Multi-objective Optimization Analysis Research Articles

Feasibility Analysis of a Direct-drive PV Heat Pump System Based on Phase Change Heat Storage

A direct-drive PV pump system based on phase change heat storage is proposed to address the current building energy consumption problem in heating/cooling. The system can solve the problems of unstable solar energy, high battery cost, and short battery service life. Taking a 45m 2 office in Shanghai as the research object, the annual dynamic simulation model of the system is built through TSNSYS. The linear weighted evaluation function method is used for multi-objective optimization analysis of system performance and cost. The results show that the selection scheme with a PV area of 26 m2, a phase change volume of 0.14 m3, and a valley electricity storage ratio of 1 has the best-combined performance and cost of the system.

Hybrid multi-objective optimization and thermo-economic analysis of a multi-effect desalination unit integrated with a fuel cell-based trigeneration system

Due to fossil fuel depletion and global warming, biomass plays a significant role in supplying the required energy in the industrial and residential sectors. In order to develop clean and efficient conversion technology, an innovative combined cooling, heating and power (CCHP) system based on biomass is introduced in this paper. The proposed system comprises a gasification process, solid oxide fuel cell, gas turbine cycle, Rankine and organic Rankine cycles, absorption chiller, and multi-effect desalination unit simulated in ASPEN. In this system, a new configuration is proposed in order to regenerate the waste heat efficiently at every level of temperature and increase energy and exergy efficiencies. Also, thermodynamics, economic, and sensitivity analyses are performed in order to investigate the energy and exergy efficiencies, the overall cost of the system, and effective parameters. The novel optimization approach (RSM-GA-TOPSIS) is introduced by combining the response surface methodology, genetic algorithm, and Shannon entropy and TOPSIS in order to model and optimize the objective functions in DESIGN EXPERT and MATLAB software, respectively. The objectives of the optimization are maximizing energy and exergy efficiencies and minimizing the overall cost of the system. As a result, based on the initial conditions, the electricity production, overall energy and exergy efficiencies, overall cost of the system, and overall exergy destruction can be acquired at 25.235 MW, 76.69%, 57.07%, 6.1063 × 106 $, and 26.03 MW, respectively. After performing the optimization method, the optimum solution (point 3) is obtained through the TOPSIS approach. In terms of thermodynamics analysis, the electricity production, energy and exergy efficiencies are acquired at 31 MW, 88.15%, and 67.30%, which are increased by 22.85%, 14.94%, and 17.93%, respectively, compared with the initial conditions of the proposed system. In terms of economic analysis of the optimum solution, the annualized total cost, COE, and COTP can be obtained at 6.594 × 106 $, 7.3857 $/GJ, and 6.7624 $/GJ, respectively. By comparing this point to the initial conditions, although the annualized total cost at this point is increased by 7.99%, the COE and COTP are decreased by 12.1% and 10.4% due to a further increase in electricity production. Therefore, for producing energy with high efficiency and low COE, the system should be operated according to the working conditions of this point (point 3).

Analysis of the Capacity of the Fenton Process for the Treatment of Polluted Wastewater from the Leather Dyeing Industry.

In this work, the capacity of the Fenton oxidation process for the degradation of color and organic matter contained in the wastewater generated in the leather dyeing stage (WWDS) of an industrial tannery was evaluated. The wastewater characteristics included, among others, high toxicity (lethal concentration for Artemia salina, 24 h test, 50% of population = 93.71 ppm), high dye concentration (36 mg/L, yellow color), high chromium concentration (3.34 mg/L), and low biodegradability index (BOD5/COD ratio = 0.083). From an experimental design, the response surface methodology, and the multiobjective optimization analysis, the following optimal operating conditions were established: initial pH = 3.15, [Fe2+] = 0.981 mM, and [H2O2] = 5.38 mM. After 10 min of oxidation (determined from kinetic studies), it reached approximately 97% decolorization, COD reduction of approximately 82%, and TOC mineralization of approximately 92%. A synergistic effect of Fenton's reagents for TOC removal (S TOC = 0.8) and decolorization (S CN = 0.28) of the WWDS under study was confirmed experimentally. An increase in the biodegradability index, to a value of approximately 0.3, was confirmed. The cost of the treatment was estimated at 0.0112 USD/m3. Thus, the Fenton oxidation process allowed compliance with current Colombian environmental regulations and considerably improved the biodegradability and toxicity characteristics of the studied industrial effluent. It can be considered as an efficient alternative, easy to carry out on an industrial batch scale, and economically viable for the treatment of wastewater from the leather dyeing stage of an industrial tannery.

