Physical Model Research Articles

Using AR to Enhance the Learning of Chirality

This study investigates the use of Augmented Reality (AR) to teach the concept of chirality—a fundamental topic in organic chemistry. Building on Cognitive Load Theory (CLT) and previous research on AR’s educational benefits, we designed an AR learning environment to facilitate students’ understanding of chirality by allowing them to interact with and superimpose virtual and physical models of chiral molecules. An initial pilot study involving 11th-grade students revealed positive student attitudes towards AR, with participants reporting enhanced comprehension of chirality and a preference for AR-based learning over traditional methods. The follow-up study refined the AR lesson based on pilot feedback, extending its scope to introduce, rather than review, the concept of chirality. Results indicated significant learning gains, low extraneous cognitive load, and high acceptance of AR technology among students. These findings underscore the potential of AR to support complex spatial learning in chemistry, though further research, such as value-added studies, is recommended to explore the generalizability and long-term impacts of AR on different student populations.

A Computational Study on Effects of PID Temperature Target and RF Frequency for PID-Controlled Nonablative RF Cosmetic Systems.

Commonly adopted in cosmetic dermatology, nonablative radiofrequency (RF) devices convert high-frequency electromagnetic energy into thermal energy to induce a wound-healing response in skin tissue. However, differences in the electrical properties of different skin layers raise questions about the impact of different RF frequencies and target temperatures on treatment effectiveness. This paper presents a finite element analysis (FEA)-based computational study aimed at simulating and optimizing the effects of a proportional integral derivative (PID)-controlled RF cosmetic devices under different combinations of these two parameters during treatment. A 3D physical model for the application of a nonablative RF device was constructed using COMSOL, which included the human tissue and RF electrodes, electromagnetic and thermal boundary conditions, as well as the PID controller. FEA was performed for each of the twelve models with parameter combinations of three RF frequencies (0.1, 0.5, and 1 MHz) and three PID-controlled target temperatures (60°C, 65°C, and 70°C) plus one group without PID control. Treatment effectiveness was quantitatively assessed using the integration of tissue thermal damage fraction, i.e., thermal damage volume. In the earlier stage of heating (0-10 s), higher RF frequency resulted in a larger thermal damage volume. At 10 s, among models with a temperature target of 70°C, there is a 6.04% difference between the thermal damage volume at RF frequencies of 1.0 and 0.1 MHz. In the later stage of heating(11-80 s), the impact of RF frequency decreases. The difference in thermal damage volume caused by higher temperature targets is more significant, at 80 s, among models with an RF frequency of 1.0 MHz, the 70°C model produces 1.15 and 1.36 times more tissue thermal damage than the 65°C and 60°C models. PID controller has ensured treatment safety and uniformity, in exchange for some efficiency. Among 12 parameter combinations, the one with a temperature of 70°C and RF frequency of 1.0 MHz achieved the highest thermal damage volume, which could potentially result in the best esthetic effect. Considering users' different susceptibility to heat, engineers or physicians can select better temperature targets and RF frequencies to bring the desired cosmetic results based on thermal damage volume curves from this study.

TRUST-DART: Land Surfaces Temperature and Emissivity Nonlinear Mapping from Nonisothermal Mixed Pixels of Satellite Images with the DART 3D Radiative Transfer Model

Abstract. Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

A key feedback loop: building electricity infrastructure and electrifying metals production.

Energy infrastructure requires metals, and metals production requires energy. A transparent, physical model of the metals-energy system is presented to explore under what conditions this dependence constrains or accelerates the transition to a net-zero economy. While the mineral (as high as 340 Mt yr-1 iron ore, 210 Mt yr-1 limestone, 250 Mt yr-1 bauxite and 5.5 Gt yr-1 copper ore in the 2040-2050 decade, assuming no improvements) and total energy (up to 22 EJ yr-1) requirements for building low-carbon energy infrastructure are significant, it compares favourably with the current extraction and energy use supporting the fossil fuel system (15 Gt yr-1 fossil minerals and ~38 EJ yr-1). There are levers to significantly reduce material use and associated impacts over time. The metals industry can play a key reinforcing role in the transition by adapting to the increasing supply of renewable electricity. Specifically, direct electrolysis can extract metal from ore close to the thermodynamic limit, to make efficient use of low-C electricity. The unique features of emerging technologies for iron extraction, molten oxide electrolysis and molten sulphide electrolysis are considered in this evolving system. Electrification enables elegant separations and provides a pathway to build out infrastructure while reducing environmental impacts, though material efficiency measures will still be crucial to meet 2050 carbon budgets.This article is part of the discussion meeting issue 'Sustainable metals: science and systems'.

