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Seismic response mitigation of offshore jacket platforms using a novel bidirectional tuned liquid column gas damper (BTLCGD)

Mohamadhosein Mohasel, Mohamad Reza Chenaghlou, Ahmad Reza Mostafa Gharabaghi

Accepted Manuscript , doi: 10.1016/j.taml.2025.100605

[Abstract] (0) [PDF 2763KB] (0)

Abstract:
This study investigates the seismic response mitigation of an offshore jacket platform via a novel damping system, the bidirectional tuned liquid column gas damper (BTLCGD). To efficiently model the complex platform structure, an equivalent single degree of freedom approach was employed. Since the mass contribution of the first mode of the platform is more than 90%, this simplification significantly reduces the computational burden while maintaining accuracy. Therefore, this structure was modeled and analyzed on a scale of 1 to 36 using the Froudian law. To address the limitations of conventional tuned liquid column gas dampers (TLCGDs), which are susceptible to the directionality of seismic excitations, BTLCGD was proposed. This innovative damper is designed to operate effectively in two orthogonal directions, thereby improving seismic performance. Through numerical simulations, the performance of both TLCGD and BTLCGD was evaluated under seismic loading. The results demonstrated that BTLCGD significantly outperforms TLCGD in terms of reducing structural responses, particularly in the direction where TLCGD is ineffective. Furthermore, BTLCGD offers advantages in terms of installation and space requirements. The results of this research offer valuable perspectives into the design and implementation of effective damping systems for offshore structures, contributing to enhanced structural integrity and safety.

Generating Airfoils from Text: FoilCLIP, A Novel Framework for Language-Conditioned Aerodynamic Design

Mingcheng Lei, Yufei Zhang

Accepted Manuscript , doi: 10.1016/j.taml.2025.100602

[Abstract] (50) [PDF 1956KB] (3)

Abstract:
Recent advances in contrastive language‒image pretraining (CLIP) models and generative AI have demonstrated significant capabilities in cross-modal understanding and content generation. Based on these developments, this study introduces a novel framework for airfoil design via natural language interfaces. To the authors‘ knowledge, this study establishes the first end-to-end, bidirectional mapping between textual descriptions (e.g., ―low-drag supercritical wing for transonic conditions‖) and parametric airfoil geometries represented by class-shape transformation parameters. The proposed approach integrates a CLIP-inspired architecture that aligns text embeddings with airfoil parameter spaces through contrastive learning, along with a semantically conditioned decoder that produces physically plausible airfoil geometries from latent representations. The experimental results validate the framework‘s ability to generate aerodynamically plausible airfoils from natural language specifications and to classify airfoils accurately based on given textual labels. This research reduces the expertise threshold for preliminary airfoil design and highlights the potential for human-AI collaboration in aerospace engineering.

Physics Field Super-resolution Reconstruction via Enhanced Diffusion Model and Fourier Neural Operator

Yanan Guo, Junqiang Song, Xiaoqun Cao, Chuanfeng Zhao, Hongze Leng

Accepted Manuscript , doi: 10.1016/j.taml.2025.100604

[Abstract] (75) [PDF 9125KB] (4)

Abstract:
With the growing demand for high-precision flow field simulations in computational science and engineering, the super-resolution reconstruction of physical fields has attracted considerable research interest. However, traditional numerical methods often entail high computational costs, involve complex data processing, and struggle to capture fine-scale high-frequency details. To address these challenges, we propose an innovative super-resolution reconstruction framework that integrates a Fourier neural operator (FNO) with an enhanced diffusion model. The framework employs an adaptively weighted FNO to process low-resolution flow field inputs, effectively capturing global dependencies and high-frequency features. Furthermore, a residualguided diffusion model is introduced to further improve reconstruction performance. This model uses a Markov chain to map high-resolution fields to low-resolution counterparts and incorporates a reverse diffusion process solved by an adaptive time-step ordinary differential equation (ODE) solver, ensuring both stability and computational efficiency. Experimental results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy and efficiency, offering a promising solution for fine-grained data reconstruction in scientific simulations.

