Optimal Dimensions Research Articles

Optimal dimensioning of renewable energy generation and storage systems

Optimal dimensioning of renewable energy generation and storage systems

Optimisation of Box Girder Design of Overhead Cranes

The subject of the research presented in this article is the dimensional optimisation of the box structure of the main girder for overhead cranes, with the aim to reduce the main girder’s own weight. Described are the applied optimisation methods and also the problems arising during the optimisation process. Loss of shape stability is analysed in more detail, as well as the methods determined for a constructional proposal of the girder cross-section. The developed optimisation procedure is a combination of the calculations, performed in the MS Excel environment, with the simulation of a parametrically modelled girder realised using Dassault Systèmes SolidWorks software v. 2023. Using the example of optimisation of an overhead crane girder with a lifting capacity of 50 tons, the effectiveness of the proposed optimisation procedure is declared, where the existing overhead crane box girder is compared with the optimised cross-section of the box girder.

Optimism and perseverance: Examining interrelations with test anxiety and academic achievement

Over the past two decades, there has been increasing attention in research into associations between student well-being and academic achievement. Notably, dimensions of perseverance and optimism have been found to influence students positively. However, results pertaining to the joint contribution of optimism and perseverance to academic achievement have rarely been examined. Furthermore, few studies have investigated these key constructs among student samples outside of the Western context, limiting our understanding of the nature of their relationships among these adolescents. In the present study, we posit that test anxiety is a pathway through which perseverance and optimism impacts academic achievement. Accordingly, the research aims to investigate the relationship between perseverance, optimism, test anxiety, and academic achievement. Additionally, we examine for gender differences in perseverance, optimism, and test anxiety. The participants were 2017 secondary one students (763 boys, 1254 girls). Results indicated that a) optimism predicted academic achievement via a direct path and a mediated path through test anxiety, b) perseverance predicted academic achievement solely through a direct pathway, and c) boys indicated a significantly higher value of optimism and a lower level of cognitive test anxiety when compared to girls. This study highlights the importance of optimism and perseverance for students. A key implication to education is that it may be essential to implement optimism interventions paired with perseverance interventions to achieve more effective results. Moreover, given the mediational role test anxiety plays, increasing optimism may also lead to reduced test anxiety and thus better academic performance.

Lubrication, Friction and Wear Characteristics of Textured Surface Slipper Pairs in Axial Piston Pumps

The study investigates the impact of textured surface parameters and pump operating parameters on the friction performance of slipper pairs in axial piston pumps. The orthogonal experimental scheme was developed, and the influence of several factors was explored, such as rotational speed, area ratio, micro-pit shape, diameter, depth-to-diameter ratio and film thickness. Optimal dimension combinations of the micro-pit were identified by numerical simulation and standard pin–disk friction experiment. In the pin–disk friction pair test, the friction coefficient of the textured surface compared to the smooth surface showed a maximum average friction reduction rate of 26.974%. Under various pump pressures (4, 8, 12 MPa) and pump displacements (10, 20, 35 L/min), the friction reduction rates of the textured surface slipper pairs (texture diameter 500 µm, depth 250 µm, area ratio 20%) ranged from 0.78% to 18.13%. The study underscores the importance of surface texture in enhancing the operational efficiency and reliability of axial piston pumps, offering valuable insights for the design and maintenance of hydraulic pumps.

Numerical studies on structure optimization and flow characteristics of a hydrogen recirculation ejector under multiple load conditions

Ejectors are ideal hydrogen recirculation devices for PEMFC systems but typically exhibit poor recirculation performance at low loads. For purpose of extending ejector's operating range in vehicular applications, the entraining performance across multiple loads should be focused. In this study, A 40 kW ejector was designed with a 1D theory and optimized using a 2D CFD model. Simulations were conducted to study the influence of various diameter and axial dimensions on the ejector's performance and flow characteristics. The results reveal that as the load increases from 20% to 100%, the suboptimal diffuser exit diameter decreases from 2.5 to 1.5 times the mixing chamber diameter, the optimal nozzle exit position increases from 0.59 to 0.63 times, and the optimal mixing chamber length increases from 2.2 to 4.3 times. A convergent nozzle with a short throat significantly improves low-load entraining performance and achieves the highest performance growth in the variable-nozzle design. Additionally, an optimization method for axial dimensions based on the proposed parameter flow uniformity was established, allowing for precise determination of the optimal dimensions through a few simulations.