Exploring Kink Solitons in the Context of Klein–Gordon Equations via the Extended Direct Algebraic Method

This work employs the Extended Direct Algebraic Method (EDAM) to solve quadratic and cubic nonlinear Klein–Gordon Equations (KGEs), which are standard models in particle and quantum physics that describe the dynamics of scaler particles with spin zero in the framework of Einstein’s theory of relativity. By applying variables-based wave transformations, the targeted KGEs are converted into Nonlinear Ordinary Differential Equations (NODEs). The resultant NODEs are subsequently reduced to a set of nonlinear algebraic equations through the assumption of series-based solutions for them. New families of soliton solutions are obtained in the form of hyperbolic, trigonometric, exponential and rational functions when these systems are solved using Maple. A few soliton solutions are considered for certain values of the given parameters with the help of contour and 3D plots, which indicate that the solitons exist in the form of dark kink, hump kink, lump-like kink, bright kink and cuspon kink solitons. These soliton solutions are relevant to actual physics, for instance, in the context of particle physics and theories of quantum fields. These solutions are useful also for the enhancement of our understanding of the basic particle interactions and wave dynamics at all levels of physics, including but not limited to cosmology, compact matter physics and nonlinear optics.

Enhancing runoff predictions in data-sparse regions through hybrid deep learning and hydrologic modeling

Amidst growing concerns over climate-induced extreme weather events, precise flood forecasting becomes imperative, especially in regions like the Chaersen Basin where data scarcity compounds the challenge. Traditional hydrologic models, while reliable, often fall short in areas with insufficient observational data. This study introduces a hybrid modeling approach that combines the deep learning capabilities of the Informer model with the robust hydrological simulation by the WRF-Hydro model to enhance runoff predictions in such data-sparse regions. Trained initially on the diverse and extensive CAMELS dataset in the United States, the Informer model successfully applied its learned insights to predict runoff in the Chaersen Basin, leveraging transfer learning to bridge data gaps. Concurrently, the WRF-Hydro model, when integrated with The Global Forecast System (GFS) data, provided a basis for comparison and further refinement of flood prediction accuracy. The integration of these models resulted in a significant improvement in prediction precision. The synergy between the Informer’s advanced pattern recognition and the physical modeling strength of the WRF-Hydro significantly enhanced the prediction accuracy. The final predictions for the years 2015 and 2016 demonstrated notable increases in the Nash–Sutcliffe Efficiency (NSE) and the Index of Agreement (IOA) metrics, confirming the effectiveness of the hybrid model in capturing complex hydrological dynamics during runoff predictions. Specifically, in 2015, the NSE improved from 0.5 with WRF-Hydro and 0.63 with the Informer model to 0.66 using the hybrid model, while in 2016, the NSE increased from 0.42 to 0.76. Similarly, the IOA in 2015 rose from 0.83 with WRF-Hydro and 0.84 with the Informer model to 0.87 using the hybrid approach, and in 2016, it increased from 0.78 to 0.92. Further investigation into the respective contributions of the WRF-Hydro and the Informer models revealed that the hybrid model achieved the optimal performance when the contribution of the Informer model was maintained between 60%-80%.

Advanced hybrid techniques for predicting discharge coefficients in ogee-crested spillways: integrating physical, numerical, and machine learning models

