Physical Model Research Articles

Advanced frequency control schemes and technical analysis for large-scale PEM and Alkaline electrolyzer plants in renewable-based power systems

Integrating electrolyzers into power systems can significantly contribute to sustainable energy via the generation of green hydrogen while also enhancing frequency stability through effective regulation of the electrolyzers’ operating power. This study gives a comprehensive analysis of large-scale electrolyzer plants when providing frequency support to power systems. First, the authors present a model predictive control (MPC)-based secondary frequency controller, combined with a droop controller as the primary frequency controller and a virtual inertia controller. Additionally, the study introduces a universal system frequency response (U-SFR) modeling approach that enables high accuracy, low computation burden, and reduced initial parameters as a testbed. Finally, an in-depth analysis is conducted, focusing on different technical aspects of large-scale electrolyzer plants when providing frequency support services. Case studies integrating PEM and Alkaline electrolyzers into the modified IEEE 39-bus system with over 50% wind power penetration are conducted. It is found that the proposed U-SFR model achieves high accuracy with lower computational time compared to detailed physical models. Additionally, model predictive controllers improve frequency quality more effectively than PID and PID-FLC methods. PEM electrolyzers are found to be more efficient in providing grid frequency support than alkaline electrolyzers due to their technical characteristics. Finally, smaller hydrogen tanks may frequently breach storage constraints, negatively impacting the system’s frequency response capability.

Enhancing VPPA welding quality prediction: A hybrid model integrating prior physical knowledge and CNN analysis

In response to the inconsistency between the features obtained by deep learning models and the quality features reflected by the physical laws of the welding process, this study proposes a solution by integrating a physical prior information model with a CNN model. Initially, the physical laws of the welding process are utilized to annotate the arc, weld pool, and weld seam features relevant to quality, which are then acquired through image processing algorithms, thereby converting the physical laws into a prior information model. Subsequently, this prior information model guides the CNN model for quality recognition, and the CNN model's attention to features is explained through visualization methods to elucidate the relationship between features and quality recognition. Experimental results demonstrate that under the guidance of the prior information model, the CNN model not only automatically focuses on features relevant to quality but also achieves a differential feature attention strategy, thereby improving the recognition accuracy of different outcomes. This research provides a new perspective for deep learning in the field of welding quality recognition.

A novel approach to aeroengine performance diagnosis based on physical model coupling data-driven using low-rank multimodal fusion method

Aeroengine health assessment plays a pivotal role in ensuring flight safety and reliability. Traditionally, this process involves diagnosing the performance of the aeroengine gas path. However, owing to the intricacies of operating conditions, non-linear performance, and the interplay of gas path performance fault characteristics, determining the aeroengine health condition directly from engine monitoring information poses a significant challenge, particularly in cases of insufficient sensor data. To address these challenges, a novel digital twin method for aeroengine performance diagnosis has been proposed. This method integrates data-driven and performance models, employing a low-rank multimodal fusion approach. By digitizing the physical system or process through mathematical models and simulation technology, this approach presents distinct advantages compared to previous methods relying solely on models or data. At the aeroengine component level, an adaptive model was implemented, and the data-driven model was constructed using flight data. Gas path fault classification employed support vector machines. The engine digital twin was established through low-order multimodal fusion. Results indicate that the proposed method attains excellent diagnostic accuracy under both steady and transient conditions. It can be harnessed to enhance engine performance monitoring and evaluation, thereby improving the reliability, availability, and efficiency of the engine.

A physics‐constrained hybrid residual neural network for the prediction of moisture content in a closed‐cycle drying system

AbstractClosed‐cycle drying technology has the advantages of safety, energy saving, and environmental protection, and has a wide application prospect. Due to the multi‐physics and multi‐scale nature of heat/mass transfer in the closed‐cycle drying process, as well as the characteristics of drying medium circulation and energy integration, it is difficult to obtain the product moisture content prediction model in closed‐cycle process based on rigorous closed‐cycle drying process mechanism model. However, accurate monitoring of product moisture content in the closed‐cycle drying process is the key to improving drying quality and process optimization. Aiming at the challenges in predicting moisture content, this paper proposes a physics‐constrained hybrid residual neural network (PC‐HRNN) for soft sensor modelling within a designed closed‐cycle drying system. The characteristic of this proposed method lies in its effective fusion of physics, knowledge, and data. First, a hybrid residual neural network (HRNN) is constructed based on the integration of a physics model and a data‐driven model. The HRNN takes the knowledge from the physics model as its auxiliary features and learns the prediction residuals of the physics model through a neural network model. Then, a new loss function that takes into account physical laws is introduced into HPNN to standardize model training and improve the generalization performance of the model. The experimental results show that the PC‐HRNN model improves prediction accuracy and model robustness, reduces the demand for data, and demonstrates stronger extrapolation capabilities under limited data.

