Multi-objective Design Optimization Approach Research Articles

Research on the Optimal Design Approach of the Surface Texture for Journal Bearings

Aiming to improve the comprehensive performance of the journal bearing system, this paper presents a multi-objective adaptive scale texture optimization design approach. A mixed lubrication model for the textured journal bearing system is established by considering the effects of cavitation and roughness. The geometrical parameters of the textures were co-optimized using a multi-objective grey wolf optimizer to obtain the optimal texture schemes that are suitable for different operating conditions. Through this approach, the influences of different texture schemes under transient operating conditions can be investigated. According to the results, it was found that different texture schemes result in different friction reduction effects. Proper surface texture is beneficial in increasing the minimum oil film thickness and reducing the possibility of asperity contact. The adaptive scale texture exhibits strong adaptability and achieves significant hydrodynamic effects. Therefore, the developed approach provides valuable insights for the optimization design of journal bearing systems.

A multi-objective optimization design approach of large mining planetary gear reducer

A two-stage computational framework is proposed to optimize the radiated noise and weight of a large mining planetary gear reducer under the rated conditions, based on a combination of response surface methodology and multi-objective optimization. The well-established transient dynamic analysis model of a large mining planetary gear reducer, which is used to analyze the mechanical strength and acoustic characteristics of the gear reducer. A unified experimental design is developed to obtain the response surface of the gearbox radiated noise and the mass of the gearbox housing. After obtaining the multi-objective optimization function, the multi-objective optimization problem for a lightweight and low-noise gearbox is performed using non-dominated sorting from the Genetic Algorithm II (NSGA-II). The research results demonstrates the effectiveness of the proposed optimization method in reducing vibrating amplitude and weight of the gearbox. This is crucial for minimizing energy consumption and enhancing the overall performance of the system. Additionally, the optimized gearbox design not only saves energy but also contributes to the reduction of carbon emissions, making it environmentally friendly.

Research on parametric design method of solar photovoltaic utilization potential of nearly zero-energy high-rise residential building based on genetic algorithm

Building a resource-saving society and achieving energy conservation and emission reduction is a strategic decision for China's current social development. Zero-energy buildings realized by the synergy of low-energy energy-saving technologies and renewable energy utilization technologies have become the ultimate quantitative index of sustainable buildings. Therefore, from the perspective of the development trend of residential buildings, near-zero-energy residential buildings oriented to zero energy consumption will become one of the goals of residential building development in China in the future. Recently, building performance simulation technology has been widely used for energy-efficient design. This study focuses on zero energy oriented residential building design, taking the integration of complex systems in terms of building design, energy technology, and economics as the guide, establishing the coupling relationship between the parameters of each system, and adopting a multi-objective optimization approach to find the optimal energy-saving design solution. Based on the parametric design tool of the Grasshopper platform and the Non-Dominated Sorting Genetic Algorithm II, the study explores a multi-objective optimization design approach for zero energy oriented residential buildings to achieve the lowest total air-conditioning and heating load, the highest photovoltaic power generation, and the lowest investment cost. Nine control variables that affect these three objective functions are quantitatively evaluated and analyzed. The design process and analysis model for the multi-objective optimization of zero energy buildings are established, and the feasibility and technical routes for achieving zero energy for typical building types in different climate zones are compared and analyzed, providing a theoretical basis and parametric design method for the practice of zero energy residential building design across different climate zones in China.

Rapid Calculation and Optimization of Vibration and Noise of Permanent-Magnet Synchronous Motors for EVs Based on Equivalent Structural Network

Optimizing electromagnetic performance and vibration noise performance simultaneously is important when designing the drive motor for electric vehicles (EVs). This has not been fully explored, and there are only a few relevant studies. To achieve simultaneous optimization, this paper proposes an equivalent structural network (ESN) of stator assembly to calculate the modal distribution and harmonic response transfer functions. Based on the ESN model, the motor’s electromagnetic vibration noise harmonic response, such as acceleration and equivalent radiated power level (ERPL), can be quickly calculated. The feasibility of the established ESN model is verified by structural-field finite-element method (FEM) and modal hammer tests. Based on the modern optimization algorithm and the ESN model, an improved multi-physics and multi-objective optimization design approach is proposed for an optimized design of a 30 kW interior permanent-magnet synchronous machine (IPMSM). The motor’s maximum output torque and ERPL were selected as optimization objectives, and then the ERPL and acceleration were recalculated using structural-field FEM to validate the accuracy of the optimal design. Finally, vibration acceleration tests were carried out on a manufactured prototype motor to verify the feasibility and validity of the proposed optimization design method.