Abstract The primary objective of this work was to examine the flow characteristics over an ogee spillway using both a numerical model and the Machine Learning (ML) approach. A 3D computational fluid dynamics (CFD) model was employed to simulate the flow over an ogee spillway, utilizing the Reynolds averaged Navier–Stokes equations. The simulation encompassed a wide variety of head ratios, ranging from 0.1 to 6.0, to extend the rating curve of discharge coefficient (C) and head ratio (H e /H 0). The formation of the negative pressure zone rapidly occurred, and the maximum velocity area developed from toe to top of the spillway surface as the head ratio increased. Then, four ML models—RF, FNN, ADB, and KNN—were utilized to estimate the discharge coefficient of the spillway. Hyperparameter tuning using the Tree-Structured Parzen Estimator (TPE) and five-fold cross-validation ensured robust model performance. The ML model’s efficacy was assessed by conducting 200 random seed simulations. The RF and ADB models exhibited the highest predictive accuracy and consistency, with mean correlation coefficient (CC) values of 0.979 and 0.975, respectively. FNN and KNN also performed well but showed greater variability in their prediction. The results demonstrated reasonably good agreement between the physical, numerical, and ML models. Both numerical simulation methods and ML models, particularly RF, proved to be cost-efficient and reliable tools for designing and analyzing flow over an ogee spillway. These findings highlight the potential of integrating numerical simulations and advanced ML techniques to enhance the prediction and analysis of hydraulic structures, providing valuable insights for the design and management of spillway systems.

Experimental decomposition of nonlinear hydrodynamic loads and hydroelastic responses in wave–structure interactions of monopile wind turbines

This study explores the nonlinear effects of hydrodynamic loads and hydroelastic responses in monopile-supported offshore wind turbines, with a focus on their harmonic structures. High-quality experimental data were collected in a shallow water basin across various irregular waves. Their harmonic components up to the fourth order were successfully isolated using a novel four-phase decomposition technique, which effectively extracts harmonics from random time series. The results highlight significant contributions of higher-order harmonics across various sea states. Specifically, two physical models were employed: a flexible monopile to capture structural hydroelastic deformations during large wave events and a rigid monopile to accurately estimate hydrodynamic loads without hydroelastic effects. Additionally, numerical results from fully nonlinear potential-flow calculations were compared, revealing that while the potential-flow solver underestimates total wave loading, especially for higher harmonics. In terms of the free-surface elevations and hydrodynamic forces, this study confirms that their higher-order harmonics align well with the scaling of their corresponding linear components in both amplitude and phase. This enables the prediction of higher-order profiles based solely on their linear components. However, this efficient model is inadequate for accurately estimating the higher-order harmonics of monopile's hydroelastic responses during these extreme wave events, highlighting the need for further refinement.

CETASim: A numerical tool for beam collective effect study in storage rings

We developed a 6D multi-particle tracking program CETASim in C++ to simulate intensity-dependent effects in electron storage rings. The program can simulate the beam collective effects due to short-range/long-range wakefields for single/coupled-bunch instability studies. It also features the simulation of interactions among charged ions and the trains of electron bunches, including both fast ion and ion trapping effects. The bunch-by-bunch feedback is also included so that the user can simulate the damping of the unstable motion when its growth rate is faster than the radiation damping rate. The particle dynamics is based on the transfer maps from sector to sector, including the nonlinear effects of amplitude-dependent tune shift, high-order chromaticity, and second-order momentum compaction factor. Users can also introduce a skew quadrupole useful for emittance sharing and exchange studies. This paper describes the code structure, the physics models, and the algorithms used in CETASim. We also present the results of its application to the PETRA-IV storage ring.

Theoretical model and verification of the laser thermal effect on the temperature measurement sensitivity of rare earth

The fluorescence temperature measurement based on rare earth ions has been studied widely. It is quite puzzling that the same thermal coupling energy levels present different relative sensitivities of temperature measurement in reports. Different relative sensitivities indicate that the level spacing of the same thermal coupling energy levels calculated by Boltzmann's formula is different. To solve this problem, a physical model is proposed to explain the influence of the laser thermal effect on the fluorescence temperature measurement. The results show that the additional temperature of the sample imposed by the laser thermal effect has an impact on the relative sensitivity, which is the higher the additional temperature, the lower the relative sensitivity. Furthermore, the source of heat generation from laser irradiation of rare earth is discussed. For Er3+ ions, the laser converted into heat mainly generated by the multiphonon transitions between 2H11/2 and 4S3/2 levels. The results show that the theoretical curve fitted well with the experimental data. It is instructive for the design of new temperature sensors.