Distributed estimation of state-of-charge of supercapacitor packs: A consensus-based approach

It is crucial to estimate the state-of-charge (SoC) of a supercapacitor pack as cells are typically connected to form packs in practical applications. However, most of the existing studies adopted a centralized approach, which is to estimate all the single cells, and then calculate the overall SoC. When the total number of the cells comes to a high level, the burden of communication and the price of a single fault rise rapidly. To address this challenge, in this article, we propose a decentralized method for estimation of pack SoC, which is free of the central observer. We use the state space equation to model the supercapacitors in the physical modeling. In the cyber modeling, we construct their communication topology and apply a definition of pack SoC. A two-layer structure is proposed in which we design a Luenberger observer for each cell to estimate SoC of each cell. We propose a consensus-based algorithm to estimate its pack SoC. A laboratory testbed has been built to verify the effectiveness of the proposed method. Experimental results show that the proposed method can effectively implement the estimation and is more robust to communication failure than the centralized method.

Magneto-acousto-electrical tomography based on frequency response compensated linearly frequency-modulated signal stimulation.

In magneto-acousto-electrical tomography (MAET), linearly frequency-modulated (LFM) signal stimulation uses much lower peak voltage than the spike pulse stimulation, lengthening the operation life of the transducer. However, due to the uneven frequency responses of the transducer, the low-noise amplifier (LNA), and the bandpass filter (BPF), MAET using LFM signal stimulation suffers from longitudinal resolution loss. In this paper, frequency response compensated linearly frequency-modulated (FRC-LFM) signal stimulation is investigated to resolve the problem. The physical model of measurement of the frequency responses of the transducer and the cascading module of the detection electrodes, the LNA, and the BPF is constructed. The frequency responses are approximated by fitting a curve to the measurement data. The frequency response compensation function is set to the reciprocal of the product of the frequency responses. The digital FRC-LFM signal is generated in MATLAB and converted to analog signal through an arbitrary waveform generator. Two groups of MAET experiments are designed to confirm the performance of the FRC-LFM signal stimulation. Pure agar phantom with rectangular through-holes and agar phantom with pork tissue inclusion serve as the samples. The pulse-compressed magneto-acousto-electrical (MAE) signal obtained using FRC-LFM stimulation has narrower main-lobe than that obtained using LFM excitation,although the signal to noise pulse interference ratio of the former is little lower than that of the latter, which is due to the limitation of the power amplifier. FRC-LFM also proves to be an effective method to utilize the frequency outside the working band of the transducer in MAET. The method in this study compensates for the longitudinal resolution loss due to the uneven frequency responses. Combining with high-capability power amplifier and high-performance LNA, the MAET using FRC-LFM signal stimulation can potentially achieve high longitudinal resolution and high sensitivity, advancing MAET toward the clinical application.

Evolution of geometric phase and explaining the geodesic rule

We use the recently developed wave model of geometric phase to track the continuous evolution of geometric phase as a wave propagates through optical elements and throughout an optical system. By working directly with the wave properties, we encounter a natural explanation of why the conventional Poincaré sphere solid angle method must use geodesic paths rather than the physical paths of the polarization state—the “geodesic rule”—and show that the existing rules for the solid angle algorithm are incomplete. Finally, we use the physical model to clarify the differences between the Pancharatnam connection and the geometric phase of a wave.

Stepped chute energy dissipation structure downstream of a low-level river crossing for embankment protection

A low-level river crossing is an economic means of providing access for lower-order roads during high-frequency flood events. Conventionally, culverts are employed to enable the flow to traverse the road, albeit with allowance for overtopping. This overtopping flow from the approach roads, however, possesses the potential to cause erosion of the downstream embankment, thereby jeopardising the foundational integrity of the structure. A physical modelling study was undertaken to investigate the hydraulic performance of stepped chutes in terms of flow patterns, air-water flow properties, and energy dissipation. Various types of stepped chutes were investigated, namely horizontal steps with a chute wall, horizontal steps without a chute wall but with riprap placed directly downstream of each chute, step-inclined steps (backward sloping), steps with pooled cascades, and steps with baffle blocks. Notably, chutes with horizontal steps were the most efficient configuration in terms of relative energy dissipation. Furthermore, this research culminated in the formulation of a new regression formula to establish a quantifiable relationship between the required step length and the overflow depth and discharge, thereby offering a valuable tool for design purposes.