Design and Analysis of a V-Shaped Permanent Magnet Vernier Motor for High Torque Density

This paper proposes a V-shaped permanent magnet vernier (V-PMV) motor for potential applications in direct drive system. By designing suitable pole slot ratio and adopting the V-shaped PM topology and dummy slots in rotor, the flux leakage can be avoided effectively. Meanwhile, the high-order harmonics with large amplitude in the airgap magnetic field are fully utilized and served as the effective working harmonics, which aim at enhancing the output torque and reducing the torque ripple. Moreover, to extensively explore the performance advantages of the V-PMV motor, a multi-objective optimization study is carried out. And, the electromagnetic performances of the V-PMV motors are analyzed and compared in detail. Finally, a prototype machine is built and tested. Both theoretical analysis and experimental results verify the validity of the motor and multi-objective optimization design approach.

PMSM design optimization concerning sensorless performance and torque ripple

This paper proposes a multi objective design optimization approach concerning sensorless performance and torque ripple for Permanent Magnet Synchronous Machine. Sensitivity analysis of design parameters has been accomplished for critical parameters selection and metamodels are generated based on critical variables. Based on these metamodels, the optimization process aims to propose an optimal machine geometry to improve sensorless performance without compromising torque ripple and average torque. The metamodel assisted optimization process has been proven to reduce computational cost with a good accuracy. The selection of the optimal solution was achieved based on multi-objective optimization process using NSGA-II. The optimal solution is validated by finite element analysis and has proved an extending of 100% of the feasibility area of sensorless control with only an increase of 3% of torque ripple. The fidelity model of optimized solution is generated and sensorless control algorithm is simulated with fidelity model to prove that the optimized solution could be operated sensorless in low speed high load conditions.

A Holistic Multi-Objective Design Optimization Approach for Arctic Offshore Supply Vessels

This article presents a new holistic multi-objective design approach for the optimization of Arctic Offshore Supply Vessels (OSVs) for cost- and eco-efficiency. The approach is intended to be used in the conceptual design phase of an Arctic OSV. It includes (a) a parametric design model of an Arctic OSV, (b) performance assessment models for independently operating and icebreaker-assisted Arctic OSVs, and (c) a novel adaptation of the Artificial Bee Colony (ABC) algorithm for multi-objective optimization of Arctic OSVs. To demonstrate the feasibility and viability of the proposed optimization approach, a series of case studies covering a wide range of operating scenarios are carried out. The results of the case studies indicate that the consideration of icebreaker assistance significantly extends the feasible design space of Arctic OSVs, enabling solutions with improved energy- and cost-efficiency. The results further indicate that the optimal amount of icebreaking assistance and optimal vessel speed differs for different vessels, highlighting the motivation for holistic design optimization. The applied adaptation of the ABC algorithm proved to be well suited and efficient for the multi-objective optimization problem considered.

Fuzzy-Based Multi-Objective Design Optimization of Buried Pipelines

This paper proposes fuzzy-based multi-objective design optimization approach for the optimal analysis of buried pipe based on the expected value of a fuzzy output variable when the membership function is computed. The design of pipe structures is usually associated with uncertainties. Therefore, the principle of fuzzy set and a multi-objective optimization algorithm is applied to account for the variabilities associated with the uncertain parameters to ensure an acceptable performance against the impact of uncertainties. Different methods such as deterministic and non-deterministic methods have been proposed to model the effect of uncertainties and analyse the performance of engineering structure in the literature. Herein, a fuzzy-based uncertainty modelling approach that employs the optimal performance of a hybrid GA-GAM for the analysis of a buried pipeline is proposed. The purpose of the strategy is to optimise the design variable while considering the adverse effect of the uncertain fuzzy variables and variability of the structural performance. The uncertain fuzzy variable is used in the analysis to take into account the subjective nature of the corrosion process, while the entropy of a fuzzy variable used as a global measure of variable dispersion is employed to measure the variability and sensitivity of the structural response. The outcome of the fuzzy-based multi-objective design optimization provides a set of optimal solution for the analysis of fuzzy structure. Finally, the applicability and characteristic of this method are demonstrated using a numerical example, and the outcome denotes acceptable analytical tools for design engineers and can be applied to analyse other engineering structures.