Physics Models and Machine Learning in Molecular Dynamics

Abstract. Tackling the complexity of particle interactions, this investigation introduces a unified computational methodology employing Moldy, Gnuplot, and Visual Molecular Dynamics (VMD). Inspired by the n-body problem's persistent intrigue, specifically the three-body dynamics within molecular systems, we leverage Molecular Dynamics (MD) simulations to forecast and scrutinize the nuanced behavior of atomic and molecular entities. Moldy, a flexible MD software, facilitated the simulation of particle trajectories and interactions across diverse scenarios, with meticulous documentation of the resulting data. For visual analytics and insight extraction, Gnuplot crafted detailed plots depicting kinetic and potential energies alongside other thermodynamic metrics over temporal scales. The integration of VMD culminated in our analysis by vividly portraying the molecular motions, enhancing comprehension of emergent patterns. This study not only endorses Moldy's precision in MD simulations but also highlights the potent alliance among simulation, analysis, and visualization in elucidating intricate particle interactions. In summary, our work underscores the efficacy of the Moldy-Gnuplot-VMD triad as a formidable resource for scientists engaged in molecular motion prediction and analysis, paving fresh paths in computational physics and chemistry research.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Unravelling eruption dynamics at El Reventador, Ecuador: linking the physiochemical properties of volcanic ash with geophysical, geochemical and satellite remote sensing data

The physiochemical properties of volcanic ash are determined by magma ascent and eruption dynamics and provide important insights into controls on the timing and style of volcanic eruptions. However, linking petrological observations to monitoring parameters remains challenging. Here we investigate the relationships between geophysical, geochemical and satellite remote sensing data with the physiochemical properties of volcanic ash to better understand eruptive dynamics at El Reventador volcano, Ecuador. Between 2016 and 2019, eruptive activity at El Reventador was characterised by frequent explosions interspersed with effusive activity from two summit vents. We found that: (1) periods of predominantly effusive behaviour were defined by ash samples with the lowest proportions of juvenile grains, frequent, more intense thermal anomalies and frequent (ca. every 25 min) but low-energy explosions that produced lower average ash column heights (< 1200 m above the summit) and; (2) periods of predominantly explosive behaviour were defined by ash samples with the highest proportions of juvenile grains and also vesicular juvenile grains, low thermal anomalies and less frequent (ca. every 70 min), higher-energy explosions that produced higher average eruption column heights (> 1500 m above the summit). Our study shows that there are strong correlations between the physiochemical properties of erupted material and the multiparametric monitoring data which in turn link to the type of surface activity observed. As a result, the development of physical conduit models and interpretations of eruptive dynamics are made more robust combining both monitoring data and the physical properties of eruptive products.

Experimental and numerical investigation of seepage erosion in sandy cobbles under coupling hydraulic and dynamic load

The internal structure of sandy cobbles strata is sensitive to disturbances in the urban underground environment, but the structural evolution process under coupling hydraulic and dynamic loads remains unexplored. This paper presents a detailed investigation into the migration patterns and mechanisms of fine particles in sandy cobbles induced by coupled hydraulic and dynamic loading. A sandy cobble specimen with a typical particle size distribution (PSD) was designed and tested using an apparatus that included a constant inlet water head control system and an eccentric-vibrator-based dynamic loading system. Based on physical modeling tests, a numerical model was constructed to replicate the internal structural evolution under hydraulic and dynamic loading by calibrating the time history of local permeability. The test results indicate that the application of dynamic load can instantly disrupt the stable internal structure of sandy cobbles under static seepage, imparting kinetic energy to fine particles that detach from the skeleton structure and migrate along the seepage direction. Significant fine particle loss occurs near the seepage outlet, but due to energy loss during migration, fine particles far from the seepage outlet are recaptured by the skeleton pore throats and clogged again in the migration path. As the intensity of the dynamic loading increases, the migration path for fine particles becomes longer, and the amount and size of fine particles lost significantly increase. The changes in the internal structure of the soil are reflected in hydraulic parameters as a transient increase in local flow velocity, an increase in local pore water pressure due to clogging, and a decrease in the overall permeability coefficient with the loss of fine particles. The findings of this study will be useful for future geotechnical engineering design and analysis.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

A novel approach for tool-narayanaswamy-moynihan model parameter extraction using multi-scale neural model