Implementing Combinative Distance Base Assessment (CODAS) for Selection of Natural Fibre for Long Lasting Composites

Eco-friendly materials are being developed as population change into more aware of the coincidental damage subordinated by synthetic materials. Research investigators have spoken a strong appetite to develop materials capable of replacing synthetic materials. As a result, there has been an increase in exigency for natural fibre-based composites in commercial applications in recent years. Natural fibres are long-lasting materials found in nature that have advantages like renewability, biodegradability, lightweight, high specific characteristics and low cost. To meet client needs in a timely manner, Within the framework of the product design process, the selection of materials should be finished, according to the concurrent engineering strategy. Due to competing requirements, Inappropriate material choice frequently causes untimely failures, which causes large losses. To deal with this objective, selection process of natural fibre for long lasting composites becomes significant. Hence, correct decision-making tool ought to be used. This paper focuses on selection of natural fibre by using CODAS technique. This technique plays a significant role in sensible managing. Since the selection of natural fibre among eight alternatives and six criteria, it was observed that Basalt is best natural for long lasting composites followed by flax and Kenaf respectively when compared with multi-objective optimization and ratio analysis (MOORA) technique.

Co-evolutionary algorithm based on problem analysis for dynamic multiobjective optimization

Dynamic multiobjective optimization problems (DMOPs) vary over time, requiring an optimization algorithm to track the position of Pareto-optimal front (PF) in a dynamic environment. To achieve that, a novel co-evolutionary algorithm based on problem analysis (CAPA) is proposed in this paper. CAPA is designed to solve DMOPs from decision space and objective space simultaneously, which is achieved by the combination of adjustable prediction (AP) and precise mapping strategy (PM). In decision space, the proposed multi-model prediction can estimate the location of new population based on the historical median points. In objective space, a novel sampling method is developed to search for sample points with better convergence or diversity. Then, mapping these sample points back to decision space based on inverse model. Through the problem analysis mechanism, the proportion of the new solutions produced by each strategy changes adaptively. CAPA is incorporated into the dynamic multiobjective evolutionary algorithm (DMOEA) based on decomposition (MOEA/D) to construct a novel algorithm. The efficacy of CAPA is validated by comparison with five state-of-the-art algorithms on 28 benchmarks. Experimental results show that CAPA has the ability to generate high quality population uniformly along PF.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale, two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells (PEMFCs) are considered a promising alternative to internal combustion engines in the automotive sector. Their commercialization is mainly hindered due to the cost and effectiveness of using platinum (Pt) in them. The cathode catalyst layer (CL) is considered a core component in PEMFCs, and its composition often considerably affects the cell performance (Vcell) also PEMFC fabrication and production Cstack costs. In this study, a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcell and Cstack. Four essential cathode CL parameters, i.e., platinum loading (LPt), weight ratio of ionomer to carbon (wtI/C), weight ratio of Pt to carbon (wtPt/c), and porosity of cathode CL (εcCL), are considered as the design variables. The simulation results of a three-dimensional, multi-scale, two-phase comprehensive PEMFC model are used to train and test two famous surrogates: multi-layer perceptron (MLP) and response surface analysis (RSA). Their accuracies are verified using root mean square error and adjusted R2. MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithm II. Compared to a typical PEMFC stack, the results of the optimal study show that the single-cell voltage, Vcell is improved by 28 mV for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by $5.86/kW for the same stack performance.