Comparative Analysis of Linear and Angular Forces in Traumatic Brain Injury: Experimental Evidence and Mechanistic Insights

Qinghang Luo, Rui Nie, Zhenwei Du, Tao Xiong, Xinyu Du, Li Yang, Kui Li, Shengxiong Liu, Aowen Duan

Accepted Manuscript , doi: 10.1016/j.taml.2025.100601

[Abstract] (81) [PDF 1842KB] (1)

Abstract:
As China’s automotive sector continues to expand, a substantial increase in per capita vehicle ownership is anticipated, potentially exacerbating traffic accident rates and increasing safety concerns. Among the various injuries sustained in such accidents, traumatic brain injury (TBI) is particularly significant, as it constitutes a leading cause of morbidity and mortality among both drivers and passengers. Current statistics reveal that the incidence rate of TBI is between 50% and 70%, with mortality rates as high as 60% to 80%^[1-3]. In response to the rising rates of traffic-related brain injuries, China implemented the New Car Crashworthiness Assessment Program (C-NCAP) decades ago, which was modeled after the U.S. federal government’s New Car Assessment Program (NCAP). This program established stringent safety requirements for vehicles, with an emphasis on protecting both occupants and pedestrians during collisions. A pivotal standard set by C-NCAP is the head injury criterion (HIC), which stipulates that the HIC value must remain below 1000. Nevertheless, these standards predominantly focus on linear acceleration, potentially overlooking the critical influence of angular forces on injury outcomes, thereby reducing their efficacy in predicting brain injuries^[4]. In reality, head impacts in vehicle collisions, falls, and sports often involve rapid rotational movements. Injuries commonly arise from complex mechanisms that combine both linear and angular forces rather than being caused by just one single type^[5]. Thus, there is an urgent need to develop a model of brain injury that accurately replicates these combined effects.

Low-Computational Time and Accurate Classification of Flow Regimes in Bubble Columns for Aquaculture Aeration Using Probability Density Functions of Bubble Velocity Standard Deviation

Natee Thong-Un, Wongsakorn Wongsaroj, Jirayut Hansot, Weerachon Treenuson, Hiroshige Kikura

Accepted Manuscript , doi: 10.1016/j.taml.2025.100598

[Abstract] (239) [PDF 2160KB] (2)

Abstract:
This study explores the combination of ultrasound technology with a detection algorithm to categorize flow regimes in bubble columns used for aeration in aquaculture. An ultrasonic velocity profiler is used to obtain the standard deviation of the bubble velocity distributed throughout the column. The bubble velocity data for three known flow regimes were used to develop a probability density function (PDF) classification model. The experimental apparatus consisted of a circular tank equipped with a bubble generator and gas hold-up monitoring systems. The flow regimes of the experimental fluid were determined, and the classification was conducted via the PDF method. The results demonstrate that the classification accuracy is not lower than that of traditional machine learning methods.

Investigation of unsteady ventilated partial cavitating flow around an axisymmetric body with particular emphasis on the vortex structure

Deshuai Cui, Xinran Liu, Tairan Chen, Guoyu Wang

Accepted Manuscript , doi: 10.1016/j.taml.2025.100596

[Abstract] (261) [PDF 1661KB] (3)

Abstract:
This paper investigates the ventilated cavity phenomena of a symmetric body under specific conditions, focusing on the factors affecting the vortex structure. The ventilated cavitating flow development process is simulated with a homogeneous free surface model combined with a filter-based turbulence model. The results show the characteristics of the pressure pulse and the bubble shedding around the axisymmetric body. A quasiperiodic pressure pulse occurs at the middle of the body. In addition, three main types of vortexes occur in ventilated partial cavitation: large-scale cloud vortices, U-type vortices, and small-scale vortices. Further analysis revealed that the cavities and vortex structures have similar influencing factors. The vorticity transportation equation (VTE) is applied to analyze the main factors influencing the vortex. The results indicate that fluid density primarily affects large-scale cloud vortices, the velocity gradient plays a dominant role in U-type vortices, and fluid angular velocity is the main influencing factor for small-scale vortices.

