Multi-objective Optimization Study Research Articles

Optimizing functionally graded tibial components for total knee replacements: a finite element analysis and multi-objective optimization study

The optimal design of complex engineering systems requires tracing precise mathematical modeling of the system’s behavior as a function of a set of design variables to achieve the desired design. Despite the success of current tibial components of knee implants, the limited lifespan remains the main concern of these complex systems. The mismatch between the properties of engineered biomaterials and those of biological materials leads to inadequate bonding with bone and the stress-shielding effect. Exploiting a functionally graded material for the stem of the tibial component of knee implants is attractive because the properties can be designed to vary in a certain pattern, meeting the desired requirements at different regions of the knee joint system. Therefore, in this study, a Ti6Al4V/Hydroxyapatite functionally graded stem with a laminated structure underwent simulation-based multi-objective design optimization for a tibial component of the knee implant. Employing finite element analysis and response surface methodology, three material design variables (stem’s central diameter, gradient factor, and number of layers) were optimized for seven objective functions related to stress-shielding and micro-motion (including Maximum stress on the cancellous bone, maximum and mean stresses on predefined paths, the standard deviation of mean stress on paths, maximum and mean micro-motions at the bone-implant interface and the standard deviation of mean micro-motion). Then, the optimized functionally graded stem with 6 layers, a central diameter of 5.59 mm, and a gradient factor of 1.31, was compared with a Ti6Al4V stem for various responses. In stress analysis, the optimal stem demonstrated a 1.92% improvement in cancellous bone stress while it had no considerable influence on the maximum, mean, and standard deviation of stresses on paths. In micro-motion analysis, the maximum, mean, and standard deviation of mean micro-motion at the interface were enhanced by 24.31%, 39.53%, and 19.77%, respectively.

Electrified Solar Zero Liquid Discharge: Exploring the Potential of PV-ZLD in the US.

Current brine management strategies are based on the disposal of brine in nearby aquifers, representing a loss in potential water and mineral resources. Zero liquid discharge (ZLD) is a possible strategy to reduce brine rejection while increasing the resource recovery from desalination plants. However, ZLD substantially increases the energy consumption and carbon footprint of a desalination plant. The predominant strategy to reduce the energy consumption and carbon footprint of ZLD is through the use of a hybrid desalination technology that integrates renewable energy. Here, we built a computational thermodynamic model of the most mature electrified hybrid technology for ZLD powered by photovoltaic (PV). We examine the potential size and cost of ZLD plants in the US. This work explores the variables (geospatial and design) that most influence the levelized cost of water and the second law efficiency. There is a negative correlation between minimizing the LCOW and maximizing the second-law. And maximizing the second-law, the states that more brine produces, Texas is the location where the studied system achieves the lowest LCOW and high second-law efficiency, while California is the state where the studied system is less favorable. A multiobjective optimization study assesses the impact of considering a carbon tax in the cost of produced water and determines the best potential size for the studied plant.

Optimising liquid impingement jet arrays for concentrated heat sources

This work seeks to investigate jet array impingement liquid cooling of CPU processors that utilise Integrated Heat Spreaders. In this study, single and centralised heat sources of various sizes (10 mm to 20 mm) were investigated, and a series of single and multi-objective optimisation studies were carried out to determine the optimum arrangement of microjets for different heat source sizes based on variable geometric inputs of the jet orifice plate. The results indicated that the design of the optimum jet orifice strongly depends on the size of the heat source as well as whether the target optimisation strategy is to achieve minimum thermal resistance, or both minimum thermal resistance and minimum pumping power simultaneously. A clustering of quite different orifice designs in optimal zones is observed, where approximately the same hydraulic and/or thermal performance is observed.

