Experimental Simulation Research Articles

On the Effect of Cooling Parameters on Solidification Structure in NdFeB Alloys

The elaboration of suitable NdFeB‐based permanent magnets is essential for high‐performance electrical machines in a number of applications. In this respect, the understanding of the relationship between heat transfer phenomena and the final solidified microstructure is the key to the control of the grain size and the improvement of the magnetic properties. The problem is even more acute with the emergence of new manufacturing processes, such as the laser powder bed fusion, where the cooling rates are much higher than those encountered in the standard strip casting process and where the microstructure can be considered as fixed from the fabrication step. The objective of the present work is to identify correlations between interstructural spacings in the solidified alloys and cooling conditions. This study is based on a methodology coupling experimental and numerical simulation approaches featuring three solidification processes: strip casting, arc melting, and laser fusion. The obtained results cover more than four orders of magnitude in terms of cooling rates R and allow to propose a reliable empirical relationship between the interstructural spacing λ and R of the form λ [μm] = 83 R [K s−1]−0.36. This relation can thus be used to estimate cooling rates from metallographic observations.

A high-quality visual image encryption algorithm utilizing the conservative chaotic system and adaptive embedding

Regarding the issue of encrypted images in the channel attracting the attention of attackers and making them vulnerable to attacks. This paper proposes a high-quality visual image encryption algorithm utilizing the conservative chaotic system, two-dimensional compressive sensing, optimization local of binary patterns, and adaptive embedding method. Firstly, a conservative chaotic system with excellent performance and resistance to reconstruction attacks is proposed. Secondly, the pseudo-random sequences generated by the chaotic system dynamically generate measurement matrices, which are optimized before compressing the image. Then, during the encryption process, by utilizing the newly proposed composite DNA computing rules, chaotic sequences dynamically encode and compute image information, which enriches the coding criteria and improves the security of encryption algorithms. Finally, in the information embedding stage, the newly proposed OLBP algorithm can identify important textured and non-textured regions of the host image, prioritize embedding non-textured regions, and then embed textured regions, which can improve the amount of information embedding and reduce damage to the host image. Experimental simulation and analysis exhibit that the encryption algorithm has high security, strong robustness and high efficiency. Meanwhile the imperceptible analysis of the steganographic images is exceed 52 dB, so the algorithm presents a high-quality visual effect of imperceptibility.

Flexural and pseudo‐ductile behavior of e‐glass uniaxial and biaxial fabric‐reinforced cementitious matrix: Experimental study and simulation

Flexural and pseudo‐ductile behavior of e‐glass uniaxial and biaxial fabric‐reinforced cementitious matrix: Experimental study and simulation

Simulation study on the effect of palladium layer thickness and temperature on the bonding properties of palladium coated copper wire

Palladium coated copper (PCC) wire is an emerging bonding wire. There are relatively few studies on the effect of palladium plating thickness and temperature on its bonding performance. In order to investigate the effects of three parameters, namely palladium plating thickness, chip preheating temperature and the free air ball (FAB) initial temperature, on the bonding performance of PCC wires, this paper establishes a transient nonlinear finite element analysis model of thermal coupling between bare copper wires and PCC wires, and simplifies the whole bonding process into two stages of impact and ultrasonic vibration to carry out the experimental simulation. Firstly, the Taguchi orthogonal test method was adopted to obtain the ranking of the degree of influence of the three factors on the bonding results, and the optimum palladium plating thickness and FAB initial temperature of 100 nm and 100 °C were derived, respectively. Then the PCC wires with 100 nm palladium plating thickness were selected, and the bare copper wires were used as the reference, and multiple one-factor simulation experiments were carried out under the condition of changing the chip preheating temperature only. The simulation results show that the FAB and pad stress levels in the bonding results of the PCC wires are significantly higher than those of the bare copper wires, but the GaN layer stress is less than that of the bare copper wires. Moreover, there is an approximate parabolic relationship between the maximum stresses of pad and GaN layer and the preheating temperature of the chip. In the bonding results of bare copper wire, there is also an approximate parabolic relationship between the maximum stress of FAB and the chip preheating temperature. This parabolic relationship allows a prediction of the optimum chip preheating temperature. Selecting the appropriate palladium plating thickness and controlling the appropriate FAB temperature and chip preheating temperature can improve the bonding quality, and this numerical simulation work can provide a reference for the selection of palladium plating thickness and temperature parameter regulation of PCC wires.

