Multi-objective Design Approach Research Articles

A novel multi-objective generative design approach for sustainable building using multi-task learning (ANN) integration

Building Performance Optimization (BPO) plays a pivotal role in enhancing building performance, guaranteeing comfort while reducing resource consumption. Existing performance-driven generative design is computational demanding and difficult to be generalized to other similar buildings with difficult to be generalized to other building types or climate conditions. To fill this gap, this paper introduces a novel framework, which integrates multitask learning (MTL), code compliance check, and multi-objective optimization through NSGA-III algorithm. This framework is able to identify Paratoo Optimal design solutions, which comply with building codes, at low computation costs. The framework begins with selecting key design variables that are critical to building energy, comfort performance and life cycle cost. It then employs MTL to enhance the model's accuracy while reducing computational costs. Next, we designed a code compliance checking module followed by the NSGA-III optimization process, with the objective of identifying solutions that comply with existing building codes. The results indicate that the proposed MTL network achieved an R2 score of 0.983–0.993 on the test set. In the particular case study where equal weights are preferred, this approach yielded noteworthy reductions of 27.65%, 19.55%, and 31.13% in Building Energy Consumption (BEC), Life Cycle Cost (LCC), and Residue of continuous Daylight Autonomy (RcDA), respectively, for a rural dwelling, and exclude solutions that fail to satisfy regulatory standards. This framework allows designer to input the weights of each objective based on their preference and can be applied to other building types and climate regions. Last, we develop a solution selection tool based on the results output by the framework we proposed, which can be found at https://github.com/LiMingchen159/Village-House-Design-Strategy-in-Hebei-Province-China.

Data driven multi-objective design for low-carbon self-compacting concrete considering durability

Self-Compacting Concrete (SCC) offers remarkable benefits in modern engineering. However, traditional SCC design faces challenges, necessitating a reduction in carbon emissions for enhanced sustainability and a shift towards multi-performance collaborative design. This study proposed an intelligent and interpretable approach for multi-objective low-carbon SCC design. By combining machine learning and optimization algorithm, key factors including sustainability, rheology, workability, strength, durability, and cost were simultaneously addressed. Partial Dependence Plots was employed for model interpretation and feature impacts reveal. The proposed three-objective optimization exhibited superior efficiency, achieving comprehensive optimization efficiencies of 42.3%. In the context of meeting other performance requirements, C40 and C50 SCC optimized using this method exhibited a significant reduction of 18.9% and 10.1% in embodied carbon. This study aims to establish a versatile intelligent framework, rather than a specific model, capable of adapting to future iterations with evolving high-quality data, to achieve multi-objective SCC collaborative design. This advancement contributes to intelligent and low-carbon practices in concrete science and the construction industry.

A Reconfigurable Cellular Remanufacturing Architecture: a multi-objective design approach

A Reconfigurable Cellular Remanufacturing Architecture: a multi-objective design approach

A Generative Design Approach to Improving the Environmental Performance of Educational Buildings in Hot Arid Climates. (Assiut National University as a Case Study)

The architectural design process is complex, involving diverse objectives that may be contradictory, and on which orientation exerts significant influence. The artificial intelligence application, Generative Design facilitates solving multi-objective design dilemmas through the creation and evaluation of numerous design alternatives. However, its exploration in educational buildings in hot arid climates remains limited. Given the impact of spaces’ function distribution, this study aims to optimize it in the typical plans of educational buildings. Employing a multi-objective design approach to enhance environmental performance. The study is conducted and evaluated in national universities in Egypt as a case study, specifically in Assiut City. The results revealed that the optimum design for a certain objective has not equated to optimal performance for other goals, highlighting an inherent contradiction between them. Among 26,334 possible alternatives for spaces’ function distribution, the difference between the optimal scenario and the least favourable one is significant for the parameters related to study spaces: natural daylighting, and visual comfort, ranging from 10% to 24%, besides around 1% difference for parameters related to the whole building, including energy consumption, thermal comfort, and carbon emission. This research offers a framework applicable to various building types. Additionally, it encourages decision-makers to adopt a no-cost sustainable design approach.

