Design Optimization Process Research Articles

Optical engineering perspectives on fractional analysis: A comprehensive study of the conformable Gross–Pitaevskii equation in the Bose–Einstein condensation

In this study, we examine the dynamic behavior of optical solitons in the nonlinear conformable Gross–Pitaevskii equation (GPE) within Bose–Einstein condensates, which refer to the phenomenon of many ultra-cold bosonic particles occupying a single quantum state. The GPE is a cornerstone of advanced statistical physics, has application in various engineering disciplines, particularly in the realm of quantum mechanics and optical engineering. By precisely encoding the GPE’s steady-state solutions, we make it possible to compute nonlinear models pertinent to engineering systems and clarify solutions within particular parameter regimes that are essential for engineering applications. Our study encompasses repulsive and attractive nonlinearities in GPE, including quadratic and double-well potentials. We focus on discovering solutions, employing the generalized Riccati equation mapping method and modified extended tanh-function method to extract soliton solutions. The study yields a variety of soliton including combined dark–bright, singular, combo dark-singular, periodic-singular, and rational solutions with physical perspectives. Through 2D and 3D representations, the internal structure of these phenomena is effectively illustrated, offering insights into their behavior and dynamics, which is significant for optimization process and engineering design.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Approximate modeling of cross-beam multi-axis force/moment sensor through Gaussian process and partial dependence plots for design optimization including stress topology

This study introduces a novel approximate model (AM) aimed at optimizing the design of cross-beam multi-axis force/moment sensor. It considers the characteristics of flexure hinges in elastic beams and the flexibility of beam joints, resulting in improved accuracy and broader solutions for equivalent stresses and natural frequencies compared to existing approximate models in the literature. Model validation is carried out by comparing the results with the finite element model in different cases that each case contains 1000 simulations. Validation cases combine different sensor dimensions with different forces, moments, material properties, and center hub shapes. Each validation case typically requires approximately 19 h using the finite element method (FEM) and just 1 sec with the AM. Furthermore, by using the Gaussian process and partial dependence plots, the sensitivity of the model accuracy on normalized sensor dimensions is investigated. The evaluation of model linearity and dimension normalization allows for the utilization of the novel approximate model beyond the provided dimension range, while still adhering to the normalized parameters. The implementation of AM in the design optimization problem demonstrates its suitability and advantages compared to existing literature and FEM results. When compared to finite element solutions within the sensor workspace, the approximate model exhibits a stiffness error of 5%, a strain error of 2%, a fundamental frequency error of 3%, and an equivalent stress error of 8%, while maintaining correlations of axial sensor properties above 98%.

Optimal Process Design for Wake-Up Free Hf0.5Zr0.5O2 Ferroelectric Capacitors: Toward Low-Power Devices with Enhanced Ferroelectric Performance

Ferroelectric hafnium and zirconium oxides have recently garnered significant attention due to their potential applications in in-memory computing. In this study, we present an optimized process design for a wake-up free 15 nm thick Hf0.5Zr0.5O2 (HZO) ferroelectric capacitor by fine-tuning the dual-oxygen process and incorporating oxygen annealing after post-metallization annealing (PMA). The optimized approach resulted in a competitive polarization of 28.6 μC/cm2, consistently exceeding 25 μC/cm2 at 3 V after 2 × 107 cycles, showcasing a current density of 3.2 mA/cm2 at 2 V after 105 cycles. The synergistic effect of oxygen vacancies and grain properties (grain size, phase proportion) enables competitive ferroelectric polarization at lower voltages, while the generation of WOx near the top electrode and increased grain size further ensure the reliability of the HZO ferroelectric capacitor. This work presents innovative perspectives for the development of non-volatile devices characterized by low leakage current and low power consumption.

Hydrogen–Water Isotope Catalytic Exchange Process Analysis by Simulation

The hydrogen–water isotope catalytic exchange process has been widely applied in the tritium-containing water treatment process. It can be compared and analyzed conveniently with process simulation software. In this study, the catalytic exchange process was simulated by Aspen Plus software (V11). According to the simulation results, the main reaction process was that HDO in the liquid phase converts into HD in the gas phase, and the reaction mainly occurred at the bottom of the column, exhibiting a two-orders-of-magnitude-higher reaction amount compared to that observed in the top section. Different side reactions occur at distinct positions along the column, exhibiting a reaction amount that is lower by one to two orders of magnitude compared to the main reaction and aligning in the same direction as the main reaction. The optimum operating temperature is 60~80 °C, with the best performance observed at 70 °C, because of the large reaction equilibrium constant and the suitable ratio of vapor to hydrogen (1:4~1:1.5) in the gas phase. The influence of the residence time was investigated by introducing reaction kinetic equations. The residence time should be more than 1 s to ensure an adequate reaction. The influence of operating conditions on the hydrogen–water isotope catalytic exchange process can be deeply investigated by process simulation, and more mass transfer process quantities can be obtained. It plays a promoting role in guiding the process design and condition optimization.

