Multi-objective optimization of dew point indirect evaporative coolers for data centers

Multi-objective optimization of hollow fiber membrane-based water cooler for enhanced cooling performance and energy efficiency

ABSTRACTEven though significant studies on diesel engine optimisation have been made, most existing approaches lack comprehensive integration of methods for effective multi‐objective optimisation (MOOP) frameworks that effectively balance engine performance and emission trade‐offs under diverse engine operating conditions. This highlights the need for more integrated, data‐driven MOOP frameworks. So, in the present work, an effective MOOP of diesel engine performance and emission carried out via adaptation of integrated methods involving Box–Behnken design of experiment (DOE), response surface methodology (RSM), Box‐Cox plot, and genetic algorithm (GA). A comprehensive experimental investigation based on Box–Behnken DOE performed to evaluate the combined effects of key engine operating parameters—fuel injection pressure (FIP), load torque, and engine speed on performance metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE), as well as emission characteristics, such as unburned hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO), and smoke opacity. RSM is used to construct predictive models and derive objective functions for each response with Box‐Cox transformation ensuring model reliability. MOOP using GA is performed to simultaneously achieve enhanced performance and reduced emissions. The integrated method‐based optimisation result is validated with optimised engine input‐based experimental results with an engine settings of 2270 rpm, 12 Nm and 250.4 bar FIP, and found to be in good agreement with modelled‐optimisation results with less error < 3% for all except BTE (6.735%) and CO (11.11%) which are well acceptable in practical scenario. Consequently, the same integrated method‐based MOOP can be applied to other fuels based engine running condition effectively.

In order to improve the machining precision, the carriage of the gantry machine tool must have high anti-vibration performance, and its mass should be as light as possible. Based on the finite element modal analysis, a precision gantry machine tool's carriage was taken as the study object, and a multi-objective optimization mathematical model of the carriage was built by taking the mass and the first four order natural frequencies of the carriage as the objective functions, and the thickness of stiffener plates as the design variables. The second-order response surface model for carriage's multi-objective optimization was established through orthogonal experimental design. The accuracy of the response surface model was verified by comparing the response surface model with finite element model. The optimal solution of carriage multi-objective optimization was obtained by solving the response surface optimization model, which made carriage's mass decreasing 9.63%, the first four natural frequencies increasing 6.48%, 7.41%, 8.36% and 10.32% respectively. Finally, the correctness of carriage's optimization design method proposed in this paper was verified by the static and dynamic experiments.

Multi-objective crashworthiness optimization of tapered thin-walled square tubes with indentations

Intercalation melt-blown nonwovens are made by intercalation process by inserting fibers such as polyester staple fiber into melt-blown nonwovens. They have many excellent properties. However, the preparation process is complicated and the parameters are many, so the control study of the process parameters will help to provide a certain theoretical basis for the establishment of the product property control mechanism.In this paper, based on data analysis and descriptive statistics, Spearman correlation coefficient, BP neural network, typical correlation analysis, multiple nonlinear regression, genetic algorithm, multiple linear regression, NSGA-II multi-objective optimization model and other algorithms and models are used to solve the performance control problems of interlayer melt-blown nonwovens.Firstly, the data of structural variables and product performance parameters before and after intercalation were compared by mapping. It was found that the thickness and porosity increased after intercalation, some groups of compression resilience increased, and some groups became smaller, and the overall trend was larger. In addition to the thickness parameter, other parameters became better after intercalation. Finally, the intercalation rate and parameter change rate were analyzed based on Spearman correlation analysis, and it was found that intercalation rate had no effect on the changes of the above 6 parameters.The BP neural network model between process parameters and structural variables is established. The error of the model is only 0.03, and the goodness of fit is 0.99. Thus, the relationship between process parameters and structural variables is obtained. Using the neural network model, 8 process parameters were substituted to obtain the predicted structural variable data.Canonical correlation analysis was used to study the relationship between structural variables and product performance, and two pairs of canonical correlation variables were found to be significantly correlated. It was recognized that the greater the thickness, the smaller the filtration resistance and the lower the filtration efficiency. Then, the relationship between structural variables and product performance was studied based on Spearman correlation analysis, and then the relationship between process parameters and filtration efficiency was studied based on multiple nonlinear regression. It is found that there is a cubic polynomial nonlinear relationship between filtration efficiency and process parameters, and a regression model is obtained. The model has good significance and high fitting degree. Finally, the regression function is taken as the objective function, and the filtering efficiency is optimized based on genetic algorithm.Multiple linear regression model was established, and the linear regression model of thickness and receiving distance and hot air speed, compression resilience and receiving distance and hot air speed, filtration efficiency and receiving distance, hot air speed, thickness, compression resilience, filtration resistance and receiving distance, hot air speed, thickness, compression resilience was obtained. Then, the regression model of filtration efficiency and filtration resistance was taken as the objective function, and multi-objective optimization based on NSGA-II model was carried out. The known information was taken as the constraint condition, and the optimal process parameters were obtained to meet the requirement of making filtration efficiency as high as possible and filtration resistance as small as possible.