Artificial neural network for preliminary design and optimisation of civil aero-engine nacelles

Abstract Within the context of preliminary aerodynamic design with low order models, the methods have to meet requirements for rapid evaluations, accuracy and sometimes large design space bounds. This can be further compounded by the need to use geometric and aerodynamic degrees of freedom to build generalised models with enough flexibility across the design space. For transonic applications, this can be challenging due to the non-linearity of these flow regimes. This paper presents a nacelle design method with an artificial neural network (ANN) for preliminary aerodynamic design. The ANN uses six intuitive nacelle geometric design variables and the two key aerodynamic properties of Mach number and massflow capture ratio. The method was initially validated with an independent dataset in which the prediction error for the nacelle drag was 2.9% across the bounds of the metamodel. The ANN was also used for multi-point, multi-objective optimisation studies. Relative to computationally expensive CFD-based optimisations, it is demonstrated that the surrogate-based approach with ANN identifies similar nacelle shapes and drag changes across a design space that covers conventional and future civil aero-engine nacelles. The proposed method is an enabling and fast approach for preliminary nacelle design studies.

Multi-objective optimization study on the performance of double Darrieus hybrid vertical axis wind turbine based on DOE-RSM and MOPSO-MODM

The Double Darrieus Vertical Axis Wind Turbine (DD-VAWT) effectively enhances wind energy efficiency at low tip speed ratios. In this study, the multi-objective optimization method is constructed for DD-VAWT. The optimization objective is to enhance the power coefficient as well as reduce the maximum instantaneous torque coefficient, while decision variables include the inner ring wind turbine diameter, inner ring wind turbine height, inner ring wind turbine blade airfoil chord length and inner and outer ring wind turbine phase angle. Firstly, the computational fluid hydrodynamics model of DD-VAWT is established and validated by experimental data from the literature. Then, the regression model between critical structural parameters and power coefficients, maximum instantaneous torque coefficients is constructed using response surface analysis. Finally, the multi-objective particle swarm optimization algorithm is used to optimize the objective and the optimal solution is selected from the Pareto frontier solution. This work provides a direction to improve the operating performance of DD-VAWT at low tip speed ratios as well as contributes to energy saving and emission reduction.

A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

Multi-objective optimization of energy-dissipating steel plate fuse links using response surface method

Steel plates with openings are used to provide energy dissipation in earthquake-resistant structures. Shear-acting steel plates with butterfly-shaped or straight links are particularly used as easily replaceable fuses in low-damage design of structures. Sensitivity analyses have shown that the cyclic response of steel plate fuses varies depending on the shape of the fuse link. However, simultaneous response optimization approaches have not been applied in the past research on steel plate fuses. A multiple-response optimization study is needed to find optimal link shapes with improved cyclic response characteristics while simultaneously considering different optimization objectives. In this study, experimentally validated finite element simulations and response surface methodology are used to predict the cyclic load-displacement response of steel plate fuses. The behavior of steel plate shear links is evaluated in terms of initial stiffness, yield strength, ultimate stiffness, effective damping, peak strength, and ductility. Predictive equations are derived and verified for each response characteristic in terms of three influential factors, including the length to end-width ratio (L/b), end-width to thickness ratio (b/t), and mid-width to end-width ratio (a/b) of shear links. The predictive equations are then employed to perform multi-objective optimization studies using a desirability approach. Four different optimization objectives are considered, such as the maximization of effective damping and ductility. Optimal ranges are determined for each factor. The results show that the energy dissipation, strength, and ductility of the fuse can be simultaneously maximized if using relatively thick and moderately long links (with b/t smaller than 6.6, L/b between 2.7 and 6, and a/b between 0.33 and 0.96). Optimal fuses links are those with an intermediate a/b between 0.37 and 0.89 and a small to moderate b/t between 3.0 and 6.6.

