Design Variables Research Articles

Commercial Soccer Ball‐Integrated Triboelectric Nanogenerator

Triboelectric nanogenerators (TENG) are representative mechanical energy harvesters that can easily generate electricity based on contact electrification and electrostatic induction. However, most TENGs have low mechanical and electrical output stabilities because of their unstable structures, in which separate TENG devices are attached to the outside surface of commercial products/items for application. Herein, a commercial soccer ball‐integrated TENG (CSB‐TENG) is proposed as a potential solution. The CSB‐TENG is designed with an electrode‐fully packaged all‐in‐one structure; thus, it can have remarkable mechanical and electrical output stability for 160 000 cycles. For one bouncing motion, the CSB‐TENG generates a maximum peak power of 480 μW. The working mechanism of the CSB‐TENG is established by considering the actual motion of a soccer ball (kicking, bouncing, and rolling). The electrical output of the CSB‐TENG is evaluated and analyzed based on various design and environmental variables (ball material, shoe material, ground material, impact force, ball drop height, and electrode size) in daily life. The CSB‐TENG demonstrates applications, including LED array illumination and capacitor charging tests.

Additive manufacturing-driven simultaneous optimization of topology and print direction for thermoelastic structures considering strength failure

Abstract This paper presents a strength-based simultaneous optimization method for optimizing thermoelastic structural topology and print direction in the presence of anisotropy induced by additive manufacturing. The approach utilizes the bi-directional evolutionary structural optimization (BESO) framework and defines design variables including element density and print-off angle. Firstly, an anisotropic thermoelastic constitutive model is established for finite element analysis. By introducing the Tsai-Hill failure criteria, the strength constraint to evaluate the stress level of additively manufactured anisotropic components is formulated. The P-norm aggregation function is employed to approximate the maximum strength failure coefficient. Then, the aggregated strength constraint is augmented to the optimization objective through a Lagrange multiplier. Sensitivity analysis of the new objective function with respect to the elemental design variables is performed, and an analytical approach is proposed to optimize the print-off angle. To improve the stability of the optimization procedure, a series of numerical algorithms and parameter updating strategies are developed. The effectiveness of our proposed method is demonstrated through typical numerical examples, highlighting a desirable match between the structural topology and the print direction can greatly improve the structural performance.

Parametric Dynamic Simulation and Bayesian Design Optimization of a Front-Loading Washing Machine

Abstract Objective This paper presents the dynamic modeling and design optimization of a fully parametrized front-loading washing machine. Methods A thorough mathematical analysis is performed to capture the effect of nine design variables (including rotation speed, spring stiffness, weight balancer mass density, damping coefficient, spring and damper angles) on vibrational characteristics of the washing machine. The parametric simulation reveals a complex relationship between the vibrational dynamics and design variables, entailing a computationally efficient optimization approach. A Bayesian Optimization (BO) framework is developed, in which a Gaussian process model and Genetic Algorithm (GA) are utilized along with carefully selected acquisition functions to enable adaptive sampling and search for optimal design values. The objective function is to minimize the maximum amplitude of the tub displacement inside the washing machine body. Two case studies are performed to consider a different number of design variables and choices of various infill methods. Results Results show that the fully parametrized front-loading washing machine model is applicable for parametric simulation and design optimization. The proposed BO is able to successfully find a set of design variable values corresponding to the lowest maximum displacement for vibration reduction. A comparison analysis is also carried out to exhibit the difference in optimization convergence and computational cost caused by the choice of acquisition functions. Conclusion It is shown that the fully parametrized washing machine model yields a lower maximum displacement than the model with fewer design variables. Of the two acquisition functions studied for optimization, the Expected Improvement function converges more rapidly than the Lower Bound criterion.

Combined application of CDWs as precursors and aggregates in geopolymer composites: A comprehensive rheological analysis