Optimization Design of Surface-mounted Permanent Magnet Synchronous Motors Using Genetic Algorithms

The permanent magnet synchronous motor (PMSM) has gained widespread popularity in various industrial applications due to its simple structure, reliable performance, compact size, high efficiency, and adaptability to different shapes and sizes. Its exceptional characteristics have made it a focal point in industrial settings. The PMSM can be categorized into two primary types based on the arrangement of the permanent magnets (PM): interior permanent magnet (IPM) and surface-mounted permanent magnet (SPM). In the IPM, the magnets are embedded into the rotor, while in SPM, they are mounted on the rotor's surface. The utilization of PMs eliminates the need for excitation currents due to their high flux density and significant coercive force. This absence of excitation losses contributes to a notable increase in efficiency. In this study, a multi-objective optimal design approach is introduced for a surface mounted PMSM, aiming to achieve maximum efficiency while minimizing material costs. The optimization task is accomplished using a genetic algorithm. Furthermore, the motor designs are simulated using the finite element method (FEM) to assess and compare designs before and after the optimization process.

Optimized Design of Solid-Liquid Dual-Impeller Mixing Systems for Enhanced Efficiency.

Flow interferences occur in the dual-impeller stirred tank between paddles as well as between paddles and baffles and the tank wall, leading to inefficient utilization of the stirring energy. To address this issue, this study investigates the flow characteristics within the mixing tank using Euler-Euler numerical simulation and the particle image velocimetry (PIV) experimental method. The three-dimensional nonconstant flow characteristics are analyzed to optimize the critical stirrer geometry. By employing the Sobol method, an approximate model is established for sensitivity analysis to identify key parameters affecting the solid-liquid dual-impeller stirred tank's performance. Numerical simulations demonstrate that the optimized stirred tank exhibits a significantly improved solid-liquid suspension capacity and considerably reduces flow losses near the wall and baffle areas. Under the designated conditions, the cloud height is increased by 8.7%, and power consumption is reduced by 15.6% compared to the prototype. PIV tests performed on the stirred tank before and after optimization confirmed the reliability of the obtained optimization results. The primary objective of this study is to enhance mixing efficiency and homogeneity in solid-liquid mixing tanks while concurrently minimizing energy consumption and cost. These results validate the feasibility of employing a multiobjective optimal design approach that combines the RBF agent model with the Sobol method. The findings offer valuable insights for the design of similar mixing tanks.

A Graph-Based Optimization Framework for Large Water Distribution Networks

Water distribution networks (WDNs) have a crucial task: to reliably provide sufficient and high-quality water while optimizing financial resources. Achieving both reliability and resilience is vital. However, oversizing capacities can be costly and detrimental to water quality due to stagnation. Designing WDNs requires the consideration of these factors, resulting in a multi-objective optimization task typically addressed with evolutionary algorithms. Yet, for large WDNs with numerous decision variables, such algorithms become impractical. Complex network analysis offers an efficient approach, particularly with mathematical graphs representing WDNs. Recently, a graph-based multi-objective design approach using a customized measure (demand edge betweenness centrality) and a surrogate method for water quality assessment in large WDNs were developed. This paper combines these graph-based approaches into an optimization framework suitable for complex, real-world WDNs. The framework aims to minimize costs, maximize resilience, and exclude designs with poor water quality. It is demonstrated on a toy example, and its computational efficiency is shown by a real case study with 4000 decision variables, obtaining results in just 18.5 s compared to weeks of computation time with a state-of-the-art evolutionary algorithm.

Optimal characteristics of natural convection in a square porous-nanofluid-filled enclosure containing three tubes

A numerical model was used to simulate natural convection heat transfer in a two-dimensional square enclosure filled with nanofluid and saturated with porous media. The enclosure contained three heated tubes with Rayleigh numbers (Ra) ranging from 102 to 3 × 104. The governing equations with Boussinesq approximation for natural convection were solved iteratively using the finite volume technique through ANSYS FLUENT-CFD commercial package. The Darcy–Forchheimer–Brinkman and Local Thermal Equilibrium models were applied for water and nanofluid flow across the porous zone. The numerical analysis was executed systematically for significant parameters that have a substantial impact on natural convection heat transfer, namely, aspect ratio (AR = 1.25 − 3.75), nanoparticle volume fraction (Φ = 0.25%, 0.5%, 0.7% and 1%), and the porosity of metal foam (ε = 0.3, 0.4, 0.5, 0.6 and 0.7). The aspect ratio was found to have a considerable impact on the averaged Nusselt number (Nuave ). For AR = 3.75, the value of Nuave for all Rayleigh numbers increased by approximately 5.22 times compared to AR = 1.25. Moreover, Nuave remained approximately constant as the concentration of alumina-water nanofluid increased from 0.25% to 1%. The novelty of this study lies in the multi-objective optimal design approach adopted by adopting DX-12 with the outcomes of ANSYS FLUENT-CFD. The averaged Nusselt number and convection heat transfer rate reached their optimal values by nearly 13 and 9 times, respectively, at the lowest possible porosity (ε = 0.3), the lowest possible nanofluid concentration (Φ = 0), and the highest possible aspect ratio value (AR = 3.75).