Comparative study on single-objective optimization and multi-objective optimization of convex textured friction couple performance

To improve the friction property of the friction pair of the hydrostatic thrust bearing, the surface with convex textures is designed at the bottom of the oil cavity, and the effect of convex textures on the interstitial oil film performance is analyzed. The interstitial oil film in contact with convex textures is simulated and studied by the computational fluid dynamics method. The study shows that the oil cavity with the convex structures can significantly improve the friction characteristics of the gap oil film in the hydrostatic thrust bearing. In order to further study the effect of texture parameters (texture radius, texture spacing , texture quantity and texture area ratio ) on its performance, the method based on the full factor test is used for single objective optimization, and the methods based on the BBD response surface and the NSGA-II algorithm are used for multiple target collaborative optimization. The full factor test with the main effect shows that the best combination with the bearing capacity is 0.5 mm, 7 mm, 7 and 1.6%, and the best combination with the rigidity is 0.3 mm, 5 mm, 5 and 1.13%. The influence of textures on the maximum temperature of oil film is basically unchanged and the bearing capacity is in inverse proportion to the friction factor. When considering the interaction between texture parameters, the BBD response surface method is used for multi-objective optimization. The study shows that the texture radius is 0.7 mm, the texture spacing is 7 mm, the quantity of textures is 6.2, and the area ratio is 3.14%, the optimal response values can be obtained, which the bearing capacity is 8519.31 N and the rigidity is 1.31868 E + 12 N m−1. The multi-objective optimization analysis of the NSGA-II algorithm based on the BBD response surface model shows that the algorithm can obtain a Pareto solution set. Each set of solutions obtained by the NSGA-II algorithm is the optimal solution. Both the BBD response surface method and the NSGA-II algorithm can be used to solve multi-objective problems.

Multi‑objective optimization of nitrile rubber and thermosets modified bituminous mix using desirability approach.

A variety of materials, including waste and rubber products, have been used in road construction to improve the performance of bituminous pavements. The present investigation is focused on modifying bitumen using Nitrile rubber (NBR) with different thermosets namely Bakelite (B), Furan Resin (FR), and Epoxy resin (ER). The emphasis of the problem is to arrive at a mix to achieve maximum Marshall Stability (MS) and minimum flow value of Modified Bituminous Concrete. Taguchi DOE technique has been used to design the experiments using Minitab software. Analysis of Variance (ANOVA) and Multi-objective optimization has been performed using the desirability approach in Design expert software. ANOVA analysis predicts that NBR, B, ER, and FR are the major significant parameters for Marshall Stability (MS) and Flow Value (FV). It has also been analyzed from SEM and EDS images of modified bitumen that sample S1 (5% NBR, 10% Bakelite, 10% FR, 2.5% ER) has a fine surface with small pores as compared to sample S34 (10% NBR, 0% Bakelite 10% FR, 2.5% ER). Multi-optimization results suggested the optimal conditions are achieved at NBR-7.6%, Bakelite-4.8%, FR-2.5%, and ER-2.6% for MS and FV. The maximum MS is 14.84 KN and the minimum FV is 2.84 mm is obtained using optimum conditions. To validate the optimization results, the confirmation runs have been conducted, and obtained results are within 5% error with optimal conditions.

Analysis of single and multi-objective optimization of the pultrusion process

This work used a computational strategy to optimize a pultrusion process. Based on mass and energy balance equations, the mathematical model was implemented into Ansys CFX® and Matlab® tools. A pultrusion die with six heaters and C-section composite geometry was used as a studied case. For the single objective optimization, the sum of the heaters' temperatures was considered as objective function with a minimum degree of cure of 0.9 imposed as a constraint. Particle swarm optimization (PSO) and sequential quadratic programming (SQP) algorithms were considered for single objective optimization. The results indicated that the SQP method could reduce the objective function and computational cost by 1.58% and 30.31%, respectively, concerning the PSO algorithm. However, the results also indicated that the performance of the SQP is highly dependent on the initial temperature estimates. The PSO and NSGA-II algorithms were applied to solve the multi-objective optimization in which pull speed maximization and minimization of heaters' temperatures were defined as objective functions. Results indicated that NSGA-II was 18.8% faster than the PSO strategy. The Pareto curve of the two methods presented a similar profile, evidencing that a higher pull speed requires greater energy consumption. Finally, the results show that the Pareto curve is helpful for predicting the set of resulting optimal points, which also makes it possible to detect unfeasible combinations of temperature and pull speed values.

