Multi-objective Parameter Optimization Research Articles

Multi-objective trajectory planning for industrial robots using a hybrid optimization approach

Abstract In this paper, a hybrid approach organized in four phases is proposed to solve the multi-objective trajectory planning problem for industrial robots. In the first phase, a transcription of the original problem into a standard multi-objective parametric optimization problem is achieved by adopting an adequate parametrization scheme for the continuous robot configuration variables. Then, in the second phase, a global search is performed using a population-based search metaheuristic in order to build a first approximation of the Pareto front (PF). In the third phase, a local search is applied in the neighborhood of each solution of the PF approximation using a deterministic algorithm in order to generate new solutions. Finally, in the fourth phase, results of the global and local searches are gathered and postprocessed using a multi-objective direct search method to enhance the quality of compromise solutions and to converge toward the true optimal PF. By combining different optimization techniques, we intend not only to improve the overall search mechanism of the optimization strategy but also the resulting hybrid algorithm should keep the robustness of the population-based algorithm while enjoying the theoretical properties of convergence of the deterministic component. Also, the proposed approach is modular and flexible, and it can be implemented in different ways according to the applied techniques in the different phases. In this paper, we illustrate the efficiency of the hybrid framework by considering different techniques available in various numerical optimization libraries which are combined judiciously and tested on various case studies.

Multi-objective optimization of key parameters of stirred tank based on ANN-CFD

In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.

A review on simulation based multi-objective optimization of space layout design parameters on building energy performance

Improving the energy performance of buildings is crucial for environmental protection, energy savings, and a better living environment. The growing emphasis on sustainable building practices has led to an increased focus on optimizing space layout design parameters to enhance building energy performance. This review explores the application of simulation-based multi-objective optimization techniques in the context of studying the impact of space layout design on building energy efficiency. The integration of advanced simulation tools with optimization algorithms allows for a comprehensive analysis of multiple conflicting objectives like energy performance, user comfort as well as cost factor. The review begins by outlining the key parameters influencing building energy performance, including spatial configurations, orientation, and space perimeter variables. Subsequently, it delves into the various simulation tools employed to model the complex interactions between these parameters and their effects on energy performance. The integration of energy simulation software is highlighted as a crucial step towards achieving accurate and realistic assessments. In summary, this review delivers a comprehensive overview of the state-of-the-art methods in simulation-based multi-objective optimization for studying space layout design parameters and their impact on building energy performance, offering insights for researchers, practitioners, and policymakers in the field of sustainable architecture. There is a requirement for a comprehensive multi-objective framework for complex structures in the investigation of building energy performance giving more focus on reducing the cooling load and optimization of space layout along with envelope parameters.

Correction: Multi-objective optimization of laser welding parameters for steel/Al based on Kriging-MSSA

Correction: Multi-objective optimization of laser welding parameters for steel/Al based on Kriging-MSSA

Multiobjective optimization of laser welding parameters for P92 steel based on MFO/MOEAD-Kriging

P92 steel is used in steam equipment of thermal power plants and nuclear power plants. It needs to withstand high temperature and high pressure during service, so high-quality welds are required. Laser welding can easily obtain good weld quality and reduce the probability of coarse grains and welding defects in the heat-affected zone. Post-weld deformation, residual stress, and weld hardness are important indexes for evaluating weld quality, which largely depends on the combination of laser welding process parameters. The multiobjective optimization methods of moth-flame optimization (MFO) and multiobjective evolutionary algorithm based on decomposition under the Kriging model were compared in this research. It was found that the MFO algorithm has a faster optimization speed and better overall effect. The Optimal Latin hypercube sampling method was used to design the simulation test. The welding seam-forming process was simulated by the SYSWELD software. The Kriging model was used to make the nonlinear relationship between the process parameters and the weld quality indexes significant. A new data set was designed to evaluate the accuracy of the model. On this basis, the efficiency of the combination of process parameters optimized by the two algorithms was compared. The simulation and experimental results were in good agreement with the multiobjective optimization results of the MFO. In the experimental verification, the optimized weld quality was analyzed from the aspects of microstructure and mechanical properties. The results showed that the multiobjective optimization method quickly and effectively reduces the probability of weld quality defects in actual welding, thereby improving weld quality. This multiobjective optimization method provides valuable research ideas for engineering research and development to effectively shorten its development cycle.

