Optimization Of Design Parameters Research Articles

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.

Optimization of pellet design parameters to achieve deep fueling depth in EAST plasma with PAM code

Abstract Deep pellet fueling depth is necessary to achieve a high-density high confinement operation and to conduct some pellet-related researches in current devices, such as trigger of internal transport barrier, and to achieve high fusion power and tritium burn-up fraction in future fusion devices. The newly developed PAM code which can make a fast evaluation on pellet ablation and deposition is applied to optimize injection parameters to achieve deep pellet fueling. Systematic scans on pellet injection parameters including pellet injection position, injection angle, size and speed are performed for optimization purpose, while at the same time demonstrating flexibility and time efficiency of the PAM code. Dependences of the pellet fueling depth on these injection parameters are revealed by simulation results and analyzed. Simulation results indicate that pellet penetration contributes more to the deep pellet deposition than the ∇B-induced plasmoid drifts in low temperature plasmas, while deep pellet fueling in reactor relevant high temperature plasmas has to rely on plasmoid drifts. Though a shallow penetration is expected in high temperature plasmas, the ∇B-induced plasmoid drift is expected to be larger than that in relatively low temperature plasmas.

Quantile-based sequential optimization and reliability assessment method under random and interval hybrid uncertainty

Under random and interval hybrid uncertainties, solving hybrid reliability based design optimization (HRBDO) can acquire an optimal balance between structural performance and reliability. Since solving HRBDO includes a triple nested framework involving minimum analysis of performance function (PF), failure probability constraint analysis and design parameter optimization, the computational complexity of HRBDO is high, especially for dealing with complex structures. Therefore, a quantile-based sequential optimization and reliability assessment method (QSORA) is proposed for reducing the computational complexity of HRBDO. In the proposed QSORA for HRBDO, failure probability constraint is firstly transformed into minimum PF (MPF) quantile one corresponding to target failure probability. Then, approximating the difference between PF and its target quantile at current iteration by that at previous one, the failure probability constraint analysis is decoupled from the design parameter optimization. Moreover, by approximating the minimum point of the PF with respect to the interval input in the current iteration by that in the previous one, the minimum analysis of PF is separated from the design parameter optimization. By the separation of minimum analysis and failure probability constraint analysis from the design parameter optimization in the proposed QSORA, the triple nested framework of HRBDO is decoupled sequentially as the deterministic design optimization, the minimum analysis of the PF and the target MPF quantile estimation, and this way of reconstructing the HRBDO from the triple nested framework to three single-loop frameworks can significantly enhance the efficiency of solving HRBDO. Furthermore, the MPF quantile at the current design parameter is estimated by stochastic collocation based statistical moment method, in which the stochastic collocation method is employed to efficiently estimate the MPF moment to approximate the probability density function of MPF. The efficiency and accuracy of the QSORA are validated by four numerical and engineering examples finally.

Composite effects of residual stress and hardness gradient on contact fatigue performance of rough tooth surfaces and optimization design of detailed characteristic parameters

Residual stress and hardness gradient significantly affect the contact fatigue failure process of rough tooth surfaces, and the meticulous design of their characteristic parameters can help improve the fatigue resistance of gears. In this paper, an efficient computational method for rough tooth surface contact is established based on the reconstruction modeling of asperities and mixed lubrication analysis. By comprehensively considering the effects of residual stress and hardness gradient, the Dangvan multiaxial fatigue criterion is adopted to predict and analyze the contact fatigue life of the gear surface. The correlation between the surface residual stress, maximum residual stress depth, surface hardness, and effective hardening layer depth, among other characteristic parameters, and the contact fatigue performance of tooth surfaces is established. The results show that: (1) Increasing the magnitude of surface residual compressive stress and surface hardness can significantly reduce the risk of fatigue failure in the near-surface layer of the gear. To achieve the same fatigue performance, there is a linear negative correlation between the design values of the two parameters. (2) Affected by the double stress peaks in the rough tooth surface stress field, the maximum residual stress depth significantly affects the depth at which the highest failure risk occurs. With changes in roughness, the optimal design value of the maximum residual compressive stress depth changes from about 0.5 times the Hertzian contact half-width depth in the subsurface layer (Sa < 0.2 μm) to about 15 μm in the near-surface layer (Sa > 0.4 μm). (3) Considering both process cost and gear surface fatigue resistance, the design threshold for the effective hardening layer depth should be greater than the Hertzian contact half-width. This paper establishes a composite correlation between the meticulous characteristic parameters of residual stress, hardness gradient, and the contact fatigue performance of tooth surfaces, providing theoretical support for optimal design of tooth surface anti-fatigue performance.

