Design Optimization Techniques Research Articles

Application of Scheffe's Simplex Lattice Model in concrete mixture design and performance enhancement

This comprehensive literature review delves into the application of Scheffe's Simplex Lattice Model for optimizing cement concrete mixtures, with a particular emphasis on its impact on material properties and sustainability. The review meticulously outlines the principles, historical context, and advantages of Scheffe's model, providing a nuanced understanding of its significance. Comparative analyses with traditional and alternative optimization techniques in concrete mix design illuminate the distinct advantages of statistical methods, especially Scheffe's model. The review critically examines the challenges and limitations associated with applying Scheffe's model, addressing issues related to the complexity of concrete mixtures and computational demands. Potential avenues for improvement are explored, suggesting refinements to handle non-linearity, incorporate advanced optimization algorithms, and streamline computational requirements. Additionally, the review highlights emerging trends in statistical modeling for concrete mixture optimization, such as the integration of machine learning and data-driven approaches, signaling the evolving landscape of concrete technology. In conclusion, the literature underscores Scheffe's Simplex Lattice Model as a valuable and versatile tool with far-reaching implications for the advancement of concrete mixture design methodologies. The call to action encourages ongoing research and development to refine the model, explore emerging trends, and address practical challenges, positioning Scheffe's model as a cornerstone in the pursuit of sustainable, resilient, and high-performance concrete materials. The study highlights Scheffe's Simplex Lattice Model as a robust statistical tool for optimizing cement concrete mixtures, demonstrating its efficacy in improving performance indicators like compressive strength and durability. This systematic approach offers a paradigm shift from empirical methods, fostering a nuanced understanding of concrete components' relationships and paving the way for tailored material properties and enhanced sustainability in construction. The call to action urges further research to refine the model, address challenges, and explore hybrid approaches, ultimately advancing concrete mixture design methodologies towards greater efficiency and precision.

Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.

A multi-objective optimization design method for underwater composite sandwich structures with high acoustic transmission and low vibration performance

ABSTRACT To address the issue of reduced structural integrity and optimality resulting from the conventional serial design of sonar dome structures, this paper establishes a multi-objective optimal design model for composite sandwich structures, utilising the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The model's stability and reliability are subsequently validated. Upon this model, a structural optimization design technique is postulated for composite sandwich structures within the realm of Fluid-Structure Interaction (FSI) multi-physics coupling. This approach aims to optimize the lightweight, high-rigidity composite sandwich structure, enhancing its acoustic transmission and minimizing vibration performance under turbulent pressure pulsations. The suggested method notably enhances the efficiency of structural design while ensuring precision. The proposed method effectively addresses the deficiency of serially designed sonar domes by neglecting the synergistic effects among multiple disciplines. In summary, this approach offers technical support and new research perspectives for the design of sonar domes under the coupling of multiple physical fields.

Multi-Step Design Optimization for the Improvement of an Outer-Rotor Brushless Direct Current Motor

Brushless Direct Current (BLDC) motors have seen significant improvements across various electrical applications. The growing focus on motor design research highlights the BLDC motor’s superior efficiency compared to traditional motors, which consume more power. BLDC motors are compact, lightweight, energy-efficient, and easy to control, making them ideal for modern applications. This study aims to enhance BLDC motor design and performance by employing the Taguchi method, Response Surface Methodology (RSM), and Finite Element Method (FEM) for multi-stage optimization. A 26-watt BLDC electric fan motor is the reference model for this study. The Taguchi method helps identify optimization points, guiding further enhancements in the second stage. The study proposes a design with improved output power, torque, and efficiency. The final design achieves a 15% higher energy efficiency than the reference model, with a 10 W increase in output power and a 0.032 Nm increase in maximum torque. The FEM analysis using JMAG software v 21.2 validates the proposed design, which shows improved configurations compared to the reference model, demonstrating the efficiency of the optimization techniques for BLDC motor design.

Dynamic analysis of drag-based vibratory swimmers using higher-order averaging

This paper discusses the mathematical modeling, dynamic analysis, and design optimization of a class of drag-based vibratory swimmers using averaging. A typical swimmer of this class consists of a surface or subsurface main body, an oscillatory mass inside the body, and a rigid fin attached to the main body using torsional springs and immersed in water. This paper takes advantage of third-order averaging techniques for dynamic analysis and design optimization of the swimmer for high frequencies. The averaged dynamics are used for determining the optimum stiffness of the springs for maximizing the average speed of the swimmer for high-frequency motion of the oscillatory mass. Experimental results confirm the general response of the mathematical model.

