Multi-objective Genetic Algorithm NSGA-II Research Articles

Multi-objective optimisation of a thermocline thermal energy storage integrated in a concentrated solar power plant

Most of CSP plants built today are all equipped with storage devices. Most are sensitive heat storages, in particular 2-tank systems. The aim of this study is to optimise a thermocline storage, designed for the Andasol CSP plant in Spain, on the basis of exergy, environmental and economic criteria. The optimum tank geometry, particle size and heat transfer fluid/filler material pair must be determined. To achieve this, six different fillers operating with Dowtherm A synthetic oil are being investigated: machined ceramics, ceramics obtained from waste materials and natural rock. The NSGA-II multi-objective genetic algorithm is used to solve the problem. This optimisation selects recycled ceramics as best filler materials. The exergy efficiency is not affected by the filler, with more than 99.8%. The application of TOPSIS method combined with weighting derived from Shannon entropy, selects recycled ceramic obtained from electric furnaces as the best filler. This tank has a tapered shape (diameter of 27.4 m, height of 22.2 m) and a very small particle diameter (6 mm). Compared with an earlier design available in literature, the environmental impact is reduced by 19%. The LCOE reaches 10.8 c€/kWhe, a 48% reduction compared with the selling price of heat in Spain (in 2021).

Artificial neural network prediction and multi-objective genetic algorithm optimization of the microchannel heat sink with trapezoidal ribs

Trapezoidal rib microchannel heat sinks (TR-MCHS) are widely used for efficient heat dissipation in electronic integrated devices. To enhance the heat transfer and hydraulic performance of TR-MCHS, an optimization method using neural network prediction combined with multi-objective genetic algorithm NSGA-II is proposed. First, the effects of trapezoidal rib geometry parameters and inlet velocity (V) on the Nusselt number (Nu¯) and pressure drop (ΔP) of TR-MCHS are investigated using numerical simulation and Sobol sensitivity analysis. The results show that the trapezoidal rib height (ht) and inlet V have the most significant effect on the TR-MCHS, and the interaction effect between the parameters can be neglected. Then, optimization is performed with Nu¯ and ΔP as objectives. On the obtained Pareto solution set, Nu¯ can be improved by 7.3 % compared to the unoptimized TR-MCHS without consuming additional pumping power. In addition, if the same Nu¯ is obtained as the unoptimized TR-MCHS, the ΔP can be reduced to 223 Pa. On this basis, the TOPSIS method based on entropy weight is developed to solve the optimal compromise solution. The best compromise solution has the Nu¯ of 50.9 and the ΔP of only 184.6 Pa, which is 87.9 Pa lower than that of the unoptimized TR-MCHS. These results have important implications for the design of TR-MCHS for electronics cooling.

The Structural Design and Optimization of Top-Stiffened Double-Layer Steel Truss Bridges Based on the Response Surface Method and Particle Swarm Optimization

A lightweight design optimization algorithm is proposed to optimize the design parameters of stiffened double-layer steel girder bridges, the aim of which is to improve structural safety and reduce superstructure works. Taking a top-stiffened double-layer steel truss bridge as the reference project, a multiscale mixed-element model of the initial design parameters is established, and its computational accuracy is verified. Considering the structural configuration and loading characteristics of the bridge, the elastic modulus of steel, the deck plate thickness, the stiffening vertical bar height, and the relative distance between the double-layer main girders are selected as the design parameters for optimization. The mid-span vertical deflection, the axial forces in the stiffeners, the bottom plate of the deck, the compressed web tube at the pier top, and the quantity of superstructure works are chosen as the objective functions to be minimized. A lightweight calculation equation reflecting the relationship between the optimization parameters and the objective functions is established using the response surface method (RSM). Subsequently, an improved weighted particle swarm optimization (WPSO) model is employed to perform the multi-objective optimization of the design parameters for the bridge, and the results are compared with those obtained from the multi-objective genetic algorithm NSGA-II. The results show that the RSM accurately fits the numerical relationship between the optimization parameters and the objective response functions. When minimizing the quantity of superstructure works as the primary control objective and minimizing the mid-span vertical deflection and the axial forces in the compressed web tube at the pier top as secondary control objectives, the optimization results achieved by WPSO outperform those obtained by NSGA-II. The optimized results lead to reductions of 11.09%, 3.92%, 7.56%, 4.45%, and 8.38% in the respective objective function values of the structure. This method has important theoretical significance for the optimization of structural design parameters.