The Multi-objective Optimization Design Approach for Carbon Fiber Hybrid Nanocomposites Containing NanoClay and NanoZnO Particles by Using OptiComp

This paper presents the multi-objective design optimization of Carbon Fiber-Reinforced Polymer hybrid nanocomposites by OptiComp. The use of nanoparticles not only improves the interface strength but also significantly improves stress and hence the propagation of strain in composite laminates, which ultimately improves the stiffness of the composite laminate. The OptiComp is the comprehensive generic procedure for design optimization of the composites. With the utilization of OptiComp, the discrete form Variable thickness approach (VTA) used in this study can discover the minimum laminate thickness, and for maximum stiffness in one step instead of two-step method. The Max stress theory and Tsai-Wu theory as constraints for the current study in optimization are performed using a direct value coding genetic algorithm. It is found that the CFRP nanocomposites up to 2 to 3 wt% range of nanoparticles can be designed for max. stiffness and min. weight.

Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets

Ensemble learning methods have already shown to be powerful techniques for creating classifiers. However, when dealing with real-world engineering problems, class imbalance is usually found. In such scenario, canonical machine learning algorithms may not present desirable solutions, and techniques for overcoming this problem must be used. In addition to using learning algorithms that alleviate the imbalance between classes, multi-objective optimization design (MOOD) approaches can be used to improve the prediction performance of ensembles of classifiers. This paper proposes a study of different MOOD approaches for ensemble learning. First, a taxonomy on multi-objective ensemble learning (MOEL) is proposed. In it, four types of existing approaches are defined: multi-objective ensemble member generation, multi-objective ensemble member selection, multi-objective ensemble member combination, and multi-objective ensemble member selection and combination. Additionally, new approaches can be derived by combining the previous ones, such as multi-objective ensemble member generation and selection, multi-objective ensemble member generation and combination and multi-objective ensemble member generation, selection and combination. With the given taxonomy, two experiments are conducted for comparing (1) the performance of the MOEL techniques for generating and aggregating base models on several imbalanced benchmark problems and (2) the performance of MOEL techniques against other machine learning techniques in a real-world imbalanced drinking-water quality anomaly detection problem. Finally, results indicate that MOOD is able to improve the predictive performance of existing ensemble learning techniques.

Potential of interactive multiobjective optimization in supporting the design of a groundwater biodenitrification process

The design of water treatment plants requires simultaneous analysis of technical, economic and environmental aspects, identified by multiple conflicting objectives. We demonstrated the advantages of an interactive multiobjective optimization (MOO) method over a posteriori methods in an unexplored field, namely the design of a biological treatment plant for drinking water production, that tackles the process drawbacks, contrarily to what happens in a traditional volumetric-load-driven design procedure. Specifically, we consider a groundwater denitrification biofilter, simulated by the Activated Sludge Model modified with two-stage denitrification kinetics. Three objectives were defined (nitrate removal efficiency, drawbacks on produced water, investment and management costs) and the interactive method NIMBUS applied to identify the best-suited design without any a priori evaluation, as for volumetric-load-driven design procedures. When compared to an evolutionary MOO algorithm, the interactive solution process was faster, more understandable and user-friendly and supported the decision maker well in identifying the most preferred solution (main design/operating parameters) to be implemented. Approach strength has been proved through both sensitivity analysis and positive experimental validation through a pilot scale biofilter operated for three months. In synthesis, without any “a priori” evaluation based on practical experience, the MOO design approach allowed obtaining a preferred Pareto optimal design, characterized by volumetric loading in the range 0.85–2.54 kgN m−3 d−1 (EBCTs: 5–15 min), a carbon dosage of 0.5–0.8 gC,dos/gC,stoich, with SRTs in the range 4–27 d.

Multiobjective Optimization Design of a Double-Rotor Flux-Switching Permanent Magnet Machine Considering Multimode Operation

The multimechanical-port double-rotor machine, as a core component in hybrid electric vehicles, has attracted considerable attention due to the merits of compact structure, control flexibility, and diverse performances. However, since there often exist two electric or mechanical ports in one compound machine frame, traditional design theory cannot be applied to it directly. Besides, because of the relatively complex machine structure and multiple design parameters, design objectives often influence even conflict with each other. In this paper, a new multiobjective optimization design approach is proposed to design and optimize a flux-modulated double-rotor flux-switching permanent magnet machine, where multiple driving modes are considered. To verify the feasibility of the double-rotor machine and the effectiveness of an optimization design method, the electromagnetic characteristics of the machine are investigated in detail. Finally, a prototype machine is built and tested. Both theoretical analysis and experimental results verify the validity of the proposed machine and multiobjective optimization design approach.