Aerodynamic optimization of supersonic airfoils using bijective cycle generative adversarial networks

Chenfei Zhao, Yuting Dai, Xue Wang, Chao Yang, Guangjing Huang

Accepted Manuscript , doi: 10.1016/j.taml.2025.100591

[Abstract] (278) [PDF 2208KB] (2)

Abstract:
An efficient, diversified, and low-dimensional airfoil parameterization method is critical to airfoil aerodynamic optimization design. This paper proposes a supersonic airfoil parameterization method based on a bijective cycle generative adversarial network (Bicycle-GAN), whose performance is compared with that of the cVAE-based parameterization method in terms of parsimony, flawlessness, intuitiveness, and physicality. In all four aspects, the Bicycle-GAN-based parameterization method is superior to the cVAE-based parameterization method. Combined with multifidelity Gaussian process regression (MFGPR) surrogate model and a Bayesian optimization algorithm, a Bicycle-GAN-based optimization framework is established for the aerodynamic performance optimization of airfoils immersed in supersonic flow, which is compared with the cVAE-based optimization method in terms of optimized efficiency and effectiveness. The MFGPR surrogate model is established using low-fidelity aerodynamic data obtained from supersonic thinairfoil theory and high-fidelity aerodynamic data obtained from steady CFD simulation. For both supersonic conditions, the CFD simulation costs are reduced by more than 20% compared with those of the cVAE-based optimization, and better optimization results are obtained through the Bicycle-GAN model. The optimization results for this supersonic flow point to a sharper leading edge, a smaller camber and thickness with a flatter lower surface, and a maximum thickness at 50% chord length. The advantages of the Bicycle-GAN and MFGPR models are comprehensively demonstrated in terms of airfoil generation characteristics, surrogate model prediction accuracy and optimization efficiency.

Probabilistic Interface Failure Model of Composite Electrodes in All-Solid-State Batteries Under Mechanical-Diffusion Coupling

Zehui Zhang, Jici Wen

Accepted Manuscript , doi: 10.1016/j.taml.2025.100593

[Abstract] (220) [PDF 1956KB] (0)

Abstract:
All-solid-state lithium metal batteries (ASSLMBs) are widely recognized as promising next-generation energy storage technologies that offer significant advantages in terms of safety and energy density. However, the long-term cycling stability of these batteries is often compromised by interfacial failures driven by coupled mechanical and diffusion effects. This study presents a probabilistic failure prediction model that quantifies interfacial damage and capacity loss in composite electrodes under the coupled influence of mechanical-diffusioninduced processes. We first develop a pseudo3D (P3D) interface failure model for a binary particle system to evaluate interfacial failure during the critical delithiation process. The P3D model is validated through mechanical-diffusion coupled simulations. Additionally, for multiparticle composite electrode films with heterogeneous particle sizes, we identify a key structural factor that governs the failure of the particle-solid electrolyte interface, which follows a three-parameter Burr distribution. Building on this, we develop a probabilistic model to predict the capacity fade in multiporject composite films. This work provides a comprehensive understanding of the critical geometric factors that influence interfacial stability, offering valuable theoretical insights and practical guidance for the rapid assessment, optimization, and enhancement of cycling stability in ASSLMBs.

Thermomechanical cubic closed-cell model of liquid-saturated soft composites with surface effects

Jun Yin, Fei Ti, Xuechao Sun, Lijun Su, Xiuwei Wan, Shaobao Liu

Accepted Manuscript , doi: 10.1016/j.taml.2025.100590

[Abstract] (222) [PDF 1502KB] (2)

Abstract:
Many animal and plant tissues, such as adipose tissue and fruits, can be taken as liquid-saturated soft composites, which have densely packed pores that are filled with liquid. Typically, when the pore dimensions are sufficiently small (at the micro- or nanoscale), surface effects significantly influence the mechanical properties of the material. To characterize the thermomechanical properties critical for animals and plants, we propose an idealized cubic closed-cell model in which liquid compressibility and surface stress (i.e., surface moduli and residual surface stress) are considered. Analytical solutions of the model are then employed to quantify how the surface stress, porosity and liquid bulk modulus affect the effective Young’s modulus, effective Poisson ratio and effective coefficient of thermal expansion (CTE) of the liquid-saturated soft composite. An increase in residual surface stress reduces both the effective modulus and effective CTE, whereas increasing the surface moduli result in a greater effective modulus and reduced effective CTE. The results provide critical insights into how surface effects govern the macroscopic thermomechanical behavior of liquid-saturated soft composites with small pores.