Experimental Analysis and CFD Simulation of Photovoltaic/Thermal System with Nanofluids for Sustainable Energy Solution

The integration of photovoltaic/thermal (PV/T) systems with nanofluids presents a promising avenue for enhancing sustainable energy solution. This study investigates the performance of such systems through experimental analysis and computational fluid dynamics (CFD) simulations. Nanofluids, engineered colloidal suspensions of nanoparticles in base fluids, are employed to enhance heat transfer within the PV/T system. The experimental setup involves measuring electrical output, thermal efficiency, and overall system performance under varying conditions. Additionally, CFD simulations are conducted to model fluid flow and heat transfer dynamics within the PV/T collector integrated with nanofluids. The results from both experimental and simulation studies provide insights into the synergitic effects of nanofluids on enhancing energy conversion efficiency and thermal management of the PV/T system. The research contributes to the development of sustainable energy solutions by demonstrating the potential of nanofluid-enhanced PV/T systems in improving energy conversion efficiency and thermal management for various environmental conditions.

Explainable AI and optimized solar power generation forecasting model based on environmental conditions.

This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power generation rates based on environmental conditions, while the EO component optimizes the LSTM model's hyper-parameters through training. The XAI-based Local Interpretable and Model-independent Explanation (LIME) is adapted to identify the critical factors that influence the accuracy of the power generation forecasts model in smart solar systems. The effectiveness of the proposed X-LSTM-EO model is evaluated through the use of five metrics; R-squared (R2), root mean square error (RMSE), coefficient of variation (COV), mean absolute error (MAE), and efficiency coefficient (EC). The proposed model gains values 0.99, 0.46, 0.35, 0.229, and 0.95, for R2, RMSE, COV, MAE, and EC respectively. The results of this paper improve the performance of the original model's conventional LSTM, where the improvement rate is; 148%, 21%, 27%, 20%, 134% for R2, RMSE, COV, MAE, and EC respectively. The performance of LSTM is compared with other machine learning algorithm such as Decision tree (DT), Linear regression (LR) and Gradient Boosting. It was shown that the LSTM model worked better than DT and LR when the results were compared. Additionally, the PSO optimizer was employed instead of the EO optimizer to validate the outcomes, which further demonstrated the efficacy of the EO optimizer. The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation patterns. Moreover, the proposed model might assist in optimizing the operations of photovoltaic power units. The proposed model is implemented utilizing TensorFlow and Keras within the Google Collab environment.

Simultaneous Desulfurization and Denitrification by H2O2/FeSO4 Preoxidation Combined with Wet Flue Gas Desulfurization Postabsorption: Experimental Investigation and Process Simulation

Simultaneous Desulfurization and Denitrification by H₂O₂/FeSO₄ Preoxidation Combined with Wet Flue Gas Desulfurization Postabsorption: Experimental Investigation and Process Simulation

Multicolor fluorescence strategy for rapid and simultaneous detection of antibiotics in wastewaters and the entire process detection mechanisms

Fluorescence nano-sensors have recently emerged as a cost-effective, convenient, and time-saving tool for high-throughput detection of antibiotics in wastewater. However, their application is primarily limited to tetracycline and penicillin due to a lack of understanding of key influencing factors. Herein, we propose a convenient strategy to expand their application range by increasing the colors of fluorescence nano-sensors. The newly three-colored nano-sensors were prepared to detect erythromycin and metronidazole, which has rarely been reported before. For in-situ detection, a smartphone app-based detection method was developed, and the detection method was optimized to obtain the widest detection limit. The fluorescence nano-sensors demonstrated satisfactory recovery rates with promising accuracy in various types of wastewaters. Moreover, a mechanism investigation comprising experimental analysis and theoretical simulation was developed to elucidate their detection mechanisms and provide ideas for improving their performance. Consequently, several improvements were proposed further to enhance the analysis selectivity, sensitivity, and accuracy. This work presents a new perspective on broadening the application of fluorescence nano-sensors and provides a novel route to analyze the detection mechanism, enabling rapid, high-throughput and in-situ detection of antibiotics.

Precise Size Determination of Supported Catalyst Nanoparticles via Generative AI and Scanning Transmission Electron Microscopy.