Optimal design of a Low-Cost SAE JA2954 compliant WPT system using NSGA-II

Wireless Power Transfer (WPT) systems for electric vehicle charging are one of the most promising methods that, given the advantages they bring, will help the desired deployment of electric vehicles. This paper presents a mathematical optimisation method applied to the design of an 11 kW S-S system that complies with the SAE J2954 standard. A proposal is made to calculate the electrical parameters of the circuit based on equations that are compared with the results obtained by simulation with finite elements and experimental measurements, achieving very tight results with a reduced computational time. The NSGA-II multi-objective genetic algorithm is then applied together with the secant method, defining three different scenarios: minimisation of the primary copper volume, minimisation of the secondary copper volume and a compromise solution optimising the total primary and secondary copper volume. The result is a set of Pareto optimal solutions, from which the one that meets the standard can be extracted that suits the designer’s needs.

Optimization of crashworthiness of CFRP thin-walled beams filled with aluminium honeycomb based on surrogate model

The CFRP thin-walled beam filled with aluminum honeycomb is a new design form in the crashworthiness design of automobile energy-absorbing component. It not only meets the requirements of strength, rigidity and lightweight under the normal load, but also ensures absorption of crash energy in a stable failure mode when a crash occurs. In this paper, under two typical conditions of axial impact and lateral impact, structural sample points are established by orthogonal experiment; a highly feasible surrogate model is constructed based on the radial basis function; the initial load peak Fmax and specific energy absorption (SEA) are taken as the main criteria to evaluate the energy absorption performance of the structure; the multi-objective genetic optimization algorithm NSGA-II is used to optimize the energy absorption design of CFRP thin-wall beams filled with aluminum honeycomb. The structure and material parameters of the minibus frame are optimized according to the crash condition to improve the energy absorption performance of the minibus in frontal and side crashes, and on this basis, an effective method to improve the crashworthiness of minibuses is explored.

Combining entropy weight and TOPSIS method for selection of tank geometry and filler material of a packed-bed thermal energy storage system

Thermocline thermal energy storage systems are promising alternatives for recovering waste heat lost by industry around the world. The aim of this work is to extend the methodology presented in previous work, by optimising an existing industrial packed-bed storage system on two geometric optimisation variables, considering exergy, environmental and economic aspects. Seven filler materials are compared for the same heat transfer fluid, to include discrete variables in the model. The multi-objective optimisation problem is solved using the NSGA-II multi-objective genetic algorithm. For each filler material, a Pareto set is obtained. The non-dominated solutions within the union of the different Pareto sets are then selected, which give a new single set of optimised solutions. A multi-criteria decision-making method (TOPSIS) is then applied to obtain the optimal solution. To avoid any subjective choice from the decision-maker by determining the objective weights of each of the optimisation criteria, the Shannon entropy is used. The combination of TOPSIS and Shannon entropy led to the selection of a recycled ceramic obtained from hard coal ashes as the best filler. This solution has a stocky tank shape (2.4 m diameter, 2.1 m height) and a small particle diameter (7 mm). The exergy and environmental performance is improved compared to the reference storage. They reach 98.0% (vs 95.6%) and 58 hab.year (vs 67 hab.year) respectively. The levelised cost of energy is close to that of the reference tank (3.35 vs 3.31 c€/kWhth).

