Design Optimization Techniques Research Articles

Bayesian design of measurements for magnetorelaxometry imaging *

The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been aligned using an external activation magnetic field that has subsequently been switched off. This work applies techniques of Bayesian optimal experimental design to (sequentially) selecting the positions for the activation coil in order to increase the value of data and enable more accurate reconstructions in a simplified measurement setup. Both Gaussian and total variation (TV) prior models are considered for the distribution of the nanoparticles. The former allows simultaneous offline computation of optimized designs for multiple consecutive activations, while the latter introduces adaptability into the algorithm by using previously measured data in choosing the position of the next activation. The TV prior has a desirable edge-enhancing characteristic, but with the downside that the computationally attractive Gaussian form of the posterior density is lost. To overcome this challenge, the lagged diffusivity iteration is used to provide an approximate Gaussian posterior model and allow the use of the standard Bayesian A- and D-optimality criteria for the TV prior as well. Two-dimensional numerical experiments are performed on a few sample targets, with the conclusion that the optimized activation positions lead, in general, to better reconstructions than symmetric reference setups when the target distribution or region of interest are nonsymmetric in shape.

Design and Fabrication of a High-Sensitivity and Wideband Cymbal Hydrophone.

So far, cymbal transducers have been developed primarily for transmitting purposes, and even when used for receiving, the focus has been mostly on improving the receiving sensitivity. In this study, we developed a cymbal hydrophone with a higher sensitivity and a wider bandwidth than other existing hydrophones. First, the initial structure of the cymbal hydrophone was established, and then the effects of structural variables on the hydrophone's performance were analyzed using the finite element method. Based on the analysis results, the structure having the highest sensitivity and widest bandwidth, with a receiving voltage sensitivity level above a certain threshold, was derived using optimal design techniques. A prototype of the cymbal hydrophone with the designed structure was fabricated, and its performance was measured, validating the effectiveness of the design by comparing the measurement results with the design values. The developed cymbal hydrophone is expected to be utilized in various underwater precision measurements, as it possesses a significantly broader reception frequency bandwidth when compared with other hydrophones used for the same purpose.

Design and Optimization of Heat Sinks for the Liquid Cooling of Electronics with Multiple Heat Sources: A Literature Review

This paper presents a detailed literature review on the thermal management issue faced by electronic devices, particularly concerning uneven heating and overheating problems. Special focus is given to the design and structural optimization of heat sinks for efficient single-phase liquid cooling. Firstly, the paper highlights the common presence and detrimental consequences of electronics overheating resulting from multiple heat sources, supported by various illustrative examples. Subsequently, the emphasis is placed on single-phase liquid cooling as one of the effective thermal management technologies for power electronics, as well as on the enhancement of heat transfer in micro/mini channel heat sinks. Various studies on the design and structural optimization of heat sinks are then analyzed and categorized into five main areas: (1) optimization of channel cross-section shape, (2) optimization of channel flow passage, (3) flow distribution optimization for parallel straight channel heat sinks, (4) optimization of pin-fin shape and arrangement, and (5) topology optimization of global flow configuration. After presenting a broad and complete overview of the state of the art, the paper concludes with a critical analysis of the methods and results from the literature and highlights the research perspectives and challenges in the field. It is shown that the issue of uneven and overheating caused by multiple heat sources, which is commonly observed in modern electronics, has received less attention in the literature compared to uniform or single-peak heating. While several design and structural optimization techniques have been implemented to enhance the cooling performance of heat sinks, topology optimization has experienced significant advancements in recent years and appears to be the most promising technology due to its highest degree of freedom to treat the uneven heating problem. This paper can serve as an essential reference contributing to the development of liquid-cooling heat sinks for efficient thermal management of electronics.

A Review on Study of Design Optimization Technique of Mechanical Component

A Review on Study of Design Optimization Technique of Mechanical Component

TENSILE STRENGTH AND HARDNESS OF POLYPROPYLENE-BAMBOO/KENAF HYBRID COMPOSITE AFTER OPTIMIZATION OF PRODUCTION PROCESS

While in use and when not, engineering materials occasionally exhibit poor characteristics and perform poorly as a result of internal or external factors. Therefore, there is a need to improve the protection of the composites from the various agents and improve their mechanical properties. This study's objective was to assess the impact of bamboo/kenaf hybrid fibres, Maleic Anhydride Grafted Polypropylene (MAHgPP), Hinder Amine Light Stabiliser (HALS), and Calcium Carbonate (CaCO3) additions on tensile strength and hardness at various levels, as well as their interactions on polypropylene (PP) composites. The manufacturing of the composites was accomplished through the compression molding process. The study was conducted using Taguchi orthogonal array L27 with fiber, MAHgPP, HALS, and CaCO3 as input parameters. A robust optimization technique for product and process design (Taguchi method ) was used to optimize the parameter settings for the tensile strength and hardness of the composite. Results showed that the tensile strength of the resultant composite ranged from 37.60 MPa to 53.57 MPa; while the hardness value ranged from 70.32 (Shore A) to 93.03 (Shore A). The optimized composite had a tensile strength of 49.12 MPa and a hardness of 79.85 (Shores A). The factors settings that gave the combined optimum tensile strength and hardness for the bamboo/kenaf fiber are 36.87% fiber, 5% MAHgPP, 1% HALS, and 3% CaCO3. therefore the best tensile strength and hardness performance of the composite was obtained from the optimum setting.

