Multi-criteria Design Optimization Research Articles

Multi-criteria optimal design of small wind turbine blades based on deep learning methods

The development of a Deep Learning (DL) model using multi-criteria optimal design of wind turbine blades is presented, focusing on the key variables TSR and Von Mises stress to predict: blade mass, power coefficient and natural frequency. The DL model was trained with data from three mathematical functions generated using distance-weighted inverse interpolation. The model allowed the generation of multiple feasible designs, which satisfy the design constraints. The BEM theory was used in the generation of the aerodynamic model of the 12.5 kW wind turbine using the NREL-S818 airfoil. Subsequently, the structural behavior of the blades was analyzed under three design load cases specified in IEC 61400-2. The results showed that in the training phase, the MAE, MSE and MSR error metrics were an essential guide in the development of the DL model. Interesting behaviors were observed due to the diverse results obtained, which are probably due to the multidimensional fits that are difficult to observe in the fit functions generated. However, training between 200 and 250 epochs performed better, with errors ranging from 0.05 to 0.120 for the MAE. The DL model exhibited the remarkable ability to predict the optimal output variables, with accuracy ranging from 90% to 98%.

Design of Performance Characteristics on Laser Treated Denim Fabric

The current paper covers an experimental study, mathematical modelling, and design optimization of the laser treatment process of denim fabrics. Laser fading is used in the finishing phase of garment production, unfortunately, it decreases fabric durability. Denim fabric is known as a cotton fabric but nowadays it is blended with other fibres to improve fabric properties. For this reason, the main purpose of this study was to test multicomponent fabric longevity after laser treatment. The knowledge from this study is used by garment production company. The tear force, abrasion resistance, and colour difference are considered as performance characteristics of laser treated denim fabrics subjected to maximization. To achieve the goals, novel, mathematical models were developed. In the preliminary tests, the design space was determined. The full factorial design of experiments was utilized in the experimental study and the values of the performance characteristics are measured. Based on the results of the experimental study the artificial neural network and Haar wavelet based mathematical models were developed to predict the behaviour of the tear force, abrasion resistance, and colour difference. The artificial intelligence-based multicriteria design optimization procedure is developed.

Multi-criteria optimization for sustainability-based design of reinforced concrete frame buildings

This paper implements the multi-criteria design optimization of three-dimensional reinforced concrete frame building structures, considering aspects such as the realistic design of the elements, including foundations within the structural assembly, or considering the soil-structure interaction. The criteria for a more comprehensive sustainable approach are related to environmental, constructive, and durability aspects. The environmental factor is measured through CO2 emissions, considering its capture due to concrete carbonation. The use of multi-objective strategies is evident in solving the multi-criteria problem. Still, it is also proposed to formulate this problem with a single function containing all the criteria to solve it as a single-objective optimization problem. Strategies are also offered to perform multi-objective optimization based on Kriging metamodels. Several alternatives for multi-criteria decision-making are explored. The results show that multi-objective metamodel-based optimization is a good strategy for solving this problem. Alternatively, the results of the single-objective optimization of the multi-criteria problem are very satisfactory. The solutions obtained are analyzed according to the type of optimization and the decision-making criteria. Optimized solutions significantly improve the sustainability indexes compared to traditional design. Multi-criteria optimization contributes significantly to achieving these indexes. Therefore, the proposed methodology allows for the sustainable design of any reinforced concrete frame structure. It highlights the importance of integrating more encompassing formulations and advanced optimization techniques into traditional design procedures to adopt cleaner production practices in the construction sector. Finally, several promising lines of research are presented.