Sustainable process design and multi-objective optimization of efficient and energy-saving separation of xylene isomers via extractive distillation based on double extractants

The focus of the chemical industry on environmental protection and energy conservation has driven the development of green distillation technology. Xylene isomers are extensively utilized industrial raw materials, whose separation is challenging and energy-intensive due to their closely similar boiling points. Therefore, it is urgent to develop energy-saving and environmental protection technology. In this paper, an energy-saving and environmental protection process for separating the xylene isomers by extractive distillation was designed combing the vapor recompression process with feed preheating (VRAPFP). The parameters of extractive distillation processes were synthetically optimized by multi-objective genetic algorithm (MOGA) with total annual cost (TAC), CO2 emission (ECO2) and separation efficiency (Eext) as the objective evaluation indexes. The economical optimal process was used further for VRAPFP design through heat integration to reduce energy consumption, and all the designed candidate processes were synthetically analyzed, compared and evaluated with performance indexes. Results show that the VRAPFP process has superior economic efficiency and ecological sustainability compared with the existing distillation process, wherein the TAC and ECO2 are reduced by 14.2 % and 30.2 %, respectively. This work provides a new reference and idea for separating xylene isomers with the economy and developing new processes for energy conservation and environmental protection.

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Optimal Design of Truss Structures for Sustainable Carbon Emission Reduction in Korean Construction

Due to the recent abnormalities in global temperature and increasing carbon emissions, the world is working to reduce carbon emissions. In particular, the construction sector accounts for about 37% of all carbon emissions, so it is one of the areas where sustainable reduction efforts must be made. Therefore, in this paper, an optimal design process was performed by evaluating carbon emissions as the objective function, a choice which differed from the objective function of the existing research used in the optimal design of truss structures. The metaheuristics algorithm used for the process was the advanced crow search algorithm. The levels of carbon emissions generated when the material of a truss structure consisted of a customary material (steel) were compared to scenarios in which timber was used, and a construction scenario centered on the Republic of Korea was established for comparison. The structures used as examples were 10-, 17-, 22-, and 120-bar truss structures. As a result, it was confirmed that truss structures using timber had fewer carbon emissions than structures using steel. In addition, it was confirmed that, even in the same timber structures, domestic timber had fewer carbon emissions than imported timber. These results confirmed that in order to achieve carbon neutrality in the construction field, carbon emissions must be considered in advance, in the design stage.

Cognitive intelligence-enabled requirements elicitation for design optimisation of smart product-service system

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification.

Performance-based target reliability analysis of offshore wind turbine mooring lines subjected to the wind and wave

Reliability assessment is a crucial aspect of the design and operation of structures, particularly in balancing safety and cost considerations. This paper introduces a novel method for evaluating the performance-based target reliability of floating wind turbine platforms in offshore environments. The method focuses on the platform's motion modes and wave frequencies, which significantly influence the system's structural integrity and performance. An improved limit state function is proposed to enhance the accuracy of reliability calculations, specifically for steady-state conditions. The platform's six degrees of freedom motions are carefully analyzed to investigate their dependence on wave frequencies. By considering the time response of these motions and accounting for uncertainties in wave characteristics, wave impact directions, and wind effects, a comprehensive reliability analysis is conducted to assess the stability modes of the platform. This paper introduces the term 'Reliability Performance-Based' (RPB) analysis as a new concept to evaluate the system's reliability at a given performance level. Furthermore, an optimal target reliability index is defined to address the economic aspect of the design process. The proposed methodology's PEB analysis focuses on capturing uncertainties in wave characteristics and wind effects on floating wind turbine platforms. This includes a detailed examination of wave and wind-induced loads and their propagation through the system concerning its performance level. Statistical models were integrated to quantify these uncertainties, applying Monte Carlo simulations to assess their effects on the platform's reliability. This approach allows for a nuanced understanding of the interactions between environmental factors and structural responses, enhancing the precision of our reliability assessments. It enables the consideration of economic efficiency alongside safety, ensuring a balanced approach to the design and operation of the floating wind turbine platform. By providing a comprehensive reliability assessment framework, it aids in the optimization of design and decision-making processes for floating wind turbine platforms.