Nonlinear impedance characteristics of a gas hydraulic energy absorber and performance prediction for railway transportation of valuable goods

It is meaningful to investigate the nonlinear impedance characteristics of gas hydraulic energy absorbers (GHEAs) and their potential application in modern railway transportation of valuable goods. Fluid-mechanics-based full parametric modelling of a GHEA used in a high-speed train is performed, both simulations with the developed mathematic model and physical experiments are carried out on the static and dynamic characteristics of the GHEA. The agreement between the results of the simulation and the experiment with tolerable deviations validates the nonlinear mathematic model. Longitudinal train dynamics (LTD) modelling and simulation of a Chinese rapid freight train with the validated GHEA model or the conventional MT-2 friction-type energy absorber model is finally conducted. The results show that when the train uses GHEAs instead of MT-2 energy absorbers, the coupler forces and vehicle accelerations of the train are significantly reduced, and the vehicles operate more steadily in situations such as train startup, emergency braking and shunting operations; thus, gas hydraulic energy absorber is protective and would be alternative for equipping rapid freight trains for the transportation of valuable goods. The obtained nonlinear mathematic model in terms of the structural, oil and gas property parameters of the GHEA is useful and meaningful in further parameter design optimization of GHEAs and optimal specification of GHEAs for rapid freight trains.

Parameters design optimization of grading ring based on electric field analysis through response surface methodology

This research delves into the primary cause of the ageing of insulated cross-arms in the field, which is attributed to the high electric stress experienced at the end fittings. To address this issue, a novel technique is introduced to meticulously analyse the influence of Grading rings on the electric field (E-field) stress, with the goal of regulating it within the prescribed limits on the surface of the insulated cross-arm. By employing advanced methodologies such as finite element analysis and design of experiment approaches, the impact of various geometrical characteristics of Grading rings on the E-field is comprehensively investigated. Through the application of the analysis of variance, the primary and interaction effects of these factors are carefully examined. Additionally, leveraging the power of the response surface technique, an optimal link between the E-field and the geometrical parameters is modelled and optimized. The results of this comprehensive study unequivocally reveal the profound influence of the geometrical parameters of Grading rings on the E-field. Furthermore, diligent efforts are made to estimate the Grading ring parameters that will effectively regulate the E-field within the suggested maximum threshold based on the derived model. The proposed technique not only significantly reduces the computational analysis time but also serves as an efficient and invaluable tool for investigating the intricate geometry of Grading rings without imposing any additional computing burden.According to the findings, the E-field is most impacted by the factors H, D, and T to optimise the shape of grading rings used in insulated cross-arms. The main objective was achieved by reducing the maximum E-field value by 65% compared to the E-field without a grading ring, which has been accomplished. The suggested technique provides a quick and easy way to analyse and improve the intricate geometry of grading rings. As a result, it lowers the cost and time related to computational analysis without needing an excessive amount of computing work to do this.

Theoretical modeling and experimental investigation of in-phase resonant MEMS mirrors with cascaded structures

This paper proposes an efficient nonlinear one-dimensional (1D) compact mass-damper-spring model to predict the dynamic response of electrostatic resonant micro-electro-mechanical system (MEMS) mirrors with cascaded structures. The time-dependent damping moment due to viscous shear and pressure drag is computed using semi-empirical analytical equations for comb-drive structures and device frames. Nonlinear electrostatic force induced by the comb drives is efficiently acquired based on the hybrid method. The optimized device is fabricated using MEMS fabrication processes based on a 4-inch silicon-on-insulator wafer. The proposed compact model with the measured key parameters from the fabricated device shows excellent capability to accurately predict nonlinear dynamic responses of the fabricated device, including parametric excitation and hysteretic frequency response, with an average error of less than 5%. In particular, our 1D model is three orders of magnitude faster than the conventional finite element method model (0.8 s versus 1 h), enabling efficient system-level optimization of the critical design parameters. Based on the parametric study, electrode gap distance and torsion spring width are found to be two critical design parameters and dimensional analysis is conducted for design optimization with scan angle enhancing from 16.8° to 24° compared with the first design.