Multicriteria Design and Operation Optimization of a Solar-Assisted Geothermal Heat Pump System

This work focuses on the determination of the design and operation parameters of a thermal system depending on the optimization objective set. Its main objective and contribution concern the proposal of a generalized methodological structure involving multiobjective optimization techniques aimed at providing a solution to a practical problem, such as the design and dimensioning of a solar thermal system. The analysis is based on system operation data provided by a dynamic simulation model, leading to the development of multiple surrogate models of the thermal system. The thermal system surrogate models correlate the desired optimization objectives with thermal system design and operation parameters while additional surrogate models of the Pareto frontiers are generated. The implementation of the methodology is demonstrated through the optimal design and operation parameter dimensioning of a solar-assisted geothermal heat pump that provides domestic hot water loads of an office building. Essentially, energy consumption is optimized for a desired domestic hot water thermal load coverage. Implementation of reverse-engineering methods allows the determination of the system parameters representing the optimized criteria.

A Multi-Criteria Optimization Approach to Cam Curve Design Combining Different Splines Given by Fixed and Adjustable Checkpoints

This study proposes a multi-criteria design optimization of cam mechanisms. This approach is based on the multistage interconnection of different tools: MATLAB, ADAMS, and mode frontier. It consists of three main issues: (i) The polynomial representation of splines given by checkpoints defining the specific motion of the follower; (ii) The connection of these splines by different degrees of fitting in order to combine them in the entire cam profile and (iii) The optimization including the adjustable checkpoints according to the objectives and the constraints formulated for a different area of the cam profile. An illustrative example based on the optimization of a production machine cam profile is discussed. The suggested methods of multi-criteria design optimization are validated by experimental tests. The given results show the effectiveness of the proposed approach and the possibility of their application in the real world of manufacturing.

Multicriteria optimal design of emamectin benzoate microparticles obtained by spray drying and ionic gelation

Emamectin benzoate (EB) is an antiparasitic used to control Caligus rogercresseyi in Chile. However, it has lost efficacy, and the parasite has been exposed to a sublethal dose. Microencapsulation has been suggested as an alternative method to protect and control the release of poorly absorbed drugs to ensure their lethal doses. Accordingly, design of experiments (DOE) and response surface methodology (RSM) were applied to find optimal conditions for EB's ionic gelation (IG) and spray drying (SD) microencapsulation. We used multiobjective (MOO) and multi-response optimization techniques as the desirability function (DFA) to obtain optimal conditions that produce microparticles that satisfy several criteria, such as low gastric digestion (GD) and high yield (Y), encapsulation efficiency (EE), load capacity (LC), and intestinal digestion (ID). The optimization process prioritized the digestion responses and was constrained according to a mass balance. MOO generated theoretical solutions that were better than any of the DOE experimental solutions. Both optimization methods achieved a more balanced performance than the responses obtained in the experimental design. Each optimization method produced better experimental responses than the other in some responses. In SD, DFA yielded higher LC, GD, and ID than MOO by 7.5%, 9.3%, and 2.1%, respectively. In contrast, MOO obtained higher Y and EE than DFA by 6.2% and 10.1%, respectively. In IG, the DFA method yielded a solution with better responses than MOO in LC (3.7%), GD (7.4%), and ID (3.2%), while the MOO solution was better in Y (14.2%) and EE (19.3%). Both multicriteria optimization techniques were suitable for obtaining optimal solutions; however, neither proved superior in all cases.

Development of a Multi-Criteria Design Optimization Methodology for Automotive Plastics Parts.

The plastic industry is undergoing drastic changes, due to the customer sustainability perception of plastics, and the eruption of new processes (such 3D printing) and materials (such as renewably sourced resins). To enable a fast transition to high-quality, sustainable plastic applications, a specific methodology could be a key competitive advantage. This novel methodology is focused on improving the objectivity and efficiency of plastic production and the design review process. It is applicable to discrete optimization events in any product lifecycle milestone, from concept design to serial production stages. The methodology includes a natural way to capture plastic-related knowledge and trends, oriented towards building a dynamic “interaction matrix”, with a list of potential optimizations and their positive or negative impacts in a comprehensive set of multi-criteria evaluations. With an innovative approach, the matrix allows the possibility to incorporate a business strategy, which could be different at every lifecycle stage. The business strategy is translated from the common “verbal” definition into a quantitative set of “Target and Restrictions”, making it possible to detect and prioritize the best potential design optimization changes according to the strategy. This methodology helps to model and compare design alternatives, verify impacts in every evaluation criteria, and make robust and objective information-based decisions. The application of the methodology in real cases of plastic material design optimization in the automotive industry has provided remarkable results, accelerating the detection of improvement methods aligned with the strategy and maximizing the improvement in product competitiveness and sustainability. In comparison with the simultaneous application of existing mono-criteria optimization methodologies (such as “Design to Cost” or “Eco Design”) and subjective expert-based reviews, the novel methodology has a reduced workload and risks, confirming its potential for future application and further development in other polymer-based products, such as consumer goods or packaging.