Multi-objective optimization study of regional integrated energy systems coupled with renewable energy, energy storage, and inter-station energy sharing

Globally, countries have established timelines and technological pathways towards achieving "carbon neutrality". Regional integrated energy systems, as an efficient and clean mode of energy provision, are particularly suitable for supplying various forms of energy to building users. However, due to the complexity of their structure and multiple energy flow couplings, regional integrated energy systems face challenges in system optimization, warranting further exploration in design optimization and benefit analysis. Therefore, a regional integrated energy system was established, integrating renewable energy, energy storage, and power/thermal sharing between stations. A multi-objective optimization model for the regional integrated energy system was established, targeting economic benefits, carbon reduction, and reliability. Overall benefits of the internal energy stations in the regional integrated energy system were meticulously analyzed, considering system benefits, inter-station energy sharing, and energy storage. Research findings indicate, the regional integrated energy system constructed in this study exhibited superior energy-saving, carbon reduction, and independence, compared to other integrated energy systems. For the proposed system, the annual total cost was 38.41 CNY/m2, primary energy consumption stood at 16.26 kWh/m2, carbon emission was recorded at 4.33 kg/m2, and the system interaction power measured 9.99 kWh/m2. Final, this study provides theoretical support for the application and promotion of regional integrated energy systems.

Advanced exergo-economic analysis of an advanced adiabatic compressed air energy storage system with the modified productive structure analysis method and multi-objective optimization study

In this study, conventional and advanced exergy/exergo economic analyses of an advanced adiabatic compressed air energy storage system (AA-CAES) system with a power output of 6 MW were performed. In addition, sensitivity analysis and multi-objective optimization study of the proposed system were also carried out. For the first time in the literature, advanced exergo-economic analysis was applied to the AA-CAES system with the Modified Productive Structure Analysis (MOPSA) method, and a new parameter was introduced as a performance criterion for this method. As a result of the study, the system's exergy efficiencies were found to be 54.51 % and 77 %, respectively, according to the conventional and advanced exergy analyses of the system. According to the conventional exergy analysis, the first three system components that should be given priority are the wind turbine (WT), the regulator (REG), and the heat exchangers-3 (HE-3), respectively. According to the advanced exergy analysis, this order changes as WT, the compressor-1 (C-1), and the compressor-2 (C-2). 40 % of the system's exergy destruction and exergy destruction cost rates are avoidable endogenous. This value shows the improvement potential of the system. As a result of the multi-objective optimization study, the modified exergy efficiency was found to be 94.58 %. The average unit product-exergy destruction cost was 59.51 $/GJ; these values were 22.83 % and 1.6 % improvement, respectively, compared to the base case.

Using Reduced Kinetic Model for the Multi-Objective Optimization of Thermal Section of the Claus Process Leading to a More Cost-Effective and Environmentally Friendly Operation

The Claus process is a sulfur recovery unit wherein hydrogen sulfide is converted into the elemental sulfur. This study aims to model the thermal section of the Claus process, which consists of a reaction furnace and a waste heat boiler, as a configuration of two reactors, and subsequently optimize the entire section. Two different reduced kinetic schemes were provided for both units. Using the validated kinetics, mathematical models were developed. The waste heat boiler was modeled as a plug flow reactor with heat transfer, instead of a heat exchanger. The main objective was to maximize the amount of elemental sulfur at the end of the thermal section. Additionally, maximizing the amount of steam generated in the WHB was considered as a secondary objective, and the multi-objective optimization problem was solved. The sulfur production was improved 14.1% and 30% as a result of single- and multi-objective optimization studies. In addition, as an alternative, the Taguchi method was also used for optimization studies, and optimum values were determined. Using the Taguchi method, we determined that an increase in sulfur production by 24% is possible.