Design and Demonstration of Hingeless Pneumatic Actuators Inspired by Plants

Soft robots have often been proposed for medical applications, creating human-friendly machines, and dedicated subject operation, and the pneumatic actuator is a representative example of such a robot. Plants, with their hingeless architecture, can take advantage of morphology to achieve a predetermined deformation. To improve the modes of motion, two pneumatic actuators that mimic the principles of the plants (the birds-of-paradise plant and the waterwheel plant) were designed, simulated, and tested using physical models in this study. The most common deformation pattern of the pneumatic actuator, bending deformation, was utilized and hingeless structures based on the plants were fabricated for a more complex motion of the lobes. Here, an ABP (actuator inspired by the birds-of-paradise plant) could bend its midrib downward to open the lobes, but an AWP (actuator inspired by the waterwheel plant) could bend its midrib upward to open the two lobes. In both the computational and physical models, the associated movements of the midrib and lobes could be observed and measured. As it lacks multiple parts that have to be assembled using joints, the actuator would be simpler to fabricate, have a variety of deformation modes, and be applicable in more fields.

The effect of Lévy index coefficient on modulational instability and rogue wave excitation in nonlocal media with cubic-quintic nonlinearities

This paper explores the modulational instability (MI) of a plane wave and its behavior in the nonlinear Schrödinger equation (NLSE) with a fractional diffraction term quantified by its Lévy index coefficient and nonlocal cubic-quintic nonlinearities. First, we analyze the stability of the plane wave solution and examine how nonlocal nonlinearities and the Lévy index coefficient affect the MI gain. We observe that the stability in the fractional NLSE exhibits new features that differ from those in the standard NLSE. Specifically, when dealing with competing cubic and quintic nonlinearities, the interaction between nonlocality and the Lévy index coefficient α can eliminate MI for low values of α, unlike the classical NLSE with α=2, where we find the plane wave to be unstable. Besides the linear stability analysis, numerical simulations are performed to understand further the plane wave dynamics from its nonlinear stage in this model. The results reveal the generation of periodic chains of localized peaks. Guided by analytical predictions and using the plane wave solution subject to Gaussian perturbation, we numerically investigate the possibility of exciting rogue waves in the parameter spaces where MI exists. We find that the different combinations of signs of the cubic and quintic nonlinearities (focusing and defocusing) and the fractional diffraction term significantly impact the formation of rogue waves. These results may pave the way for the theoretical and experimental study of nonlinear phenomena in physical models with fractional derivatives and nonlocal nonlinearities.

Physics-Informed Deep-Learning Models Improve Forecast Scalability, Reliability

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4042557, “Physics-Informed Deep-Learning Models for Improving Shale and Tight Forecast Scalability and Reliability,” by Kainan Wang, SPE, Lichi Deng, and Yuzhe Cai, SPE, Chevron, et al. The paper has not been peer reviewed. _ In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty. Introduction In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions. A novel approach known as physics-informed machine learning (PIML) has gained prominence. These models leverage the strengths of machine learning while honoring the constraints imposed by physical laws. By incorporating observational, inductive, or learning bias, they bridge the gap between empirical data and fundamental physics. In this paper, the authors showcase the application of a PIML system tailored specifically for unconventional reservoirs. Their methodology involves the selection of an appropriate physics modeling framework, coupled with the development of multiple machine-learning models that augment the limited field data set. Data Acquisition and Generation Bottomhole pressure (BHP) data are key to reliable forecasting because they reflect constraints to well production. Downhole pressure gauges can provide measurements of BHP accurately, but these measurements are not always available. Numerical models can be used to correlate surface measurements to BHP with some accuracy, taking into consideration the well configuration and fluid properties, yet it is too time-consuming to develop such models for many wells in relation to the speed needed for unconventional reservoir development. An in-house machine-learning model was developed for BHP calculation to further augment the data set of gauge measurements and numerical modeling. Through a workflow that chooses the wells with higher-fidelity data as a training set, and careful calibration and iterations between incrementally adding wells of high-fidelity data to reduce uncertainty, the trained machine- learning model is able to predict BHP accurately for many more wells. Another type of engineering data important for reliable well-performance analysis and forecasting is reservoir-fluid description. In this study, the authors leveraged an internally developed pressure/volume/temperature (PVT) machine-learning model trained on proprietary PVT data, which was also shown to provide reliable predictions to key PVT properties such as saturation pressure, formation volume factor, and viscosity from limited inputs at the reservoir and separator level. These PVT models not only enhance scalability of any downstream forecasting models but also play a crucial role in physics-informed uncertainty analysis for these models.