The study of rheological characterization of construction and demolition waste (CDW)-based geopolymeric materials has gained interest due to digital innovation in construction and the increased focus on sustainable materials. Understanding the rheological behavior of these materials is essential due to their rapid geopolymeric reaction kinetics and viscosity behavior. This study investigated the impact of various CDW-based fine recycled aggregates, including crushed concrete aggregates (CRC), crushed ceramic-tile aggregates (CTA), and crushed brick aggregates (CBA), on the rheological behavior of geopolymer mortars (GPMs). Natural sand (NSA) was utilized as a reference for comparison purposes. In addition to CDW-aggregates, the GPMs were also made from optimized CDW-based precursors of brick waste powder (BP), ceramic-tile waste powder (TP), or concrete waste powder (CP). As a result, twelve different GPMs were considered, each containing 100 % individual CRC, CTA, and CBA fine aggregates, blended with BP, TP, and CP precursors. The study evaluated the relationship between the design variable of SiO2/Al2O3 molar ratio as well as the packing densities (PD) of GPM systems and their rheological characteristics, such as flow, apparent and plastic viscosity, yield and shear stresses, thixotropy and shear strains employing the compressible packing method. The results indicate that all GPMs possess shear-thinning or pseudoplastic characteristics. Enhanced SiO2/Al2O3 molar ratio generally resulted in increased rheological parameters. However, using TP as a precursor and CBA as fine aggregates in GPM matrices led to significant enhancement in rheological parameters due to the higher specific surface area of TP and the greater irregularity, convexity, and absorption capacity of CBA particles. Notably, although the enhanced non-colloidal frictional interactions and colloidal flocculation of CRC, CTA, and CBA resulted in reduced flow tendency of GPMs, they have been found to improve the thixotropy during the initial geopolymeric gelation stage.

Knowledge graph-guided data-driven design of ultra-high-performance concrete (UHPC) with interpretability and physicochemical reaction discovery capability

Traditional methods for designing concrete materials typically rely on labor-intensive laboratory experiments, resulting in time and cost inefficiencies. Recently, designing concrete using artificial intelligence (AI) methods has shown high efficiency, but existing AI methods often rely solely on data, which can lead to violation with scientific principles and result in models lacking reasoning abilities. To overcome these challenges, this paper presents an interpretable knowledge graph-guided data-driven design approach. By integrating advanced computing techniques with domain knowledge via knowledge graphs, this approach enables the interpretation of data-driven models and uncovers the underlying mechanisms behind predictions. This approach is applied to ultra-high-performance concrete (UHPC) involving complex physicochemical reactions. The domain knowledge about UHPC is imparted using a knowledge graph, and UHPC properties are predicted using a machine learning model considering mixing proportions, processing methods, and physiochemical properties of materials via natural language processing. The results show that the knowledge graph displays crucial design variables and their effects on UHPC properties, aiding in selecting variables for machine learning models and interpreting their results. The prediction accuracy of the machine learning model reached 0.95. The research paves the way for more transparent and scientific AI models for material design and AI-enabled discovery of scientific knowledge.

Prediction of allowable compression load for notched composite laminates combining FEA simulation and machine learning

Determining the allowable compression load (ACL) of notched composite laminates (NCL) requires consideration of buckling, intra- and inter-laminar damage, which is a costly and repetitive task due to the high dimensional and nonlinear relation of ACL to the numerous design variables. In this work, a high-throughput finite element analysis (FEA) and machine learning (ML) combined approach is proposed to predict ACL of laminates. First, the high-throughput FEA model of NCL covering all of the design variables is generated, and the corresponding critical compression loads in terms of buckling, intra- and inter-laminar damage are obtained. Then, ML models are trained, with inputs being the design variables of NCL and outputs being the critical compression loads. ACL and initial failure mode are determined by the minimum compression load of the three failure modes. Moreover, transfer learning is introduced to laminates with new design variables of new ply number, notch shape and laminate type. By fine-tuning the pre-trained ML model using scarce new data, the fine-tuned models are suitable for new laminates. Consequently, ACL and initial failure mode of notched laminates with various design variables are predicted.

Optimal coordination of directional overcurrent protection relays using hybrid optimizer

In order to minimize or maximize an objective function, electrical engineering problems might benefit from optimization methodologies inspired by nature. This study employs a hybrid particle swarm optimizer (HPSO) to get rid of local optimum solutions and find the best global one for directional overcurrent protection relays (DOPR) protection. DOPR coordination is a highly constrained, mixed-integral nonlinear optimization problem. An adaptive defense method has been created to counteract the intentionally challenging design. Therefore, design variables such as time multiplier setting (TMS) and plug setting (PS) are utilized for each relay in the circuit. The goal function is to reduce the amount of time spent to trace the fault by all the relays. Reducing the time required for each relay to trace the fault is the aim of function. After analyzing and comparing two test studies of the IEEE benchmark using various optimization strategies, we found that the results for the IEEE 15-bus system has minimum time of 8.9 s, which is improved by 22.92% to 82.89%, and those for the IEEE 30-bus system minimum time of 22.45 s that is improved by 4.41% to 39.6%. In terms of overall DOPR operation and computational time needed to get the global optimum solution, the acquired results are demonstrably superior to those obtained using conventional and state-of-the-art methods.