Multi-criteria optimal design and seismic assessment of SMA RC piers and SMA cable restrainers for mitigating seismic damage of simply-supported highway bridges

Recent strong earthquakes demonstrated that multi-span simply-supported highway bridges have high possibilities in experiencing serious damage of piers, unseating of bridge spans, and bearing failure, e.g. the Yematan bridge under 2021 Maduo earthquake. A newly proposed novel bridge system with shape memory alloy reinforced piers, i.e., SMA RC pier, and restraining devices, i.e., SMA cable restrainer may be a potential alternative to prevent the bridge from such catastrophic collapses. This paper aims at investigating the effectiveness of the novel bridge system, identifying the main influential factors, and optimizing the design of SMA RC piers and SMA restrainers. Nonlinear time history analysis is fist conducted to investigate the efficiency of the novel bridge system for a typical simply-supported bridge. Then, a sensitivity analysis is performed to estimate the effects of the main influential factors. Lastly, the design optimization of SMA RC piers and SMA restrainers is explored through a multi-criteria decision-making process. The results revealed that the novel bridge system is efficient in improving the overall safety of simply-supported highway bridges. The pounding force between the adjacent decks can be effectively reduced by installing SMA restrainers in the bridge. The optimal range of the allowable relative displacement between the superstructure and substructure is between 0.22 and 0.51 times of the seat width; the optimal value of the target residual drift of SMA RC pier is recommended as 0.87%; the most effective range of the normalized design force of the restrainers varies from 1.0 to 1.4. The optimized novel bridge system can well balance the seismic responses of each vulnerable component.

Multi-criteria optimal design of hybrid clean energy system with battery storage considering off- and on-grid application

Abstract This paper examines the optimal design of a hybrid photovoltaic-wind generator system with battery storage (PV-wind-battery) with off-grid and on-grid operation modes. The objective of the study is to supply annual load demand considering environmental emissions and energy generation cost, as well as the cost of load losses. The decision variables include the optimal size of photovoltaic and wind resources, transfer of the power of the inverter to the load, battery storage bank size, and photovoltaic panel angle. The angle is determined optimally using a meta-heuristic algorithm, i.e., the spotted hyena optimisation (SHO), considering minimisation of the objective function and satisfying the constraints. The objective function is defined as the sum of net present cost (NPC) of the system components and load losses and also cost of emissions (TNPC). The constraints are a maximum and minimum size of the system components and also reliability constraint as load interruption probability (LIP). In this study, first, an off-grid PV-wind-battery system was designed without considering emissions; then, an on-grid PV-wind-battery system was designed considering emissions. It was then optimized using SHO. The simulation results indicated that the PV-wind-battery and wind-battery combinations are the best and worst system combinations, respectively, in terms of TNPC and reliability indices. Additionally, the performance of the SHO algorithm was compared with that of the particle swarm optimisation (PSO), and the results proved the superiority of SHO in system design with a lower cost and better reliability indices. Moreover, the results cleared that the TNPC and LIP achieved 1.286 M$ and 0.13%, respectively, which were lower in the on-grid mode of the hybrid PV-wind-battery system designing compared to the results of the off-grid mode with 1.295 M$ and 0.19%. The obtained results showed that reliability can be improved by purchasing power from the network and considering minimisation of emissions in the on-grid operation mode. The impact of increasing the maximum available power of the network (MAPnet) was examined on the hybrid PV-wind-battery system designing. The results confirmed that the system reliability was improved and TNPC decreased by increasing the MAPnet, and vice versa.