Topology optimization for fluid–structure interaction problems considering heat transfer performance

Yuhui Jing, Li An, Sinan Yi, Jing Li, Pai Liu, Yaguang Wang, Xiaopeng Zhang

Accepted Manuscript , doi: 10.1016/j.taml.2025.100592

[Abstract] (209) [PDF 2131KB] (0)

Abstract:
Effectively controlling the deformation and temperature of heated structures is crucial for achieving high-performance active cooling through fluid flow. In this study, the topology optimization design of structures considering fluid–structure interactions and heat transfer performance was investigated, and then optimized designs of two-dimensional/three-dimensional cooling impingement systems obtained using the proposed method were obtained. In the optimization model, the objective function was constructed as a weighted combination of the mechanical deformations at specific locations and the average temperature within the designated solid channel structures. Additionally, explicit functional interpolation models were introduced to establish connections between the thermal, fluid, and solid properties, along with the element densities. In the analysis model, the strongly coupled structural mechanical deformation and fluid velocity field were analyzed via a dynamic-grid-based finite element model with a Winslow elliptic smoother to automatically track the fluid–structure interface during the process of optimization. To solve the optimization problems, the globally convergent moving asymptotic optimizer method was used to adjust the design variables on the basis of the sensitivity analysis. A demonstration of the efficacy of the proposed algorithm is provided through the presentation of several optimization examples. Furthermore, two- and three-dimensional cooling impingement systems were designed with the proposed method.

Aerodynamic uplift force improvement in single-strip high-speed pantograph via key parameter regulation with mechanism investigation

Yafeng Zoua, Xianghong Xu, Rui Zhou, Zichen Liu, Liming Lin

Accepted Manuscript , doi: 10.1016/j.taml.2025.100588

[Abstract] (227) [PDF 0KB] (0)

Abstract:
This study addresses the significant disparity in aerodynamic uplift forces experienced by single-strip high-speed pantographs under different operating directions. A systematic numerical investigation was conducted to evaluate the influence of key geometric parameters on aerodynamic characteristics, culminating in two targeted adjustment strategies. The reliability of the computational methodology was validated through comparative analysis, which revealed less than a 6% deviation in aerodynamic drag between the numerical simulations and wind tunnel tests. Aerodynamic decomposition revealed that the operating direction critically impacts the uplift force, which is governed by two factors: streamwise cross-strip positioning and the angular orientation of the arm hinge. These factors collectively determine the divergent aerodynamic responses of the panhead and frame during directional changes. By establishing a parametric database encompassing four strip-to-crossbar spacing configurations and six arm diameter variations, nonlinear response patterns of the uplift forces under different operating directions to geometric modifications were quantified. Both adjustment approaches, simultaneously reducing both streamwise and vertical strip-to-crossbar spacings to half of the original dimensions or increasing the upper arm spanwise diameter to 1.45 times and decreasing the lower arm spanwise diameter to 0.55 times the baseline values, successfully constrained aerodynamic uplift force deviations between operating directions within 3%.

Molecular understanding of phase behavior of hydrocarbon mixtures in nanopores and their influence on recovery dynamics

JingShan Wang, Yan Wang, BinHui Li, QingZhen Wang, SiWei Meng, RuoShi Chen, HengAn Wu, FengChao Wang

Accepted Manuscript , doi: 10.1016/j.taml.2025.100589

[Abstract] (230) [PDF 0KB] (0)

Abstract:
Understanding the phase behavior of hydrocarbons and their mixtures, especially under confinement, is crucial for the extraction of shale oil and gas. In this study, we employed molecular dynamics simulations to investigate the phase behaviors of three typical hydrocarbons (methane, pentane, and octane) in the bulk phase and in nanopores. We find that the confinement effect can alter the phase behavior of a single-component hydrocarbon. For the mixture of methane and octane in nanopores, a rather high proportion of methane could inhibit the capillary condensation of octane. We also studied the influence of phase behavior on the recovery dynamics of hydrocarbon mixtures from blind nanopores of different sizes at different gas‒oil ratios. The capillary condensation of the heavy hydrocarbon components in the nanopore throat could hinder the transport of light. These findings increase the understanding of the occurrence states of shale oil and gas and their migration through nanopore throats, providing practical guidance for shale oil and gas development.