Research on Multi-Objective Optimization of High-Speed Solenoid Valve Drive Strategies under the Synergistic Effect of Dynamic Response and Energy Loss

Under high-frequency operating conditions, the high-speed solenoid valve (HSV) experiences energy loss and heat generation, which significantly impacts its operational lifetime. Reducing the energy loss of an HSV without compromising its opening response characteristics poses a significant challenge. To address this issue, a finite element simulation model of an HSV coupled with a current feedback model is constructed to investigate the synergistic effects of dynamic response and energy loss. Prediction models for the opening response time, HSV driving energy, and Joule energy using a back propagation neural network (BPNN) are established. Furthermore, a multi-objective optimization study on the current driving strategy using a non-dominated sorting genetic algorithm II (NSGA-II) is conducted. After optimization, although there was a 6.24% increase in the opening response time, both HSV drive energy and Joule energy were significantly reduced by 15.67% and 22.49%, respectively. The proposed multi-objective optimization method for an HSV driving strategy holds great significance for improving its working durability.

An integrated assessment of the financial and environmental impacts of exterior building insulation application

In the literature, optimum thermal insulation thickness on building's exterior was examined by many researchers. Optimum insulation thickness was generally studied with the aim of minimizing the total cost, and suitable values were determined within this scope. In this study, it has also considered the amount of CO2 released into the atmosphere due to the fuels used in building heating and cooling processes, as well as insulation materials, in addition to the total cost. This study has been focused on optimization of the determination of the ideal insulation thickness of buildings by simultaneously minimizing the total cost and CO2 emission, using the knee point approach. In this context, a multi-objective optimization study has been conducted for thermal insulation applied to buildings located in different climatic zones considering the solar radiation. A comprehensive analysis has been carried out to determine the impact of the heating source type (natural gas, electricity, fuel oil), insulation material (expanded polystyrene, extruded polystyrene, mineral wool, polyurethane foam), climatic zone (I, II, III, IV) and the purpose of the building's use (summer house, winter house or all-season house) on the optimum insulation thickness. The degree-day approach has been used to calculate annual energy consumption, and accordingly the life-cycle cost (LCC) method has been used in economic analysis. The optimum insulation thicknesses have been calculated with two separate objectives: one to minimize only the total cost and the other to minimize only the CO2 emissions. It has been found that when the primary goal of insulation application is to minimize CO2 emissions, the calculated optimum insulation thicknesses are consistently higher compared to situations where the main goal is minimizing total cost. Furthermore, for various climatic zones and different purpose of the building's use, an ideal insulation thicknesses that simultaneously minimizes both the cost and CO2 emissions have been identified. In this research, the equations providing environmentally optimum insulation thicknesses to minimize CO2 emissions have been derived and presented to researchers. Finally, the conclusion is drawn that the knee point approach can be applied in insulation scenarios where two objective functions need simultaneous optimization.

Enhancing Cutting Efficiency and Minimizing Forces for Nomex Honeycomb Core Using Grey Relational Analysis and Desirability Function Analysis.

Nomex Honeycomb core is the foundational building block for manufacturing aerospace composite components. Its usage requires machining honeycomb in complex aerodynamic profiles where the quality of the core is governed by accuracy and precision of cut profiles. The assessment of accuracy and precision is directly related to forces induced in the cutting tool and cutting efficiency. These two parameters form the basis of a multi-objective function that this paper aims to optimize for the milling operation. The parameter of depth of cut considered in this paper has not been analyzed in a multi-objective optimization study of the Nomex Honeycomb core previously. A Taguchi-based array of Design of Experiments followed by Analysis of Variance and correlation analysis is utilised. The results indicate that the most significant factor is the feed rate, with a percentage contribution of 72% for the cutting forces and depth of cut, with a percentage contribution of 85% in the case of cutting efficiency. The two parameters are optimized using Desirability Function Analysis and Grey Relational Analysis. The results are validated through experimental runs with an error within 5% of the statistical predictions, with the percentage improvement in cutting forces for optimum runs as compared to the worst experimental run at 47.8%. The percentage improvement in cutting efficiency likewise is 11%.