Analysis and optimization of the heat transfer behavior in the melt-spinning process of Nd-Fe-B ribbons

Heat transfer plays a significant role on the quality of Nd-Fe-B ribbons in the melt-spinning (MS) process. In this paper, the heat transfer behavior in the MS process of Nd-Fe-B ribbons was investigated by experiment and simulation. A full flow channel heat transfer model with high accuracy is established using the “fluid-thermal” coupling method. The temperature distribution of the cooling roller and fluid field in steady-state is obtained. The simulated and experimental results of the outlet temperature are compared and the relative error is within 5%. The microstructures of the free surface and roller-sticking surface of Nd-Fe-B melt-spun ribbons are characterized by FE-SEM, and the refined grains are formed on the roller-sticking surface of Nd-Fe-B ribbons due to rapid solidification caused by the heat transfer from the roller. Moreover, a multi-nozzle structure is proposed for the MS process. The temperature distribution on the surface of the cooling roller in the axial direction is more uniform compared with the original single-nozzle structure, and the average convective heat transfer coefficient is increased by about 1.27 times after the modification of multi-nozzle structure, with a productivity of three times. Finally, the experimental design method of Latin hypercube sampling response surface design is used to obtain the response surface relationships of rotational speed, inlet flow rate, and water inlet temperature on the surface temperature of the cooling roller, respectively. NSGA-II multi-objective genetic algorithm (MOGA) is adopted for multi-objective optimization of process parameters. The optimized combination of process parameters is obtained with a relative error of about 0.41%. The research results are helpful to understand the heat transfer behaviors in the MS process of Nd-Fe-B ribbons, improve the utilization rate of the cooling roller and enhance the production efficiency in the actual application.

Mechanical analysis and biodegradation of oxides-based magneto-responsive shape memory polymers for material extrusion 3D printing of biomedical scaffolds

The development of advanced smart polymers, characterized by superior strength, exceptional quality, biodegradability, and lightweight structures, stands as a paramount concern in the context of biomedical implant materials. Aiming at the requirements of a tracheal scaffold, this research focuses on developing magnetic shape memory polymers using solvent evaporation method and characterizing their morphology, viscoelastic, thermodynamic, mechanical and shape memory properties. Furthermore, an assessment of degradation is conducted, along with an evaluation of tensile strength both pre- and post-degradation. The findings reveal that the dispersion of Fe3O4 nanoparticles exhibits homogeneity at a 15 wt% concentration but tends to aggregate at high proportions. The glass transition temperature increases with the addition of Fe3O4, while less than 10% ratio of the Fe3O4 cannot activate the magnetic actuation. Notably, the tensile and elastic modulus for the 15 wt% Fe3O4 composition measure at 59.70 MPa and 24.68 MPa, respectively, and exhibit a diminishing trend with higher nanofiller content. Subsequently, it demonstrates favorable thermal and magnetic shape memory behavior, yielding reasonable shape recovery ratios. Over a degradation period of 120 days, a reduction in average weight loss was observed for 15 wt% concentration, while higher for higher concentrations due to their non-uniform dispersion. Post-degradation, the tensile strength experiences a decline with proportional reduction of 41.15%, 37.61%, 47.13%, 42.46%, and 54.77%, respectively. Based on the finite element analysis, the multi-objective optimization of the structural parameters of the tracheal stent was carried out, and manufactured by material extrusion 3D printing. Finally, the stability, flexibility and compression properties of the scaffold were tested by relevant experimental tests, which ensured the suitability (foldability) of scaffolds. In conclusion, the developed magnetic shape memory polymers showed promising potential for biomedical scaffolds preparation.

Multi-objective optimization for impeller structure parameters of fuel cell air compressor using linear-based boosting model and reference vector guided evolutionary algorithm

As a pivotal part of cathode air supply system, centrifugal air compressors play a central position in ensuring efficient operations of onboard fuel cells. To improve the overall performance of compressors, comprehensive performance tests were conducted and an integrated simulation model was developed by using computational fluid dynamics (CFD) methods. On this basis, the prediction performance of Linear-based Boosting models was investigated, and multi-objective optimization of impeller structural parameters was carried out through the Reference Vector Guided Evolutionary Algorithm (RVEA). Simulation results indicate that the impeller of the original compressor exhibits significant entropy increase, insufficient gas compression and serious energy dissipation, highlighting considerable room for design optimization. The eXtreme Gradient Boosting (XGBoost) model with 29 estimators has superior generalization ability and prediction performance, making it the preferred Boosting model for the compressor. Multi-objective optimizations have strong universality and rationality, resulting in a well-distributed and diversified final non-dominated solution set. The isentropic efficiency and pressure ratio of the Max_σ solution are improved by 18.7% and 70.1%, while those of the Max_ηc solution are enhanced by 23.0% and 48.9%, respectively. After optimization, the gas experiences reduced shock loss, diminished entropy increase and enhanced flow stability. These findings can provide data support, theoretical basis and directional guidance for performance improvement of centrifugal air compressors.