Design and optimization of Anisotropy–Inspired AlSi10Mg metamaterials with tailored mechanics and mass transport properties in tissue engineering

Metamaterials are widely used for achieving tailored multi–physical properties. However, it is challenging to satisfy multiple property requirements simultaneously. For example, the mechanical and mass transport properties of bionic scaffolds have to be compromised. In this work, 3D–printed metamaterials with adjustable topological features are proposed, and a topological optimization strategy for coordinating multiple properties is developed. Tradeoffs between the mechanics, energy absorption capacity, and mass transport properties are accomplished under given a porosity. Anisotropic metamaterials with permeabilities of 2.34 ∼ 3.44 × 10-7 m2 and elastic moduli of 1.37 ∼ 3.14 GPa were obtained, and the gradient scaffolds were further designed to realize adjustable local characteristics. The optimized design technique described here can serve as an effective way to design more complex multilayered scaffolds with structural characteristics and biomechanical properties similar to those of natural tissue, so as to achieve an unprecedented level of tailoring multi–physics properties in tissue engineering, especially in the design of gradient scaffolds. Our study also represents advances in property decoupling, individual customization and collaborative design of metamaterials, which can be generalized to fluid and heat transport fields.

Ultra‐fast finite element analysis of coreless axial flux permanent magnet synchronous machines

AbstractLarge‐scale design optimisation techniques enable the design of high‐performance electric machines. Electromagnetic 3D finite element analysis (FEA) is typically employed in optimisation studies for accurate analysis of axial flux permanent magnet (AFPM) machines, which require extensive computational resources. To reduce the computational burden, a FEA‐based mathematical method relying on the geometric and magnetic symmetry of coreless AFPM machines is proposed to estimate the machine performance indicators using the least number of FEA solutions, thereby significantly lowering the running time. This method is generally applicable to AFPM machines with low saturation effects and cogging torque as exemplified for a printed circuit board (PCB) stator coreless AFPM machine. To further reduce the computation time, a systematically simplified equivalent 3D FEA model for planar PCB coils integrated with this machine is also proposed. The practical implementation of the introduced method is elaborated based on an example optimisation study, and an analytical method for fast design scaling is also discussed. The results of the proposed approach are compared with detailed transient FEA results, and a prototype 26‐pole PCB stator coreless AFPM machine was also used to validate the results experimentally.

Smart Circuit Design Machine Learning-Driven Optimization for Enhanced Performance in Electronics and Computer Engineering.

In the realm of Electronics and Computer engineering, achieving optimal performance of circuits amidst escalating complexity poses significant challenges. Traditional manual optimization techniques are often inadequate to navigate the intricacies of modern electronic systems. This paper advocates for the adoption of machine learning-driven optimization as a transformative approach to smart circuit design. By leveraging machine learning algorithms, engineers can systematically explore the expansive design space, discern complex relationships between circuit parameters and performance metrics, and ultimately enhance the efficiency and effectiveness of electronic circuits. This paper comprehensively reviews the application of machine learning techniques in circuit design optimization. Supervised learning algorithms such as neural networks, support vector machines, and decision trees enable the modeling of intricate interdependencies within electronic circuits. Unsupervised learning techniques, including clustering and dimensionality reduction, facilitate efficient exploration of the design landscape by identifying patterns and correlations. Additionally, reinforcement learning algorithms offer an autonomous approach to circuit optimization through iterative learning and refinement. Real-world applications of machine learning-driven optimization in electronics and computer engineering span various domains, including power-efficient integrated circuits, signal processing algorithm optimization, and layout optimization for enhanced performance and reliability. Moreover, machine learning techniques play a crucial role in mitigating variability in semiconductor manufacturing processes, ensuring robustness and reliability of electronic systems in the face of uncertainties. Despite the promising potential of machine learning in circuit design optimization, challenges such as dataset acquisition, model interpretability, and scalability to complex circuits persist. Addressing these challenges requires innovative research endeavors, including the development of hybrid optimization techniques and novel hardware architectures. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6339

Improving the Groundnut Oil Extraction Efficiency using RSM and Central Composite Design (CCD) Optimization Techniques

Improving the Groundnut Oil Extraction Efficiency using RSM and Central Composite Design (CCD) Optimization Techniques