Research on multi-physical field coupling of solid electrothermal storage unit

In recent years, the solid-state electro-thermal storage device has been connected to the power supply side of the power system for peak regulation by virtue of its high voltage and large capacity characteristics, which plays a positive role in the consumption of wind power, photovoltaic and other new energy. Based on the coupling effects of heat, current, electricity, magnetism, force and other physical fields in the equipment, this paper studies the optimal design of thermal storage structure. A multi-physical field and multi-criteria collaborative optimization design method was proposed to design the surface load of heating power, heat transfer channel of heat storage body, and porosity of body. Through the multi-field coupling numerical simulation and experiment verification, the thermal field, flow field, stress field, electric field and other changes are analyzed, and it is verified that the design method can effectively overcome the malpractice of low heat transfer rate temperature imbalance of thermal storage materials. The experimental results provide theoretical guidance for device design and engineering application.

Energy, exergy, economic and exergoeconomic (4E) multicriteria analysis of an industrial waste heat valorization system through district heating

The purpose of this article is to determine the multicriteria-optimal design of an industrial waste heat recovery system for district heating in Grenoble (France). Energy, exergy and cost flow balances were applied unit by unit allowing to assess the process performance based on two innovative methods. First, the performance assessment includes all units involved in the heat valorization process, not only focusing on the recovery system. Second, the yearly management of energy flows was optimized through mixed-integer linear programming, anticipating fluctuations in residential demands and waste heat availability. This multicriteria analysis with a systemic-anticipatory approach allowed to select the appropriate inlet temperature and storage capacity. It was found that the most promising inlet temperature and heat storage capacity are 35 °C and 30 MWh, respectively. With this design, the system recovers 41% of industrial waste heat, covers 48% of residential needs, has an estimated net present value of 11.7 million euros over 20 years, and reduces the district’s overall exergy destruction and exergy destruction costs by 20% (4.2 GWh/year) and 9% (286 k€/year), respectively. Remarkably, none of the mono-criterion analyses prioritized this design. Therefore, the multicriteria analysis with systemic-anticipatory approach was of utmost importance for detecting the most promising solution.

Multi-criteria PSO-based optimal design of grid-connected hybrid renewable energy systems

ABSTRACT Human-induced climate change through the over liberation of greenhouse gases, resulting in devastating consequences to the environment, is a concern of considerable global significance which has fuelled the diversification to alternative renewable energy sources. The unpredictable nature of renewable resources is an impediment to developing renewable projects. More reliable, effective, and economically feasible renewable energy systems can be established by consolidating various renewable energy sources such as wind and solar into a hybrid system using batteries or back-up units like conventional energy generators or grids. The precise design of these systems is a critical step toward their effective deployment. An optimal sizing strategy was developed based on a heuristic particle swarm optimization (PSO) technique to determine the optimum number and configuration of PV panels, wind turbines, and battery units by minimizing the total system life-cycle cost while maximizing the reliability of the hybrid renewable energy system (HRES) in matching the electricity supply and demand. In addition, by constraining the amount of conventional electricity purchased from the grid, environmental concerns were also considered in the presented method. Various systems with different reliabilities and potential of reducing consumer’s CO2 emissions were designed and the behavior of the proposed method was comprehensively investigated. An HRES may reduce the annualized cost of energy and carbon footprint significantly.