Optimisation of a Multi-Element Airfoil for a Fixed-Wing Airborne Wind Energy System

Airborne wind energy systems benefit from high-lift airfoils to increase power output. This paper proposes an optimisation approach for a multi-element airfoil of a fixed-wing system operated in pumping cycles to drive a drum-generator module on the ground. The approach accounts for the different design objectives of the tethered kite’s alternating production and return phases. The airfoil shape is first optimised for the production phase and then adapted for the requirements of the return phase by modifying the flap setting. The optimisation uses the multi-objective genetic algorithm NSGA-II in combination with the fast aerodynamic solver MSES. Once the optimal shape is determined, the aerodynamic performance is verified through CFD RANS simulations with OpenFOAM. The resulting airfoil achieves satisfactory performance for the production and return phases of the pumping cycles, and the CFD verification shows a fairly good agreement in terms of the lift coefficient. However, MSES significantly underpredicts the airfoil drag.

Integration of geothermal-driven organic Rankine cycle with a proton exchange membrane electrolyzer for the production of green hydrogen and electricity.

Engineers and scientists are increasingly interested in clean energy options to replace fossil fuels in response to rising environmental concerns and dwindling fossil fuel resources. There has been an increase in the installation of renewable energy resources, and at the same time, conventional energy conversion systems have improved in efficiency. In this paper, several multi-generation systems based on geothermal energy are modeled, assessed, and optimized which employ an organic Rankine cycle and a proton-exchange membrane electrolyzer subsystem in five different configurations. Based on the results, the evaporator mass flow rate and inlet temperature, turbine efficiency, and inlet temperature are the most influential parameters on system outputs, namely, net output work, hydrogen production, energy efficiency, and cost rate. In this study, the city of Zanjan (Iran) is selected for a case study, and the results of system energy efficiency for changes in ambient temperature are examined during the four seasons of the year. To determine the optimal values of the objective functions, energy efficiency, and cost rate, NSGA-II multi-objective genetic algorithm is employed, and a Pareto chart is derived. The system's irreversibility and performance are gauged by energy and exergy analyses. At the optimum state, the best configuration yields an energy efficiency and cost rate of 0.65% and 17.40 $/h, respectively.

Multi-objective Optimization of Multi-cells Foam Mattress

Body pressure dispersion mattresses are useful tools for preventing pressure ulcers in patients with limited mobility who experience prolonged body weight-related compression loads at their body contact areas over time. The objective of this study is to propose and optimize a multicell finite element (FE) model of foam mattress to prevent patients from developing pressure ulcers (bed sores), by improving the contact pressure distribution on the upper mattress surface and immersion in the mattress. The NSGA-II multi-objective genetic algorithm was used to predict different configurations of cell materials to provide a more comfortable sleep. Our mattress model contains many cells (50 × 50 × 50), each of which can contain one of the nine different foam firmnesses. The NSGA-II algorithm attempts to combine the properties of soft and firm foams into a single mattress. however, the complexity and intersection of the fitness function objectives and the high number of possible chances forced the optimal solutions set to extend into the area under the result of foams that have a compressive strength between soft and firm. Based on the overall optimization results, the standard deviation ranged from 0.00325 to 0.00175 MPa and the maximum mattress immersion ranged from 50 mm to less than 20 mm. Mattresses with optimal configurations disperse body pressure smoothly to fit the patient's body shape.

Optimization of the driver's seat belt and injury biomechanical analysis in real-world minivan small offset impact accident scenarios.

To minimize injuries and protect the safety of the driver in minivan small offset collisions, an optimized pre-tensioned force-limiting seat belt was proposed herein. An accident with detailed information, such as medical reports, vehicle inspection reports, and accident scene photographs, was reconstructed using HyperMesh software. The effectiveness of both the accident model and the pre-tensioned force-limiting seat belt was evaluated. To obtain the optimal seat belt parameters for driver protection, first, force-limiting A, pre-tensioned force B, and pre-tensioned time C factors were selected in designing an orthogonal test with different factor levels. The influence laws of each factor on the injury biomechanical characteristics of the driver were analyzed via the direct analysis method. Moreover, each kind of critical injury value of the human body was synthesized, and the radial basis function surrogate model was constructed. The three seat belt parameters were optimized using the NSGA-II multi-objective genetic algorithm. The results showed that the optimal balance variable parameter of the seat belt was 4751.618 N-2451.839 N-17.554ms (A-B-C). Finally, the optimal scheme was verified in a system simulating a minivan small offset collision. The results showed that after optimization, the skull von Mises stress was reduced by 36.9%, and the stress of the cervical vertebra cortical bone and cancellous bone decreased by 29.1% and 30.8%, respectively. In addition, the strains of the ribs and lungs decreased by 31.2% and 30.7%, respectively.