Solving higher dimensional expensive black box global optimization problems using sparse directional search on surrogates

AbstractWe present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationally expensive objective functions that can be multimodal. DOSS introduces three new techniques not previously used in earlier RBF algorithms, including using a combination of the coordinates knowledge level, fewer initial sampling points, and the surrogate’s gradient information. Numerical results on a test set including 14 test problems with 36, 48, and 60 dimensions show that DOSS outperforms two recently published algorithms RBFOpt and TuRBO, and earlier RBF algorithms such as DYCORS. TuRBO is a Gaussian process based optimization algorithm, which outperformed earlier state-of-the-art methods. DOSS algorithm also has a good performance on real-world optimization problems, including robot pushing and rover trajectory planning problems. Almost sure convergence for the DOSS algorithm is also proven in this paper. An implementation of DOSS is available at https://doi.org/10.5281/zenodo.13731558.

Multi-objective ergonomics design model optimization for micro electric cars via response surface methodology

Despite advancements in ergonomic comfort assessments in automotive design, optimizing seat dimensions within the constrained spaces of micro-electric cars presents a substantial challenge. In this study, response surface methodology (RSM) is utilized for the ergonomics design of a micro electric car in the conceptual design phase. Specifically, five critical seat dimensions are analyzed: Seatback Angle, Seat Base Angle, Steering Wheel Height from Car Base, Distance from Seat Base to Pedals, and Distance from Seat Base to Steering Wheel. The analysis took place using a 2-level full factorial design L32 orthogonal array. The optimal dimensions are determined using RSM, such as the mean comfort level and signal-to-noise ratio are maximized, and the standard deviation for multiple drivers is minimized. Three regression models are formulated for the three responses, and their adequacy is checked using different methods. The optimal dimensions are obtained and verified through digital human modeling simulation. This research offers practical guidelines for the ergonomic seating design in micro-electric vehicles, enhancing comfort without compromising space efficiency.

Multi-input Fourier neural network and its sparrow search optimization

In engineering applications, the back-propagation (BP) neural network often encounters many limitations due to its slow convergence and high noise sensitivity, and meanwhile the reported Fourier neural networks have no ability to extract the features of multi-attribute input data. Hereby, This work proposes a gradient descent-based multi-input Fourier neural network after integrating the multi-layer perceptron with an overlapping Fourier neural network. Thereafter, related to the difficulty of deciding the global optimal parameter settings, an improved sparrow search algorithm is developed to optimize the parameter settings and solve high dimensional function optimization problems, after the Cat chaotic map and the mechanisms of population-size adjustment and parameter adaptiveness are designed to promote the sparrow search algorithm's ability to balance global exploration and local exploitation. The theoretical analysis shows that the improved algorithm's computational complexity is decided by its population size and the optimization problem's dimension. Numerically comparative experiments have validated that not only the acquired Fourier neural network can effectively extract the features of multi-attribute data with strong generalization ability, but also the improved algorithm has significant advantages in coping with high dimensional function optimization problems.

Machine learning-assisted SCAPS device simulation for photovoltaic parameters prediction of [formula omitted] perovskite solar cells

Perovskite solar cells (PSCs) have garnered significant research attention because of their remarkable surge in power conversion efficiency (PCE) surpassing 26% within a short timeframe. However, the widespread adoption of these highly efficient PSCs is hampered by the inherent toxicity associated with lead (Pb)-based perovskites, which currently dominate the field. Researchers have explored alternative cations like Ge2+ and Sn2+ Owing to their identical oxidation states to Pb2+, paving the way for lead-free PSC development. By examining the critical factors influencing CsSnI3 thin film solar cells’ performance as well as their interdependencies, this work seeks to improve the efficiency of these devices. To do so, machine learning algorithms (ML) are employed to figure out the critical factors influencing the completion of CsSnI3 solar cells. A new dataset of 7870 data points is generated through simulations with SCAPS-1D to effectively depict the perovskite solar cell. Five ML regression algorithms are thereafter utilized to predict the photovoltaic parameters of solar cells, such as the fill factor (FF), open circuit voltage (VOC), short circuit current (JSC), and PCE. The study shows that random forest (RF) and decision tree (DT) are the best performing algorithms, among which DT produces high accuracy and low error for predicting the PCE with a coefficient of determination R2 of 0.99, Pearson’s coefficient (Rvalue) of 0.99, Spearman’s correlation coefficient (ρ) of 0.99 and root mean square error of 0.08 on the testing set. Finally, this study offers valuable guidance for further experiment optimization by shedding light on the optimal device dimensions and essential components.