The physical information LSTM surrogate model for establishing a digital twin model of reciprocating air compressors

Reciprocating air compressors play a crucial role in industrial production processes. However, due to the complex structure and long operating time of reciprocating air compressors, real-time monitoring to grasp the operating status of reciprocating air compressors has become particularly important. Digital twin is a technology that can reflect the behavior of physical entities in real time, accurately predicting and evaluating the operation status of reciprocating air compressors. However, the establishment of a digital twin model for reciprocating air compressors requires a significant amount of computational resources, which can result in the inability to meet the requirements of real-time performance evaluation. To overcome this limitation, this paper proposes a method for constructing a digital twin model of reciprocating air compressors based on a surrogate model. The surrogate model is constructed based on a long short-term memory neural network with physical information(PILSTM). This model can accurately describe the changes in cylinder pressure by combining physical information. According to the characteristics of cylinder pressure changes, regularization formulas are added to ensure the smoothness of the predicted pressure. The experimental results show that the digital twin model based on the surrogate model has high prediction accuracy and real-time performance. Therefore, this model provides a new method for monitoring the operating status of reciprocating air compressors.

Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and energy sectors, and facilitating water transfer to the Segura River Basin, a key semi-arid region. Given the basin's near-total allocation of water resources, accurate streamflow simulations are essential to optimize the socio-economic distribution and ensure sustainable management across both interconnected basins. This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. An ensemble machine learning technique is then applied to assess improvements in streamflow estimation. Results revealed that AI-based models significantly surpassed the SWAT+ model in performance. Furthermore, the application of an ensemble technique enhanced the precision of rainfall-runoff modeling by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period, compared to individual AI-based models. Additionally, the SWAT+ model's precision improved by 44 to 74% and 40 to 55% for the respective periods. The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. This research offers significant contributions to hydrological modeling, highlighting the importance of ensemble techniques in elevating predictive accuracy for various river basins.

Hyperslip velocity of melting ice sliding down inclined parallel ridges

A geometric and physical model for melting ice sliding over inclined superhydrophobic (SH) surfaces with parallel ridges is presented. By analyzing the micro-shear flows of molten liquid films between the ice layer and SH surfaces, the hyperslip velocities of melting ice sliding are investigated. The stick-slip boundary condition of the SH surface is used to establish the dual-series equations analytically, and the numerical solutions are implemented by truncating Fourier series and transforming the dual-series equations into linear algebraic equations to determine the hyperslip velocities of melting ice sliding. The numerical results indicate that the non-dimensional hyperslip velocities increase nonlinearly from near 0 to approximately 1.1 for longitudinal sliding and from near 0 to approximately 0.55 for transverse sliding with an increasing air groove ratio (a). The hyperslip velocities increase with increasing δ at the beginning initially (δ < 1), after which they tend toward asymptotic solutions as δ = 1. The hyperslip velocity ratio (Wh/Uh) shows that longitudinal ridges are at least twice as effective as transverse ridges in enhancing the ice hyperslip velocity, with the velocities accounting for more than 60% of the ice sliding velocities for arbitrary θ at a = 0.95 and δ = 0.1. The relative deviations between the numerical and asymptotic solutions are less than 5% at δ = 1, with the maximum relative deviation occurring at a = 0.65 for arbitrary θ.

Physical Modeling for Micropattern-Trench IGBT and p-i-n Diode With Dynamic Control Lumped Charge

Physical Modeling for Micropattern-Trench IGBT and p-i-n Diode With Dynamic Control Lumped Charge

A new similarity method for the ultimate strength of box girders subjected to the combined load of bending and lateral pressure

The ultimate strength is an important property for hull structures nowadays. For large-scale structures, scale models are needed to perform physical model tests. Therefore, similarity methods are especially important. Traditional similarity methods consider similarity criteria of cross-section properties, and modern similarity methods introduce some strength-related similarity criteria to consider the nonlinear collapse procedure. However, these two kinds of criteria may be conflicting and are hard to meet simultaneously. Most existing works concentrated on simple structures, like stiffened plates, due to the difficulty in meeting all the similarity criteria. In this paper, a new similarity method is proposed for box girders subjected to the combined load of bending moment and lateral pressure. The proposed method considers only the strength-related similarity criteria, ignoring the criteria of cross-section properties. The column slenderness, the plate slenderness, and the torsional slenderness are selected as the similarity parameters for every stiffened plate in the box girder. The new similarity relations of the ultimate moment and the applied lateral pressure are derived according to the proposed criteria. A structured step-by-step scale model design procedure is proposed to design corresponding scale models. 4 numerical examples are presented to show the correctness and advantages of the proposed method.