Simultaneous process optimization and heat integration for ethylene-to-ethylene oxide process: A surrogate model-based approach

Since the production process of ethylene to ethylene oxide (EO) is highly energy-intensive, it is necessary to perform a whole process heat integration. However, due to the technical barriers, it is always a challenge to design the heat integration system while optimizing the process parameters by mechanistic models. In response to this issue, this study presented a novel surrogate model-based framework that allows for the seamless integration of process optimization and heat exchanger network (HEN) design. The EO production process is modeled and simulated to provide the essential data for surrogate model training. Considering the inherent complexity of the process and the challenges associated with model training, the whole production process is decomposed into parts and the output parameters relevant to heat integration are included within the surrogate models training by Artificial Neural Network. Building upon these preliminary steps, a flexible optimization framework built upon the Genetic Algorithm, integrating the developed process surrogate model and any HEN synthesis model, is constructed to achieve synchronous optimization. The results show that the obtained process saves 22.93 % utility cost and finally leads to a 2.60 % increase in total profit than the alternative designed by the conventional stepwise method, showing the priority of the proposed method.

Optimal design of low frequency rubber vibration isolator

Abstract Low frequency vibration isolator is a common structure vibration control device, widely used in transportation and machinery manufacturing fields. This paper is centered around enhancing the optimization design of rubber isolators operating at low frequencies. In the initial section, the paper introduces the structural framework and operational principles of rubber isolators. Furthermore, it delves into the analysis of their significance and potential applications within engineering contexts. Subsequently, the paper narrows its focus to address the design challenges concerning rubber isolators with low-frequency capabilities. A comprehensive three-dimensional model is constructed using ANSYS software, employing finite element analysis techniques. The paper employs a multi-objective optimization algorithm through a direct optimization module to facilitate the optimization design process. Within this study, the NSGA-II algorithm was employed to drive the optimization of design, with the objective of achieving a 10 Hz frequency for a series of dual rubber isolators, while simultaneously constraining static deformation to within 8 mm. The outcomes of this investigation emphasize the successful attainment of optimization goals by meticulously fine-tuning the structural dimensions of the isolators.

Data-efficient surrogate modeling of thermodynamic equilibria using Sobolev training, data augmentation and adaptive sampling

Modern thermodynamic models, such as the PC-SAFT equation of state, are very accurate but also computationally intensive, which limits their applicability to process design optimization, for example. Surrogate models, which can be evaluated quickly, can be used to approximate the thermodynamic equilibria. However, this requires many data points from the flash calculation routine. In this paper, we investigate three approaches to reduce the number of samples and thus the effort needed to train the surrogate models. First, Sobolev training is used, where the surrogate model is trained not only on the output values, but also on derivative information. Second, data augmentation along the tie lines in LLE systems is proposed to generate samples without additional flash calculations. Third, adaptive sampling is revisited with a novel quality criterion. It is shown that the combination of these techniques can be used to significantly reduce the number of samples required.

Advancements in Hybrid Additive Manufacturing: Integrating SLM and LMD for High-Performance Applications

Additive manufacturing (AM) technologies enable near-net-shape designs and demand-oriented material usage, which significantly minimizes waste. This points to a substantial opportunity for further optimization in material savings and process design. The current study delves into the advancement of sustainable manufacturing practices in the automotive industry, emphasizing the crucial role of lightweight construction concepts and AM technologies in enhancing resource efficiency and reducing greenhouse gas emissions. By exploring the integration of novel AM techniques such as selective laser melting (SLM) and laser metal deposition (LMD), the study aims to overcome existing limitations like slow build-up rates and limited component resolution. The study’s core objective revolves around the development and validation of a continuous process chain that synergizes different AM routes. In the current study, the continuous process chain for DMG MORI Lasertec 65 3D’s LMD system and the DMG MORI Lasertec 30 3D’s was demonstrated using 316L and 1.2709 steel materials. This integrated approach is designed to significantly curtail process times and minimize component costs, thus suggesting an industry-oriented process chain for future manufacturing paradigms. Additionally, the research investigates the production and material behavior of components under varying manufacturing processes, material combinations, and boundary layer materials. The culmination of this study is the validation of the proposed process route through a technology demonstrator, assessing its scalability and setting a benchmark for resource-efficient manufacturing in the automotive sector.

Multiscale Equation-Oriented Optimization Decreases the Carbon Intensity of Shale Gas to Liquid Fuel Processes.