Hydraulic-mechanical-electrical coupled model framework of variable-speed pumped storage system: Measurement verification and accuracy analysis

With the large-scale operation of wind and solar energy, the demand for energy storage in power grids has increased sharply. As a reliable means of long-term energy storage, the variable-speed pumped-storage power station (VSPSU) is a new development direction for pumped storage that has attracted increasing attention owing to its advantages of more rapid and flexible action. However, accurately predicting the transient process of a VSPSU is a key challenge in its design and operation. The inconsistent time step required for the fine simulation of the hydraulic-machine subsystem (millisecond level) and the electrical subsystem (microsecond level) is the main challenge that hinders the coordination of system simulation efficiency and accuracy and restricts further development of the entire coupling system simulation. Simultaneously, a coupling refinement model verified by multi-condition measurements has not been reported. This paper proposes a VSPSU hydraulic-mechanical-electrical coupling model framework that has been verified experimentally. The innovation of the model framework lies in the realization of the fine solution of the hydraulic and electrical systems through the coupling strategy of the variable simulation step size rather than using model simplification. Based on a real micro-VSPSU system, we conducted experiments under various operating conditions and compared the measurements with the numerical simulation results. The results showed that in the generation mode, the maximum simulation error of the proposed coupling model framework on the hydraulic-mechanical side was 2.29 %, and the maximum simulation error on the electrical side was 9.59 %. Finally, we quantified the contribution of key parameters in the model framework to the dynamic response process and clarified the influence of physical parameters on the transient process in the VSPSU. Compared with conventional models, the advantage of the proposed coupling framework is that it can realize the coupling of a hydraulic-mechanical-electrical system without simplification, which has been verified by measuring multiple working conditions. This framework provides a reliable tool for the parameter design and operation optimization of VSPSU.

Hybrid intelligent algorithm and CAD secondary development technology in parameter optimization design of transmission gear set

Hybrid intelligent algorithm and CAD secondary development technology in parameter optimization design of transmission gear set

Optimisation of design parameters for a novel two-piston ionic compressor applied in hydrogen storage

The ionic compressor is an advanced and prospective compression technology for hydrogen storage. However, a complex wave phenomenon was observed inside the compression chamber due to the fluid flow vortex. This complex gas-fluid interaction can be intervened by a new mechanical design of the motion piston. In this paper, the thermodynamic performance of a novel homocentric two-piston ionic compressor was carried out by mathematical modelling of one compression cycle. The optimisation of the design parameters of the compressor proposed was conducted by the Taguchi method. This study found that the piston diameter ratio was the dominating factor accounting for more than 80 % of the contribution to the mass delivered, power consumption and specific energy consumption. The optimal design parameters were obtained as 0.7 for the piston diameter ratio, 0.5 for the stroke diameter ratio of Piston A and 0.1 for the trajectory parameter. Under the optimal design parameters, the specific energy consumption was obtained as 1563.38 kJ/kg.

A Parameter Optimization Design Method for Single-Phase Dual Active Bridge AC-DC Converter

The single-stage dual active bridge (DAB) AC-DC converter has the advantages of high power density, low cost, and simple control; it has a broad potential for application in the field of onboard chargers (OBC). However, the lack of fast and accurate quantitative parameter optimization design methods in single-stage DAB AC-DC converters limits the overall efficiency of the converter. Based on the above problem, in order to improve the overall operating efficiency of the converter by optimizing the parameter transformer ratio and power inductance, this paper proposes a parameter design method considering a multi-timescale strategy by combining the steady-state analysis model of the converter in the line cycle and switching cycle and step-by-step reducing its design space through the constraints on the parameters. The first step is to obtain a safe design space for the parameters under the converter’s transmitted power and current stress constraints. The second step obtains the optimization design space of the parameters under the optimization of conduction loss and switching loss of the converter. Finally, the optimal parameters are determined by the loss analysis model. The proposed parameter optimization method entirely takes into account the steady-state characteristics of the DAB AC-DC converter during the line cycle, and the step-by-step constraints greatly accelerate the parameter design process. In addition, the proposed parameter optimization design method applies to all types of single-stage DAB AC-DC converters, which can be well applied to engineering practice.