The steering system has an important impact on the energy consumption, maneuverability, and driving stability of vehicles. In order to improve the system’s comprehensive performance, this research takes electro-hydraulic active steering (EHAS) system as the design object and utilizes the dynamic constraints collaborative optimization (DCCO) method for the parametric optimization of the EHAS system. Steering modality is combined with dynamic performance evaluation indices such as steering energy consumption, steering road feel, steering sensibility, and steering stability, to evaluate the comprehensive performance of the EHAS system. Then, a hierarchical multidisciplinary optimization model of EHAS system is established based on DCCO method, which takes the comprehensive performance of steering system as the overall system and steering energy consumption, steering road feel, steering sensibility, and steering modality as subsystems respectively. The multidisciplinary performance evaluation indices of EHAS system are derived for the first time, among which the evaluation index of steering modality is obtained by combining of design of experiments (DOE) design method and response surface method. The optimization results show that the EHAS system optimized by DCCO method can effectively improve steering energy consumption, steering road feel, and steering sensibility.

To avoid drawbacks of conventional structural op- timization approaches,a neural network(NN)response surface optimization method is proposed for the design of composite stiffened structures.Such NN-based structural analysis re- sponse surfaces can reflect the global mapping relationship between design inputs and structural response outputs.By using the orthotropic experiment method to select the appropriate structural finite element analysis samples,neural network re- sponse surfaces can be trained with reasonable accuracies.The constructed response surfaces can be either used as objective function or constraints or both.Together with other conven- tional constraints,an revised optimization design model can be formed which can be solved by using genetic algorithm(GA). Taking a hat-stiffened composite panel of blended wing-body aircraft as example,the structural weight response surface is developed as objective function,and strength and buckling factor response surfaces as constraints.All these neural net- works are trained by finite element samples computed through PATRAN/NASTRAN software.The optimization results illus- trate that it can significantly reduce the cycles of finite element model analysis and achieve highly accurate response approxi- marion results.Eventually,the approach can greatly save the com- putation time and raise the efficiency of optimization process.

Carbon emission is one of the factors that have recently gained more negative environmental impact. In this project, optimization has been done to obtain the optimum values of the machining parameters- speed, feed, and depth of cut in computer numerical control turning of Grey Cast Iron material to reduce carbon emissions. Other objective functions are processing time, which characterizes the production rate, and surface finish, which characterizes material quality. The optimization has been done using genetic algorithm and response surface methodology.

Multi-objective optimization of corrugated tube inserted with multi-channel twisted tape using RSM and NSGA-II

In this study, a new method was proposed for the design of composite magnetoelectric actuator. Design of experiment (DOE) was utilized to investigate the mutual effect of geometric parameters. Moreover, the effect of impedance phase angle, magnetic field, and bias field were studied through finite element (FE) modeling. Resonance frequency, displacement value, magnetoelectric coefficient, and mode shape were considered as response variables. Analysis of variance (ANOVA), regression modeling and response surface method (RSM) were used to investigate the pair-wise effect of input parameters on the response variables. ANOVA results showed that the magnetoelectric length and piezoelectric thickness are the most important parameters affecting the magnetoelectric performance. The optimization process was performed using Metaheuristics algorithm. Optimum results were verified using magnetoelectric measurement setup and Laser Doppler Vibrometry device. The accuracy of the FE model in resonance frequency prediction was estimated at 97%. The prediction error of the FE model for the magnetoelectric voltage parameter was 14.6%, which was about 12.9% better than the regression model. The confirmation test showed that the regression modeling can only predict magnetoelectric behavior and for determining magnetoelectric performance, a precise FE model would be more reliable. Such proposed optimization technique can be used in the design of magnetoelectric composites.