Weight Optimization of Stainless Steel (SS304) Helical Spring

This study deals with the elaborate design optimization technique for helical compression spring with analytical method. Concentration is made on reducing weight of helical compression spring keeping the induced stresses within permissible limits. The spring is acted upon by compressive force so the spring gets compressed. The entitled work comprises, design of helical compression spring made from wire of solid circular section for a problem and optimizing the weight of solid wire spring by redesigning it with hollow circular section instead of solid circular section. After analytical design procedure the solid wire spring and optimized spring (i.e. spring made from hollow circular wire) are manufactured. After manufacturing of springs, analytical results are validated experimentally and in ANSYS.

Optimum design technique for two lenses telescopic system using collimation testing and Zernike polynomials

Optimum design technique for two lenses telescopic system using collimation testing and Zernike polynomials

Statistical analysis for remediation of As(III) ions from water using pristine and derivatized Phyllanthus emblica seed coat

Abstract The aim of this study is to determine the optimal conditions for remediation of As(III) ions from water using pristine Phyllanthus emblica (PPE) seed coat and derivatized Phyllanthus emblica (DPE) seed coat, by using Box -Behnken design (BBD) and central composite design (CCD) optimization techniques. pH, initial ion concentration, dosage, and contact time were taken as process parameters while designing the experiment. The desirability factor is 1.0 for the BBD and 0.8 for CCD for both adsorbents. The regression coefficient for both adsorbents was in the range of 0.993 -0.999 for the BBD and 0.965 -0.969 for the CCD. The BBD is found to be more suitable for optimization of variables for maximum removal, and estimation of removal percentage in different conditions. The adsorption of ions at equilibrium (qe) is found to be 43.59 mg/g at pH 7.13, initial concentration of arsenic of 99.02 mg/L, contact time of 105.13 min, and dosage of 0.12 g/L for PPE using the BBD. However, the adsorption of ions at equilibrium (qe) is found to be 48.79 mg/g at pH 7.31, initial ion concentration of 98.82 mg/L, contact time of 126.99 min, and dosage of 0.12 g/L for DPE using the BBD.

Development of a parametric optimization concept for the sun control of the outdoor spaces: the case study of 5A business park, Cairo, Egypt

PurposeComfortable outdoor workspaces are important for employees in business parks and urban areas. Prioritizing a pleasant thermal environment is essential for employee productivity, as well as the improvement of outdoor spaces between office buildings to enhance social activities and quality of outdoor workplaces in a hot arid climate has been subjected to very little studies Thus, this study focuses on business parks (BPs) landscape elements. The objective of this study is to enhance the user's thermal comfort in the work environment, especially in the outdoors attached to the administrative and office buildings such as the BPs.Design/methodology/approachThis research follows Four-phases methodology. Phase 1 is the investigation of the literature review including the Concept and consideration of BP urban planning, Achieving outdoor thermal comfort (OTC) and shading elements analysis. Phase 2 is the case study initial analysis targeting for prioritizing zones for shading involves three main methods: social assessment, geometrical assessment and environmental assessment. Phase 3 entails selecting shading elements that are suitable for the zones requiring shading parametrize the selected shading elements. Phase 4 focuses on the optimization of OTC through shading arrangements for the prioritized zones.FindingsShading design is a multidimensional process that requires consideration of various factors, including social aspects, environmental impact and structural integrity. Shading elements in urban areas play a crucial role in mitigating heat stress by effectively shielding surfaces from solar radiation. The integration of parametric design and computational optimization techniques enhances the shading design process by generating a wide range of alternative solutions.Research limitations/implicationsWhile conducting this research, it is important to acknowledge certain limitations that may affect the generalizability and scope of the findings. One significant limitation lies in the use of the shade audit method as a tool to prioritize zones for shading. Although the shade audit approach offers practical benefits for designers compared to using questionnaires, it may have its own inherent biases or may not capture the full complexity of human preferences and needs.Originality/valueFew studies have focused on optimizing the type and location of devices that shade outdoor spaces. As a result, there is no consensus on the workflow that should regulate the design of outdoor shading installations in terms of microclimate and human thermal comfort, therefore testing parametric shading scenarios for open spaces between office buildings to increase the benefit of the outer environment is very important. The study synthesizes OTC strategies by filling the research gap through the implementation of a proper workflow that utilizes parametric thermal comfort.

Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings

AbstractThe construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer‐aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed, whose architecture and loss function are specifically designed for CMQ estimation. With a heterogeneous feature fusion mechanism, the GNN can automatically extract features from all CMQ‐related information, in contrast to the existing data‐driven methods that rely heavily on manually selected features. By further incorporating a prior knowledge inclusion strategy, the GNN can avoid fundamental errors that might be encountered by purely data‐driven methods. To enrich the diversity of the CMQ dataset, a data augmentation method is proposed incorporating generative adversarial networks and parametric modeling. Numerical experiments and case studies show that the proposed CMQ estimation method is superior to the existing data‐driven methods in terms of accuracy and is 500 times faster than typical commercial structural design software. This study is anticipated to benefit the objective evaluation of computer‐aided design, thereby facilitating the promotion of low‐cost and low‐carbon‐emission building designs.

Harnessing the power of floating photovoltaic: A global review

As the global demand for energy continues to increase, floating photovoltaic (FPV) power is gaining more attention as a promising clean energy source. This paper summarizes the unique advantages of FPV, such as its freedom from land restrictions, higher energy output, and potential integration with other forms of energy. However, FPV also faces challenges due to complex and harsh environmental conditions such as wind, waves, corrosion, and biological fouling. Therefore, the integrated mechanism design and the use of lightweight hydrophobic materials are crucial for ensuring the safety and stability of FPV systems under such conditions. This article also highlights certain key design points and optimization techniques that can improve the structural reliability of FPV systems. With continuous technological advancements and the accumulation of experience, the production efficiency of FPV has improved, leading to the expansion of FPV projects and a reduction in investment costs. As a result, the development of FPV has been accelerating globally, with numerous high-capacity projects being constructed. Building upon the previous literature reviews, this paper provides a concise review of the latest FPV case studies, innovative technologies, challenges in marine environments, economic costs, and market prospects from various perspectives. The primary objective is to encourage further research and application in the field of FPV.

A scalable problem to benchmark robust multidisciplinary design optimization techniques

A scalable problem to benchmark robust multidisciplinary design optimization techniques

Machine learning-based cognitive load prediction model for AR-HUD to improve OSH of professional drivers.

Augmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load. A novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design. Experimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting. In this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers.

Estimating the robust optimal parameters of tuned mass damper

The tuned mass damper (TMD) is one of the oldest and most extensively used passive vibration control systems in dynamically excited buildings. However, the uncertainties or randomness in the loading and the primary structure parameters (i.e., stiffness and damping coefficient) significantly affected the TMD performance. In this framework, Robust Design Optimization (RDO) technique is advantageous, aiming to simultaneously minimize the performance function’s mean and variance. Simulation-based RDO techniques are particularly effective at solving difficult nonconvex and multimodal issues. Therefore, in the present study, a stochastic simulation-based approach is proposed for the RDO of TMD. The performance function’s mean and variance are considered robustness measures. The TMD’s frequency and damping ratios are considered design parameters. TMD attached to a Multiple-Degree-of-Freedom (MDOF) structural system is analyzed to demonstrate the efficacy of the proposed approach. The top floor displacement variance ratio of the protected structural system to that of the unprotected structural system is adopted as the performance function. The simulation results show that the proposed strategy successfully identifies the optimal design for all levels of uncertainty. The necessity of considering the uncertainties is well highlighted. In conclusion, the proposed approach is found to be effective and offers the designer more information for an optimal design decision.

Optimization of biodiesel production, engine exhaust emissions, and vibration diagnosis using a combined approach of definitive screening design (DSD) and artificial neural network (ANN).

In this study, definitive screening design (DSD) optimization and artificial neural network (ANN) modelling techniques are applied for the production of palm oil biodiesel (POBD). These techniques are implemented to examine the vital contributing factors in achieving maximum POBD yield. For this purpose, seventeen experiments are conducted randomly by varying the four contributing factors. The results of DSD optimization reveal that a biodiesel yield of 96.06% is achieved. Also, the experimental results are trained in ANN for predicting the biodiesel yield. The results proved that the prediction capability of ANN is superior, with a high correlation coefficient (R2) and low mean square error (MSE). Furthermore, the obtained POBD is characterized by significant fuel properties and fatty acid compositions and observed within the standards (ASTM-D675). Finally, the neat POBD is examined for exhaust emissions and engine cylinder vibration analysis. The emissions results confirm a significant drop in NOx (32.46%), HC (40.57%), CO (44.44%), and exhaust smoke (39.65%) compared to diesel fuel at 100% load. Likewise, the engine cylinder vibration measured on top of the cylinder head reveals a low spectral density with low amplitude vibrations observed for POBD at measured loads.