Shale gas is revolutionizing the U.S. energy and chemical commodity landscape and can ease the transition to a sustainable decarbonized economy. This work develops an equation-oriented (EO) multiscale modeling framework using the open-source IDAES-PSE platform that tractably incorporates microkinetic detail in process design via reduced-order kinetic (ROK) models. Using multiobjective optimization with embedded heat integration and life-cycle analysis, we simultaneously minimize the minimum selling price of liquid hydrocarbons (e.g., liquid fuels/additives from shale gas) and process emissions (via a CO2 tax). Optimization reduces greenhouse gas emissions per MJ of fuel produced by over 35% compared to the literature and achieves a carbon efficiency of 87%. The optimizer changes the recycling rate, temperatures, and pressures to mitigate the effect of ROK model-form uncertainty on product portfolio predictions. Moreover, we show that the optimal process design is insensitive to changing CO2 tax rates. Finally, the EO framework enables a fast sensitivity analysis of shale gas composition variability across 12 regions of the Eagle Ford basin. These results highlight the benefits of the open-source EO framework: fast, scalable, customized, and reproducible system analysis and optimization for sustainable energy technologies beyond shale utilization.

Machine learning assisted multi-objective design optimization for battery thermal management system

The rapid expansion of the electric vehicle (EV) industry necessitates the development of advanced battery thermal management systems (BTMSs) to safeguard the cyclic properties and security of lithium-ion batteries. However, the assessment of the performance of BTMS often overlooks the importance of considering not only the thermal regulation effectiveness on batteries but also its own energy efficiency. This study investigated the synergistic effects of BTMS design and control strategies on both thermal performance and energy utilization of its own. A multiphysics-based model was developed, featuring an 18,650 Lithium-ion battery module with curved cooling channels, to systematically evaluate the impact of cooling channel width and warping angle, inlet coolant temperature and velocity, and charging rate on system performance and efficiency. To further expedite the design optimization process, a Gaussian process (GP)-based surrogate model was implemented. An uncertainty quantification analysis was subsequently performed to validate the robustness of the optimized designs against stochastic variations. The findings indicate that the channel width and coolant velocity play a pivotal role in enhancing BTMS efficiency. Through a rigorous multi-objective optimization process, the energy efficiency was improved by 126 %, while maintaining battery temperatures below 28.4 °C and coolant pressure drops under 3.6 kPa. The integration of multiphysics simulation and machine learning assisted optimization technique represents a pioneering step forward in the development of sophisticated and efficient BTMS solutions for future electric vehicles.

Large Pool Fires in the Process Industry. A Simple Model for Safety Distances Estimation

In recent years, the global production growth and the complexity of processes operation have increased safety concern, especially during the design project, considering potential accidental events as fires, explosions, and their related domino effects. Pool fire is one of the most frequent accidental events. Some procedures for safety distances estimation from pool fires were reported. However, there is a lack of adequate simple models to directly estimate safety distances covering a wide range of substances and pool diameters, considering a continuous interval of receptor vulnerabilities (from damage to people to domino effect prevention). In this paper, a simple model to estimate safety distances is proposed, suitable to be incorporated into complex models (e.g. safety management systems, layout optimization). A surrogate model is achieved by adopting a reference one. Methodologically, the main variables that play a key role modelling a pool fire were selected and their influence on the safety distance was analyzed to propose simple mathematical expressions. NLP optimization problems were solved to optimize the model parameters. A conservative, accurate and easy-to-use model was achieved with the aim of developing safer plants or for their safe operation by improving the optimal process design, or implementing safe management systems, considering risks.

Modeling of cell cultivation for monoclonal antibody production processes considering lactate metabolic shifts.

Demand for monoclonal antibodies (mAbs) is rapidly increasing. To achieve higher productivity, there have been improvements to cell lines, operating modes, media, and cultivation conditions. Representative mathematical models are needed to narrow down the growing number of process alternatives. Previous studies have proposed mechanistic models to depict cell metabolic shifts (e.g., lactate production to consumption). However, the impacts of variations of some operating conditions have not yet been fully incorporated in such models. This paper offers a new mechanistic model considering variations in dissolved oxygen and glutamine depletion on cell metabolism applied to a novel Chinese hamster ovary (CHO) cell line. Expressions for the specific rates of lactate production, glutamine consumption, and mAb production were formulated for stirred and shaken-tank reactors. A deeper understanding of lactate metabolic shifts was obtained under different combinations of experimental conditions. Lactate consumption was more pronounced in conditions with higher DO and low glutamine concentrations. The model offers mechanistic insights that are useful for designing advanced operation strategies. It can be used in design space generation and process optimization for better productivity and product quality.

Integrated Design Optimization Process for Building Projects

In this study, a cost-design optimization is conducted for a reinforced concrete structure with irregular voids and inclined axes, which is a real-world application. Data transfers are performed on a 3D model to achieve the optimization. Data exchange is managed automatically by the created software program. As an application example, a hospital building with 10 beds is considered. Optimum design and minimum cost values are obtained for optimization processes using the Rao algorithms. The construction cost values are compared both among algorithms and with actual cost values, indicating a successful optimization process. The software program developed in this study accelerates the processes in optimization operations, thereby contributing to the realization of automatic data transfers.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.