Transmission electron microscopy (TEM) plays a crucial role in heterogeneous catalysis for assessing the size distribution of supported metal nanoparticles. Typically, nanoparticle size is quantified by measuring the diameter under the assumption of spherical geometry, a simplification that limits the precision needed for advancing synthesis-structure-performance relationships. Currently, there is a lack of techniques that can reliably extract more meaningful information from atomically resolved TEM images, like nuclearity or geometry. Here, cycle-consistent generative adversarial networks (CycleGANs) are explored to bridge experimental and simulated images, directly linking experimental observations with information from their underlying atomic structure. Using the versatile Pt/CeO2 (Pt particles centered ≈2nm) catalyst synthesized by impregnation, large datasets of experimental scanning transmission electron micrographs and physical image simulations are created to train a CycleGAN. A subsequent size-estimation network is developed to determine the nuclearity of imaged nanoparticles, providing plausible estimates for ≈70% of experimentally observed particles. This automatic approach enables precise size determination of supported nanoparticle-based catalysts overcoming crystal orientation limitations of conventional techniques, promising high accuracy with sufficient training data. Tools like this are envisioned to be of great use in designing and characterizing catalytic materials with improved atomic precision.

Seafloor Landing Dynamics of a Novel Underwater Robot Using ALE Algorithm

Seafloor landing research is crucial for underwater robots tasked with long-term fixed-point observation missions, particularly those requiring prolonged operations on the seabed. Developing safe and stable landing technology is essential for the success of these missions. This paper introduces a novel underwater robot by establishing its dynamic model and using the arbitrary Lagrangian–Eulerian (ALE) method to conduct a comprehensive study of its deep-sea landing process. Prior to detailed parameter analysis, the ALE algorithm’s effectiveness and accuracy were validated through experimental data and simulation results from previous studies. This study examines the penetration depth and force conditions during the landing process by varying factors such as seabed soil properties, landing velocity, and the robot’s mass. Findings indicate that seabed soil properties significantly influence penetration depth and pressure, with variations in soil density and strength affecting the robot’s landing behavior. In contrast, the robot’s mass has a relatively minor effect, suggesting that the choice of structural materials has limited impact on penetration depth and pressure during landing. Additionally, landing velocity was found to significantly affect penetration depth and pressure; higher velocities result in greater penetration depths and pressures. This highlights the importance of controlling landing velocity to minimize impact forces and protect the robot. The results of this study provide theoretical support and data for developing deep-sea landing technology for novel underwater robots and inform the selection of structural materials, ensuring the successful execution of long-term fixed-point observation missions in deep-sea environments.

Quasi-static/dynamic energy absorption characteristics and micromechanical behavior of Al/Ep cast metal braided tubular structures

As a low-density, high-strength material with high energy release potential, Al/Ep cast metal braided tubes have a wide range of application prospects in the combatant shell, which is one of the most important means to realize the integrated destructive effect of kill and blast. In this paper, Al/Ep composites with mass ratios of 3:7, 4:6 and 5:5 were prepared, and the intrinsic model of Al/Ep was established by studying its quasi-static and dynamic mechanical properties. The Al/Ep cast three-dimensional braided metal skeletons were used to obtain the required cast braided tubes with different mass ratios, and the destruction behaviors of the tubes under axial and radial compressive loads were analyzed by a combination of experimental and numerical simulations. The results show that the addition of Al particles can significantly improve the elastic-plastic properties of Al/Ep composites, and the energy storage and absorption capacities are positively correlated with the ratio of the relative contents of Al powder. When the mass ratio of Al powder to epoxy resin is 5:5, the energy absorption of Al/Ep composites is 23.16 MJ/m3, which is optimal among similar specimens and much higher than that of pure epoxy resin materials (the energy absorption of epoxy resin is 9.63 MJ/m3). The strength of binary resin matrix composites is improved, and the intrinsic models of three kinds of different mass ratios of Al/Ep composites are obtained. The intrinsic models of various Al/Ep cast braided tubes with crushing force efficiency CFE, energy absorption capacity, and specific energy absorption SEA decreased with the ratio of mass occupied by the braided filaments.

Consensus Control of Leader–Follower Multi-Agent Systems with Unknown Parameters and Its Circuit Implementation

With the development and progress of Internet and data technology, the consensus control of multi-agent systems has been an important topic in nonlinear science. How to effectively achieve the consensus of leader–follower multi-agent systems at a low cost is a difficult problem. This paper analyzes the consensus control of complex financial systems. Firstly, the dynamic characteristics of the financial system are analyzed by the equilibrium points, bifurcation diagrams, and Lyapunov exponent spectra. The behavior of the financial system is discussed by different parameter values. Secondly, according to the Lyapunov stability theorem, the consensus of master–slave systems is proposed by linear feedback control, wherein the controllers are simple and low cost. And an adaptive control method for the consensus of master–slave systems is investigated based on financial systems with unknown parameters. In theory, the consensus of the leader–follower multi-agent systems is proved by the parameter identification laws and linear feedback control method. Finally, the effectiveness and reliability of the consensus of leader–follower multi-agent systems are verified through the experimental simulation results and circuit implementation.