Modeling and techno-economic comparison of two types of coaxial with double U-tube ground heat exchangers

Among different types of ground heat exchangers (GHEs), multi-external chamber coaxial GHE is recently a promising design due to its geometrical characteristics of concentric tube-in-tube configuration and maximal heat exchange performance. Nevertheless, the characterization and commercialization of multi-external chamber coaxial GHEs need a deep understanding of the effect of different opt-geometrical and operational parameters on their thermo-economic performance. Therefore, accurate numerical modeling of the transient heat transfer proficiencies and the techno-economic feasibility of the multi-external chamber coaxial GHE and coaxial GHE compared to conventional double U-tube GHE is crucial to promise an optimal design of ground-coupled heat pump systems. This study introduces a comprehensive systematic and techno-economic analysis of the transient heat transfer characteristics of coaxial and multi-external-chamber coaxial GHEs compared to conventional double U-tube GHE. A three-dimensional numerical model was adopted and validated by field experiments. Moreover, a sensitivity analysis was conducted to investigate the impacts of several key operating and design parameters on heat transfer performance under heating and cooling modes conditions. Furthermore, various case studies, along with a comprehensive analysis of multi-objective optimization for minimization of the GHE number and energy consumption, were also carried out. In addition, a techno-economic analysis was also carried out using the total annual cost method to assess the economic viability of the proposed GHE designs. The results reveal that, compared to double U-tube GHE, coaxial GHE has the best thermal performance with maximum and average heat transfer rates of 127.54% and 17.67%, while the multi-external-chamber coaxial GHE is estimated to be 62.38% and 11.72%, respectively. Also, the corresponding total thermal resistance of coaxial GHE is the lowest by 14.86%, and for multi-external-chamber coaxial, it is 10.30%; also coaxial GHE has the lowest pressure drop by 76.45%, followed by multi-external-chamber coaxial GHE with 57.18%. In the case studies, replacing double U-tube GHE with coaxial or multi-external-chamber coaxial GHEs, the GHE number can be decreased by 13.3% for both the designs, or the energy consumption of the water pump by 33.91% and 30.97%, and the energy consumption of the entire system by 17.21% and 10.54%, respectively. The techno-economic analysis results indicate that replacing double U-tube GHE with coaxial GHE is the most feasible alternative, which can decrease the total annual cost by 13.58%, followed by multi-external-chamber coaxial GHE by 6.77%. Further discussion for the deep applications was performed, which concluded the optimal GHE design. The findings of this paper can be used as a guide for future research on GHEs for potential applications of ground-coupled heat pump systems.

Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network.

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

Development of ventilation and sound-absorbing materials using specimens generated by the multi-objective optimization method

In hot and humid tropical countries, the houses are designed to be naturally ventilated with vent louvers. However, these louvers play as direct paths for noise entering the house. It is necessary to create geometries that can maximize the noise transmission loss and the ventilation performance of the housing structure. The present study aims to develop ventilation and sound-absorbing material using the multi-objective optimization method. This material has a specific design that enables wind from the exterior of the building to pass through while reducing noise. This study is presented in two parts. This paper, Part 1, explains the multi-objective optimization method and analysis calculation result. In the following paper, Part 2, the acoustic experiment of the ABS specimens manufactured by the 3D printer and results will be reported. An optimization problem is formulated by the Digital Fourier transformation, which searches the patterns of existing and blank elements. The sum of the magnitudes of the flow velocities in all the elements and those in the bottom elements are obtained from fluid potential flow analysis and used as by-objective functions. Multi-objective optimization is performed using "Platypus," an external library of Python. As a result, models that maximized the sum of the magnitude of each flow velocities are obtained.