Precision Optimization Analysis of Online Measurement System for Laser Melting Pool Morphology in Additive Manufacturing Process

Selective Laser Melting (SLM), as the most precise and highest quality processing technology in additive manufacturing, is widely used in fields such as aerospace. Online measurement of the laser melting pool, the basic unit of the SLM process, reflects the precision and process stability of SLM. The accuracy of the online measurement system for laser melting pool morphology, which is based on the principle of visual measurement, is crucial for accurate evaluation of the laser melting pool morphology. This paper firstly conducts research on visual calibration technology for the online measurement system of laser melting pool morphology, achieving precise positioning of the visual system and effectively calculating target-related feature information, which is then converted into corresponding digital information for further processing and analysis. Secondly, the paper completes the system error analysis and parameter optimization design, effectively improving the accuracy of the visual measurement system. Finally, theoptimized online measurement system is verified for accuracy and evaluated for precision, proving the feasibility and effectiveness of the methods presented in this paper.

A review on simulation based multi-objective optimization of space layout design parameters on building energy performance

Improving the energy performance of buildings is crucial for environmental protection, energy savings, and a better living environment. The growing emphasis on sustainable building practices has led to an increased focus on optimizing space layout design parameters to enhance building energy performance. This review explores the application of simulation-based multi-objective optimization techniques in the context of studying the impact of space layout design on building energy efficiency. The integration of advanced simulation tools with optimization algorithms allows for a comprehensive analysis of multiple conflicting objectives like energy performance, user comfort as well as cost factor. The review begins by outlining the key parameters influencing building energy performance, including spatial configurations, orientation, and space perimeter variables. Subsequently, it delves into the various simulation tools employed to model the complex interactions between these parameters and their effects on energy performance. The integration of energy simulation software is highlighted as a crucial step towards achieving accurate and realistic assessments. In summary, this review delivers a comprehensive overview of the state-of-the-art methods in simulation-based multi-objective optimization for studying space layout design parameters and their impact on building energy performance, offering insights for researchers, practitioners, and policymakers in the field of sustainable architecture. There is a requirement for a comprehensive multi-objective framework for complex structures in the investigation of building energy performance giving more focus on reducing the cooling load and optimization of space layout along with envelope parameters.

Optimization of design parameters and improvement of human comfort conditions in an upper-limb exosuit for assistance

Optimization of design parameters and improvement of human comfort conditions in an upper-limb exosuit for assistance

Cloud Based Ccmc Model for Application of Genetic Algorithm Based on Cloud Computing in Art Product Design

Art design has entered a new era with the integration of cloud computing technology, revolutionizing the creative process and expanding artistic possibilities. By leveraging the vast computational power and storage capabilities of cloud platforms, artists can explore innovative techniques, collaborate with peers remotely, and access a wealth of digital resources from anywhere in the world. Cloud computing enables artists to work with large-scale datasets, create complex visualizations, and render high-resolution artworks with ease. Moreover, cloud-based tools and applications offer flexibility and scalability, allowing artists to experiment freely and iterate rapidly. Whether creating digital paintings, 3D animations, or interactive installations, cloud computing empowers artists to push the boundaries of their creativity and bring their visions to life in ways previously unimaginable. This paper presents an innovative application of genetic algorithm (GA) leveraging cloud computing technology for art product design, with a focus on the Centralized Clustering Middle Chain (CCMC) framework. By harnessing the computational power and scalability of cloud platforms, GA facilitates the optimization of design parameters to generate novel and aesthetically pleasing art products. The CCMC framework streamlines the design process by centralizing data clustering and analysis, enabling efficient exploration of design space and identification of optimal solutions. Through simulated experiments and empirical evaluations, the effectiveness of the GA-based approach in art product design is assessed. Results demonstrate significant improvements in design quality and efficiency, with the GA-enabled cloud computing solution achieving a 40% reduction in design iteration time and a 30% increase in product innovation compared to traditional methods.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