Optimised multi-objective design of weir structures

Diversion head works, also called weirs or barrages, are structures constructed across rivers or canals to store water or raise the water level. The design of diversion weir structures involves calculating the depth, length and thickness of the horizontal and sloping aprons and sheet pile cut-offs. The design parameters of a diversion weir structure, which have complex non-linear relationships, are traditionally determined using empirically derived recommendations and iterations to achieve structural stability against failures due to scour, uplift, sliding, piping and overturning. However, current design approaches do not explicitly explore the trade-offs between the many relevant design objectives and thus fail to reveal possibly superior designs. A multi-objective optimisation design approach for a diversion weir structure is proposed in this article. A free and open-source code that can be used as a design tool is also provided. The method is demonstrated on a stylised design problem. The results show that the method reveals solutions with diverse balances of stability metrics and cost, with the optimal relationships of parameter values of components also varying based on the relative cost of the construction materials for sheet piles and aprons.

Development of a Multi-Objective Optimal Design Approach for Combined Water Systems

Recently, due to extreme climate change, damage from flooding has been increasing; however, water shortages are being announced simultaneously. Moreover, the water distribution system’s ability to supply consumers is being overwhelmed because of urbanization, population concentration, and increases in water consumption. For this reason, to solve the water shortage problem, water reuse technologies are developing and improving that perform simple chemical treatment processes to reuse water for flushing toilets, washing, gardening, etc. but not as drinking water. However, most water reuse systems are designed and operated as independent systems, such as reusing water used in individual buildings or using rainwater. Therefore, this study develops an optimal design for the combined water systems, which is modeling and designing water distribution systems, urban drainage systems, and water reuse systems simultaneously to solve the water shortage and reduce flooding damage. To consider the combined water systems design, the existing water distribution system (WDS) demand is divided into drinking water and other uses, and the resource of other water is assumed by the rainwater storage tank for covering the amount of exceeding precipitation. To derive optimal design solutions for the combined three water systems, single- and multi-objective optimization techniques are applied considering various design criteria (i.e., construction cost, system resilience, and flooding volume on the exceeding design rainfall intensity). The developed combining water system design techniques could be used to create designs that solve the problems of medium and long-term water shortages and sustainable water systems development.

Evolutionary Multiobjective Design Approach for Robust Balancing of the Shaking Force, Shaking Moment, and Torque under Uncertainties: Application to Robotic Manipulators

In this paper, the environmental uncertainties are taken into account when designing a robotic manipulator to balance the shaking force, shaking moment, and torque. The proposed robust balancing design approach does not consider the probability distributions of the uncertainties and is addressed without dependence on specific trajectories. This is expressed as a nonlinear constrained multiobjective optimization problem in which the nominal performance in the time-independent terms of the shaking force balancing, the shaking moment balancing, and the torque delivery, as well as their three sensitivities to uncertainties, are simultaneously optimized to provide a set of link shapes that match link mass distributions in a single stage. The proposal is applied to a three-degree-of-freedom serial-parallel manipulator, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the associated problem. Comparative results with other design approaches reveal that the selected design achieves a suitable tradeoff in balancing the shaking force balancing, the shaking moment balancing, and the torque delivery and their sensitivities, leading to a reduction in their values and variations under mass changes in the manipulator end-effector with different operating conditions (tasks).

De Novo Drug Design by Iterative Multi-Objective Deep Reinforcement Learning with Graph-based Molecular Quality Assessment.

Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potentials of the generated molecules during the generation process. In this study, we present a novel de novo multi-objective quality assessment-based drug design approach QADD, which integrates an iterative refinement framework with a novel graph-based molecular quality assessment model on drug potentials. QADD designs a multi-objective deep reinforcement learning pipeline to generate molecules with multiple desired properties iteratively, where a graph neural network-based model for accurate molecular quality assessment on drug potentials is introduced to guide molecule generation. Experimental results show that QADD can jointly optimize multiple molecular properties with a promising performance and the quality assessment module is capable of guiding the generated molecules with high drug potentials. Furthermore, applying QADD to generate novel molecules binding to a biological target protein DRD2 also demonstrates the algorithm's efficacy. QADD is freely available online for academic use at https://github.com/yifang000/QADD or http://www.csbio.sjtu.edu.cn/bioinf/QADD. Supplementary data are available at Bioinformatics online.

Application of the preference selection index method in multi-objective lightweight design of heavy commercial vehicle frames

An optimization strategy integrating contribution analysis, design of experiment, an approximate model and a preference selection index (PSI) method is proposed to establish an efficient multi-objective lightweight design approach for heavy commercial vehicle frames. First, finite element and rigid–flexible coupling virtual prototype models are established and their accuracy is verified using experiments. Secondly, the fatigue life of the frame is calculated using the power spectral density function, frequency response function and corrected stress–life curve. Finally, the thicknesses of frame components are regarded as design variables, and the mass and maximum root mean square stress are considered as design objectives in the process of lightweight design. Three optimization strategies are used to investigate the characteristics of the PSI method. The results showed that the optimization strategy combined with the PSI method demonstrates high computational efficiency and reliability.

A pareto-based multi-objective network design approach for mitigating the risk of hazardous materials transportation

Hazardous materials (HazMat) transportation is one of the indispensable links in the chemical process industry and is closely related to safety, security, and environmental concerns. HazMat transportation routing is a major proactive risk management measure. Most related studies focus on diminishing the transportation risk for one single HazMat shipment. This work addresses a global perspective on the problem where the correlations of multiple simultaneous HazMat shipments are considered. A multi-objective transportation network design model is defined to optimize the objectives of the transportation risk and the transportation cost. A well-designed Pareto-based hybrid heuristic algorithm is proposed for model solving, which integrates a linear decomposition procedure for initial population construction, a Pareto-based scatter search procedure for population evolution, and a variable neighborhood search procedure for solution improvement. Computational results indicate that the proposed algorithm is competitive in solving large-scale instances since it is able to strike a great balance between effectiveness and efficiency. The detailed solution analysis shows that the proposed approach can effectively coordinate multiple simultaneous HazMat transportation processes. Moreover, we find that performing multiple short-distance shipments would significantly reduce the total transportation risk, but results in the higher transportation cost. This work can provide a set of Pareto optimal transportation networks for different industrial application scenarios to achieve the trade-off between economic and safety.

Artificial Neural Networks-Based Multi-Objective Design Methodology for Wide-Bandgap Power Electronics Converters

Design methodology of power electronics converters is critical to fully explore the potential of wide-bandgap power semiconductors at the converter level. However, existing design methods largely rely on complex mathematical models which significantly increases the computational time, complexity and further leads to problems including poor constraint handling capabilities, inaccurate design, difficult parameter tuning and inadequate problem dimension. These all could generate sub-optimal designs that make the whole design process meaningless. To overcome the aforementioned problems, in this paper, an artificial neural network (ANN)-based multi-objective design approach is proposed, which offers significant advantages in reducing the repetitive usage of complex mathematical models and hence the computational time of design. The computational time was reduced by up to around 78% and 67% compared to the numerical modeling and geometric program (GP) methods as validated through a hardware design process. The proposed method was implemented in MATLAB/Simulink to design a 1 kW single-phase inverter, resulting in a design with an optimized efficiency (98.4%) and power density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {(\mathrm{\text{4.57}~kW/dm^{3}})}$</tex-math></inline-formula> . The accuracy of the design is verified through experimental prototyping and the measured efficiency and power density are 98.02% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathrm{\text{4.54}~kW/dm^{3}}$</tex-math></inline-formula> , respectively, so the errors of efficiency and power density are both less than 1%.