Peakons and compactons of the (2+1)-dimensional modified dispersive water-wave system

For a higher-dimensional nonlinear dynamical system, there exist abundant coherent excitations. The variable-separated method is a powerful approach to deriving these structures, as its solutions allow for arbitrary functions. Previous works have produced numerous results, including solitons, chaos and fractals. As the molecule structure appears, constructing the multi-soliton molecule through this technology is a meaningful work, especially considering the local peakons and compactons that were seldom discussed before. In this paper, after taking the Bäcklund transformation, the variable-separated solution for the (2+1)-dimensional modified dispersive water-wave system is first derived, which is an important physical model in describing the nonlinear and dispersive long gravity waves. As a result, the multi-peakons and multi-compactons are constructed through the derived universal formula with the aid of the variable functions p and q. These solitons include two general clusters of M × N peakons and compactons, from which the multi-soliton molecules and their interactions are presented.

Experimental Study Of Anisotropic And Dispersion Signature Of Shale Reservoirs: Implications For Synthetic Seismic Well Tie

To investigate velocity anisotropy and dispersion in lacustrine shales, we conducted laboratory measurements of acoustic properties at seismic frequencies (2–100 Hz) and ultrasonic frequencies (1 MHz) under varying effective pressures. Our study reveals significant transverse isotropy, accompanied by notable velocity dispersion and attenuation, with anisotropy coefficients ranging from 0.45 to 0.74 for P-waves and 0.44 to 0.95 for S-waves. Attenuation exhibited weak dependence on frequency and pressure but showed a strong positive correlation with clay content. We interpreted the data by integrating geological and physical analyses. Using rock physics modeling and the propagation matrix method, we found that velocity dispersion primarily caused the mismatch between seismic data and synthetic seismograms, while anisotropy had minimal impact on near-offset seismic data. Our findings suggest that anisotropy and dispersion observed at the core scale persist at the seismic scale. Incorporating these effects into seismic workflows is crucial for improving data processing, inversion, and reservoir characterization. This study provides valuable insights for exploration and production strategies in the study area and similar settings.

Cationic dye remediation in water treatment with Lizardite–Rice Husk composite: A statistical physics approach

This study presents the development of a novel composite adsorbent, Lizardite-Rice Husk Composite (LRHC), synthesized from lizardite, a mineral derived from chromite ore waste, and rice husk, an agricultural byproduct. The primary objective was to evaluate the efficiency of LRHC in the adsorption of cationic dyes, Malachite Green (MG) and Methylene Blue (MB), from aqueous solutions. Systematic investigations were conducted to assess the influence of adsorbent mass, initial dye concentration, stirring speed, and contact time on adsorption performance. The results revealed that LRHC exhibited optimal adsorption capacities at pH values above 5, with MB and MG adsorption capacities reaching 309.75 mg/g and 51.43 mg/g, respectively. Adsorption equilibrium for MB was achieved within 30 min, indicating rapid dye uptake. Isotherm analysis demonstrated that the Temkin model best described MB adsorption, while the Freundlich model was more suitable for MG, with favorable Langmuir constants. Kinetic studies confirmed that the adsorption followed a second-order model, suggesting chemisorption as the rate-determining step. Intraparticle diffusion and mass transfer were identified as significant contributors to the adsorption process. Thermodynamic studies indicated that MB adsorption was spontaneous (ΔG° = −4.69 kJ/mol for the concentration of 50 mg/L at 298 K) and that MG adsorption was non-spontaneous (ΔG° = 1.52 kJ/mol for the concentration of 50 mg/L at 298 K), with spontaneity increasing at higher initial concentrations. Statistical physics modeling, particularly the monolayer model, provided detailed insights into the adsorption mechanisms and geometry. Furthermore, LRHC demonstrated excellent reusability, maintaining over 80% of its initial adsorption capacity after four regeneration cycles via a heterogeneous Fenton-like reaction. The combination of easy availability, cost-effectiveness, and environmental friendliness makes LRHC a superior adsorbent for the removal of cationic dyes compared to conventional alternatives. This research contributes novel insights into the design and application of composite adsorbents, offering a sustainable solution for water treatment and addressing significant environmental challenges.

A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping

Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC–HMS and HEC–RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC–HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a “Sigmoid Focal Loss” function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC–RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data.