Pump-and-treat (PAT) techniques are often applied to the remediation of dissolved \nchemicals from shallow aquifers. A related management problem typically consists of \nthe selection of the pumping strategy and the most appropriate treatment method, in \norder to minimize the total cleanup cost while meeting a set of technical, economic \nand social constraints. However, due to scarcity of information about the \nhydrogeological system, stochastic modeling approaches seem more appropriate. Of \nprimary concern is the inherent spatial variability of hydraulic conductivity. In \ngeneral, the implementation of a remediation strategy assessed based on uncertain \nhydrogeological parameters leads to a decision involving the risk of constraint \nviolations. The decision-making process may then be formulated as a multiobjective \noptimization framework where the optimality of a pumping pattern is traded off \nagainst its reliability. Operations may be structured into a stochastic optimal \ncontrol problem, in which the remediation strategy is sequentially updated based \nupon new measurements collected during the actual cleanup process. The procedure \nrequires the implementation of an inverse simulation model to estimate the \nstochastic hydrogeological parameters based on a set of potential measurements. In \nthis work, we follow a geostatistical conceptual model where the spatial \ndistribution of hydraulic conductivity is represented as a realization of a log-\nnormally distributed stationary process, characterized by an exponential covariance\nfunction. Using the maximum likelihood method, the parameter estimation problem is \nsolved by determining the set of geostatistical parameters -- average, variance, \nand correlation scales. Available data may include direct measurements of hydraulic \nconductivity, water table elevation at a number of monitoring wells, and \ncontaminant mass extracted from active remediation wells. A rigorous solution to \nthis optimization problem would require a stochastic flow and transport model to be \nincluded in the optimization loop to calculate the expected values and the \ncovariance matrix of the available measurements as functions of the decision \nvariables. Because of the overwhelming computational effort involved, a surrogate \nmodel or response surface is introduced to approximate the objective function. The \nsurrogate model is estimated using a multidimensional kriging interpolation over a \nset of data points or measurements obtained from a series of stochastic flow and \ntransport simulations for pre-established combinations of the decision variables. \nSince the goodness of the solution is ultimately determined by the error of \nestimation of the objective function, which in turn depends on the number and the \nlocation of measurements, an optimal search procedure is used in order to optimize \nthe pattern of data points. The method turns out to be computationally efficient \nand produces results that well approximate the actual geostatistical distribution of\nhydraulic conductivity.

Abstract: This paper is about the optimization of a small circular loop antenna using a Genetic Algorithm (GA) to significantly enhance its radiation efficiency for operation at 38.22MHz. Traditional small loop antennas are known for their poor radiation characteristics due to extremely low radiation resistance and high conductor losses, rendering them unsuitable for practical RF communication without a complex matching network. This research applies a Genetic Algorithm implemented in MATLAB to optimize two critical design parameters: the loop radius and the wire radius (thickness). An analytical electromagnetic model was developed to compute radiation resistance, conductor loss resistance (accounting for skin effect and radiation efficiency. The objective function was designed to maximize efficiency by minimizing its negative value. Results from the GA were analysed using convergence plots and two-dimensional contour maps, revealing the efficiency distribution across the design space. The optimized antenna achieved a loop radius of 0.02458m and a wire radius of 0.0099m, leading to a dramatic improvement in radiation efficiency from 0.02% (traditional design) to 99.4%. Additionally, gain and bandwidth performance were significantly improved, making the antenna suitable for terrestrial RF applications. The study demonstrates that evolutionary optimization techniques like GA can overcome the inherent limitations of traditional small loop antennas, by intelligently navigating the design space, the Genetic Algorithm identified configurations that would be difficult to obtain through manual tuning. These findings highlight the practical potential of computational intelligence in modern antenna design particularly for compact efficient and broadband systems in wireless and communication technologies.

Phenolic compound extraction from Nigerian Azadirachta Indica leaves: Response surface and neuro-fuzzy modelling performance evaluation with Cuckoo Search multi-objective optimization

Aiming at the urgent need for miniaturization and high efficiency of the driving mechanism of scraper conveyors, this paper proposes an axial flux permanent magnet motor, which is different from the current asynchronous motors or radial flux permanent magnet motors. Aiming at the problems of many parameters and strong coupling in the multi-objective optimization process of the motor, a multi-objective optimization method based on a genetic algorithm is proposed. Through OptiSLang software and Maxwell joint simulation, multi-objective optimization analysis is carried out to achieve the goals of cogging torque and output torque fluctuation. Through parameter sensitivity analysis, strong correlation variables are selected to improve optimization efficiency. The response surface between the optimization objectives and variables is established, and the multi-objective optimization is carried out by a genetic algorithm. Finally, the correctness and tangibility of the algorithm and the results are verified by finite element simulation. It provides a new idea for miniaturization and high efficiency of the drive motor of the scraper conveyor.

This paper presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.