Multi-objective optimization for gene expression noise reduction in a synthetic gene circuit

Stochasticity in biological systems often referred to as gene expression noise is ubiquitous. The main sources of this noise come about in two ways. The implicit randomness of the biochemical reactions generates intrinsic noise inside the cell. Other cellular processes are themselves products that vary over time and from each cell to another, producing the so-called extrinsic noise. Controlling the mean expression level of a gene while reducing its noise is a challenge in many applications of Synthetic Biology. In previous works, we proposed a gene synthetic circuit to reduce gene expression noise while achieving a desired mean expression level. The circuit combines a negative feedback loop and a cell-to-cell communication mechanism based on quorum sensing. In this work, we use a multi-objective optimization design approach to find the best values for the tunable-in-the-lab parameters that, for a given desired mean expression value, achieve minimization of gene expression noise caused by intrinsic and extrinsic fluctuations. Our approach allows tuning the circuit parameters required to minimize noise effects, providing results which prove in accordance with genome-wide experimental data reported in the literature. Exploring different scenarios, either considering only intrinsic noise or considering both extrinsic and intrinsic ones, we find that the design strategies obtained for both cases are not transferable. Thus, designing the circuit parameters taking into account only intrinsic noise yields a sub-optimal design with decreased performance when evaluated in a scenario where both extrinsic and intrinsic noise are present.

Mixing augmentation induced by a vortex generator located upstream of the transverse gaseous jet in supersonic flows

The mixing process plays a very important role in the engineering realization of the scramjet engine, and sufficient mixing between the incoming supersonic air and the fuel relates to improve the overall performance of the airbreathing hypersonic propulsion system. In the current study, the delta wing is placed in front of the injector to promote the mixing of fuel and supersonic crossflow, and the effects of the delta wing height and the jet-to-crossflow pressure ratio have been investigated numerically based on grid independency analysis and code validation. The obtained results predicted by the three-dimensional Reynolds-average Navier–Stokes (RANS) equations coupled with the two equation k–ω shear stress transport (SST) turbulence model show that the delta wing has a highly remarkable improvement on mixing characteristics such as mixing efficiency and fuel penetration depth. However, the delta wing also shows additional losses of stagnation pressure. In the case of higher values of delta wing height and jet-to-crossflow pressure ratio, higher penetration and more losses of stagnation pressure are shown. At the same time, the mixing efficiency decreases with the increase of the jet-to-crossflow pressure ratio irrespective of the height of the delta wing, and there is an optimum height of the delta wing for each jet-to-crossflow pressure ratio to achieve the maximization of rapid fuel–air mixing. In addition, the hydrogen content in the recirculation region between the orifice and the delta wing is a result of both the jet-to-crossflow pressure ratio and the height of the delta wing. In conclusion, the design of the flow field with the delta wing to realize objective comprehensive optimal performance is a multi-objective problem, and it should be solved by the multi-objective design optimization approach.

A multi-objective design optimization approach for floating offshore wind turbine support structures

This paper presents a multi-objective design optimization approach for floating wind turbines with a design space that spans three stability classes of floating wind turbine support structures. A single design parameterization scheme was used to define the geometries of tension-leg, spar buoy, and semi-submersible candidate designs in terms of nine design variables. The seakeeping analysis of any particular platform configuration was completed using a simplified frequency-domain dynamic model applying linearized dynamics for the floating platform, mooring system, and a reference 5 MW wind turbine that were derived using existing functionality in FAST and WAMIT. Evaluation and comparison of different platforms was performed using a Pareto front pursuing multi-objective Genetic Algorithm (GA) optimization method to find the locus of platform cost minima and wind turbine performance maxima for a given environmental condition and sea state spectrum. Optimization results for the single-body platforms indicated a dominance of tension-leg platforms in this subset of the design space. Results for multi-body platforms showed that semi-submersible platforms with four floats demonstrated better stability and were more cost effective than other semi-submersible designs. In general, the full exploration of the design space demonstrated that four float semi-submersible platforms with angled taut mooring systems are a promising concept that can be used as a foundation for a detailed design and costing study. The results generated here are subject to the specifics of the targeted environmental conditions, cost model, linearized dynamics and choice of performance metric. As these elements evolve, the optimization framework presented here should be reapplied to track how the Pareto fronts for the different classes of platforms respond.

Design Optimization of Asymmetric Salient Permanent Magnet Synchronous Machines

This paper sets forth a design paradigm for a permanent magnet synchronous machine (PMSM) architecture that employs rotational asymmetry to obtain an improved constant power speed range performance and increased torque density. A population-based multiobjective optimization design approach is used to design the new machine topology and compare it to a conventional symmetrical surface mounted PMSM. Results obtained demonstrate that improved performance is attainable with the asymmetrical design. Validation of the results is carried out by means of three-dimensional finite element analyses.