Stiffness performance analysis of a 3-PRPS kinematically redundant parallel mechanism

The stiffness performance of a spatial kinematically redundant parallel mechanism (KRPM) with three relative degree-of-freedom (RDOF) is analyzed. Firstly, the composition principle of the KRPM is introduced, the RDOF is analyzed, the kinematics model is established, and the overall Jacobian matrix is established based on the screw theory. Secondly, the workspace of the KRPM is drawn, and the theoretical stiffness model is established considering the internal restraint force and the external load. The stiffness variation in the workspace is drawn, and the effectiveness of the theoretical stiffness model is verified through finite element analysis. Finally, according to the theoretical stiffness model, four task-based stiffness performance evaluation indexes are obtained, and they are optimized based on the NSGA-II multi-objective genetic algorithm. The results show that adding and adjusting redundant actuators in a parallel mechanism can effectively improve the stiffness performance of the mechanism. In the meantime, different stiffness optimization schemes can be proposed for different task paths based on the NSGA-II genetic algorithm.

Performance Optimization of the High-Pressure Compressor in Series Two-Stage Turbocharging System Based on Low-Speed Performance Requirements of Diesel Engine

Abstract The performance of the high-pressure (HP) compressor is very important for the two-stage turbocharging system. However, the performance of HP compressor on the engine most of the time is poor at low speed and low mass flow conditions. These will lead to poor engine performance at low engine speed. The purpose of this paper is to improve the performance of the HP compressor at low speed and low mass flow conditions. The Latin hypercube design of the experiment method is used to establish the Kriging model and global optimization by multi-objective genetic algorithm NSGA-II to optimal HP compressor. The simulation results indicated that the flow field within the compressor was improved and the high entropy generation area was reduced. The new design delayed the mixing between the tip clearance leakage vortex flow and main flow. The low-speed performance of the HP compressor was improved. The turbocharger gas stand tests and engine bench tests were carried out. The results showed that the efficiency and pressure ratio of the optimized design is increased by 2.1% and 3%, respectively. The engine achieved better performance in low-speed conditions. The pumping means effective pressure (PMEP) and intake airflow increased by 7% and 4.98%, respectively, while brake specific fuel consumption (BSFC) and soot emissions decreased by 0.56% and 32.8%, respectively.

Structural Optimization of Jet Fish Pump Design Based on a Multi-Objective Genetic Algorithm

Jet fish pumps are efficient hydraulic machinery for fish transportation. Yet, the complex flow phenomenon in it is the major potential risk for damage to fish. The dangerous flow phenomena for fish, such as radial pressure gradient and exposure strain rate, are usually controlled by the structural parameters of jet fish pumps. Therefore, the injury rate of fish can be theoretically decreased by the structural optimization design of jet fish pumps. However, there is a complex nonlinear relation between flow phenomena and key structural parameters. To solve this problem, the present paper established a complex mapping between flow phenomena and structural parameters, based on computational fluid dynamics and a back-propagation neural network. According to this mapping, an NSGA-II multi-objective genetic algorithm was used to optimize the structure of jet fish pumps. The results showed that the optimized jet fish pumps could reduce the internal radial pressure gradient, exposure strain rate and danger zone to 40%, 12.5% and 50% of the pre-optimization level, respectively. Therefore, the optimized jet fish pump could significantly reduce the risk of fish injuries and keep the pump efficiency at a high level. The results could provide a certain reference for relevant structural optimization problems.