An analytical approach to optimization of isotope production by bremsstrahlung radiation

Based on analytical description of isotope production by bremsstrahlung (X-ray) radiation, an algorithm is proposed for calculating the optimal dimensions of a cylindrical target of given mass positioned at a given distance from a bremsstrahlung converter to ensure the maximum yield of the isotope product. The expressions are derived for the total activity and its distribution along the target axis. A technique of γ-spectrometric measuring the activity of a thick production target is proposed. The novel approach is validated by the 100Mo(γ,n)99Mo reaction induced in a natural molybdenum target by mass in the range 10-100g with the X-ray photons at an end-point energy of 40 MeV. The analytical predictions are in good agreement with the results of Monte-Carlo simulations and experiment.

Evaluation of four-bar mechanisms for bicycle pedals

As the Global Warming destroys gradually the life on the earth, vehicles driven by clean energy are of importance. Therefore, in this paper, mechanisms for bicycle drivetrain are scrutinized starting from its initial invention to attract attentions of the people in the field of mechanism. Some recent health problems caused by design of modern bicycles are highlighted. Optional several mechanisms such as crank rocker, double crank, double rocker, slider crank, and inverted slider crank for the drivetrain of bicycle are evaluated. Each of them was assessed in the perspective of applicability by using shelf-ready products. A double-rocker mechanism is schematically designed by using GeoGebra. An analysis for the geometric constraints of the design and torque are presented. Finally, the double rocker was concluded to design a pedal drivetrain. An optimization was carried out by using Microsoft Excel. The optimal dimensions were delivered. The maximum torque 90 Nm. The maximum energy can be produced is 600W.

FACIAL PARAMETERS AS SUPPLEMENTARY LANDMARKS FOR DETERMINATION OFVERTICAL DIMENSIONS OF OCCLUSION: DISTANCE ECTOKANTHION - CHEILION

Background: The successful recovery of the functions and the aesthetics appearance of the edentulous patients often is a kind of challenge. The use of correlation between facial parameters could facilitate the determination of the vertical occlusion dimensions. The purpouse of this research was to investigate the interrelation between vertical dimensions of occlusion and distance external ocular angle (Ectokanthion) - labial commissure (Cheilion) among persons with natural dentition. Material and Methods: In the study were examined 120 persons from Bulgarian origin (59 male and 61 female), aged 20 to 25. The head of each and every participant was hold up into position so that the occlusal plane is parallel to the horisontal plane. The teeth were closed in central occlusion. Four reference points were marked: Ectokanthion, Cheilion, Subnasale and Gnathion. The researched distances were measured with the assistance of Vernier digital caliper. Statistical analysis of the obtained data was performed. Results: Moderate correlation was found between the distance Ectokanthion-Cheilion and the vertical occlusion dimension. The dataanalysisshows that coefficients of correlation are: 0.5681 for male and 0.5481 for female. Conclusions: It was found moderate correlation between the distance Ectokanthion-Cheilion and vertical dimensions of occlusion defined between point Subnasale and point Gnathion. The use of distance Ectokanthion-Cheilion as basic point of reference is not reliable, but it could be applied combined with other clinical methods or as part of mathematical models for determination of optimal vertical occlusion dimensions during prosthetic treatment of edentulous patients.

Inhibitory impact of a mesoporous silica nanoparticle-based drug delivery system on Porphyromonas gingivalis-induced bone resorption

Controlling and reducing plaque formation plays a pivotal role in preventing and treating periodontal disease, often utilizing antibacterial drugs to enhance therapeutic outcomes. Mesoporous silica nanoparticles (MSN), an FDA-approved inorganic nanomaterial, possess robust physical and chemical properties, such as adjustable pore size and pore capacity, easy surface modification, and high biosafety. Numerous studies have exploited MSN to regulate drug release and facilitate targeted delivery. This study aimed to synthesize an MSN-tetracycline (MSN-TC) complex and investigate its inhibitory potential on Porphyromonas gingivalis (P. gingivalis)-induced bone resorption. The antibacterial efficacy of MSN-TC was evaluated through bacterial culture experiments. A P. gingivalis-induced bone resorption model was constructed by subcutaneously injecting P. gingivalis around the cranial bone of rats. Micro-computed tomography was employed to assess the inhibitory impact of MSN and MSN-TC on bone resorption. Furthermore, the influence of MSN and MSN-TC on osteoclast differentiation was examined in vitro. The MSN exhibited optimal pore size and particle dimensions for effective loading and gradual release of TC. MSN-TC demonstrated significant bacteriostatic activity against P. gingivalis. MSN-TC-treated rats showed significantly reduced cranial bone tissue destruction compared to MSN or TC-treated rats. Additionally, both MSN and MSN-TC exhibited inhibitory effects on the receptor activator of nuclear factor kappa-Β ligand-mediated osteoclast differentiation. The MSN-TC complex synthesized in this study demonstrated dual efficacy by exerting antibacterial effects on P. gingivalis and by resisting osteoclast differentiation, thereby mitigating bone resorption induced by P. gingivalis.Graphical