Cost optimization of tall buildings having tube composite columns using social spider algorithm

SummaryThis study aims to develop an algorithmic approach to obtain optimum designs for tall buildings having composite columns and investigate the material cost advantages of these buildings over steel structures. The social spider optimization (SSO) algorithm, a new meta‐heuristic optimization method that has shown promising results in optimizing frame structures, was used to obtain the optimum designs. Concrete‐filled steel tube sections were chosen for composite columns. To define the optimization problem, we considered the material cost of the structure as the objective function, the size of columns (strength, deflection, drift, and geometric limitations) as the constraint functions, and ready steel sections as the design variables. We tested eight different frame structures of varying heights and irregularities to analyze how cost varied according to these parameters. Our results demonstrate that composite columns are a more cost‐effective option than steel structures, even for buildings that are not considered high rises. We found that the difference in cost between the two types of structures increases with building height and irregularity. Additionally, our optimization algorithm was unable to find feasible designs for steel structures taller than 180 m using ready steel profiles.

Wave Reflection Analyses On Laser Scan Data From A Model Salt Marsh

Physical or numerical models are common tools to investigate the interaction between waves and marine structures. The decomposition of the water level into incident and reflected wave components is often required, as most design variables (overtopping, run-up) are linked to the incoming wave characteristics. Also, an accurate solution can provide information on the distribution of energy in the wave spectrum and the spread of energy from the fundamental wave components to the lower and higher frequencies (Lin and Huang, 2004). Thus, utilizing an appropriate wave reflection analysis is critical in the analysis of such experiments.

Construction of design requirements knowledgebase from unstructured design guidelines using natural language processing

In the manufacturing industry, unstructured documents such as design guidelines, regulatory documents, and failure cases are essential for product development. However, due to the large volume and frequent revisions of these documents, designers often find it difficult to keep up to date with the latest content. This study presents a method for analyzing the characteristics of unstructured design guidelines and automatically constructing a knowledgebase of design requirements from them. A knowledgebase is structured data that a computer can understand, and that can be used to assist designers in the design process. The knowledgebase is constructed using the sections of the document, including design variables and design requirements. The construction process involves pre-processing the documents, extracting information using natural language processing models, and generating a knowledgebase using predefined rules. A requirements knowledgebase was experimentally constructed from a standard document on the general requirements for the design of pressure vessels (American Society of Mechanical Engineers Section VIII Division 1) using the proposed method. In the experiment, the accuracy of information extraction was 86.3 %, and the generation process took 3 min and 50 s. Thus, the proposed method eliminates the need for specialized training of deep learning models and can be applied to various design guideline documents with simple modifications to the design vocabulary and rules. The knowledgebase has applications in design validation, and is expected to enhance the efficiency of the product development process and contribute to reducing the overall development timeline.

Occupant's thermal comfort augmentation and thermal load reduction in a typical residential building using genetic algorithm

The uncontrollable rise in energy consumption become a most significant issue in recent decades. One of the largest consumers of energy resources across all industries is the residential building sector. Researchers have suggested several strategies to reduce energy loss, including enclosing insulation in wall structures because air conditioning systems account for the majority of energy use inside homes. The main goal of this article is to increase residents' thermal comfort (Tc) while reducing their heating load (HL) and cooling load (CL). Using the EnergyPlus program, the building model was simulated in sample cities with various climatic conditions. For optimization, the first seven design variables were determined in Jeplus software and then multi-objective optimization was performed by the Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) algorithm. As a result, Tc, HL, and CL values improved by 38–62, 61 to 100, and 17 to 39 percent, respectively.

Optimization of polyoxymethylene spur gear pair using meta-heuristic algorithms: A comparative study

Design-optimization of power transmitted elements such as gears is a complicated process to accomplish as the mathematical model acquires to be in compliance with the optimization algorithm used. Utilization of technical standards and handbooks assisted engineers and designers with designing the gears, however the results were inefficient, thus meta-heuristic algorithms were approved for optimization, for example, genetic algorithm. In this paper, optimization of single stage spur gear pair made of polyoxymethylene material was carried out using genetic algorithm (GA), artificial bee colony (ABC), JAYA, grey wolf optimizer (GWO), and whale optimization algorithm (WOA) in MATLAB. Three single functions were used as objectives, those are, weight, power loss, and center distance. Module ( m), face width ( b), number of pinion teeth ( z1), and finally profile shifts ( x1, x2) served as design variables. Unlike steel gears which were constrained by handling root bending stress and flank pressure, more constraints were added to the problem with the increase of complexity such as root and flank temperature, tooth deformation, and wear abrasion. The results showed a significant decrease in all three objectives when optimizing each objective solely with variation of the variables. It is also observed that JAYA has the superiority in decreasing the three objectives in comparison to GA and ABC nevertheless, in agreement with GWO, and WOA.

Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction

Scramjet engines are a promising propulsion technology for future space transportation systems, and its design exploration represents a significant task toward development of high-performance scramjets. While sensitivity analysis is expected to facilitate acquisition of physical insights for effective scramjet design, the substantial computational cost incurred due to numerical simulations required for statistical analysis poses a challenge to the application, limiting the capability of this analysis. The present study has conducted sensitivity analysis using deep-learning-based flowfield prediction that can significantly reduce the computational cost for numerical simulations providing flowfield data. Global sensitivity analyses have been employed to investigate influential design variables on the flowfield and performance. These have allowed for identifying the design variables that dominantly influence or determine the performance parameters. Local sensitivity analyses have been performed to elucidate the design rationales and the characteristic flow structures for high-performance intake designs. The sensitivity analysis methods in conjunction with flowfield prediction have enabled generation of rich insights that would otherwise be difficult to acquire without this approach, demonstrating the capabilities of the proposed methodology.

A domain knowledge-informed design space exploration methodology for mechanical layout design

Layout designs have a large number of design variables and various physical constraints. Conventional design exploration approaches are time-consuming and may require human intervention. A unique feature about physical layout designs is the availability of domain knowledge, which can be utilised to speed up the design process. In this paper, we propose a generative deep learning-based design space exploration (DSE) methodology that is capable of learning design constraints in the layout design without explicit supervision. Moreover, it can incorporate domain knowledge in the generated layouts, thereby speeding up the design process. This is realised by constructing a layout generation variational autoencoder (LGVAE) model, which uses a latent space as an interface to generate the layouts. By training the LGVAE model, significantly lower-dimensional representations can be learned compared to the original dimensionality of the design space. Therefore, the number of design variables is greatly reduced. We showcase the performance of the proposed DSE approach by solving the heat source layout design problem encountered in thermal management of chips. Experiments demonstrate that the LGVAE model is capable of generating compressed latent representations that capture the characteristics of the input samples, which makes the DSE cost-effective.

Energy and structural optimization of mid-rise light-frame timber buildings for different climates and seismic zones in Chile

AbstractLocation determines not only the climatic condition but also the structural loads that the structure must withstand. Given the broad variety of climatic and seismic requirements of Chile, the design of lightweight timber buildings considering both energy and seismic design parameters and boundary conditions becomes a difficult task. The main objective of this research is to analyze and quantify the effect of climates, seismic loads, lateral anchorage, and story number on the optimal energy design solutions, including the seismic behavior in a light-frame timber building. Furthermore, the optimal design was parametrically analyzed considering five Chilean cities that consider different climates, seismic zone, number of stories, and lateral anchorage systems to prevent rocking (overturning) due to lateral seismic forces. The optimal wall insulation thickness, stud spacing, and thermal mass exhibited significant variations depending on the buildings' number of stories, lateral anchorage system, climate, and seismic zone. Therefore, the results of this investigation reinforce the necessity of integrating energy and seismic designs for light-frame timber buildings. The optimal designs obtained in this investigation showed considerable variations depending on the combination of climatic and seismic loads as well as the number of stories and anchoring systems. The article's main contributions are the evidence of the structural and energy design interconnection of light-frame timber buildings and how design variables, such as stud spacing, floor concrete thickness layer, and wall insulation thickness, are related and change according to the different climates, seismic loads, lateral anchorage, and story number.

Multi-objective design optimization of polymer spur gears using a hybrid approach

Polymer gears are used in applications requiring small to moderate loads to effectively transmit power and use the limited place available as possible. Various commercial standards have been provided designers with the rating criteria and acquaintance of different polymer material properties for the process of design. However, the result was unsatisfactory in terms of economy, time, and the optimality of the product. Thus, classic and stochastic algorithms have been embraced to reach the best design of polymer gears. Taking advantage of the former and latter algorithm’ methods, optimal design of gears could be attained with an increased gear life span and decreased failure modes. In this study, polyoxymethylene (POM) spur gear set has been optimized combining the mathematical model from plastic standards and hybrid optimization approach of multi-objective genetic algorithm (MOGA) and sequential quadratic programming (SQP). Weight and power loss were the objective functions. Five design variables were optimized with the satisfaction of bending and contact stresses, temperature, wear, and deformation as constraints. Solutions of the problem were formulated as Pareto optimal set. The results of multi-objective were compared with previously published single-objective optimization. The results favored multi-objective optimization (82.67%, 31.39% reduction in weight and power loss respectively) as it gave the best applicable solution fitting in real life situations. The results also went hand in hand with literature confirming the efficiency of the proposed algorithm. With the variation of operating parameters, various optimal designs could be obtained where the designers can choose the design that is suitable for a particular application.