Active Ankle–Foot Orthosis Design and Computer Simulation with Multi-Objective Parameter Optimization

There are many current active orthosis designs to assist with disabilities related to foot-drop, with most of them intervening during the whole gait cycle. We propose that, for the treatment of foot-drop, it is possible to design an ankle–foot device that will assist a walking user only during the dorsiflexion stages of the gait, avoiding interference with other stages, by using a single actuator with a simple transmission and a suspension block. This design can be improved by the use of multi-objective optimization to obtain a static set of parameters that are applicable to varying initial conditions. We present a computer simulation study of an active ankle–foot orthosis design, based on the interaction of a cam and lever with a suspension block, with the objective of assisting only with dorsiflexion during the gait cycle, leaving the rest of the movements unimpeded while reducing the complexity and weight of the device. This design is validated using a full simulation environment that includes the movements of the lower leg and foot, as they interact with our device and a ground element. As part of the design and validation, we found sets of mechanical and control parameters that provoke adequate output behavior of the orthosis to help the wearer perform a moderate-speed, normal gait. To optimize the design, we proposed three objectives to warrant ankle angle accuracy, minimal oscillations, and low energy consumption. A set of solutions was obtained with multi-objective optimization algorithms NSGA-II and RVEA to tune the parameters of the active orthosis. The solutions set from RVEA resulted in lower mean and standard deviation values for the oscillations and energy objectives in comparison to the solutions from NSGA-II, while for the MSE objective, NSGA-II obtained lower mean and standard deviation; for the energy consumption objective, the mean score using RVEA is 17% less than with NSGA-II. The orthosis is shown to be robust to differences in initial ankle angles. We observed that it is possible to obtain a broad set of solutions with a good performance during the gait cycle in controlled spaces and that in this application, the RVEA algorithm results in a better option for optimization to balance the objectives.

A STUDY ON MULTI-OBJECTIVE OPTIMIZATION OF PULSED TIG WELDING PARAMETERS AND THEIR EFFECT ON JOINT PROPERTIES OF HIGH MANGANESE STEELS USING TAGUCHI-BASED GRA APPROACH

This investigation focuses on the study of the effect of process parameters like peak current ([Formula: see text], base current ([Formula: see text], pulse frequency ([Formula: see text], shielding gas flow rate ([Formula: see text] on mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and flexural strength (FS) of the welded joints during pulsed TIG welding of SAILMA 450 and EN14 B steels. Taguchi’s L[Formula: see text] orthogonal array has been used for conducting the tests. Multi-objective optimization has been performed using Grey relational analysis (GRA) in order to maximize the mechanical strength and to find out the optimal set of parameters. The optimum parametric combination is obtained at a peak current of 220[Formula: see text]Amps, base current of 120[Formula: see text]Amps, pulse frequency of 5[Formula: see text]Hz and shielding gas flow rate of 17[Formula: see text]l/min. Analysis of variance (ANOVA) is used to predict the significant process parameters. It has been observed from the ANOVA analysis that peak current and pulse frequency have more influence on the output responses than the shielding gas flow rate and base current. The results of the confirmatory test show an improvement of 0.5801 in the GRG, which is satisfactory. A microstructure study has been performed using scanning electron microscopy (SEM) for the optimal set of process parameters.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Multi-objective parameter optimization of the Z-type air-cooling system based on artificial neural network

The performance of electric vehicles is significantly influenced by the battery thermal management system (BTMS). In this article, the Z-type air cooling BTMS is studied, and a method combining an artificial neural network (ANN) model as a surrogate model with non-dominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization is proposed to enhance the heat dissipation performance. The ANN model is trained by using 1529 sets of numerical simulation data of Z-type air cooling system, and the fast and high-precision prediction of heat dissipation performance is realized, which effectively avoids the repeated calculation of CFD. The design variables studied include geometric structure parameters and inlet wind speed (vinlet), and the target variables are maximum temperature (Tmax), temperature difference (ΔTmax) and pressure loss (ΔP). From the new perspective of genetic evolution process of NSGA-II, it is proved that the design of tapered inlet and outlet is better than that of flat and expansion outlet, the wide channel near the inlet and narrow channel near the outlet are more conducive to the improvement of heat dissipation performance. Further analysis shows that there is a linear relationship among vinlet, Tmax and ΔP in the Pareto solution set of Z-type air-cooling system, which simplifies the Pareto model effectively, and engineers can predict the optimal performance of BTMS by this relationship. In addition, through sensitivity analysis, it is found that in the Z-type BTMS, the operating parameter vinlet has the greatest influence on the heat dissipation performance and pressure loss, while the geometric parameters have less influence. Compared with the benchmark case, the optimized design scheme reduces Tmax, ΔTmax and ΔP by 1.97 K, 6.32 K and 2.82 Pa, respectively, which significantly improves the heat dissipation performance.

Multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

Multi-objective optimization and machine learning assisted design and synthesis of magnesium based novel non-equiatomic medium entropy alloy

Non equiatomic compositionally complex alloys have gained significant attention due to their unique combinations of properties including high strength, good ductility, excellent thermal stability and corrosion resistance. The non equiatomic compositions provide flexibility in tailoring the mechanical, thermal, magnetic, biomedical and other functional properties to meet the specific application requirements. In this study, a novel design approach was proposed to identify quaternary non equiatomic medium entropy alloy for biomedical application. The design methodology employs multi-objective optimization of empirical phase formation parameters and ML algorithm to design solid solution containing non equiatomic medium entropy alloy. The suggested approach scans over a large composition space to find possible non-equiatomic compositionally complex alloy compositions (CCA) with desired phases. The proposed methodology is an alternative to trial-and-error alloy fabrication and CCA design approach reported in the literature. This step significantly reduced the time and resources utilized in CCA design by decreasing the number of experimental runs. Based on the design strategy, Mg60Ti24.24Zn9.66Nb6.1 alloy was designed and selected for fabrication through powder metallurgy route by employing microwave sintering. The Mg rich alloy exhibited the presence of two solid solution phases viz HCP and BCC in both prealloyed powder and final sintered specimen. The developed alloy showed a high microhardness value of 153.9 ± 0.85HV with a measured density of 2.82 ± 0.03 g/cm3. The mechanical properties such as yield strength, ultimate compressive strength, elastic modulus and fracture strain of the developed alloy were found to be superior to that of pure Mg and AZ31 fabricated through the same manufacturing route. Mechanical properties of local phases present within the alloy were assessed through nanoindentation technique. Elastic modulus (E) and hardness (H) values for the Ti rich BCC phase were 118.9±7.2 GPa and 4.65±1.1 GPa, respectively, whereas the Mg rich HCP phase showed values of 65.2±7 GPa and 1.6±0.43 GPa, respectively. Furthermore, with minor changes to the constraint equations and correct ML dataset selection, the suggested design technique can be applied to any multi-principal element alloy system to design non-equiatomic compositions.

Research on multi-objective parameter optimization of split flow dual flash thermodynamic cycle

In addition to considering the intermediate pressure variables, the split flow double flash thermodynamic cycle also needs to consider the operating conditions and split flow coefficient of the cycle. Traditional algorithms require too much computation and have a longer response time. Faced with sudden operating conditions or changes in production conditions, it is difficult to quickly re-optimize the intermediate parameters of the split flow double flash thermal cycle and carry out corresponding system control. This article uses intelligent optimization algorithms instead of global search methods to optimize multi-objective parameters in a split flow double flash thermodynamic cycle. By summarizing and analyzing the characteristics of algorithms such as SIO, EA, and ANN, it is believed that the GWO algorithm in the SIO class is the most suitable for multi-objective parameter optimization of the split flow double flash thermal cycle. It is combined with the SA optimization algorithm to propose the SA-GWO algorithm with faster optimization speed and higher optimization accuracy, taking the initial position of the wolf pack as the optimization objective. Combined with the cycle thermodynamic model, a multi-objective parameter optimization model for the split flow double flash thermal cycle is established.

