Teerapong Poltue, Chao Zhang, Frédéric Demoly
et al.
Active composite (AC) plates, composed of active and passive materials, can undergo complex shape transformations when stimulated. Leveraging 4D printing—which combines additive manufacturing with stimuli‐responsive materials—digitally encoded design patterns offer flexibility in shape morphing. However, performing inverse design, i.e., determining the pattern to achieve a desired shape, remains challenging due to the vast design space. Recently, machine learning (ML) has been applied to inverse design tasks with promising results. Nevertheless, these approaches require large datasets, and even then, inverse design remains difficult, often demanding multiple strategies and trials to obtain optimal results. To address these challenges, this work introduces an iterative data curation strategy combined with transfer learning. This method ensures that newly curated data is nonredundant and distinct from existing datasets, reducing the required training data by a factor of eight while maintaining performance. Additionally, ML models are integrated with a genetic algorithm (ML‐GA) to further fine‐tune the generated design patterns. The results show that ML‐GA enhances accuracy in achieving the desired shape while reducing computational effort. This framework offers an efficient and scalable approach for inverse design, reducing data needs and improving performance, making it a valuable tool for AC plate design and 4D printing.
Computer engineering. Computer hardware, Control engineering systems. Automatic machinery (General)
Perovskite, oxide, organic, and dye-sensitised solar cells are studied from 2015 to 2025, and their current standing and future Mott-Schottky (MS) analysis in photovoltaic (PV) research are highlighted in this review. The incorporation of MS characterisation methodology with solar cell capacitance simulator one dimension (SCAPS-1D) simulations, ab-initio calculations, impedance spectroscopy, and nascent data-driven models is addressed. The MS approach will always be at the forefront in the extraction of the flat band potential, doping concentration, depletion region width, and built-in potential. This is the link between the energetics of the semiconductors and the charge transport of the solar cells and other PV. With MS-validated doping profile optimisation, interface engineering achieves (37.66%) power conversion efficiencies, 1.52 V (open-circuit voltages) and fill factors above (87%). Unfortunately, there are limitations of the frequency-dependent capacitance, parasitic elements, trap states, and non-ideal depletion layer of some architectures, like organic and hybrid ones. The MS and simulations to be used together, and machine learning adoption and analytical models to improve the electronic characterisation, have the potential to resolve the problems. This study offers a critical evaluation of current methods and inherent constraints in MS analysis, offering a strategic framework for the systematic design of efficient, durable, and sustainable solar technologies.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
To study the effect of spiral scanning laser power on the arc thermal field, droplet transfer, and porosity defects in double pulsed cold metal transfer (CMT) additive manufacturing of aluminum alloy, comparative experiments of laser arc additive manufacturing with variable power were conducted. The infrared thermography, spectral measurement, and high-speed imaging technology were used to analyze the radial temperature field distribution of the arc near the molten pool surface, the plasma behavior of the intersection region, the droplet transition, and the porosity defects when the scanning laser was introduced. The results indicate that the radial temperature field of the arc near the molten pool surface exhibits significant fluctuations and a steep temperature gradient with low arc stability in the double pulsed CMT. When the laser power is set to 600–1200 W, the radial temperature distribution of arc becomes more uniform, and the temperature gradient significantly decreases; the arc temperature near the molten pool surface is notably elevated (a maximum increase of 900 ℃), which promotes the ionization of Mg and Al atoms, resulting in more generation of Mg II, Mg III, Al II, and Al III ions, and improving the arc stability. At laser power of 600 W and 1200 W, the electron density increases by 6% and 17%, and the electron temperature rises by 400 K and 800 K. The laser metal vapor effectively reduces the droplet transfer frequency, lessens the droplet impact on the molten pool, and contributes to greater stability. The number of pores in the components decreases significantly, with their distribution becoming more dispersed; at a laser power of 1200 W, the porosity is almost eliminated.