Multidisciplinary Optimization of Thermodynamic Cycles for Large-Scale Heat Pumps With Simultaneous Component Design

Abstract The performance of high temperature heat pumps (HTHPs) is highly dependent on the efficiency of its main components, which need to be optimally matched especially in closed cycles. The design process is therefore a challenging task as many disciplines and varying modeling depths need to be considered. Consequently, this is usually a sequential procedure beginning with cycle definition and raising the fidelity for component design. Fundamental design decisions are made based on assumptions for component performance. Mistakes in the phase of cycle definition are hard to reverse in later design stages. Therefore, this work introduces holistic approaches to the multidisciplinary design of closed Brayton cycles. Aerodynamic compressor design with two-dimensional throughflow analysis and geometry-based heat exchanger sizing are simultaneously optimized with thermodynamic cycle parameters. The presented methodologies make use of highly sophisticated design tools drawing on many years of experience in gas turbine design. The results demonstrate that holistic heat pump optimization can be successfully performed with reasonable computational effort. The advantages compared to conventional sequential design are elaborated. A comparison of two optimization concepts indicates that splitting up the design vectors of cycle and components shows the tendency to improve robustness. Finally, the tradeoff between system compactness and performance is demonstrated with a multi-objective optimization study.

Multiobjective Optimization Study On the Aerodynamic Performance and Anti-Erosion Characteristics of a Single-Stage Dusty Flue Gas Turbine

Abstract Solid particle erosion of dusty energy recovery turbine blades has a great impact on the operating economics and safety of the unit. To mitigate the erosion of blade and improve the aerodynamic performance of the turbine, a multiobjective optimization method for turbine cascade based on the experimental design method, genetic algorithm and CFD multiphase flow simulation was developed. The optimization results show that the number of stator and rotor blades and the trailing edge angle at 50% blade span are the main parameters affecting the efficiency and blade erosion of the dusty turbine. By reducing the number of stator blades and the circumferential bending angle of the stator trailing edge, the impingement velocity and impingement probability of particles impinging on the stator trailing edge decrease by 7.5%~16.8% and 8.9%~46.2%, respectively. Additionally, compared with the original design, the flow separation loss and secondary flow intensity of the rotor cascade are suppressed by adjusting the load distribution and inlet attack angle of the rotor; thus, the turbine efficiency effectively improves by 2.28%. Meanwhile, the optimized blade reduces the particle impingement velocity and probability on the rotor leading edge, and the erosion condition of the rotor leading edge decreases by 70%.

A multi-objective optimization approach for the production of polyhydroxybutyrate via Chlorogloea fritschii under diurnal light with single-stage cultivation

The present study aims to optimize the nutrients for maximization of cyanobacterial biomass with high content of polyhydroxybutyrate (PHB), a bioplastic, and recovery of biomass by auto-sedimentation under diurnal light mimic to sunlight. The multi-objective optimization with desirability approach was used to improve dry cell weight (DCW), PHB content (% w/w), and auto-sedimentation concentration factor (SCF) of biomass. Initially, NaNO3, K2HPO4, TRACE (micronutrient solution), Na2EDTA, and MgSO4.7H2O were screened as important media compositions. Screening was followed by the application of response surface methodology for the development of a model used in multi-objective optimization. The optimized media selected from many optimal solutions, a set of Pareto solutions generated by multi-objective optimization was validated in a flat panel photobioreactor. Using a single-stage cultivation strategy under diurnal light, Chlorogloea fritschii TISTR 8527 has shown capability to produce DCW of 1.23 g/l with PHB content of 31.78 % and SCF of 93.63 with optimal media. This leads to the enhancement of both PHB content (2.72 fold) and SCF (1.64 fold) were observed when compared to the non-optimal medium. This is the first multi-objective optimization study for media optimization using cyanobacteria reported till now under diurnal light mimic to sunlight for bioplastic production.

