triple-objective optimization

Optimizing several properties simultaneously based on small data-driven machine learning in complex black-box scenarios can present difficulties and challenges. Here we employ a triple-objective optimization algorithm deduced from probability density functions of multivariate Gaussian distributions to optimize the γ′ volume fraction, size, and morphology in CoNiAlCr-based superalloys. The effectiveness of the algorithm is demonstrated by synthesizing alloys with desired γ/γ′ microstructure and optimizing γ′ microstructural parameters. In addition, the method leads to incorporating refractory elements to improve γ/γ′ microstructure in superalloys. After four iterations of experiments guided by the algorithm, we synthesize sixteen alloys of relatively high creep strength from ~120,000 candidates of which three possess high γ′ volume fraction (>54%), small γ′ size (<480 nm), and high cuboidal γ′ fraction (>77%).

Triple-Objective Optimization of SCO2 Brayton Cycles for Next-Generation Solar Power Tower

In this paper, the SCO2 Brayton regenerative and recompression cycles are studied and optimized for a next-generation solar power tower under a maximum cycle temperature of over 700 °C. First, a steady-state thermodynamic model is developed and validated, and the impacts of different operating parameters on three critical performance indexes, including the cycle thermal efficiency, specific work, and heat storage temperature difference, are analyzed. The results reveal that these performance indexes are influenced by the operating pressures, the SCO2 split ratio, and the effectiveness of the regenerators in complex ways. Subsequently, considering the three performance indexes as the optimization objectives, a triple-objective optimization is carried out to determine the optimal operating variables with the aim of obtaining Pareto solutions for both cycles. The optimization indicates that the regenerative cycle can achieve the maximum heat storage temperature difference and the maximum specific work of 396.4 °C and 180.6 kW·kg−1, respectively, while the recompression cycle can reach the maximum thermal efficiency of 55.95%. Moreover, the optimized maximum and minimum pressure values of both cycles are found to be around 30 MPa and 8.2 MPa, respectively. Additionally, the distributions of the optimized values of the regenerator effectiveness and the SCO2 split ratio show different influences on the performance of the cycles. Therefore, different cycles with different optimized variables should be considered to achieve specific cycle performance. When considering thermal efficiency as the most important performance index, the recompression cycle should be adopted. Meanwhile, its SCO2 split ratio and the regenerator effectiveness should be close to 0.7 and 0.95, respectively. When considering heat storage temperature difference or specific work as the most important performance index, the regenerative cycle should be adopted. Meanwhile, its regenerator effectiveness should be close to 0.75. The results from this study will be helpful for the optimization of superior SCO2 cycles for next-generation solar tower plants.

Exergoeconomic and exergoenvironmental analysis and optimization of an integrated double-flash-binary geothermal system and dual-pressure ORC using zeotropic mixtures; multi-objective optimization

The current study focuses on proposing a double-flash geothermal system with a dual-pressure organic Rankine cycle that utilizes zeotropic mixtures. This system aims to harness the geothermal energy efficiently and sustainably. The performance of the system is evaluated using the exergy, exergoeconomic, and exergoenvironmental approaches, taking into account the utilization of different mass fractions of the R123/R142b mixture. The results of the analysis indicate that the designed system is capable of producing a net power output of 6336.04 kW with an exergetic efficiency of 66.70%. The payback period of the system is estimated to be 3.48 years, indicating its economic viability. Moreover, the electricity sale price has a substantial impact on the system's payback period and revenue, surpassing the influence of the geofluid purchase cost. Through the application of triple-objective optimization scenarios, the best optimum state of the system is determined to achieve a 66.96% exergetic efficiency, 3017.82 mPts/h exergoenvironmental impacts, and a payback period of 3.38 years. Furthermore, this state yields a revenue of 11.1 M$. Overall, the study highlights the potential of the proposed double-flash geothermal system with a dual-pressure organic Rankine cycle utilizing zeotropic mixtures for sustainable power generation.