Design and analysis of a conical origami tube (COT) composed of quadrilateral unit cells

Abstract Kresling origami are a topic dat are constantly being researched, especially when it comes too,, teh cylindrical mechanisms made up of special quadrilateral units. It's also fascinating dat teh conical mechanisms based on teh Kresling pattern are gaining more attention lately. However, designing conical mechanisms wif stable behavior and solving teh stress concentration in crease areas for practical engineering applications remains less researched. they'refore, dis study considers quadrilateral unit cells as teh research object, designs a conical origami tube (COT), and establishes a theoretical model wif five variables too,, systematically investigate teh COT. Based on dis, teh design domain related too,, teh design variables are proposed, and teh COT are analyzed in three cases. We further explored teh influence of η (conical degree) on teh COT. In addition, dis study implements a cutting design on each crease vertex too,, reduce teh impact of stress concentration on teh nonlinear response through finite element shell models and uniaxial compression experiments. Teh findings of dis study reveal dat teh setting of η and teh crease-cutting design has a remarkable impact on teh mechanical properties and stability behavior.

CFD-based multi-objective Bayesian optimization approach for compact and efficient hydrogen production in steam reforming reactor

This paper introduces a multi-objective Bayesian optimization approach for concurrently optimizing the size and productivity of a steam methane reforming reactor. Seven design variables, resulting in a total of 3,929,310 possible combinations, were explored, leading to three Pareto optimal designs after 100 iterations. Parametric studies were conducted first, and the results of Bayesian optimization were suggested with initial samples obtained via Latin Hypercube Sampling. The Pareto optimal designs revealed significant reduction in size and improvement in productivity. Furthermore, adjusting operating conditions, specifically the steam-to-carbon ratio, showed additional improvements of productivity. This study can contribute to efficiently proposing Pareto optimal designs with potential applications in hydrogen supply for compact and highly efficient systems.

Artificial intelligence-based surrogate modeling for computational cost-effective optimization of hydrogen liquefaction process

The chemical processes are inherently complex, and obtaining desired product quality and high energy efficiency by optimizing the design variables is challenging. The progress in computing technologies can be a blessing since machine learning models can process the information and data for learning purposes and eventually help ease the simulation and optimization efforts. Against this backdrop, an artificial neural network-based surrogate model is developed in the present study and applied to the challenging process of hydrogen liquefaction. Optimization of hydrogen liquefaction processes is still an open challenge because of convergence issues and many design variables; only a handful of algorithms are considered successful. One of those algorithms, particle swarm optimization (PSO), is used here to compare computational effort and results accuracy. The percentage error of prediction of the ANN-based surrogate model compared to PSO was 4% for the minimum internal approach temperature, which is usually considered as a constraint, and 0.04% for specific energy consumption considered as the objective function. The optimization of the proposed model significantly reduced the time needed for PSO optimization of the existing model by more than 99.99%. As an extension of the current work, the ‘shallow' neural network model, with one hidden layer used in the present study, can be used to analyze the whole liquefaction process.

Lightworks, a scientific research framework for the design of stiffened composite-panel structures using gradient-based optimization

Efficient structural optimization remains integral in advancing lightweight structures, particularly concerning the mitigation of environmental impact in air transportation systems. Varying levels of detail prove useful for different applications and design phases. The lightworks framework presents a modular approach, for the consideration of individual design parameterizations and structural solvers for the numerical optimization of thin-walled structures. The framework provides the combination of lightweight fibre composite design and the incorporation of stiffeners for a gradient-based optimization process. Therefore, an analytical stiffener formulation is implemented in combination with different continuous composite material parameterizations. This approach allows the analysis of local buckling modes, as well as the consideration of load redistribution between stringer and skin. The flexibility achieved in this way allows a tailored configuration of the optimization problem to the required level of complexity. A verification of the framework’s implementation is carried out using established literature results of a simplified unstiffened wing box structure, where a very good agreement is shown. The accessibility of solvers with different fidelity through a generic solver interface is demonstrated. Furthermore, the usage of the implemented continuous composite parameterizations as design variables is compared in terms of computational performance and mass, providing different advantages and disadvantages. Finally, introducing stringer into the wing box use case demonstrates a 38% mass reduction, showcasing the potential of the inline optimization of stiffeners.