Design Optimization Process Research Articles

Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method.

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. We show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

Variable functioning and its application to large scale steel frame design optimization

To solve complex real-world problems, heuristics and concept-based approaches can be used to incorporate information into the problem. In this study, a concept-based approach called variable functioning (Fx) is introduced to reduce the optimization variables and narrow down the search space. In this method, the relationships among one or more subsets of variables are defined with functions using information prior to optimization; thus, the function variables are optimized instead of modifying the variables in the search process. By using the problem structure analysis technique and engineering expert knowledge, the Fx method is used to enhance the steel frame design optimization process as a complex real-world problem. Herein, the proposed approach was coupled with particle swarm optimization and differential evolution algorithms then applied for three case studies. The algorithms are applied to optimize the case studies by considering the relationships among column cross-section areas. The results show that Fx can significantly improve both the convergence rate and the final design of a frame structure, even if it is only used for seeding.

A Proposed Methodological Considerations for Sustainable Chemical Process Design

Chemical manufacture is a significant value-added industry in both local and global economies. Along with highly relevant issues such as unsustainable process design, energy consumption, and waste generation linked with the sector, societal issues relating to chemical product health and safety are part of the greater sustainability concerns. There is a need to design a sustainable strategy while ensuring economic stability at the corporate level. Hence, this paper presents a methodological proposal of integrated modelling approach for sustainable chemical process design at early design process. The step of achieving this objective is separated into several parts. The first step is to classify parameters from established tools as a benchmark for sustainability metrics. The next step is to transform quantification value of triple bottom line impact (economic, environment and social) to normalized weightage by using Multi Criteria Analysis. The final stage is to develop alternative in terms of technical optimization of process design. The implementation multimodal Multi Criteria Analysis and optimized process design exhibits significant improvements in sustainability impacts according to the waste reduction (WAR) algorithm.

A Fast Aerodynamic Model for Aircraft Multidisciplinary Design and Optimization Process

A multidisciplinary design analysis and optimization process is developed at ONERA for the design of tube and wing and blended wing–body aircraft configurations. This process is composed of different disciplinary modules (geometry, propulsion, aerodynamics, structure, handling qualities and flight mission), and the overall process considers different fidelity levels for these modules at each step of the design process. This article describes the low-fidelity aerodynamic module used during the preliminary design optimization process. Analytical formulations retained for lift and drag components are presented in the first part. Then, the performances estimated by the aerodynamic module on some reference configurations are compared with both numerical and experimental data, showing a quite good agreement for both tube and wing and blended wing–body configurations not only for global performance but also for individual drag components.

On the Use of Multi-Step Dies for Improving the Performance against Hydrogen Embrittlement of Cold Drawn Prestressing Steel Wires.

The main cause of in-service failure of cold drawn wires in aggressive environments is hydrogen embrittlement (HE). The non-uniform plastic strains and residual stresses generated after cold drawing play a significant role in the matter of HE susceptibility of prestressing steels. In this paper, a new and innovative design of the drawing scheme is developed, geared towards the reduction in both manufacturing-induced residual stresses and plastic strains. To achieve this goal, three innovative cold drawing chains (consisting in diverse multi-step dies where multiple diameter reductions are progressively carried out in a single die) are numerically simulated by the finite element (FE) method. From the residual stress and plastic strain fields revealed from FE numerical simulations, hydrogen accumulation for diverse times of exposure is obtained by means of FE simulations of the hydrogen diffusion assisted by stress and strains. Thus, an estimation of the HE susceptibility of the cold drawn wires after each process was obtained. Results reveal that cold drawn wire using multi-step dies exhibits lower stress and strain states nearby the wire surface. This reduction causes a decrease in the hydrogen concentration at the prospective damage zones, thereby improving the performance of the prestressing steel wires in hydrogenating environments promoting HE. Thus, the optimal wire drawing process design is carried out using special dies with several reductions per die.