Experiment-Based Determination of the Optimized Current Level to Achieve Multiple Constant Current Charging for Lithium-Ion Batteries

This study experimentally tested a multi-stage constant-current (CC) charging method for lithium-ion batteries controlled by the state-of-charge (SoC) discretization. The Taguchi method was used in the experiments to take three different charge performance objectives, including the charge time, charge efficiency and average cell surface temperature rise, into consideration at the same time to determine the most preferable one. Under the triple-objective optimization, with an equal discretized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoC</i> strategy, the three-stage CC (3SCC) protocol was found to yield much shorter charge time than the four- and five-stage CC protocols, while no significant differences were found in the charge efficiency and average cell surface temperature rise when the three CC protocols with different stages were employed. Thus, an additional performance objective, the energy loss, was used in a further investigation on the 3SCC protocol. In this investigation, a quad-objective problem was formed to determine whether the 3SCC protocol with unequally discretized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoC</i> intervals in each stage would produce better outcomes than the protocol with equally discretized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoC</i> intervals or vice versa. The results indicate that the 3SCC with the unequal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoC</i> discretization strategy reduces the energy loss by 1.82%, decreases the charge time by 4.27%, increases the charge efficiency by 0.32%, and lowers the average cell surface temperature rise by 12.5%, compared to the results yielded with the equal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SoC</i> discretization in each stage.

Design and multi-objective optimization of a multi-generation system based on PEM electrolyzer, RO unit, absorption cooling system, and ORC utilizing machine learning approaches; a case study of Australia

The geothermal systems' capability to produce various products persuades the design of a novel hybrid system, including power, cooling, freshwater, and hydrogen. Australia, with numerous geothermal sources and the tendency of hydrogen utilization as fuel, is considered a case study. The designed system is analyzed from energy, exergy, and economic viewpoints. Also, the artificial neural network is implemented to optimize the system's operation. Hence, the accuracy of four artificial neural networks in optimizing and predicting systems' performance is compared. Furthermore, four double-objective and four triple-objective optimization scenarios are considered to achieve the best optimum state from different viewpoints. The mean absolute Error value of 2.28×10−14 to predict the exergetic efficiency in the testing procedure is the best algorithm. The system provides 1263 kW net power with 39.89% exergy efficiency and 2.13 years of payback period at the base condition. The exergy efficiency-net present value scenario represents the best performance in which the net power, hydrogen, and freshwater production rates reach 3946 kW, 8.73 kg/h, and 152 kg/h, respectively. Subsequently, the exergy efficiency, payback period, and net present value are estimated at 46.27%, 1.84 years, and 19.52 M$.

Multi-criteria assessment and triple-objective optimization of a bio-anode microfluidic microbial fuel cell

Microfluidic microbial fuel cell has lower costs and greater potential than typical microbial fuel cell due to the elimination of proton exchange membrane. However, the development has mostly relied on experiments, and there has been little research on numerical simulations. Based on experimental validation, a reliable and universal model for microfluidic microbial fuel cell without quantifying the biomass concentration is proposed. Subsequently, the primary work is to study the output performance and energy efficiency of the microfluidic microbial fuel cell under different operating conditions and to comprehensively optimize the cell performance by employing the multi-objective particle swarm algorithm. Compared the optimal case with the base case, the increase ratios of maximum current density, power density, fuel utilization and exergy efficiency are 40.96%, 20.87%, 61.58% and 32.19%, respectively. On the basis of improving energy efficiency, the maximum power density and current density can reach 1.193 W/m2 and 3.51 A/m2.

Thermal process simulation and multi-variable study/optimization of a novel geothermal-driven multi-generation process using bi-evaporator with zeotropic mixture