Optimization Design and Injury Analysis of Driver’s Restraint System in Sedan Small Offset Collision

A combination of airbag, seatbelt, and other restraint systems greatly reduces injury to drivers in small offset collisions. However, the airbag causes accidental injury to the driver in the deployment process. To maximize the protection effect of the restraint system on the driver, this study proposes a pre-tensioned force-limiting seatbelt. A small offset collision accident with video information was simulated by using a Neon sedan and the THUMS (v.4.0.2) finite element model. The effectiveness of the accident model and the matching use of a pre-tensioned force-limiting seatbelt and airbag for driver protection were verified. To obtain the best parameter matching of protection effect, first, the seatbelt force-limiting A, pre-tensioned force B, pre-tensioned time C, airbag ignition time D, and mass flow coefficient E were selected as influencing factors, and orthogonal tests with different factor levels were designed. Then, the direct analysis method was applied to analyze the influence laws of each factor on driver dynamic response and injury. In addition, the radial basis function surrogate model was constructed by synthesizing each kind of critical injury value to the human body. Combined with NSGA-II multi-objective genetic algorithm, the structural performance parameters of the restraint system were optimized and matched. Results showed that the optimal protection matching parameters of the restraint system were 4933.5 N−2499.9 N−16 ms−15.3 ms−0.5 (A−B−C−D−E). Finally, the best matching parameters were input into the accident model for verification. After optimization, the WIC and Nij of drivers were reduced by 37.9% and 45.3%, respectively. The results show that the optimized restraint system can protect the driver the most.

Use of optimised MLP neural networks for spatiotemporal estimation of indoor environmental conditions of existing buildings

Controlling the indoor environmental quality in real time is essential for the health, well-being and productivity of occupants of a building. In recent years, research has focused on improving monitoring devices and strategies and developing techniques for estimating indoor conditions. The use of machine learning algorithms in this context has increased considerably. However, monitoring data in real time from large multizone working areas is challenging. The aim of this work is to provide an interpolation methodology based on the use of optimised multilayered perceptron neural networks to estimate the indoor environmental conditions of a building in real time. These estimations are obtained without the need for neither monitoring in the occupied working area nor human intervention and considering low-cost sensors. The neural network is optimised by implementing the multiobjective genetic algorithm NSGA-II to find the best architecture in terms of error and complexity. This method was applied to the building of a research centre in north-western Spain, where interpolated values for indoor air temperature, relative humidity and CO2 concentration were obtained. The results of this case study yielded relative errors close to 6% for temperature, 5% for relative humidity, and 12% for CO2 concentration. These values validate the methodology developed for the estimation of indoor environmental conditions and the contribution of this research to the improvement of the monitoring and control of the indoor environmental quality of a building.

Structural Strength and Reliability Analysis of Important Parts of Marine Diesel Engine Turbocharger

Supercharging is the main method to improve the output power of marine diesel engines. Nowadays, most marine diesel engines use turbocharging technology, which increases the air pressure and density into the cylinder and the amount of fuel injected correspondingly so as to achieve the purpose of improving the power. In a marine diesel engine, the turbocharger has become an indispensable part. The performance of turbochargers in a harsh working environment of high temperature and high pressure for a long time will directly affect the performance of diesel engine. Based on the market feedback data from manufacturers, the failure modes of compressor impeller, turbine blade, and turbine disk of marine diesel turbocharger are analyzed, and the statistical model of random factors is established. Using DOE design, the structural strength simulation data of 46 compressors and 62 turbines are obtained, and the response surface model is constructed. On this basis, Monte Carlo sampling is carried out to analyze the reliability of the compressor and turbine. The reliability of the compressor is good, while that of the turbine disk is 0.943 and that of the turbine blade is 0.96, which still has the potential of reliability optimization space. Therefore, a multiobjective optimization method based on the NSGA-II genetic algorithm is proposed to obtain the multiobjective optimization scheme data with the reliability and processing cost of turbine disk and blade as the objective function. After optimization, the reliability of turbine disk and blade is 1, the stress value of turbine blade is optimized by 4.7941%, the stress value of turbine disk is optimized by 3.0136%, the machining cost of the turbine blade is reduced by 15.5087%, and the machining cost of turbine disk is reduced by 3.9907%. At the same time, it is verified by simulation, the data based on NSGA-II multiobjective genetic algorithm are more accurate and have practical engineering reference value. The optimized data based on NSGA-II multiobjective genetic algorithm are used to manufacture new turbine samples, and the accelerated test of simulation samples is carried out. The cycle life of the optimized turbine can reach 101,697 cycles and 118,687 cycles, which is 51.75% and 77.11% longer than that of the unoptimized turbine. It can be seen that the optimized turbine can meet the requirements of the reliability index while reducing the manufacturing cost.