Fractal-based supervised approach for dimensionality reduction of hyperspectral images

Dimensionality reduction is one of the most challenging and crucial issues apart from data mining, security, and scalability, which have retained much traction due to the ever-growing need to analyze the large volumes of data generated daily. Fractal Dimension (FD) has been successfully used to characterize data sets and has found relevant applications in dimension reduction. This paper presents an application of the FD Reduction (FDR) Algorithm on geospatial hyperspectral data, examining its usefulness for data sets with a relatively high embedding dimension. We examine the algorithm at two levels. First is the conventional FDR approach (unsupervised) at the image level. Alternatively, we propose a pixel-level supervised approach for band reduction based on time-series complexity analysis. Techniques for determining an optimal intrinsic dimension for the dataset using these two techniques are examined. We also develop a parallel GPU-based implementation for the unsupervised image-level FDR algorithm, reducing the run-time by nearly 10 times. Furthermore, both approaches use a support vector machine classifier to compare the classification performance of the original and reduced image obtained.

Uniformity enhancement of flow field through optimizing nozzle structure for jet electrochemical machining

To improve the processing quality during the Jet electrochemical machining (JECM) process, a single-orifice nozzle and dual-orifice nozzles with different inclination angles were designed, where the fluid dynamic behavior was numerically simulated. Then, through the L49 (74) orthogonal design and error analysis, the influence of nozzle dimension on the flow field characteristics was analyzed. Finally, the nozzle with optimal structure and dimension was obtained and validated by experiment. Results show that the dual-orifice nozzles had better flow field uniformity than the single-orifice nozzle. In the dual-orifice nozzle, the increase of the inlet inclination angle can improve the flow field uniformity. The influence of the nozzle dimension parameters on the flow field uniformity is prioritized as follows: the outlet slit width > upper chamber diameter > the height of the inflow chamber > the height of the transition chamber. After optimization, the optimal dimension parameters were obtained under the upper chamber diameter of 16 mm, the jet outlet slit width of 0.4 mm, the height of the inflow chamber of 7 mm, and the height of the transition chamber of 23 mm. The flow velocity dispersions under the pressures of 0.3, 0.4, and 0.5 MPa can be reduced by 25.41 %, 25.17 %, and 28.16 %. The findings can help understand the flow behavior and guide the structure optimization for improving JECM quality.

Impact of trapezoidal ribs on the performance of solar air collector: A numerical solution with optimized rib dimensions for better performance

In the present study, a 2D numerical analysis of the solar air collector (SAC) of an indirect solar dryer having trapezoidal corrugations on the absorber plate was performed. Corrugation pitch, p (twelve values ranged from 20 to 160 mm) and height, e (six values ranged from 1 to 10 mm) were varied and analyzed for six values of Reynolds numbers (Re). The output characteristics such as Nusselt number (Nu), friction factor (f) and thermo-hydraulic performance index (Thp) were calculated for different p, e and Re. The total work was categorized into two parts (part-I for optimizing p and part-II for optimizing e). 18 domains (twelve for part-I and six for part-II simulations) were generated and 108 simulations were executed to find the optimum dimensions (p, e and corrugation angle, α) of the corrugation. ANSYS Fluent-v15 was used to solve the problem. The maximum Nu for the corrugated sheet was 2.663 times greater than the flat absorber plate. The maximum Thp range was from 1.435 to 1.699 and obtained at the optimal values of p = 140 mm, e = 4 mm and α = 38.66° The numerical results were compared with the existing literature.

Flexible droplet microfluidic devices for tuneable droplet generation

Droplet microfluidics is a promising technology for applications that require precise handling of minute fluid volumes. This technology has broad applications in the pharmaceutical industry, food and beverage, and material synthesis. Droplet size and frequency are the two key parameters in these applications. Current droplet microfluidic devices are rigid platforms where the geometry and dimensions of microchannels are fixed after the device fabrication. This poses a significant limitation when adjusting the droplet generation characteristics. Repetitive design, fabrication, and testing are often needed to achieve the optimal microchannel dimensions. To overcome this bottleneck, we develop a proof-of-concept stretchable microfluidic device that can control droplet size and generation frequency by precisely controlling the microchannel dimensions in real-time. Theoretical analysis, numerical modelling, and experimental characterisation were conducted to study the influence of device lateral stretching on these characteristics. We found that the lateral stretching of the device increased the droplet diameter and spacing but reduced the droplet generation frequency. Droplet diameter and spacing increased by ∼20 %, and droplet frequency decreased by ∼45 % when the device was strained up to 25 %. We believe this innovative, flexible droplet microfluidic platform will provide an alternative way to precisely control the droplet formation by modifying the channel dimensions on-site and in real-time.