Bi-evaporator technology is a robust tool, operating in two levels of coolant production for normal cooling and freezing purposes. The current study is motivated to assess the feasibility of integrating a bi-evaporator technology and a single-flash geothermal power plant. The main novelty of this paper is to design a novel combined cooling and power (CCP) production cycle relying on an organic Rankin cycle and a modified bi-evaporator refrigeration cycle. In addition, the use of a zeotropic mixture (Pentane/Isobutane) fed to the CCP is another difference between the current research and the available literature review. In addition to the power and cooling, the entire system is boosted by the use of a multi-generation process, producing heating via a heating production unit and hydrogen (H2) via a low-temperature electrolyzer. The proposed system was analyzed from the energy, exergy, specific exergy costing, and net present value points of view. Therefore, a coherent single- and dual-sensitivity study is done, and the variability of the main performance metrics is viewed against the decision parameters. In addition, owing to the incompatible trend of the newly devised plant's chief performance metrics, an advanced triple-objective optimization through a NSGA-II method is regraded from the standpoints of thermodynamics and economics. The objective functions include the coefficient of performance, exergy efficiency, and sum unit cost of products, and the optimum solution reveals their values of 33.56%, 63.2%, and 6.0 $/GJ, respectively.

Integration of wind turbine with biomass-fueled SOFC to provide hydrogen-rich fuel: Economic and CO2 emission reduction assessment

Addressing the energy crisis and global warming issues would entail urgent development of efficient and environmentally-benign power generation systems to mitigate the future clean energy policies. The biomass-driven SOFC systems are considered in this regard as pioneer technologies to supply clean power, particularly for decentralized applications. However, enough biomass availability and supply is a main challenge to run these systems. In current research to reduce biomass consumption, the biomass-driven SOFC is hybridized with wind turbines to produce pure hydrogen via a PEM electrolyzer. Produced hydrogen is added to the anode of SOFC to enrich the hydrogen content of the synthesis gas fuel. Feasibility assessment of proposed hybrid SOFC/wind turbine structure is examined considering first and second laws. Then, comprehensive economic and environmental appraisals are considered to inspect trade-offs between increased costs (associated with wind turbine and electrolyzer) and increased power as well as decreased CO2 emission. Finally to determine the optimal conditions for proposed system operation, triple-objective optimization via genetic algorithm is implemented. Obtained results have revealed exergy efficiency enhancement by 7.3% and CO2 emission reduction by 13.0 %, via incorporation of wind turbine. These technical and environmental performance enhancements are achieved at the expense of around 6.4 % increase in unit electricity cost, due to the increment of capital expenditures associated with the wind turbine and water electrolyzer. From parametric analysis, it is found that, the proposed framework yields lower electricity price and CO2 emission under higher cell temperatures and lower fuel utilization factors.

Modeling and optimizing of an actual municipal sewage plant: A comparison of diverse multi-objective optimization methods

The activated sludge process of an actual municipal sewage treatment plant was systematically modeled, calibrated, and verified in this study. Identified multi-objective optimization (MOO) methods were employed to optimize the process parameters of the validated model, and the optimal MOO algorithm was obtained by comparing Pareto solution sets. The optimization model consisted of three key evaluation indicators (objective functions), which are the average effluent quality (AEQ), overall cost index (OCI), and total volume (TV) of the biochemical tank, along with 12 more process parameters (decision variables). Three optimization algorithms, i.e., adaptive non-dominated sorting genetic algorithm III (ANSGA-III), non-dominated sorting genetic algorithm II (NSGA-II), and particle swarm algorithm (PSO), were adopted using MATLAB. The comparison of these algorithms demonstrated that the ANSGA-III algorithm had better Pareto solution sets under the triple objective optimization, and the effluent quality of COD, TN, NH4+-N, and TP after optimization decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. Additionally, the simulated AEQ was reduced by 13% compared to the original effluent, and the OCI and TV decreased from 21,023 kWh d−1 and 17,065 m3 to 20,226 kWh d−1 and 16,530 m3, respectively. The reported ANSGA-III algorithm and the proposed multi-objective method have a promising ability for energy conservation, emission reduction, and upgrading of municipal sewage treatment plants.