Parametric design and multi-objective optimization of daylight performance in gallery skylight systems: A case study on the high museum expansion

In the realm of museum design, the meticulous planning of natural lighting plays a vital role in enhancing energy efficiency, improving the indoor atmosphere, and preserving and showcasing artifacts. A significant challenge is to ensure visitors' visual comfort while minimizing excessive natural light to prevent damage to exhibits. This study takes Renzo Piano's High Museum Expansion as a case study and employs the medium sensitivity exhibit standards from IESNA as the evaluation criteria. During the preliminary design phase, computer modeling was utilized, daylighting simulations were carried out using the Ladybug tool, and the Octopus genetic algorithm plugin was applied to investigate, optimize, and evaluate the performance of the point skylight system in museum interiors. The findings indicate that optimized point skylight systems not only block direct sunlight, reducing indoor glare, but also ensure that the Annual Cumulative Illuminance (ACI) on interior surfaces stays below 480,000 lx, while the annual effective Useful Daylight Illuminance (UDI) is increased to 75 %. In terms of design parameters, a skylight height of 0.6 to 0.7 m, combined with a shading panel to horizontal angle of 65 to 70 degrees, is found to be effective in enhancing indoor lighting conditions.

Parametric design optimization and thermodynamic analysis of plate fin heat exchanger for helium liquefaction system

The plate-fin heat exchanger is one of the most important components of helium liquefaction system as it recovers cold energy from helium gas in the cold box. Because of larger heat transfer surface area and compact geometrical configuration, plate-fin heat exchangers are widely used. The design of heat exchanger using helium as a cryogenic fluid needs high thermodynamic performance with minimum pressure drop which particularly requires the optimization of dimensional array and its geometrical configurations. In this regard, a parametric investigation was performed to compute the effective geometrical parameters and its effect on thermodynamic (effectiveness and log-mean temperature difference) performance has been discussed in detail. The present study aims to design optimization and thermodynamic performance evaluation of plate-fin type heat exchangers used in helium liquefaction systems. The developed helium liquefaction model has been simulated for the most critical liquefier with and without liquid nitrogen precooling, mixed mode, and refrigeration mode. Each component of the liquefaction model has been technically and thermodynamically analyzed and, in particular, four plate-fin heat exchangers have been considered and designed accurately using Aspen EDR®. Initially, the sensitivity analysis was carried out to characterize the most significant geometrical parameters (fin thickness, height, number and arrangement of layers, frequency, and fin type) and their effect on thermodynamic performance and pressure drop. After that, the artificial intelligence model was used to determine the optimal range of such geometrical variables in which the heat exchanger has maximum effectiveness and log mean temperature difference. Finally, the operating conditions obtained from cycle modeling and optimized geometrical datasets have been applied in commercial simulation software Aspen EDR® for plate-fin heat exchangers design. Finally, a comparative thermodynamic analysis has been carried out to determine the performance of all the designed heat exchangers operating at four different modes (boundary conditions).

A novel flat coil tube heat exchanger for metal hydride hydrogen storage reactors

Metal hydride (MH) based hydrogen storage is one of the potential methods for achieving higher volumetric storage densities. However, its poor thermal conductivity limits heat transfer during exothermic hydriding and endothermic dehydriding processes. Hence, heat transfer augmentation is essential in MH reactors to speed up the charging and discharging of hydrogen. The present study proposes a novel flat coil tube heat exchanger with a spiral fin for heat transfer augmentation. It offered superior performance than conventional single and double helical tube heat exchangers, taking 35.3 and 16.7% less time for 90% storage time. Further, optimization of design parameters (pitch and fin thickness) is carried out considering several key parameters such as weight ratio (WR), gravimetric exergy output rate (GEOR), and energy efficiency (EE). A pitch of 30 mm with a fin thickness of 0.25 mm is optimum according to a novel performance index. The influence of the operating parameters (H2 supply pressure and heat transfer fluid inlet temperature) on the absorption performance is investigated for the optimized design. Finally, a single desorption case is presented at 1 bar and 333 K to gain insights about desorption behavior.

Optimization of screw turbine design parameters to improve the power output and efficiency of micro-hydropower generation

Optimization of screw turbine design parameters to improve the power output and efficiency of micro-hydropower generation