Optimal Solar Panel Area Computation and Temperature Tracking for a CubeSat System using Model Predictive Control

Recently, there has been a rising interest in small satellites such as CubeSats in the aerospace community due to their small size and cost-effective operation. It is challenging to ensure precision performance for satellites with minimum cost and energy consumption. To support maneuverability, the CubeSat is equipped with a propellant tank, in which the fuel must be maintained in the appropriate temperature range. Simultaneously, the energy production should be maximized, such that the other components of the satellite are not overheated. To meet the technological requirements, we propose a multicriteria optimal control design using a nonlinear dynamical thermal model of the CubeSat system. First, a PID control scheme with an anti-windup compensation is employed to evaluate the minimum heat flux necessary to keep the propellant tank at a given reference temperature. Secondly, a linearization-based controller is designed for temperature control. Thirdly, the optimization of the solar cell area and constrained temperature control is solved as an integrated nonlinear model predictive control problem using the quasilinear parameter varying form of the state equations. Several simulation scenarios for different power limits and solar cell coverage cases are shown to illustrate the trade-offs in control design and to show the applicability of the approach.

Multi-criteria design optimization and thermodynamic analysis of a novel multi-generation energy system for hydrogen, cooling, heating, power, and freshwater

The main focus of this paper is to present thermodynamic and economic analyses and multi-objective optimization of a novel geothermal-solar multigeneration system. The system aims to produce hydrogen, freshwater, electricity, cooling load, and hot water and designed based on geothermal and solar energy. After modeling and thermodynamic and economic analysis, exergy destruction rate, exergy efficiency and, cost rate were calculated for each component of the system. The results showed that the highest amount of exergy destruction was related to parabolic trough collectors (PTCs) and absorption chillers. To select the geothermal fluid of the organic Rankine cycle (ORC), several different fluids were investigated, among which isobutene was selected. By using the Group method of data handling (GMDH) neural network, a mathematical relationship was obtained between the inputs and outputs of the problem and were given as inputs to the non-dominated sorting genetic algorithm II (NSGAII)alg. The final optimal point was obtained applying the technique for order of preference by similarity to ideal solution (TOPSIS) decision criterion at which the exergy efficiency and cost rate were calculated to be 21.63% and 63.89 $/h, respectively. The meteorological data of the Zanjan, Isfahan, and Bandar Abbas cities were used to calculate the performance accurately at the TOPSIS selection point. To provide a comparison between different cities, the performance of the system was evaluated on September 17 as a sample day. On this day, the proposed system produces 26.38 kg of hydrogen and 373.8 m3 of freshwater in Isfahan.

Multi-criterion optimization of integrated photovoltaic facade with inter-building effects in diverse neighborhood densities

Inter-building effects have been proved to have significant impacts on the energy and indoor environmental performance of adjacent building developments in densified urban environment. Therefore, this study presents a multi-criterion design optimization and decision-making approach for an integrated PV facade design considering miscellaneous inter-building factors as well as corresponding criteria specification in major green building assessment schemes. The impact of the surface reflectance and façade geometry is quantified for the lighting energy demand, HVAC energy demand, photovoltaic (PV) energy supply and indoor visual discomfort time respectively with a robust global sensitivity approach in diverse urban density scenarios. Wall reflection is determined to be the most important urban design factor contributing to 34%–66% variation of different performance indicators as per the availability of solar radiation on different building surfaces. Multi-dimension parametric analyses and design optimizations of PV envelope design parameters are then conducted for different neighborhood densities with fixed surface properties of peripheral building facades. Sensitivity indices of window and shading design parameters are subject to significant variation when considering surface properties of peripheral buildings. Furthermore, the performance of two different decision-making strategies (i.e. Technique for Order of Preference by Similarity to Ideal Solution and Minimum Distance to the Utopia Point methods) is compared based on criteria weightings from green building assessment schemes. The final optimum solution closest to the ideal is then determined by two-dimensional charts of objective functions. The integrated design optimization considering peripheral reflection can change the estimated net energy demand by up to 8.84% (mainly in PV supply and lighting demand). These research findings can promote the integration of synergetic energy and indoor environment design strategies and expand the penetration of renewable applications in urban areas.