Multi-objective robust design approach usage in integration of bond graph and genetic programming

ABSTRACT In this paper, a new hybrid algorithm, based on multi-objective genetic programming and bond graphs in combination with Monte Carlo simulation, is proposed for Pareto optimal robust design of dynamical systems. For this purpose, first, an embryo model is considered for a dynamical system. Then, genetic programming is used to help this embryo model evolve through generations and achieve an optimum model. It is clear that without considering the uncertain parameters of the designing system, unwanted behaviors could occur. To overcome this, Monte Carlo simulation has been used to minimize the variations of objective functions by generating some samples around the nominal values of the uncertain parameters. In other words, the mean and variance of the objective function are minimized simultaneously through sampling, generated by Monte Carlo simulation, to reach a robust behavior. The proposed algorithm has been employed for robust design of two case studies, namely a closed-loop typewriter drive system and an optimum controller for quarter-car suspension system. A comparison between the results obtained using the proposed algorithm and those obtained through other approaches in the literature shows that more robust behavior in the presence of uncertainties can be observed in the proposed method.

Comparison between deep learning and fully connected neural network in performance prediction of power cycles: Taking supercritical CO 2 Brayton cycle as an example

AI is becoming increasingly important in promoting the energy revolution of carbon-neutral to achieve sustainable development. Induced by the large implementation of renewable energy, the more complexities and uncertainties in the future carbon-neutral energy systems make their designs hard accessible to the conventional methods, so machine learning (ML) especially the neural network becomes under focus. Here, we design a deep learning architecture based on convolutional neural networks (DL-CNN) known for its powerful predicting ability, and first utilize it in a case study of performance prediction of supercritical CO2 Brayton cycle. The design paradigm of DL-CNN architecture for performance prediction of power cycle is proposed. We also summarize the commonly used fully connected neural network (FC-NN) in related studies of power cycle design. Through systematically comparing the prediction performance of DL-CNN and FC-NN, their respective advantages and application scenarios in energy system design are discussed. In addition, a multiobjective design approach based on DL-CNN combined with random search is proposed and proved to be feasible by comparing with genetic algorithm. The results show that our proposed DL-CNN model is much more competitive than FC-NN model when the training data is sufficient and the prediction condition is complex, in which the prediction accuracy can achieve 99.6%. In the future, our deep learning model may help solve the complex design problems of hybrid carbon-neutral energy systems.

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.

Quieter and Greener rotorcraft: concurrent aerodynamic and acoustic optimization

Within the DLR project VicToria an aerodynamic and aero-acoustic optimization of helicopter rotor blades is performed. During the optimization, three independent flight conditions are considered: hover, cruise and descent flight. The first two flight conditions drive the power requirements of the helicopter rotor, while the descent flight is the loudest flight condition for current helicopter generations. To drive down the required power and the emitted noise, a multi-objective design approach coupled with surrogate models is utilized to find a Pareto optimal set of rotors. This approach allows to identify the trade-offs to be made when laying emphasis on either goal function. The underlying CFD simulations utilize fourth-order accurate spatial schemes to capture the vortex dominated flow of helicopter rotor blades. The paper presents the validation of the setups, the optimization results and the off-design analysis of a chosen set of blades from the Pareto front. The conclusion is that the utilization of the Pareto front approach is necessary to find good rotor designs, while the utilization of high-order methods allows for efficient CFD setups.

Optimal Electromagnetic-Thermal Design of a Seven-phase Induction Motor for High-Power Variable-Speed Applications

Induction motors have been traditionally used in industrial applications ranging from a fraction of horse-power up to several Megawatts due to their substantial benefits. Induction drives with more than three-phases are superior to the 3-phase induction drives in terms of overall-volume, torque fluctuations, current passing each stator-winding, ohmic-loss, efficiency and reliability in the case of stator-windings open-circuit fault. These benefits are especially more attractive in variable speed drivers due to the reduced capacity of power-electronics switches. This paper aims to develop an optimal electromagnetic-thermal design procedure of a high-power seven-phase induction motor suitable for variable-speed applications. In this multi-objective design approach, the objective function is defined aiming to increase the efficiency, power-factor, power-to-weight ratio, starting-torque as well as decrease the starting-current. Furthermore, the electrical, mechanical, dimensional, magnetic and thermal limitations are included in this optimization study in order to ensure practical realization of the designed machine. The coupled-circuit method is employed for nonlinear electromagnetic modeling while the current displacement phenomenon is considered in rotor parameters calculations. A lumped-parameter-thermal model is established for calculating heat rises of different parts in each iteration of optimization study. Finally, the performance characteristics of the optimally designed 1-MW 4-pole motor are verified using 2D-FE analyses.