Optimizing the fuzzy PI controller for quadruple tank system using multi-objective Jaya algorithm

This article proposes the Jaya optimization algorithm for the multi-objective optimization of parameters for a Fuzzy Proportional-Integral (Fuzzy PI) controller, referred to as MOJaya. The purpose is to control the water level of a quadruple-tank system (QTS). The controlled plant is a nonlinear and uncertain Multiple-Input Multiple-Output (MIMO) system, which is affected by various factors such as parameter uncertainties, external disturbances, and significant time delays. These factors have a considerable impact on the stability of the control system. The parameters of the fuzzy controller and the normalization coefficients of the Fuzzy PI controller are optimized offline using the multi-objective Jaya optimization (MOJaya) algorithm. To demonstrate the effectiveness of the proposed algorithm, it was compared with the Fuzzy PI and PI-MOJaya algorithms. The simulation results indicate that the proposed algorithm outperforms the other algorithms in terms of control quality.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

Performance enhancements of semi-active vehicle air ISD suspension

To explore an innovative approach for enhancing the vibration isolation performance of vehicle air suspension systems, this study introduces the inerter element into the vehicle air suspension and investigates the dynamic behavior of the semi-active vehicle air ISD (inerter-spring-damper) suspension. Initially, a dynamic model of the quarter semi-active vehicle air ISD suspension is established, followed by conducting multi-objective optimization of the core parameters using the genetic algorithm. Subsequently, the dynamic performance of the semi-active vehicle air ISD suspension is thoroughly examined through simulations and analyses in both the time and frequency domains. The results demonstrate notable improvements in various aspects: the root-mean-square (RMS) value of the vehicle body acceleration in the semi-active vehicle air ISD suspension is reduced by 12.8%, the RMS value of the suspension working space is decreased by 37.3%, and the RMS value of the dynamic tire load experiences an 8.9% reduction. The findings of this paper indicate that the proposed semi-active vehicle air ISD suspension outperforms both the passive vehicle air suspension and the semi-active vehicle air suspension without an inerter, significantly enhancing the vehicle’s ride comfort, handling stability, and driving safety.

Design and experiment study of a novel dual-channel independent-coil magnetorheological grease damper

In order to address the issue of reduced damping force dynamic range in magnetorheological (MR) damper caused by the high zero-field viscosity of MR grease, known for its sedimentation stability, this paper introduces a novel dual-channel independent-coil MR damper (DCICMRD). Firstly, the dual-channel configuration and the magnetic circuit structure of independent coils were meticulously designed, and a genetic algorithm was employed to conduct multi-objective optimization of the structural parameters for DCICMRD. The optimization results indicate that all performance metrics of the damper post-optimization exhibit improvements exceeding 15%. Then, the theocratical model of the damping force for DCICMRD under three operational modes are established, and the output damping force of various input currents for different operating mode is obtained. Finally, the DCICMRD was manufactured and subjected to dynamic performance testing. The results revealed that the damping force dynamic range in Mode III, i.e. dual-channel structure, can achieve approximately 15 times, whereas Mode I, i.e. traditional single-channel structure, only attains approximately 9 times. The aforementioned research holds significant implications for expanding the further engineering applications of MR dampers.

Multi-objective optimization of emission parameters of a diesel engine using oxygenated fuel and pilot injection strategy based on RSM-NSGA III

In this work, a diesel engine fueled with PODE3/n-butanol/diesel blends at high altitudes was optimized using the RSM-NSGA III hybrid optimization methodology to achieve a reduction in emissions. The target parameters of the optimization were soot, NOx, and HC emissions, and the decision variables were the mass ratio of pilot injection (MRPI), pilot injection timing (PIT), mass ratio of PODE3 (MRP) and mass ratio of n-butanol (MRB). Firstly, a three-dimensional simulation model of the CI engine was built in CONVERGE, and the model was verified through experiments. Secondly, the effects of the pilot injection strategy, also known as the pre-injection strategy, on the combustion characteristics of the CI engine were analyzed. Then, an experimental design based on Box-Behnken Design (BBD) was conducted and a mathematical model of the target parameters was obtained using RSM. Finally, the mathematical model of RSM was optimized using NSGA III to obtain the Pareto frontier, and the optimal solution was selected from the Pareto frontier using TOPSIS. The results showed that a suitable pilot injection strategy helped alleviate the problem of low cylinder pressure (CP) and high soot and HC emissions from the CI engine at high altitudes. The soot and HC emissions exhibited a decreasing and then increasing trend as the PIT advanced. As MRPI increased, both soot and HC emissions decreased. The NOx emissions showed the opposite trend. Compared with the non-optimized results, at an altitude of 2000 m, the optimized engine exhibited a 65.29% decrease in soot emissions, a 24.7% increase in NOx emissions, and a 74.94% decrease in HC emissions. Compared with the non-optimized results, at an altitude of 4000 m, the optimized engine demonstrated a 54.70% decrease in soot emissions, a 15.71% increase in NOx emissions, and a 65.78% decrease in HC emissions. This research will contribute to the application of oxygenated fuels in plateau diesel engines and the optimization of plateau diesel engines.