<p>Accurate wind speed determination at the height of the rotor swept area is critical for resource assessments. ERA5 data combined with short-term measurements through the “measure, correlate, predict” (MCP) method are commonly used for offshore applications in this context. However, ERA5 poses limitations in capturing site-specific wind speed variability due to its low resolution. To address this, we developed random forest models extending near-surface wind speed up to 200 m, focusing on the Dutch part of the North Sea. Based on public 2-year floating lidar data collected at four locations, the 15 % testing subset shows that the random forest model trained on the remaining 85 % of site-specific wind profiles outperforms the MCP-corrected ERA5 wind profiles in accuracy, bias, and correlation. In the absence of rotor height measurements, a model trained within a 200 km region handles vertical extension effectively, albeit with increased bias. Our regionally trained random forest model exhibits superior accuracy in capturing wind speed variations and local effects, with an average deviation below 5 % compared to corrected ERA5 with a 20 % deviation from measurements. The 10 min random-forest-predicted wind speeds capture the mesoscale section of the power spectrum where ERA5 shows degradation. For stable conditions the root mean squared error and bias are 12 % and 29 % larger, respectively, compared to unstable conditions, which can be attributed to the decoupling effect at higher heights from the surface during stable stratification. Our study highlights the potential enhancement in wind resource assessment by means of machine learning methods, specifically random forest. Future research may explore extending the random forest methodology for higher heights, benefiting a new generation of offshore wind turbines, and investigating cluster wakes in the North Sea through a multinational network of floating lidars, contingent on data availability.</p>
Balaji Panchal, Chia-Hung Su, Chun-Chong Fu
et al.
Biodiesel has the potential to significantly contribute to the elimination of the current global energy and climate change challenges. However, its production and commercialization have been hindered by the diverse nature of feedstocks, and production techniques. This comparative review evaluates the production of biodiesel by electrolysis method with other methods such as (trans)esterification, supercritical transesterification, emulsion or micro-emulsion, and thermal cracking or pyrolysis, microwave-assited transesterification, and photocatalysis in terms of their environmental impact and commercial feasibility. Also, this study focuses on the availability of different biodiesel feedstocks and summarizes their characteristics affect biodiesel properties. It also outlines the criteria for selecting feedstocks for sustainable and low-cost biodiesel production. Waste cooking oil based third-generation feedstocks have been shown to be superior in comparison. Among all biodiesel production processes, electrolysis is the most suitable because it is an eco-friendly method with properties comparable to diesel. Recent research provides an update on the current challenges and opportunities for biodiesel commercialization, taking into account techno-economic and environmental considerations. The review concludes with future perspectives and suggestions regarding the selection criteria of feedstocks and production techniques to make biodiesel production cost-effective, efficient, and environmentally friendly.
AbstractA balancing control algorithm for a wheel-legged robot is designed for current logistics and distribution mobile robots. Since the wheel-legged robot is a nonlinear underactuated system, it is crucial to realize the balanced control and robustness of the wheel-legged robot in the absence of an accurate dynamics model. In this paper, an optimal control scheme for the wheel-legged robot based on the fusion control of variable-height adaptive fuzzy PID and conventional PID is designed. The balance of the wheel-legged robot is controlled by adaptive fuzzy PID control, the lifting and attitude changes of the two sides of the wheel-legs are controlled with high precision by traditional PID control, and the speed and steering control of the wheel-legged robot is not affected by the structure of the model, and is controlled by traditional PID control with linearization. The attitude of the robot is detected in real time using the BMI088 attitude sensor to realize the positional control. Experimental and simulation results show that the control algorithm of the wheel-legged robot designed in this paper is reliable, and the robot runs smoothly and robustly.
AbstractThe relationship and the applicable conditions between Mohr–Coulomb model and linear Drucker-Prager model were discussed, and the results showed that when the friction angle was less than 22° Drucker-Prager model was more suitable for modelling the soil unit; When the friction angle was more than 22°, the Mohr–Coulomb model should be used. In order to further study the compaction characteristics of vibratory roller and the dynamic relationship between vibratory roller and soil, the finite element model of “vibrating roller-soil” was established. The simulation results showed that vertical stress of the soil distributed symmetrically along the axial direction of the vibrating drum, shift forward along the direction of the vibration drum, and decreased sharply with the increasement of the depth. Then experiments carried out showed that acceleration of the vibrating drum was positively correlated with the compaction times, which verified the model was basically correct. Moreover, the regression equation between the compaction degree and the effective value of acceleration was obtained, which provided the idea for the new compaction degree monitoring system.