The waste heat of a biofuel-powered SOFC for green hydrogen production using thermochemical cycle; Economic, environmental analysis, and tri-criteria optimization

Biofuels are becoming more and more attractive recently due to their environmental benefits and various applications. In transport section and automotive industries, the biofuels and hydrogen energy are considered promising alternatives for decarburization. In the present research, two biofuel-driven systems for cogeneration of hydrogen and electricity, are suggested and investigated, in which biomass digestion or biomass gasification are compared to provide the required biofuel. The suggested schemes are comprised of SOFC units and thermochemical water splitting cycles, fueled with either biogas or syngas. To comprehensively compare the two schemes, thermoeconomic investigations are established to assess thermodynamic efficiencies as well as produced electricity and hydrogen costs. To examine adverse impacts on the environment, the greenhouse gas emission index is also considered. A sensitivity appraisal is presented for studying the influence of vital design/operation factors on systems’ responses and performances. Finally, triple-objective optimization is implemented to detect the best practical operation of the systems for achieving maximum efficiency and minimum emission and product costs. The obtained results under the optimum operation of the systems revealed an interesting feature. It is concluded that each of the two examined systems might be preferred to the other one regarding the desired objective. For instance, if higher hydrogen production (with lower prices) is favored over electricity production, then the gasification-derived scheme is chosen; however, the digestion-based scheme must be selected to gain more electricity.

Triple-objective optimization and electrochemical/technical/environmental study of biomass gasification process for a novel high-temperature fuel cell/electrolyzer/desalination scheme

To meet demands in agricultural sectors, the way of waste-to-useful products is an alternative to conventional processes capable of generating on-site products. This is an innovative method leading to designing a novel combined system in the current work. By means of a precise chemical and electrochemical simulation, the rice husk (an agricultural biomass fuel) is processed through a gasifier with a steam agent and is utilized in a molten carbonate fuel cell. The fuel cell's waste heat is delivered to thermal-based desalination utilizing humification and dehumidification processes. In addition, a solid oxide electrolyzer cell creates hydrogen by consuming power supplied by the fuel cell. Consequently, the system is able to meet some demands like electricity, irrigation, and chemical fertilizers. Accordingly, the system's applicability is measured by comprehensive electrochemical, technical, and environmental sensitivity analyses along with an advanced triple-objective optimization using the method of artificial neural network (ANN) + multi-objective grey wolf optimization. Regarding the objective functions, i.e., exergetic efficiency (ExEtot), exergoenvironmental impact index (EIItot), and carbon dioxide emission (CDEtot), the optimum state brings ExEtot=29.98%, EIItot=2.28, and CDEtot=391.1kg/MWh. Also, the variability of studied variables is further affected by the fuel cell's current density; its mean sensitivity index equals 0.48.

Design and multi-scenario optimization of a hybrid power system based on a working gas turbine: Energy, Exergy, Exergoeconomic and Environmental evaluation

The rising demand for electricity, along with the need to minimize carbon footprints, has motivated academics to investigate the flexible and efficient integration of energy conversion technologies. A novel hybrid power generation system based on environmentally friendly and cost-effective technologies to recover the waste heat of a working gas turbine is designed and assessed in different scenarios of multi-objective optimization from energy, exergy, exergoeconomic, and environmental (4E) perspectives. In the proposed system, a steam methane reformer and a water gas shift reactor are utilized for hydrogen production, while a polymer electrolyte membrane fuel cell (PEMFC) and steam/organic Rankine cycles are run for generating additional power. Aspen Plus, in conjunction with Fortran, Microsoft Excel, and MATLAB, is used to model and simulate the designed plant. The response surface methodology (RSM) is utilized to determine accurate surrogate models to describe the evaluation criteria, and the Non-dominated Sorting Genetic Algorithm II technique is employed to seek the optimal conditions. Moreover, TOPSIS and LINMAP decision-making approaches are used to find the best final solution among Pareto frontiers. The analysis of variance (ANOVA) and sensitivity analysis are also applied to evaluate the importance of the design variables. In this regard, three single-objective optimizations and four multi-objective optimization scenarios based on the maximization of the ecological coefficient of performance (ECOP) and the minimization of CO2 emissions and total system product cost (Ċp) are carried out. It is demonstrated that the system’s evaluation criteria have the highest and lowest sensitivity to the variation of reformer temperature and ORC pressure, respectively. From the triple-objective optimization procedure, the decision variables including reformer temperature, ORC pressure, Rankine cycle I pressure, and Rankine cycle II pressure are 544 °C, 4.35 bar, 158.12 bar, and 52.82 bar, respectively. At these conditions, the total hybrid system’s energy efficiency, exergy efficiency, exergy destruction, net generated power, and total investment cost rate are 45.96%, 46.83%, 215.72 MW, 203.67 MW, and 9791 $/h, respectively. The findings of this paper conclude that it is necessary to address all objective functions simultaneously in the system’s ultimate optimum design. Furthermore, the objective of this paper becomes even more apparent when there is no choice but to cut greenhouse gas emissions while also addressing the rising global energy demand.