Mechanics of origami/kirigami structures and metamaterials

Hongbin Fang, Haiping Wu

Accepted Manuscript , doi: 10.1016/j.taml.2025.100587

[Abstract] (245) [PDF 0KB] (1)

Abstract:
The ancient arts of paper folding and cutting— origami and kirigami— have long captivated both artists and engineers. Today, these techniques are inspiring the creation of adaptive structures and innovative metamaterials that challenge conventional mechanical paradigms. Whereas early research in origami/kirigami primarily addressed design principles and folding kinematics to achieve vast shape transformations, breakthroughs since the 2010s have unlocked new avenues in folding- and cutting-induced mechanics. By harnessing folding-induced deformations and leveraging strong geometric nonlinearities, researchers now realize exceptional mechanical properties such as auxetic behavior, high reconfigurability, programmable stiffness, impact absorption, and bi-stability or multi-stability.

Study of an adaptive bump control mechanism for shock wave/boundary layer interactions in supersonic flows

Shan-shan Tian, Liang Jin, Wei Huang, Yang Shen, Kai An

Accepted Manuscript , doi: 10.1016/j.taml.2025.100586

[Abstract] (220) [PDF 2023KB] (0)

Abstract:
The stability of supersonic inlets faces challenges due to various changes in flight conditions, and flow control methods that address shock wave/boundary layer interactions under only one set of conditions cannot meet developmental requirements. This paper proposes an adaptive bump control scheme and employs dynamic mesh technology for numerical simulation to investigate the unsteady control effects of adaptive bumps. The obtained results indicate that the use of moving bumps to control shock wave/boundary layer interactions is feasible. The adaptive control effects of five different bump speeds are evaluated. Within the range of bump speeds studied, the analysis of the flow field structure reveals the patterns of change in the separation zone area during the control process, as well as the relationship between the bump motion speed and the control effect on the separation zone. It is concluded that the moving bump endows the boundary layer with additional energy.

Quantification and reduction of uncertainty in aerodynamic performance of GAN-generated airfoil shapes using MC dropouts

Kazuo Yonekura, Ryuto Aoki, Katsuyuki Suzuki

Accepted Manuscript , doi: 10.1016/j.taml.2024.100504

[Abstract] (615) [PDF 2212KB] (2)

Abstract:
Generative adversarial network (GAN) models are widely used in mechanical designs. The aim in the airfoil shape design is to obtain shapes that exhibits the required aerodynamic performance, and conditional GAN is used for that aim. However, the output of GAN contains uncertainties. Additionally, the uncertainties of labels have not been quantified. This paper proposes an uncertainty quantification method to estimate the uncertainty of labels using Monte Carlo dropout. In addition, an uncertainty reduction method is proposed based on imbalanced training. The proposed method was evaluated for the airfoil generation task. The results indicated that the uncertainty was appropriately quantified and successfully reduced.

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Hydrodynamic characteristics around offshore pipelines and oscillatory pore pressures in sand beds under combined random wave and current loads

Yue Xu, Lin Cui, Dong-Sheng Jeng, Mingqing Wang, Ku Sun, Bing Chen

Theoretical and Applied Mechanics Letters 15 (2025) 100575. doi: 10.1016/j.taml.2025.100575

[Abstract] (288) [PDF 4215KB] (1)

Abstract:
This paper experimentally investigates the wave pressure and pore pressure within a sandy seabed around two pipelines under the action of random waves (currents). The experiments revealed that when the random wave plus current cases are compared with the random wave-only case, the forward current promotes wave propagation, whereas the reversed backward current inhibits wave propagation. Furthermore, the wave pressure on the downstream pipeline decreases as the relative spacing ratio increases and increases as the diameter increases. However, alterations in the relative spacing ratio or dimensions of the downstream pipeline exert a negligible influence on the wave pressure of the upstream pipeline. Moreover, the relative spacing ratio between the pipelines and the dimensions of the pipelines considerably influence the pore pressure in the sand bed. When the relative spacing ratio remains constant, increasing the downstream pipeline diameter will increase the pore-water pressure of the soil below the downstream pipeline.