Thermodynamic study of calcium d-pantothenate drug in the presence of aqueous solutions including some amino acids: Volumetric and compressibility properties

The impressive and remarkable efforts have been made over the past years to improve the production, formulation and purification of active ingredients pharmaceutical which leads to the discovery of novel drug preparations and delivery systems. The optimal design of such processes requires a molecular-level understanding of the interactions between drug molecules and biological membranes. The thermodynamic investigation provides deep and complete knowledge of interactions and the choice of appropriate and suitable compounds in pharmaceutical fields. Particularly, the analysis of drugs + biomolecules in aqueous solutions is the central issue in many types of research because they exert their impact by interacting with biological membranes. This work is aimed to measure the density and speed of sound for the calcium d-pantothenate, vitamin B5 calcium salt (CDP) in aqueous solutions of amino acids (glycine, l-alanine, and l-leucine) at temperature ranges (293.15 to 308.15) K. By correlating of the measured physical quantities in with standard relations, apparent molar volumes, Vϕ,m, and apparent molar isentropic compression, κs,ϕ,m, are calculated. In addition, apparent molar isobaric expansion, Eϕ,m0, and Hepler’s constant are computed. For fitting the experimental Vϕ,m and κϕ the Redlich-Meyer equation was employed that the important quantities; standard partial molar volume, Vϕ0 , and partial molar isentropic compression, κϕ0, were obtained.

Process performance maps for membrane-based CO2 separation using artificial neural networks

Membrane-based gas separation processes are currently being implemented at different scales for several industrial applications. The optimal design of such processes, which is of key importance for their large-scale commercial deployment, has been extensively studied through parametric analyses and optimisation procedures. Nevertheless, the applicability of such design methodologies is generally limited by the large computational time and effort they require. In this work, surrogate models based on artificial neural networks are developed to circumvent the lengthy optimisation of a one-stage and two-stage cascade membrane-based gas separation process. In 200 ms, the surrogate model generates a Pareto front that describes the optimal trade-off between the process specific electricity consumption and productivity based on given input data, i.e., membrane material properties, feed composition and separation target. Whereas the surrogate model is applicable to any binary gas mixture, here its features are illustrated by creating process performance maps for post-combustion CO2 capture. Such maps provide valuable insights on: (i) attainable gas separation regions in term of CO2 recovery and CO2 purity, and (ii) the impact of membrane material, feed composition and separation target on the Pareto fronts and the optimal operating conditions.

Data-driven discovery of dimensionless numbers and governing laws from scarce measurements

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data. The proposed methodology, called dimensionless learning, is a physics-based dimension reduction technique. It can reduce high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless parameters, greatly simplifying complex process design and system optimization. We demonstrate the algorithm by solving several challenging engineering problems with noisy experimental measurements (not synthetic data) collected from the literature. Examples include turbulent Rayleigh-Bénard convection, vapor depression dynamics in laser melting of metals, and porosity formation in 3D printing. Lastly, we show that the proposed approach can identify dimensionally homogeneous differential equations with dimensionless number(s) by leveraging sparsity-promoting techniques.

A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision- and policy-making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open-source datasets and tools, major findings, challenges, and future directions. Discussions focus on current methods of uncertainty quantification and optimization and how they are applied in different dimensions of a digital twin. Additionally, this paper presents a case study where a battery digital twin is constructed and tested to illustrate some of the modeling and twinning methods reviewed in this two-part review. Code and preprocessed data for generating all the results and figures presented in the case study are available on Github.

Development of a Transient Synchronization Analysis Tool for Line-Start PM Motors

With more stringent IEC energy efficiency standards, electrical machine industry increasingly focuses on new motor technologies. Amongst others, the line-start permanent magnet synchronous machine (LSPMSM) is considered as an attractive alternative to induction machine, especially for low power and fixed-speed applications. However, the design of LSPMSMs is rather complex as both steady-state and transient synchronization performances need to be considered. The synchronization capability determination of a LSPMSM design usually relies on time-consuming transient finite-element simulations, which is impractical for use in an iterative design optimization process. This paper compares and evaluates various existing analytical synchronization analysis methods in an attempt to identify most suitable equations and methods for fast synchronization analysis. Using the selected methods, a software tool is developed that can seamlessly work with ANSYS Electronics Desktop to perform rapid transient synchronization analysis. Given its ability to quickly determine the critical inertia factor of a LSPMSM design, the software tool is further adapted for use in a highly iterative, multi-objective design optimization procedure. It shows that the developed software tool can be successfully used in the design of LSPMSMs.

Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. The result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

System approach in optimal management problems for two-stage technological lines

Purpose of work is to research and design process optimization and optimal operational management for a two-stage technological line. Methodology. To achieve the goal, we apply a comprehensive approach, which consists of the formalization and analysis of operational management tasks and technological lines designing.The methods of probability theory are used to determine the optimal level between the maximum bandwidths of the stages of the technological lines and for processing mathematical models of random variables. Research results. The need for a system-wide approach to the management of two-stage technological lines is substantiated, which involves joint consideration of design and operational management tasks. Three optimization problems are considered, which differ in their formulation by initial data, goals and optimization parameters.This is the problem of design optimization when creating equipment, the problem of optimal design of technological schemes and the problem of optimal operational control.These three types of tasks must be considered comprehensively, in mutual connection to achieve a global optimum in terms of the economic criterion.The formalization of such a complex problem for two-stage technological schemes was carried out in a general form and approaches to its solution. The methodology for solving such problems is proposed, based on information about the specific consumption of the resource and the characteristics of the flow of the processed material. Scientific novelty. The formulation of the complex optimization problem for the functioning of two-stage technological lines, including the choice of the equipment and a type of operational management, and a solving approach for the considered problem was provided. Practical significance of work results. The proposed approach makes it possible to find the optimal solution to the problem of management and design of two-stage technological lines from the point of view of the economic criterion.

Optimal and systematic design of large-scale electrodes for practical Li–air batteries

The battery market to enable the broad uses of electric vehicles and renewable energy sources is undergoing a rapid expansion given the requirements of light, safe, and low-cost materials. Li–air batteries are considered suitable candidates that can overcome the existing challenges in this field. Continuous progress in performance improvements triggered by the structure and design evolution of battery electrodes is essential to utilize Li–air batteries in future applications. In this study, an optimal design process is devised for the practical application of Li–air batteries with large-scale area and capacity levels. The primary difficulty in the large-scale design for practical use originates from the design complexity caused by the one-side (air cathode) open-cell structure for the proper air flow. To examine problems related to practical cell manufacturing, we attempted to explore the large-scale electrode on the charge/discharge performance with two current collectors, specifically Ni foam and a gas diffusion layer (GDL), in single and stacked Li–air cells. A large-scale stacked 1.07 Ah cell was manufactured using commercial carbon mixtures with tabless current collector of Ni foam. The single-cell capacity of tabless Ni foam presented approximately 440 mAh at full-depth scan, showing that the cell-specific gravimetric and volumetric energy densities were 385 Wh/kg and 428 Wh/L, respectively. In the case of tabless GDL, the cell capacity was 630 mAh at full-depth scan. The cell-specific gravimetric and volumetric energy densities of tabless GDL were 680 Wh/kg and 756 Wh/L, respectively. However, the use of a GDL current collector resulted in structural deformation during the charge/discharge reaction due to a self-reaction in spite of the 1.56 times higher energy density than that of Ni foam. Although the GDL helps to enhance the reactivity and performance by increasing the capacity when used with an active material, the extended cycle stability of the Li–air cell is hindered due to the self-reaction of the current collector. These results present case‐based evidence that extends our knowledge about the trade‐offs between capacity enhancements and long-cycle stability regarding the choices of materials for the manufacturing of Li-air cells, thus proving practical insights with regard to the fabrication of large-scale Li–air cells for commercialization.

Improvements to the ADM1 based Process Simulation Model: Reaction segregation, parameter estimation and process optimization

Anaerobic digestion is a sustainable organic waste treatment technique with energy recovery via biogas generation. This work presents a novel Aspen Plus ADM1-based flowsheet for this process. Three reactor segments were chosen: stoichiometric for the hydrolysis step, kinetic for acido-aceto-methanogenesis, and equilibrium for hydrogenotrophic methane production. Selected parameters- conversion ratios, kinetic pre-exponent and inhibitor factors- were controlled to best fit model and experimental results. The parity plot fitting had an R2 = 0.999, a slope of 1.0058 and an intercept of −0.8651. Obtained parameter values stressed the importance of inhibitions, and simulation results showcased the bell-shaped curve for acetic and volatile fatty acid reduction. The model was used for a subsequent sensitivity analysis as well as an optimization runs, leading to a 50% higher methane production ratio. The proposed model presents itself as a significant contribution for optimal anaerobic digestion process design.

A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github .