Design and performance analysis of integrated sensing and communication scheme based on LoRa signals

LoRa (Long Range) has garnered widespread adoption in Internet of Things (IoT) communications due to its extensive transmission range and robust resistance to interference. Furthermore, its capability to linearly discern deviations across both frequency and time domains renders it an ideal signal for sensing applications. Consequently, this article proposes an Integrated Sensing and Communication (ISAC) scheme based on the LoRa signal. On the communication side, while the modulation and demodulation processes of the LoRa signal are currently kept confidential, this article has theoretically derived its principles and further implemented the communication functionality of LoRa through experimental simulations. On the sensing side, through theoretical derivation and experimental simulation, the influence of different modulation information on the peak amplitude and position of pulse compression was analyzed. Two signal processing schemes are proposed to overcome the impact of modulation information, thereby enabling the sensing function. The communication and sensing performance of the proposed ISAC scheme was evaluated. Experimental results demonstrated that, even in low signal-to-noise ratio (SNR) environments, the communication function maintained a low bit error rate (BER), while the sensing function achieved a high target detection rate, both of which exhibited excellent performance.

Experimental and theoretical simulations to examine the influence of nonionic surfactant on the corrosion control of mild steel in hydrochloric acid

The increasing demand for corrosion prevention strategies that are both effective and sustainable is part of the research the background. Nonionic surfactants offer a potential replacement for traditional corrosion inhibitors. These surfactants are well-known for their low toxicity and biodegradability. The research involved conducting experimental tests (such as weight loss, polarization and impedance spectroscopy) and theoretical computations to investigate the role of nonionic surfactant (polyoxyethylene (7) tribenzyl phenyl ether) (PETPE) in controlling the corrosion of mild steel in hydrochloric acid (1.0 M HCl) environment. The results of the study demonstrated that PETPE exhibited significant corrosion inhibition properties for mild steel in HCl solution. The inhibition efficiency of PETPE was found to increase with increasing PETPE concentration. PETPE is an excellent corrosion inhibitor because it significantly reduces the rate of corrosion, as seen by the notable inhibition efficiency result (95.4%) at a relatively low dose of PETPE (100 ppm). Thermodynamic studies were used to discuss the fundamental mechanisms that control PETPE-acid interactions. The adsorption process followed Langmuir adsorption isotherm, indicating a monolayer adsorption of the PETPE on the mild surface. Theoretical computations confirm the strong inhibition behavior of PETPE. The innovative feature of this research is its comprehensive strategy, which integrates experimental studies and theoretical simulations to evaluate the impact of PETPE on the corrosion control of mild steel in hydrochloric acid. The combined effort has the ability to supply valuable knowledge into the mechanisms of corrosion that will lead to the establishment of powerful corrosion control strategies.

Multi-feature Fusion Recognition Method of Rail Surface State Based on SVM Algorithm

Abstract In order to solve the problems that the rail surface status is still judged by manual experience and the recognition efficiency is low, a multi-feature fusion recognition method of rail surface status based on SVM algorithm is proposed. First, the rail surface image database is obtained by preprocessing the collected rail surface images in different states. The preprocessing includes image graying, image denoising and image extraction. Then, the gray mean and variance of the contact surface region are calculated to describe the color features of the rail surface image. And the texture features of the contact surface region are extracted using the gray level co-occurrence matrix. Then the two features are fused as the basis for judging the rail surface state. SVM is used to recognize the rail surface state. Finally, the proposed method is verified and analyzed by experimental simulation, and the results prove the effectiveness of the proposed method.

Impact of liquid crossflow on the discharge coefficient of a gas jet hole on a flat plate

This study combines the experimental and numerical simulation methods to deeply analyze the impact of liquid crossflow on the discharge coefficient of a gas jet hole on a flat plate. Experiments were conducted to examine the influence of momentum flux ratio and theoretical momentum flux ratio on the discharge coefficient under various crossflow Reynolds numbers. It was found that the variation of the discharge coefficient with the theoretical momentum flux ratio clearly reflects the impact of the crossflow boundary layer velocity profile on the discharge coefficient. The rapid growth of velocity in the boundary layer near the wall in the direction normal to the wall surface, or the decrease in the thickness of the boundary layer, both enhance the shearing effect of the crossflow, leading to a decrease in the discharge coefficient. Analysis of the cavity morphology at the hole exit captured by high-speed camera revealed that the averaged profile of the gas–liquid boundary on the symmetrical plane of the jet below the hole can be approximated as a straight line within the scale of the hole diameter, and the sine of the angle between this line and the upper wall surface is roughly equivalent to the normalized discharge coefficient. This relationship was physically interpreted through the analysis of effective and equivalent flow cross-sectional shapes derived from numerical simulation at different crossflow Reynolds numbers and theoretical momentum flux ratios. Additionally, this paper introduces an innovative method for predicting jet flow rate based on image processing technology. A notable feature of this method is that it does not require the measurement of the pressure inside the gas chamber.