A Review of Axial Flux Permanent Magnet Machine Technology

Axial flux permanent magnet machines (AFPM) are popular for applications that benefit from high torque density and an axially compact form factor, such as in-wheel traction drives. Although the radial flux permanent magnet machine (RFPM) and the AFPM work based on the same underlying principle, the differences in their geometry introduce complexities in analysis of the AFPM. In this paper, the different AFPM design variants, their sizing approaches, computationally efficient design optimization techniques, and manufacturing techniques reported in literature are reviewed. In addition to classical AFPM machines, emerging variants and research opportunities with potential to push the boundaries of electric machine technology are reviewed. These include bearingless AFPMs, magnetically geared AFPMs, and combined radial-axial flux machines.

Virtual data-based generative optimization using domain-adaptive designable data augmentation (DADDA): Application to electric vehicle design

In the field of virtual product development, product performance is evaluated at an early stage through metamodel-based optimization using simulation, reducing development time and cost, and improving product quality. However, it takes a lot of time to construct a simulation model. The model must go through calibration and validation process to reduce errors with actual systems. This study proposes a novel engineering design process that can perform virtual data-based generative optimization by adapting the design domain of existing systems to those of new systems without requiring many simulations. A domain-adaptive designable data augmentation (DADDA) algorithm is proposed using inverse generator-implemented data augmentation, domain adaptation, and design optimization techniques. The DADDA algorithm can quickly provide optimal design variables for new systems compared to the genetic algorithm-based approximate optimization. The proposed process was applied to electric vehicle design. Driving responses and design variables related to safety performance were selected, and a small amount of training data was obtained using Modelica-based electric vehicle. As a result, it was shown that DADDA could significantly reduce the time required to derive optimal design variables by approximately 53% compared with the existing method. In addition, it is possible to derive the optimal performance for a new electric vehicle, which is approximately 21% better than that of an existing vehicle.

Development of Optimal Design Technique of RC Beam using Multi-Agent Reinforcement Learning

Development of Optimal Design Technique of RC Beam using Multi-Agent Reinforcement Learning

Multidisciplinary design and optimization of winged architectures for reusable launch vehicles

Partially reusable launch vehicle design has raised a great interest in order to minimize the costs of space transportation by recovering and refurbishing the first stage. Several configurations such as toss-back architectures are now operational. These concepts require additional rocket propellant in order to carry the boost-back and landing maneuvers, that induce several losses in terms of payload mass performance for the ascent mission. In order to limit these losses, this paper focuses on the design of two types of architectures to carry out the return-to-launch-site mission. The first, named glide-back, uses the main propulsion system of the first stage to perform a boost-back burn and returns to the landing site by a gliding mode using additional lifting surfaces. The second, named, fly-back, performs the return-to-launch-site mission using several air-breathing engines located in the nose of the first stage combined with lifting surfaces. In this paper, the design of a reusability kit, allowing to provide the first stage with both expendable and reusable capabilities, is investigated. This kit is composed of the lifting surfaces, the nose including the air-breathing propulsive system (for fly-back configuration), landing gears and additional avionics. This paper addresses the design of such reusability kits and presents the mission specifications, the design process relying on Multidisciplinary Design Optimization techniques, the vehicle performance and the optimal trajectories. Different analyses (aero-propulsive, optimal control, operational considerations, etc.) are detailed and trade-offs between the studied configurations are assessed.

LiteHST: A Tree Embedding based Method for Similarity Search

Similarity search is getting increasingly useful in real applications. This paper focuses on the in-memory similarity search, i.e., the range query and k nearest neighbor (kNN) query, under arbitrary metric spaces, where the only known information is the distance function to measure the similarity between two objects. Although lots of research has studied this problem, the query efficiency of existing solutions is still unsatisfactory. To further improve the query efficiency, we are inspired by the tree embeddings, which map each object into a unique leaf of a well-structured tree solely based on the distances. Unlike existing embedding techniques (e.g., Lipschitz embeddings and pivot mapping) for similarity search, where an extra multi-dimensional index is needed to index the embedding space (e.g., Lp metrics), we directly use this tree to answer similarity search. This seems to be promising, but it is challenging to tailor tree embeddings for efficient similarity search. Specifically, we present a novel index called LiteHST, which is based on the most popular tree embedding (HST) and heavily customized for similarity search in the node structure and storage scheme. We propose a new construction algorithm with lower time complexity than existing methods and prove the optimality of LiteHST in the distance bound. Based on this new index, we also design optimization techniques that heavily reduce the number of distance computations and hence save running time. Finally, extensive experiments demonstrate that our solution outperforms the state-of-the-art in the query efficiency by a large margin.