Carbon Dioxide Capture on Oxygen- and Nitrogen-Containing Carbon Quantum Dots.

To address global climate change challenges, an effective strategy involves capturing CO2 directly at its source using a sustainable, low-cost adsorbent. Carbon quantum dots (CQDs), derived from lignin, are employed to modify the internal surface of an activated carbon adsorbent, enabling selective adsorption based on electrostatic interactions. By manipulating charge distribution on CQDs through either doping (nitrogen) or functionalization (amine, carboxyl, or hydroxyl groups), the study confirms, through classical molecular dynamics simulations, the potential to adjust binding strength, adsorption capacity, and selectivity for CO2 over N2 and O2. For simulations with a single component gas, maximum selectivities of 3.6 and 6.7 are shown for CO2/N2 and CO2/O2, respectively, at 300 K. Simulations containing a wet flue gas indicate that the presence of water increases the CO2/N2 and CO2/O2 selectivities. The highest CO2/H2O selectivity obtained from a CQD/graphite system is 4.3. A comparison of graphite and lignin-based carbon composite (LBCC) substrates demonstrated that LBCC has enhanced adsorptive capacity. The roughness of the LBCC substrate prevents the diffusion of the CQD on the surface. This computational study takes another step toward identifying optimal CQD atomic architecture, dimensions, doping, and functionalization for a large-scale CQD/AC adsorbent solution for CO2 capture.

A comprehensive study on active mixer performance using liquid metal droplets: an experimental approach

In this experimental study, we delve into the performance of an active mixer that utilizes liquid metal droplets. Our mixer configuration follows an innovative Y-type design, where two liquid metal-based pumping mechanisms operate within the minichannel inlet branches. By applying alternating current voltage to both sides of the liquid metal, we induce a surface tension gradient, propelling the fluid from the reservoirs toward the minichannel. Notably, the two entering liquids are distinctly colored, allowing us to quantify mixing using a Mixing Index (MI) definition and image processing techniques. Our investigation centers on four key variables governing mixer performance: applied voltage, liquid metal droplet diameter, input angle between the branches, and minichannel width. Given the experimental nature of our research, we employ a central composite design method within the framework of design of experiments (DoE). From our obtained experimental results, we establish correlations between MI and the other variables. Our examination of the four mentioned parameters revealed that liquid metal droplet diameter exerts the most significant influence on the mixing index. Following this, in descending order of impact, we find applied voltage, input angle, and channel width. In the optimal dimensions of our mixer, we achieve a remarkable maximum MI of 0.867.

Novel Design and Performance Analysis of 1R1T Remote Center-of-Motion Mechanisms With Partially Decoupled T- and R-Motions

Abstract Remote center-of-motion (RCM) mechanisms provide a way for surgical instruments to pass through a remote center (e.g., skin incision) under geometrical constraints, facilitating safer operations in minimally invasive surgery (MIS). One rotation and one translation (1R1T, pitch and insertion) are the basic requirements for RCM mechanisms. To make the structure simpler and control easier, a novel concept of 1R1T RCM mechanisms with partially decoupled motions, inspired by the double-parallelogram 1R RCM mechanisms, is proposed in this article, by investigating and proving its motion combination principle based on the screw theory. New evolution procedures based on the configuration evolution method have been derived to design 1R1T RCM mechanisms based on two approaches of inserting the T-motion in an original 1R RCM mechanism, resulting in two types of 1R1T RCM mechanisms with partially decoupled motions and base-locating actuators. The kinematic models of one typical proposed mechanism (including the forward and inverse kinematics) and its Jacobian matrix are derived. The performance analysis is presented, including RCM validation, velocity, singularity, and workspace analysis. Then, the dimensional optimization based on the discrete solution method is derived. Finally, a prototype of the proposed mechanism is presented with preliminary experiments performed to verify the feasibility of the synthesized RCM mechanisms. The results show that the RCM mechanism performs the 1R1T partially decoupled motion, and it can be used as the basic element of an active manipulator of an MIS robot.