Robust calibration of surround-view camera systems with Planar-Eccentric Constraint: A novel Apriltag approach

This research tackles calibrating surround view camera systems for automated driving, where traditional methods require extensive manual measurements and precise conditions. We introduce a novel automatic calibration method that ensures portability and accuracy using only custom-designed calibration boards placed in the cameras’ common view. To address visual interference from extraneous objects, we design a unique board with AprilTag 2D codes, offering clear advantages over traditional checkerboards. Additionally, we’ve developed an Adaptive Angle Projection to counteract fisheye lens distortions. We also introduce a hierarchical pose optimization strategy that transitions from local to global, incorporating a “Planar-Eccentric constraint” to leverage the calibration board’s geometric positioning. Our method achieved 4.01 cm measurement error, with 0.075°angle error and 1.18 cm translation error. Our method’s efficacy and robustness were confirmed through extensive testing on an experimental vehicle and simulation dataset, proving its superiority in achieving precise calibration and measurement with minimal manual input.

Influence of multimodal fiber distribution on the permeability of fibrous media

This article deals with the impact of fiber size distribution on the permeability of nonwoven fibrous media, a major issue in the general field of filtration. The conventional method to determine the permeability of nonwoven fibrous media involves equating complex fibrous media with structures composed of a single equivalent fiber diameter. Based on numerical simulations of flow through tridimensional structures with polymodal and polydisperse fiber size distributions, we proposed a new equivalent diameter. In conjunction with a packing density function, the approach is compared to experimental data and simulations available in the scientific literature and shows better permeability predictions than other existing models for a wide range of solid volume fractions (1 % to 20 %) and fiber diameters (1.5 µm to 30 µm). This predictive model of permeability is an essential step in developing a decision-making tool for the design of fibrous media.

Experiment and dynamic simulation of micro gas turbine combined with concentrated solar power system

The solar micro gas turbine (SMGT) system is a promising solution to address the instability and intermittency of renewable energy sources. Its dynamic characteristics under unstable input conditions and uncertain output load demands are crucial for peak shaving. This paper constructs an SMGT system based on experiments with micro gas turbine (MGT) and concentrated solar power (CSP), analyzing the impact of integrating CSP system on the MGT. Then the performance of the SMGT under varying ambient conditions and load demands is studied. Results show that in grid-connected mode, the turbine speed of MGT and SMGT is determined by ambient temperature and set power, with electrical efficiency decreasing as temperature and DNI increase. The solar receiver in the SMGT should maximize temperature increase while maintaining pressure loss below 55 kPa. Additionally, a higher heat capacity can reduce sensitivity to ambient changes but also extends stabilization time. Under real solar radiation, the SMGT system can keep constant power within 0.6% fluctuation, with a solar share of 36.5%. When combined with photovoltaic, the hybrid system’s maximum power fluctuation does not exceed 2.6%, with a solar share of 48.8%. This study provides guidance for optimizing SMGT systems and their application in peak shaving scenarios.

TiO2 Films with Macroscopic Chiral Nematic-Like Structure Stabilized by Copper Promoting Light-Harvesting Capability for Hydrogen Generation.

Cellulose nanocrystals (CNCs) have inspired the synthesis of various advanced nanomaterials, opening opportunities for different applications. However, a simple and robust approach for transferring the long-range chiral nematic nanostructures into TiO2 photocatalyst is still fancy. Herein, a successful fabrication of freestanding TiO2 films maintaining their macroscopic chiral nematic structures after removing the CNCs biotemplate is reported. It is demonstrated that including copper acetate in the sol avoids the epitaxial growth of the lamellar-like structure of TiO2 and stabilizes the chiral nematic structure instead. The experimental results and optical simulation demonstrate an enhancement at the blue and red edges of the Fabry-Pérot reflectance peak located in the visible range. This enhancement arises from the light scattering effect induced by the formation of the chiral nematic structure. The nanostructured films showed 5.3 times higher performance in the photocatalytic hydrogen generation, compared to lamellar TiO2, and benefited from the presence of copper species for charge carriers' separation. This work is therefore anticipated to provide a simple approach for the design of chiral nematic photocatalysts and also offers insights into the electron transfer mechanisms on TiO2/CuxO with variable oxidation states for photocatalytic hydrogen generation.