Analytical finite-integral-transform and gradient-enhanced machine learning approach for thermoelastic analysis of FGM spherical structures with arbitrary properties

Palash Das, Dipayan Mondal, Md. Ashraful Islam, Md. Abdullah Al Mohotadi, Prokash Chandra Roy

Theoretical and Applied Mechanics Letters 15 (2025) 100576. doi: 10.1016/j.taml.2025.100576

[Abstract] (277) [PDF 3960KB] (2)

Abstract:
This study introduces a novel mathematical model that combines the finite integral transform (FIT) and gradient-enhanced physics-informed neural network (g-PINN) to address thermomechanical problems in functionally graded materials with varying properties. The model employs a multilayer heterostructure homogeneous approach within the FIT to linearize and approximate various parameters, such as the thermal conductivity, specific heat, density, stiffness, thermal expansion coefficient, and Poisson’s ratio. The provided FIT and g-PINN techniques are highly proficient in solving the PDEs of energy equations and equations of motion in a spherical domain, particularly when dealing with space-time dependent boundary conditions. The FIT method simplifies the governing partial differential equations into ordinary differential equations for efficient solutions, whereas the g-PINN bypasses linearization, achieving high accuracy with fewer training data (error < 3.8%). The approach is applied to a spherical pressure vessel, solving energy and motion equations under complex boundary conditions. Furthermore, extensive parametric studies are conducted herein to demonstrate the impact of different property profiles and radial locations on the transient evolution and dynamic propagation of thermomechanical stresses. However, the accuracy of the presented approach is evaluated by comparing the g-PINN results, which have an error of less than 3.8%. Moreover, this model offers significant potential for optimizing materials in high-temperature reactors and chemical plants, improving safety, extending lifespan, and reducing thermal fatigue under extreme processing conditions.

Explainable artificial intelligence model for the prediction of undrained shear strength

Ho-Hong-Duy Nguyen, Thanh-Nhan Nguyen, Thi-Anh-Thu Phan, Ngoc-Thi Huynh, Quoc-Dat Huynh, Tan-Tai Trieu

Theoretical and Applied Mechanics Letters 15 (2025) 100578. doi: 10.1016/j.taml.2025.100578

[Abstract] (284) [PDF 3877KB] (5)

Abstract:
Machine learning (ML) models are widely used for predicting undrained shear strength (USS), but interpretability has been a limitation in various studies. Therefore, this study introduced shapley additive explanations (SHAP) to clarify the contribution of each input feature in USS prediction. Three ML models, artificial neural network (ANN), extreme gradient boosting (XGBoost), and random forest (RF), were employed, with accuracy evaluated using mean squared error, mean absolute error, and coefficient of determination (R²). The RF achieved the highest performance with an R² of 0.82. SHAP analysis identified pre-consolidation stress as a key contributor to USS prediction. SHAP dependence plots reveal that the ANN captures smoother, linear feature-output relationships, while the RF handles complex, non-linear interactions more effectively. This suggests a non-linear relationship between USS and input features, with RF outperforming ANN. These findings highlight SHAP’s role in enhancing interpretability and promoting transparency and reliability in ML predictions for geotechnical applications.

The impact attenuation behavior of three-dimensional soft elastomeric lattices

Bin Xia, Wenmiao Yang, Junwei Shi, Lichen Wang, Zeang Zhao

Theoretical and Applied Mechanics Letters 15 (2025) 100579. doi: 10.1016/j.taml.2025.100579

[Abstract] (296) [PDF 2568KB] (1)