Multi-objective optimization and 4E analysis of an integrated system based on waste-to-energy, solar PV, power-to-gas, and HDH-RO desalination

Waste-to-energy technology is among the most important parts of a sustainable waste management system nowadays. By adding a renewable source of electricity (solar photovoltaics) and power to gas (proton exchange membrane electrolyzer and the Sabatier reactor) to convert CO2, the environmental problems of waste to energy plants (here incineration and landfill) can be tackled. To solve freshwater scarcity issues, two desalination units (reverse osmosis and humidification and dehumidification) are added to the system. The main novelty of this study is to produce beneficial outputs from municipal solid waste and solar energy. The system is analysed comprehensively from energy, exergy, economic, and environmental viewpoints. The energy and exergy efficiency of the system is 17.48 % and 15.15 %, respectively, and the net power production is 8.42 MW. From economic and environmental points of view, the total annual cost is €12.12 million and CO2 conversion is 99.96 %. The parametric study is conducted on the effect of important parameters. Furthermore, multi-objective optimization is performed by using a genetic algorithm with the objectives of maximizing exergy efficiency and minimizing total annual cost. The optimum solution provides the exergy efficiency of 20.70 % with the total annual cost of €12.75 million.

Fusion Processing Techniques and Bio-inspired Algorithm for E-Communication and Knowledge Transfer

This study suggests employing a dynamic natural and bio-inspired algorithm (DNBIA) to strengthen the confidentiality, integrity, and availability of digital information exchanges. You may think of the suggested method as a clever approach to Fusion Processing. Fusion Processing is the practice of combining and analyzing information from many databases. The efficiency and reaction time of e-communication systems may be increased by the use of the suggested DNBIA algorithm, which processes and integrates data from different sources. It is also possible to see the multi-objective optimization study presented in this work as a type of Fusion Processing. Cyberattacks and other types of computer security risks are the focus of this study, which seeks to optimize numerous objectives concurrently in order to eliminate them. The study can give a complete solution to improve the security of e-communication systems by combining different goals. The suggested method of enhancing e-communication and information transmission using DNBIA and multi-objective optimization analysis can be seen as a type of Fusion Processing. Efficient e-communication systems may be achieved by collecting data from a variety of sources and analyzing the results.

Multi-Objective Optimization and Tradespace Analysis of a Mechanical Clock Movement Design

Abstract Pendulum clocks were the prevalent time keeping standard for centuries to regulate commerce and public activities. These mechanical movements were the most accurate timekeepers globally until replaced by electric clocks. Although mainly used for decorative purposes today, the pendulum clock's working principles and mechanical behavior can serve to demonstrate fundamental science and engineering concepts. The tradeoff between a clock's quality factor, pendulum properties, and period can best be explored with multiple objective optimization and tradespace analysis methods. In this project, a Multi-Objective Genetic Algorithm (MOGA-II) and a Multi-Objective Simulated Annealing (MOSA) optimization approaches are applied to evaluate a Graham escapement street clock for pendulum mass and time accuracy with a range of the period. These clock designs vary the pendulum length, pendulum bob radius, and bob thickness. Horological concepts are used to calculate the overall performance and general utility. The numerical results show a 0.7% increase in the quality factor, and a 0.56% reduction in the mass, while maintaining the designed period by modifying the clock parameters. More importantly, these changes can provide material cost savings in a mass production scenario. Overall, the study highlights the tradeoff designer engineers have considered for decades which can now be visualized using computer tools for greater insight.

Simultaneous optimization of working fluid and temperature matching for heat pump assisted geothermal cascade heating system

The heat pump assisted geothermal cascade heating system (GCHS) has a promising prospect in space heating. In this paper, considering thermodynamic, economic and environmental performance, the optimal working fluid from four hydrocarbon candidates and temperature-matching for the GCHS are determined simultaneously based on multi-objective optimization and grey relational analysis. The results show that according to the optimal compromise solutions, the GCHS with R600 has the highest thermodynamic performance (COPtot) of 22.58 and the least annual carbon emission (ACE) of 2.80 × 103 ton while R290 has the largest net present value (NPV) about 45.83 × 106 CNY. Additionally, all temperature variables considered generally tend to reach their extreme value except for the temperature lift of middle circulation water in the secondary plate heat exchanger. The optimal working fluid based on comprehensive performance is considered to be R600 with the largest grey relational degree of 0.752. Besides, the global sensitivity analysis on the GCHS with R600 indicates that the pinch point temperature difference of condenser is the most important influential parameter for COPtot with total Sobol index about 0.918 and the geothermal tail water temperature plays a significant role for both NPV and ACE with total Sobol index up to 0.806 and 0.992, respectively.