A multi-objective optimization design for a new linear compliant mechanism

This paper develops a hybrid optimization approach for multi-criteria optimal design of a compliant positioning platform for nanoindentation tester. The platform mimics the biomechanical behavior of beetle so as to allow a linear motion. Structure of the beetle-liked mechanism consists of six legs arranging in a symmetric topology. Amplification ratio and static characteristics of the platform are analyzed by finite element analysis (FEA). To improve the performances of the platform, the main geometric parameters of the platform are optimized by an efficient hybrid approach of the Taguchi method (TM), response surface methodology (RSM), improved adaptive neuro-fuzzy inference system (ANFIS), and teaching learning based optimization (TLBO). Numerical data are collected by integrating of the RSM and FEA. Signal to noise ratios are determined and the weight factor of each response is calculated. The suitable ANFIS’s parameters are optimized through the TM. The results found that trapezoidal-shaped MFs is the best type for the safety factor and the displacement. The optimal ANFIS’s parameters for the safety factor and the displacement were determined at the number of input MFs of 4, trapmf, hybrid learning method, and linear output MFs. According to improved ANFIS establishments, TLBO algorithm is utilized for solving the multi-objective optimization. Analysis of variance and sensitivity are investigated to determine the significant effects of design factors on the responses. The simulated and experimental validations are in a good agreement with the predicted results.

Multicriteria Optimization via Parameter Space Investigation for Ultralow-Altitude Airdrop L1 Adaptive Controller

This paper presents preliminary results of the application of the parameter space investigation method for the design of the L1 flight control system implemented on the ultralow-altitude airdrop process. In particular, the study has established the task performance level of airdrop mission which is chosen as the optimization objective in this paper. According to the characteristics of ultralow-altitude airdrop process, 12 optimization criteria are designated as the constraints during the multicriteria optimization. After two iterations, the task performance level of the airdrop is improved from “Moderate” to “Expected”. The results in the paper confirms the suitability of the parameter space investigation method for the multicriteria design optimization of an airdrop flight control system subject to desired control specifications. In addition, the robustness and dynamic response of the flight control system are significantly improved after optimization. Moreover, the analysis of optimization results has contributed to a deeper understanding of the relationship between L1 controller parameters and airdrop task performance.

Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design.

As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method.

Optimal design through the utility function parameterization method on electromechanical actuators

This paper presents an analytical and computer aided procedure for the multicriteria design optimization of the electromechanical actuator. The constraints are defined to find the basic constructive parameters which defined have to by simultaneous optimized through objectively severe functions. For the defined to find the multi-criteria optimization model of the electromechanical actuator a computer program based in interactive dialogue in developed in an algorithm. The results of the program are presented considering a multicriteria optimization algorithm of the new electromechanical actuator.

Methodology for multi-criteria design optimization of plastic products with a focus on highly competitive markets

Plastic products are currently key in highly competitive markets because of their cost-efficiency and performance. However, plastic product markets are undergoing substantial changes due to new factors, such as sustainability and waste reduction, rising petrol and energy prices, and the arrival of new production technologies such as 3D printing. These changes make it necessary to update plastic product design to ensure competitiveness and market success. Several pure optimization strategies have been fully developed for manufacturing process optimization (such as LEAN or KAIZEN methodologies) that are recognized and useful (if not mandatory) in any competitive industry. However, there is a lack of equivalent methodologies being applied to product design optimization, even if a product design review is much more powerful to maximize returns. There are several advantages to an optimization methodology over other strategies, because of its easier implementation and compatibility with any existing organization or design strategy, because it is based on discrete optimization events, with application to the entire product lifecycle (from the early design phase to mature products), maximizing real profitability. The work here is focused on developing a plastic product design optimization methodology with several tools integrated with “variational” information flow to help users detect and evaluate improvements and to make information-based decisions, with the ability to be updated with experience and market changes. The application scope is initially limited to plastic products due to current improvement need, and ensuring deep approach, but also with development opportunities to other markets.