Abstract:
Cellular structures, distinguished by their porous characteristics, are frequently adopted in designs aimed at impact isolation, owing to their lightweight attributes and exceptional ability to absorb energy during impact events. Lattice structures often rely on plastic deformation to absorb energy. However, in applications such as sports protection and robotic grasping, there exists a requirement for a reusable structure designed to isolate impacts, which can be effectively achieved by three-dimensional flexible lattice structures. In this work, a theoretical calculation method for soft lattice structures is proposed, and in light of this method, a three-dimensional soft lattice structure aimed at isolating impacts has been carefully designed. The predictive theory for the quasistatic mechanical properties, including stiffness and buckling strength for three-dimensional soft lattice structures is described. On the basis of the quasizero stiffness characteristics inherent in body-centered cubic, octahedral, and regular diamond structures, a soft impact isolation structure is designed. The soft structure, fabricated with thermoplastic polyurethane material, demonstrated a peak impact isolation efficiency of 83%, despite possessing a thickness of 24 mm described. The work provides a novel design methodology for three-dimensional soft lattice structures and supports the development of reusable impact isolation structures for applications such as reconfigurable robots and space capture missions.

Transient analysis of functionally graded curved shells using a nonuniform shape parameter integrated radial basis function approach

Vay Siu Lo, Andrzej Katunin, Thien Tich Truong

Theoretical and Applied Mechanics Letters 15 (2025) 100580. doi: 10.1016/j.taml.2025.100580

[Abstract] (1) [PDF 3942KB] (0)

Abstract:
In this study, an improved integrated radial basis function with nonuniform shape parameter is introduced. The proposed shape parameter varies in each support domain and is defined by , where is the maximum distance of any pair of nodes in the support domain. The proposed method is verified and shows good performance. The results are stable and accurate with any number of nodes and an arbitrary nodal distribution. Notably, the support domain should be large enough to obtain accurate results. This method is then applied for transient analysis of curved shell structures made from functionally graded materials with complex geometries. Through several numerical examples, the accuracy of the proposed approach is demonstrated and discussed. Additionally, the influence of various factors on the dynamic behavior of the structures, including the power-law index, different materials, loading conditions, and geometrical parameters of the structures, was investigated.

Revisiting Taylor bubble motion in an oscillating vertical tube

Quan Yao, Guangzhao Zhou

Theoretical and Applied Mechanics Letters 15 (2025) 100581. doi: 10.1016/j.taml.2025.100581

[Abstract] (264) [PDF 2097KB] (1)

Abstract:
In this study, we numerically investigate the rise of a Taylor bubble in a vertically oscillating round tube. The results show that increasing the oscillation frequency and amplitude reduces the bubble rise velocity, which is consistent with previously reported experimental findings. Analysis of the flow in the annular film region indicates that the influence of tube wall oscillations is minimal. This suggests that the effect of tube oscillations is essentially equivalent to that of an oscillating piston above the bubble, leading to a similar mechanism for bubble deceleration. Using a theoretical formula from the literature, we demonstrate that at sufficiently high frequencies, the amplitude of the tube velocity oscillations becomes the sole control parameter affecting bubble deceleration. This study enhances our understanding of Taylor bubble behavior in mechanically oscillating environments and provides useful insights into the design of control strategies for Taylor bubble motion in vertical slug flows.

DeepSeek vs. ChatGPT vs. Claude: A comparative study for scientific computing and scientific machine learning tasks

Qile Jiang, Zhiwei Gao, George Em Karniadakis

Theoretical and Applied Mechanics Letters 15 (2025) 100583. doi: 10.1016/j.taml.2025.100583

[Abstract] (234) [PDF 3770KB] (2)

Abstract:
Large language models (LLMs) have emerged as powerful tools for addressing a wide range of problems, including those in scientific computing, particularly in solving partial differential equations (PDEs). However, different models exhibit distinct strengths and preferences, resulting in varying levels of performance. In this paper, we compare the capabilities of the most advanced LLMs—DeepSeek, ChatGPT, and Claude—along with their reasoning-optimized versions in addressing computational challenges. Specifically, we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems. We designed all our experiments so that a nontrivial decision is required, e.g, defining the proper space of input functions for neural operator learning. Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems, with ChatGPT o3-mini-high generally offering the fastest reasoning speed.