Parameter Sensitivity Analysis of Mounting Pedestals and Multi-Objective Optimization for a Multi-Support Rigid Body System.

In this paper, a parameter sensitivity analysis of mounting pedestals and a multi-objective optimization design for vibration reduction in a multi-support rigid body system, taking an aeroengine-lubricating oil tank supported by multiple mounting pedestals as an example, are conducted based on the third version of non-dominated sorting genetic algorithm (NSGA-Ⅲ) combined with Sobol’s sensitivity analysis method (SSAM). An aeroengine-lubricating oil tank with three mounting pedestals is simplified as a three-support dynamic system, and its dynamics model is established. Several structural parameters of mounting pedestals are taken as the design variables, and the system vibration response and the reaction force of the front and rear mounting pedestals are considered as the objective functions. The first-order results and total sensitivity index of different design parameters for each objective function are obtained via SSAM, and the five most sensitive parameters are selected. Based on the above five design parameters, multi-objective optimization designing for vibration reduction in a simplified lubricating oil tank system is conducted based on NSGA-Ⅲ, and the results of the above triple-objective optimization are obtained as a Pareto-front surface with an obvious frontier. It can be observed from the simulation results that the oil tank vibration of the optimized system is effectively suppressed under the unbalanced excitation of two typical engine speeds. The established method and the main results can provide guidance for designers of aeroengine external structural systems, which can help to achieve superior system dynamic performances in engineering applications.

Triple-objective optimization of He Brayton cycles for ultra-high-temperature solar power tower

In this study, three He regenerative Brayton cycles, including the cycle with intercooling (1T2C), single-reheat cycle with intercooling (2T4C) and double-reheat cycle with intercooling (3T6C), are designed to enhance the cycle performance of the ultra-high-temperature (>1300 °C) solar power tower. The effects of various operating parameters are firstly studied using a thermodynamic model. Results indicate that three crucial performance metrics, including the cycle energetic efficiency, the temperature difference of the thermal energy storage, and the specific work of He, are influenced by the parameters in different ways. Then, the three cycles are optimized by using the three metrics as the optimization objectives. The results indicate that the optimized 3T6C cycle can achieve the greatest energetic efficiency of 65.32% and the largest specific work of 4947.1 kW·kg−1, and the optimized 1T2C cycle achieves the widest temperature difference of 1028.5 °C. Then, the optimal cycle designs are selected from the Pareto frontiers of the three cycles. It is found that the optimal 1T2C, 2T4C and 3T6C cycles achieve the energetic efficiencies of 55.91%, 61.57% and 65.28%, respectively. Meanwhile, the temperature differences and the specific work of the optimal 1T2C, 2T4C and 3T6C cycles are 877.1 °C and 2432.1 kW·kg−1, 713.4 °C and 4294.7 kW·kg−1, 539.0 °C and 4947.1 kW·kg−1, respectively. Moreover, the 3T5C cycle can achieve the same performance as the 3T6C cycle but it needs fewer components. The above comparisons indicate that only the 1T2C cycle can meet the requirement when the temperature difference needs to be wider than 1000 °C. The 3T5C cycle is recommended if the specific work and energetic efficiency are required to be as large as possible. When the three metrics need to be balanced, the 2T4C cycle should be recommended. The aforementioned analyses indicate that the proposed He cycles should be promising for improving the power cycle performance of the ultra-high-temperature solar power tower.