Design of low-carbon and cost-efficient concrete frame buildings: a hybrid optimization approach based on harmony search

ABSTRACT The development of sustainable building structures has been a crucial measure for carbon reduction. With consideration of both carbon emissions and costs, the sustainable design of building structures is complicated and time consuming for designers. To simplify the process of sustainable design optimization, the present study proposes a multi-objective harmony search algorithm for the design of concrete frame buildings considering low-carbon and cost-efficient requirements. This hybrid method combines structural analysis with local component optimization. Process-based life cycle assessment was used to identify carbon-intensive and high-cost structural members, and these members were optimized using discrete design variables and complicated constraints. Furthermore, a case study was conducted on a four-story frame building, in which the embodied emissions and costs were estimated as 1270.91 tCO2e and 0.67 million USD in the initial design scheme. Beams were identified as the optimization target for this building, and a set of Pareto-optimal solutions was obtained, indicating potential emission and cost reductions of 17.9% and 12.3%, respectively, compared with the initial designs. This study provides an easy and practical approach for the sustainable design of concrete frame buildings and enables designers to better understand building structural optimization from an economic and low-carbon perspective.

An innovative additively manufactured implant for mandibular injuries: Design and preparation processes based on simulation model.

Objective: For mandibular injury, how to utilize 3D implants with novel structures to promote the reconstruction of large mandibular bone defect is the major focus of clinical and basic research. This study proposed a novel 3D titanium lattice-like implant for mandibular injuries based on simulation model, which is designed and optimized by a biomechanical/mechanobiological approach, and the working framework for optimal design and preparation processes of the implant has been validated to tailored to specific patient biomechanical, physiological and clinical requirements. Methods: This objective has been achieved by matching and assembling different morphologies of a lattice-like implant mimicking cancellous and cortical bone morphologies and properties, namely, an internal spongy trabecular-like structure that can be filled with bone graft materials and an external grid-like structure that can ensure the mechanical bearing capacity. Finite element analysis has been applied to evaluate the stress/strain distribution of the implant and bone graft materials under physiological loading conditions to determine whether and where the implant needs to be optimized. A topological optimization approach was employed to improve biomechanical and mechanobiological properties by adjusting the overall/local structural design of the implant. Results: The computational results demonstrated that, on average, values of the maximum von-Mises stress in the implant model nodes could be decreased by 43.14% and that the percentage of optimal physiological strains in the bone graft materials can be increased from 35.79 to 93.36% since early regeneration stages. Metal additive manufacturing technology was adopted to prepare the 3D lattice-like implant to verify its feasibility for fabrication. Following the working framework proposed in this study, the well-designed customized implants have both excellent biomechanical and mechanobiological properties, avoiding mechanical failure and providing sufficient biomechanical stimuli to promote new bone regeneration. Conclusion: This study is expected to provide a scientific and feasible clinical strategy for repairing large injuries of mandibular bone defects by offering new insights into design criteria for regenerative implants.

Research on optimization of index system design and its inspection method: data quality diagnosis, index classification and stratification

PurposeThe purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.Design/methodology/approachUsing modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.FindingsBased on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.Originality/valueThe innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

Optimal design of the ternary azeotrope separation process assisted by reactive-extractive distillation for ethyl acetate/isopropanol/water

There are numerous issues with the separation process for ternary systems containing multi-azeotropes. Specially, for the aqueous azeotrope as ethyl acetate-isopropanol-water, the presence of water results in a ternary azeotrope, three binary azeotropes and a heterogeneous region. Reactive distillation is introduced to break the azeotropes and simplify complexity by consuming water through ethylene oxide hydration reaction. The triple-column extractive distillation process (TED) and two enhanced processes combined with reactive distillation (i.e., double-column reactive-extractive distillation process (DRED) and reactive-extractive dividing-wall column process (REDWC)) are designed for the separation in this work. A simultaneous optimization strategy is proposed to optimize the parameters through the Particle Swarm Optimization algorithm taking the total annual cost (TAC) as the optimization objective. Then, the CO2 emissions and thermodynamic efficiency are calculated to assess these three processes. Exergy analysis and entropy production are applied to quantify each component to the total irreversibility. Results show that reactive-extractive distillation effectively realize lower TACs with better environmental performance. The TACs of DRED and REDWC processes are reduced by 39.04% and 46.01% compared with TED process, respectively. The thermodynamic efficiency of DRED and REDWC processes increased from 5.40% to 42.88% and 43.64%, respectively.