Effect of the Flow Passage Area Ratio on a Double Slit-Type Pintle Injector

Due to the structural characteristics of the pintle injector, a recirculation zone is generated under the pintle, so the pintle tip is directly exposed to a high-temperature environment. Therefore, the cooling technique and protective coating on the pintle tip are essential for improving the reusability and durability of the pintle injector. Although various cooling methods are known, this study applied a forced flow passage cooling method by adding internal structures called inserts to the pintle injector. The characteristics of the insert were analyzed experimentally and numerically based on change of its area ratio. When the internal flow changes, the spray angle, droplet size, and cooling performance also change in accordance with an increase in the insert’s central area. When the insert structure is installed in the pintle post, upward flow may occur, which can worsen the spray angle and droplet distribution characteristics. Nevertheless, owing to the insert structure, the surface temperature of the pintle post is reduced dramatically as intended. Thus, an optimal design technique for cooling by changing the area ratio of the first- and second-row slits of the pintle post can be suggested to overcome the limitations of the insert structure. The results of this study will be helpful in understanding the spray characteristics of the insert structure and optimize the design of cooling channels for reusable pintle injectors in the future.

ERGO-II: An Improved Bayesian Optimization Technique for Robust Design With Multiple Objectives, Failed Evaluations, and Stochastic Parameters

Abstract In this work, the efficient robust global optimization (ERGO) method is revisited with the aim of enhancing and expanding its existing capabilities. The original objective of ERGO was to address the computational challenges associated with optimization-under-uncertainty through the use of Bayesian optimization (BO). ERGO tackles robust optimization problems which are characterized by sensitivity in the objective function due to stochasticity in the design space. It does this by concurrently minimizing the mean and variance of the objective in a multi-objective setting. To handle the computational complexity arising from the uncertainty propagation, ERGO exploits the analytical expression of the surrogate model underlying BO. In this study, ERGO is extended to accommodate multiple objectives, incorporate an improved predictive error estimation approach, investigate the treatment of failed function evaluations, and explore the handling of stochastic parameters next to stochastic design variables. To evaluate the effectiveness of these improvements, the enhanced ERGO scheme is compared with the original method using an analytical test problem with varying dimensionality. Additionally, the novel optimization technique is applied to an aerodynamic design problem to validate its performance.

Qualitative data augmentation for performance prediction in VLSI circuits

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180 nm and 22 nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog circuits and digital cells. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.

An analysis of polymer material selection and design optimization to improve Structural Integrity in 3D printed aerospace components

This paper presents an analysis of material selection and design optimization techniques to enhance the structural integrity of 3D printed aerospace components. The study highlights the importance of considering material characteristics and design factors such as shape, orientation, and support structures in order to achieve reliable and high-performance components. Various materials, including metals and polymers, commonly used in aerospace applications are evaluated, along with their properties and limitations in the context of 3D printing. Furthermore, the impact of different printing parameters on the structural integrity of the components is discussed. The study identifies optimization strategies such as topology optimization, lattice structures, and infill patterns, which can significantly improve the strength and durability of 3D printed parts. The results demonstrate the potential of these techniques to optimize the design and material selection of aerospace components, leading to lighter, more efficient, and reliable parts for air and space vehicles.

Finite Element Analysis of Castellated Beams with Different Web Openings

Due to the recent developments in design optimization techniques and advanced construction technologies, uses of castellated beams have become very popular. Castellated beams because of their high strength to weight ratio and low maintenance and painting cost have effectively replaced built-up girders in light to medium constructions for medium to long spans. Longitudinal cuts in the form of openings along the web of castellated beams also results in stronger and stiffer beams than their parent beams without any increase in the self-weight. In this study, attempts are made to compare the behavior of castellated beams with their parent beams. Studies on effect of different web openings on deflection are carried out using finite element analysis.

A Review of the Key Points in the Design of Small-Radius Curved Ramp Bridges

This article explores the fundamentals of small-radius curved ramp bridges. It covers the selection of box girder spans, support methods, and forms, along with design optimization techniques for this type of bridge structure. The purpose of this paper is to provide robust support for enhancing the design quality of these bridges and ensuring their efficacy in real-world applications.