AbstractComprehensive exploration of ball-end milling processes is presented in this paper, with a primary focus on the modelling of milling forces and the execution of finite element analysis during the machining of Inconel 718, a material known for its challenging machinability. A detailed milling force model, considering various parameters such as cutting speeds, feed rates, and depths of cut, has been developed, providing valuable insights into the optimization of machining parameters. Temperature and stress distributions within the tool during milling, particularly in the context of difficult-to-machine materials like Inconel 718, were investigated through finite element analysis. Critical temperature profiles at the tool tip, rake face, and flank face, which have an impact on tool wear and lifespan, were identified through the temperature field analysis. Notably, a maximum tool tip temperature of 682 °C was observed during the machining of Inconel 718. Challenges posed by difficult materials were unveiled through the stress field analysis, aiding in stress mitigation and enhancing the understanding of machining processes. In conclusion, a significant contribution is made by this paper to the understanding of ball-end milling processes.
Power transmission and control include the conversion, transmission, and distribution of power, and the actuating of control in the end implement block; it plays an important part in the power and the vehicle of machines [...]
AbstractIn recent years, the new energy vehicle industry has been vigorously developed, and the demand for new energy vehicle practitioners and their knowledge of electric vehicles has been continuously increasing. In the undergraduate education of vehicle engineering, there is an urgent need for an electric drive demonstration teaching platform. This design aims to solve the teaching demonstration function of multiple configurations of electric vehicle drive systems on the same platform. The electric vehicle drive demonstration teaching platform is designed as an expandable way, which can meet the requirements of various combinations of different motor forms, battery types and capacities, control methods, etc. for platform loading and demonstration, achieving diversification of the teaching platform. It can also conduct experiments on various types of new energy vehicle configurations through connecting racks The expansion of the demonstration teaching platform meets the needs of teaching demonstrations and experiments related to electric vehicle drive systems.
AbstractCavitation bubble collapse, which generates strong shock waves and high-velocity liquid jets, is responsible for the erosive damage to hydraulic components. In order to assess the fluctuation of near-wall pressure, in this work, an open-source package OpenFOAM is utilized for solving the Navier–Stokes equation. To track the liquid–air interface, the volume of fluid (VoF) method-based compressibleInterFoam solver is selected, and its shipped dynamic contact angle model is modified to obtain better accuracy when considering the wettability of substrates. Numerical methods are first validated by comparing with experiment, and then it is extended to study the effect of bubble diameter, pressure difference, and surface wettability on the fluctuation of near-wall pressure. Simulation results show that the initial sphere bubble goes through three stages of growth, shrinkage, and collapse near the wall. A larger bubble size leads to higher impact pressure due to the higher speed of the liquid jet. The difference in initial pressure in and out of the bubble has a great effect on the collapse behaviour. In addition, a hydrophobic surface, meaning hard liquid pining, can speed up the damping of near wall pressure. The findings in this work will be a guide to designing hydraulic components for limiting the erosive damages of cavitation bubble collapse.
AbstractIn response to the low design efficiency caused by the numerous design parameters of existing shock absorbers, this research presents the design of an efficient software tool for obtaining the external characteristics of Continuous Damping Control (CDC) shock absorbers. Firstly, a mathematical model for the damping force of the shock absorber was established based on principles from fluid dynamics, elasticity mechanics, and throttle orifice models in the rebound and compression valve systems. Secondly, a Graphical User Interface (GUI) was developed to visualize the external characteristics and conduct simulation studies of the CDC shock absorber. Finally, experiments were performed on a shock absorber test bench to validate the external characteristics of the CDC shock absorber using initial structural parameters. The GUI software interface enables direct adjustment of the shock absorber's structural parameters and signal excitation, thereby enhancing the practical efficiency of the shock absorber. A comparison between simulation and experimental results reveals that the relative error rate is generally high when the velocity amplitude is 0.052m/s, with a maximum relative error rate of 24.44%. For other excitation velocity amplitudes, the relative error rate remains within 10%. This demonstrates the high accuracy and reliability of the established mathematical model for the CDC shock absorber, providing a solid theoretical foundation for studying the shock absorber's external characteristics.