Multiobjective optimization of mechanical properties based on the composition of adhesives

A mixed numerical-experimental approach capable to predict and optimize the performance of the footwear adhesive joints, based on the weight composition of used raw materials was presented. The approach based on the optimal design of adhesive composition to achieve the targets of minimum creep rate (CR) and maximum peel strength (PS) under manufacturing. Two stages are considered in the proposed approach. In the first stage, an approximation model is built based on planned experimental measurements and artificial neural network (ANN) developments. The ANN learning procedure uses a genetic algorithm. In the second stage an optimal design procedure is developed based on multi-objective design optimization (MDO) concepts. The MDO algorithm based on dominance concepts and evolutionary search is proposed aiming to build the optimal Pareto front. The model uses the optimal ANN to evaluate the fitness functions of the optimization problem. Furthermore, a ANN-based Monte Carlo simulation procedure is implemented and the sensitivity of the CR and PS relatively to weight compositions of raw materials is determined. The approach shown robustness to establish the trade-off between minimum CR properties and minimum inverse PS (maximum PS) using the weight composition of used raw materials. The optimal results for both CR and PS based on proposed approach are reached when large quantities for polyurethanes (Pus) and for some additives are considered. The performances of adhesive joints measured by CR and PS are very sensitive to the influence of some PUs and in some way are moderately sensitive to additives. The proposed MDO approach supported by experimental tests shows improved explorative properties of raw materials and can be a powerfully tool for the designers of adhesive joints in footwear industry.

Pareto optimal design of sectored toroidal superconducting magnet for SMES

A novel multi-objective optimization design approach for sectored toroidal superconducting magnetic energy storage coil has been developed considering the practical engineering constraints. The objectives include the minimization of necessary superconductor length and torus overall size or volume, which determines a significant part of cost towards realization of SMES. The best trade-off between the necessary conductor length for winding and magnet overall size is achieved in the Pareto-optimal solutions, the compact magnet size leads to increase in required superconducting cable length or vice versa The final choice among Pareto optimal configurations can be done in relation to other issues such as AC loss during transient operation, stray magnetic field at outside the coil assembly, and available discharge period, which is not considered in the optimization process. The proposed design approach is adapted for a 4.5MJ/1MW SMES system using low temperature niobium–titanium based Rutherford type cable. Furthermore, the validity of the representative Pareto solutions is confirmed by finite-element analysis (FEA) with a reasonably acceptable accuracy.

Semiconductor device design using the BiMADS algorithm

Designing high-performance semiconductor devices is a complex optimization problem, which is characterized by multiple and, often, conflicting objectives. In this research work, we introduce a multi-objective optimization design approach based on the Bi-Objective Mesh Adaptive Direct Search (BiMADS) algorithm. First, we assess the performance of the algorithm on the design of a n+-n-n+ silicon diode using a standard drift–diffusion model, showing that BiMADS is able to find the best solutions and to outperform the state-of-the-art algorithms. Successively, we tackle the design of MESFET and MOSFET devices, using a Maximum Entropy Principle (MEP) model; BiMADS is able to locate new designs that minimize the size of the device and provide an increased output current. Moreover, it is proved that BiMADS is able to locate promising solutions with a tight budget of objective function evaluations, which makes it suitable for large-scale industrial applications.

Vibro-Acoustic Optimization of Turbulent Boundary Layer Excited Panel with Curvilinear Stiffeners

The present work addresses the structural-acoustic design optimization study of straight or curvilinearly stiffened panels excited by turbulent boundary layer (TBL) pressure fluctuation. The Corcos model of representing TBL is used to capture the correlation of TBL pressure excitation. Validation of the Corcos TBL model, implemented in a framework, named EBF3PanelOpt, is also performed. For a comparative study, two optimal baseline designs are obtained for a panel with two and four stiffeners, respectively. The optimal designs obtained by performing acoustic optimization have reduced the maximummean square pressure radiated in the far field by 11.4 dB for a panel with two stiffeners and 18.5 dB for a panel with four stiffeners. The reduction of mass and the acoustic response during structural-acoustic optimization are conflicting in nature. Therefore, amulti-objective design optimization using the nondominated sorting genetic algorithm-II (NSGA-II) in VisualDoc is performed. The best Pareto-optimal design with four stiffeners, obtained using the multi-objective design optimization approach, has reduced the maximum mean square pressure radiated in the far field by 4.8 dBwhen comparedwith the baseline designwith four stiffeners. The obtained optimal designs meet all of the constraints, such as buckling eigenvalue, von Mises, and crippling stresses.