Design of a Wheelchair-Mounted Robotic Arm for Feeding Assistance of Upper-Limb Impaired Patients

This paper delineates the design and realization of a Wheelchair-Mounted Robotic Arm (WMRA), envisioned as an autonomous assistance apparatus for individuals encountering motor difficulties and/or upper limb paralysis. The proposed design solution is based on employing a 3D printing process coupled with optimization design techniques to achieve a cost-oriented and user-friendly solution. The proposed design is based on utilizing commercial Arduino control hardware. The proposed device has been named Pick&Eat. The proposed device embodies reliability, functionality, and cost-effectiveness, and features a modular structure housing a 4-degrees-of-freedom robotic arm with a fixing frame that can be attached to commercial wheelchairs. The arm is integrated with an interchangeable end-effector facilitating the use of various tools such as spoons or forks tailored to different food types. Electrical and sensor components were meticulously designed, incorporating sensors to ensure user safety throughout operations. Smooth and secure operations are achieved through a sequential procedure that is depicted in a specific flowchart. Experimental tests have been carried out to demonstrate the engineering feasibility and effectiveness of the proposed design solution as an innovative assistive solution for individuals grappling with upper limb impairment. Its capacity to aid patients during the eating process holds promise for enhancing their quality of life, particularly among the elderly and those with disabilities.

A distributed photonic crystal fiber reverse design framework based on multi-source knowledge fusion

Recent advances in artificial intelligence (AI) have inspired researchers to explore machine learning (ML)-based optimization and reverse design techniques for photonic crystal fibers (PCFs). These studies often seek to improve model generalization, particularly for data that the model has not previously encountered. Traditional centralized training methods are challenging for devices with limited resources, as they rely on aggregating expansive datasets, which is hindered by constraints in storage capacity and communication efficiency. This paper introduces an innovative distributed framework for optimizing PCF parameters, utilizing decentralized training to amalgamate knowledge across various institutions while maintaining data privacy. Each institution develops a lightweight neural network using a small subset of local data, contributing to the construction of a collective and robust global model. This approach is advantageous for both internal and external applications in PCF engineering. Rigorous empirical experiments conducted with real-world PCF optimization data substantiate the efficacy and benefits of the proposed framework. This framework shows promise in achieving an equilibrium between data protection and resource efficiency, offering a novel platform for the reverse design of microstructured optical fibers.

A Desirability Function-Based Multi-Characteristic Robust Design Optimization Technique

A Desirability Function-Based Multi-Characteristic Robust Design Optimization Technique

Optimization of Synergistic Leaching of Valuable Metals from Spent Lithium-Ion Batteries by the Sulfuric Acid-Malonic Acid System Using Response Surface Methodology

A new environmentally friendly and economical recycling process for extracting metals from spent lithium-ion batteries (LIBs) using sulfuric acid and malonic acid as leaching agents is proposed. By applying Box-Behnken design (BBD) and response surface methodology (RSM) optimization techniques, the global optimal solution of the maximum leaching rate of metals in spent LIBs is realized. The results show that under the optimal conditions of 0.93 M H2SO4, 0.85 M malonic acid, liquid/solid ratio of 61 g·L–1, the temperature of 70 °C and 5 vol% of 30% H2O2, 99.79% Li, 99.46% Ni, 97.24% Co and 96.88% Mn are recovered within 81 min. The error between the theoretical value and the actual value of the metal leaching rate predicted by the regression model is less than 1.0%. Additionally, the study of leaching kinetics reveals that the leaching process of Li, Ni, Co, and Mn in spent cathode materials was affected by the synergistic effect of interfacial mass transfer and solid product layer diffusion. Economic analysis reveals that evaluation index should be fully considered when formulating recovery processes for different metals. This process can reduce the environmental risks of heavy metal disposal, and allow the reuse of metals recovered from spent LIBs.

Machine learning techniques for sequential learning engineering design optimisation

When designing a fusion power plant, many first-of-a-kind components are required. This presents a large potential design space across as many dimensions as the component’s parameters. In addition, multiphysics, multiscale, high-fidelity simulations are required to reliably capture a component’s performance under given boundary conditions. Even with high performance computing (HPC) resources, it is not possible to fully explore a component’s design space. Thus, effective interpolation between data points via machine learning (ML) techniques is essential. With sequential learning engineering optimisation, ML techniques inform the selection of simulation parameters which give the highest expected improvement for the model: balancing exploitation of the current best design with exploration of uncertain areas in the design space. In this paper, the application of an ML-driven design of experiment procedure for the sequential learning engineering design optimisation of a fusion component is shown. A parameterised divertor monoblock is taken as a typical example of a fusion component requiring HPC simulation to model. The component’s geometry is then optimised using Bayesian optimisation, seeking the design which minimises the stress experienced by the component under operational conditions.