Multi-objective optimization study of hydrothermal aging on NOx-assisted soot catalytic combustion and secondary pollution emission in regeneration of CeO2 catalytic diesel particulate filter

The present study aimed to explore the effect of hydrothermal aging on the heterogeneous catalytic combustion process of soot in CeO2 catalytic diesel particular filter and the formation of secondary pollutants (N2O and CO).The present study was based on a fixed-bed experimental bench for NOx-assisted soot catalytic oxidation in CeO2-CDPF with different soot loading and catalyst coating amounts. A non-linear regression surrogate model and a multi-objective optimization decision approach were used to analyze the trade-off relationship between the design variables and the optimization objectives. The results showed that lower levels of soot loading (2.5 g/L), CeO2 catalyst coating amount (2.5 g/L) and higher NOx concentration (1000 ppm) in fresh catalysts were favorable for improving the performance of soot-assisted catalytic combustion and minimizing the formation of N2O and CO. However, it was observed that higher levels of catalyst coating amount (10 g/L) resulted in better soot catalytic combustion performance with lower N2O and CO emissions. Global sensitivity analysis showed a positive correlation between N2O and CO peak concentrations and peak temperatures with soot loading, but a negative correlation with catalyst loading. Over time, hydrothermal aging of CDPF caused the inhibitory effect of CeO2 catalyst on N2O to shift from negative to positive correlation, while also decreasing the synergistic performance of NOx in soot catalytic combustion.

Multi-objective optimization study of CCHP-GSHP-PV-ST system under different climatic zone conditions

In this research, a system with multiple energy sources of combined cooling, heating, and power (CCHP)/ground source heat pump (GSHP)/photovoltaic (PV)/solar thermal collector (ST) (CCHP-GSHP-PV-ST system) is constructed. And this system is combined with the operation strategy of following total electric demand, that is, giving priority to meeting the electrical load of the building, the additional power of the coupling system, and the power of driving the GSHP. Therefore, a model for multi-objective optimization of the CCHP-GSHP-PV-ST system based on energy, environment, and economic indicators is established, and the optimization model is resolved using the genetic algorithm. The load simulation of office buildings in representative cities of five climate zones in China (Harbin (severe cold climate), Beijing (cold climate), Kunming (mild climate), Guangzhou (hot summer and warm winter climate), Changsha (hot summer and cold winter climate)) was selected as the basis for coupling system optimization, and the multi-objective optimization study of CCHP-GSHP-PV-ST system under different climate zone conditions was carried out. The findings indicate: (1) Under various climatic zone conditions, the coupling system’s optimized composition varies a little; (2) The multi-energy coupled system of office buildings in Beijing (cold climate) has primary energy saving rates, carbon dioxide emission reduction rates, annual total cost saving rates, and comprehensive performance that are respectively 46.06%, 60.43%, 44.05%, and 50.18% better than the split-production system when compared to other climate zones.

Empirical study of evolutionary computation-based multi-objective Bayesian optimization for materials discovery

Empirical study of evolutionary computation-based multi-objective Bayesian optimization for materials discovery

Multi-objective optimization and parametric study of a hybrid waste gasification system integrated with reverse osmosis desalination

The increasing population has created two fundamental issues on the islands (in this case, Kish Island): an expansion in waste production and handling and a rise in the need for freshwater for daily consumption. Waste gasification can be used to reduce waste and generate energy. Sea salt water can be converted into drinkable freshwater using energy. This article describes a computational code using a combination of EES (Engineering Equation Solver) and MATLAB software for a hybrid cycle that includes waste gasification and reverse osmosis to generate freshwater and power. Kish Island waste data was carefully collected onsite. An exergy evaluation has been conducted to verify the cycle's irreversibility. Lastly, optimization has been performed to identify the best operation mode. In terms of irreversibility, a gasifier is far more irreversible than any other type of plant equipment. Based on the parametric analysis, salt water and waste flow significantly impact freshwater production. Approximately 20,000 m3/day are consumed on Kish Island each day, so if the entire production power of the steam turbine is used in the reverse osmosis high-pressure pump, 2860 m3/day can be obtained each day (14% of the required amount).