A multi-objective model-based vertical handoff algorithm for heterogeneous wireless networks

The emergence of 5G communication systems will not replace existing radio access networks but will gradually merge to form ultra-dense heterogeneous networks. In heterogeneous networks, the design of efficient vertical handoff (VHO) algorithms for 5G infrastructures is necessary to improve quality of service (QoS) and system resource utilization. In this paper, an optimized algorithm based on a multi-objective optimization model is proposed to solve the lack of a comprehensive consideration of user and network impacts during the handoff process in existing VHO algorithms. The Markov chain model of each base station (BS) is built to calculate a more accurate value of the network state that reflects the network performance. Then, a multi-objective optimization model is derived to maximize the value of the network state and the user data receiving rate. The multi-objective genetic algorithm NSGA-II is finally employed to turn the model into a final VHO strategy. The results of the simulation for the throughput and blocking rate of networks demonstrate that our algorithm significantly increases the system throughput and reduces the blocking rate compared to the existing VHO strategies.

Multi-Objective Big Data View Materialization Using NSGA-II

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.

The application of support vector regression and mesh deformation technique in the optimization of transonic compressor design

With the development of modern aero-engine, the compressor is required to achieve higher performance such as transonic operation, high stage pressure ratio and efficiency. At present, the traditional blade design methods have the disadvantage of highly relying on designer's experience and heavy computational burden. In order to reduce design variables used to define blade geometry, the present work introduces the Free-Form Deformation technique. It can achieve high degree-of-freedom deformation and update mesh and geometry simultaneously. To further alleviate computational cost, the Support Vector Regression surrogate model is adopted to replace the time-consuming numerical simulation. It performs well in small sample learning problems. On this basis, a surrogate-based optimization design framework is established by combining the Advanced Latin hypercube sample and NSGA-II multi-objective genetic algorithm. Then an aerodynamic optimization of the transonic NASA Rotor 37 is conducted as a validation. The results show that the pressure ratio and isentropic efficiency are increased by 4.2% and 2.5%, respectively. The optimized shape sweeps forward with a slight increase in twist angle, and shock loss is reduced effectively. Compared with traditional optimization method, the proposed framework can improve the optimization efficiency by decreasing design variables and training samples.

Trajectory Identification for Moving Loads by Multicriterial Optimization.

Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is performed indirectly, based on the measured mechanical response of the structure. However, trivial solutions that directly fit the mechanical response tend to be erratic due to measurement and modeling errors. To achieve physically meaningful results, these solutions need to be numerically regularized with respect to expected geometric characteristics of trajectories. This paper proposes a respective multicriterial optimization framework based on two groups of criteria of a very different nature: mechanical (to fit the measured response of the structure) and geometric (to account for the geometric regularity of typical trajectories). The state-of-the-art multiobjective genetic algorithm NSGA-II is used to find the Pareto front. The proposed approach is verified experimentally using a lab setup consisting of a plate instrumented with strain gauges and a line-follower robot. Three trajectories are tested, and in each case the determined Pareto front is found to properly balance between the mechanical response fit and the geometric regularity of the trajectory.