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.

Technical assessment of novel organic Rankine cycle driven cascade refrigeration system using environmental friendly refrigerants: 4E and optimization approaches.

Hydrocarbons are a promising working fluid for organic Rankine cycles and refrigeration systems that have gained increasing attention in recent decades as a replacement for the currently used higher global warming potential fluids. A novel standalone cascade refrigeration system (CRS) has been developed in this study for simultaneous ultra-low temperature cooling and heating production to medical applications most suitable for rural and power outages. Using the energy utilization factor (EUF) metrics, neopentane was chosen as the working fluid for the organic Rankine cycle (ORC) system among the selected hydrocarbon (HC) refrigerants. The standalone ORC-CRS system performance was investigated in terms of cooling and heating capacity, EUF, exergy, and environmental metrics by varying both ORC and CRS key temperature parameters. The exergy destruction of individual components was evaluated, and it was found that the ORC evaporator has the highest exergy destruction, followed by the expander. The highest EUF and exergy efficiency for a combined ORC-CRS system was about 0.64 and 38%. The maximum cooling and heating capacity of novel standalone CRS system was achieved by 70kW and 225kW respectively at a typical operating condition. At a higher ORC evaporation temperature, the exergy efficiency of the ORC-CRS system is decreased, leading to reduction in SI. The standalone ORC-CRS system at ORC evaporator temperature of 150°C achieved the maximum CO2 emission tax savings of $ 6.4 × 107 over a 15-year lifetime period. The total annualized cost index of the combined cooling heating system is in the range between 35 and 60 $/h for the variable operating condition. Non-dominated sorting genetic algorithm II (NSGA-II) method was used to conduct the multi-objective optimization analysis, and the technique for order of preference by similarity to ideal solution (TOPSIS) method used to found the optimal point, which had the following values of TAC 42.22 $/h and exergy efficiency was around 46.10%.

Multi-objective optimization design and analysis of large deformation composite material

In the future, advanced aircraft proposes to reduce the gaps on the surface of the body to improve the flight performance and stealth performance of the aircraft. This requires a sealing device between the movable surface of the leading and trailing edges of the wing and the fixed wing surface. Based on the sealing of the wing gap, this paper uses CATIA software combined with HyperWorks and ABAQUS software to design and optimize a large-deformation composite structure that can follow the large-scale movement of the movable surface without affecting the movement of the movable surface of the wing.

Application of yaw dampers with frequency-selective damping to improve the locomotive adaptability to low/high conicity stability

High-speed locomotives are prone to the carbody or bogie hunting under extreme wheel-rail contact conditions, which can cause negative impacts on vehicle dynamics performance. The influence of key suspension parameters on the carbody lateral ride comfort and bogie lateral acceleration is investigated by the multi-objective optimization and Sobol global sensitivity analysis methods, which is based on the Radial Basis Function Neutral Network surrogate model. The results illustrate that the yaw damper damping is sensitive to the two hunting stabilities, but conventional yaw dampers with fixed damping fail to satisfy both the stability demands simultaneously. Therefore, this paper presents a damping-adjustable yaw damper integrating an FSD (frequency-selective damping) valve to guarantee the lateral stability especially in abnormal wheel-rail contact states, and the application effects of the FSD yaw damper for the locomotive are researched by dynamics simulations. The results show that the FSD yaw damper can improve the adaptability of the locomotive to both the low and high wheel-rail contact conicity service conditions compared with the conventional yaw dampers.