The accurate determination of parameters in the Tool-Narayanaswamy-Moynihan (TNM) model, which describes the viscoelastic behavior of glass-forming materials, is crucial for predicting material responses through various thermal histories.Traditional methods rely heavily on curve-fitting techniques; however, these often fail due to noise in the data. Furthermore, traditional methods are computationally intensive and prone to inaccuracies, particularly when dealing with complex datasets or when the initial parameter guesses are far from optimal; also, they require a skilled personnel.In this study, we propose the application of a multi-scale convolutional neural network (MCNN) as a machine learning approach to address these challenges. The MCNN model is trained on a comprehensive simulated dataset encompassing a wide range of TNM parameters, allowing it to learn intricate patterns and dependencies within the data that are difficult to capture with conventional methods. Our results show that the MCNN significantly improves the accuracy of the parameter estimations for β and x across the entire spectrum of tested conditions, achieving performance that is not only comparable to, but often surpasses, traditional curve-fitting methods. Furthermore, the MCNN demonstrates superior robustness when initial parameter estimates are suboptimal or when the dataset exhibits significant noise. Although the prediction accuracy for the activation energy Δh∗ and the pre-exponential factor log(A) was somewhat lower, the method still provides valuable estimates that can be refined with supplementary techniques.This work highlights the potential of machine learning approaches like MCNN to revolutionize the parameter extraction process in complex physical models, reducing the reliance on manual curve-fitting and providing a more automated, scalable solution. We also analyze the primary sources of prediction errors in the MCNN outputs and offer insights into future improvements, including model architecture refinements and the integration of additional physical constraints. Our findings suggest that this approach can be extended to other domains where similar models are employed, paving the way for broader applications of machine learning in materials science.

A hybrid model for accurate prediction of composite longitudinal elastic modulus

This research presents a novel hybrid model that integrates a physical-based empirical model with an Artificial Neural Network (ANN) to accurately predict the longitudinal modulus of elasticity for composites under compression. The study focuses on a composite material with a pore inclusion within an ABS plastic matrix, exploring various pore volumes, orientations, and shapes. As part of the proposed hybrid model, a regression-type neural network was trained in MATLAB® to predict and correct discrepancies between the Generalized Stiffness Formulation (GSF) homogenization-based modeling method and the collected compression experimental test results. Using MATLAB® neural network, random error datasets were used to train the feed-forward neural network, and the remaining error datasets were used for validating the performance of the proposed hybrid modeling scheme.The hybrid model demonstrated superior performance, achieving the lowest Mean Error (ME) of 0.1684864, Mean Absolute Error (MAE) of 1.051846, Mean Squared Error (MSE) of 3.500952, and highest R-squared of 0.998797. The proposed hybrid model outperformed both the Generalized Stiffness Formulation (GSF) and standalone ANN models. The significant improvement in prediction accuracy underscores the novelty and robustness of the hybrid approach in composite material modeling. Furthermore, this method can be used to refine any existing physical model by focusing on improving these established models to match experimental results and reducing the discrepancies, which offers a more efficient and attractive strategy for accurate predictions.

Investigation of oil-based CO2 foam EOR and carbon mitigation in a 2D visualization physical model: Effects of different injection strategies

Investigation of oil-based CO2 foam EOR and carbon mitigation in a 2D visualization physical model: Effects of different injection strategies

Ultrasonic dissolution of solid Al in liquid Sn during soldering: Modeling and equation, trend prediction, accelerating effect

Soldering of ceramics/metals using an inactive commercial solder with the advantage of low cost has wide application prospects. The dissolution behavior of base metal could not be quantified, which has been a basic issue for the joining design. This work investigated the dissolution of the solid Al in liquid Sn with and without the ultrasound. The physical model for the dissolving process was established based on the experiments. The relationship equation of the average concentration of Al (Ca) and soldering time was derived by using the Nernst-Brunner principle. The key parameters of limiting solubility (C0L) and dissolution rate constant (K) were obtained through dissolving experiments of liquid Sn to solid Al. The calculation results show that the effect of soldering temperature, and thickness Sn layer on the Al concentration (Ca) and dissolution rate (dCa/dt) under ultrasound is weaker than that without ultrasound. Compared to the condition without ultrasound, the value K, the maximum value of Ca and dCa/dt was increased by 5.8 times, 4.5 times, and 52 times at 250 °C under the ultrasound action, respectively, and the activation energy of dissolution was reduced by 41 %. The mechanism of ultrasonically accelerating dissolution of the solid Al by the liquid Sn has been revealed by using the bubble dynamics principle. It will provide a guideline for the design of soldering ceramics/metals using an inactive commercial solder.