Hydrodynamic performance of bionic streamlined remotely operated vehicle based on CFD and overlapping mesh technology

Bin Guan, Junjie Li

Theoretical and Applied Mechanics Letters 15 (2025) 100584. doi: 10.1016/j.taml.2025.100584

[Abstract] (221) [PDF 5078KB] (0)

Abstract:
To meet the intelligent detection needs of underwater defects in large hydropower stations, the hydrodynamic performance of a bionic streamlined remotely operated vehicle containing a thruster protective net structure is numerically simulated via computational fluid dynamics and overlapping mesh technology. The results show that the entity model generates greater hydrodynamic force during steady motion, whereas the square net model experiences greater force and moment during unsteady motion. The lateral and vertical force coefficients of the entity model are 4.32 and 3.13 times greater than those of the square net model in the oblique towing test simulation. The square net model also offers better static and dynamic stability, with a 24.5% increase in dynamic stability, achieving the highest lift-to-drag ratio at attack angles of 6°∼8°. This research provides valuable insights for designing and controlling underwater defect detection vehicles for large hydropower stations.

Gradient-free optimization of non-differentiable hybrid neural solvers for spatially heterogeneous composites

Hanfeng Zhang, Tengfei Luo, Jian-Xun Wang

Theoretical and Applied Mechanics Letters 15 (2025) 100585. doi: 10.1016/j.taml.2025.100585

[Abstract] (238) [PDF 3364KB] (2)

Abstract:
The data-driven machine learning paradigm typically requires high-quality, large-scale datasets for training neural networks, which are often unavailable in many scientific and engineering applications. Integrating physics equations into machine learning models, either fully or partially, can mitigate these data requirements and improve generalizability; however, such approaches frequently rely on differentiable programming frameworks. This ability poses significant challenges when legacy or commercial numerical solvers, which are often non-differentiable and difficult to modify without introducing code changes, are integrated. This work addresses these challenges by leveraging the mini-batching iterative ensemble Kalman inversion (EKI) algorithm as a gradient-free training framework for hybrid neural models. The use of stochastic mini-batching significantly enhances the computational efficiency and convergence of EKI, making it well-suited for high-dimensional learning problems. The proposed method is demonstrated for modeling a fiber-reinforced composite plate, where heterogeneous local constitutive laws are parameterized by a trainable neural network embedded within the FEniCS finite element solver. Using the displacement field as indirect data, the hybrid neural FEM solver successfully predicts deformations by learning the local constitutive laws, even for unseen fiber volume fraction distributions and varying test loading conditions. These results demonstrate the effectiveness of iterative EKI in training hybrid neural models with non-differentiable components, paving the way for broader adoption of hybrid neural models in scientific and engineering applications.

Fine-tuning a large language model for automating computational fluid dynamics simulations

Zhehao Dong, Zhen Lu, Yue Yang

Theoretical and Applied Mechanics Letters 15 (2025) 100594. doi: 10.1016/j.taml.2025.100594

[Abstract] (229) [PDF 1430KB] (5)

Abstract:
Configuring computational fluid dynamics (CFD) simulations typically demands extensive domain expertise, limiting broader access. Although large language models (LLMs) have advanced scientific computing, their use in automating CFD workflows is underdeveloped. We introduce a novel approach centered on domain-specific LLM adaptation. By fine-tuning Qwen2.5-7B-Instruct on NL2FOAM, our custom dataset of 28,716 natural language-to-OpenFOAM configuration pairs with chain-of-thought (CoT) annotations enables direct translation from natural language descriptions to executable CFD setups. A multi-agent system orchestrates the process, autonomously verifying inputs, generating configurations, running simulations, and correcting errors. Evaluation on a benchmark of 21 diverse flow cases demonstrates state-of-the-art performance, achieving 88.7% solution accuracy and 82.6% first-attempt success rate. This significantly outperforms larger general-purpose models such as Qwen2.5-72B-Instruct, DeepSeek-R1, and Llama3.3-70B-Instruct, while also requiring fewer correction iterations and maintaining high computational efficiency. The results highlight the critical role of domain-specific adaptation in deploying LLM assistants for complex engineering workflows. Our code and fine-tuned model have been deposited at https://github.com/YYgroup/AutoCFD.

Evaluations of large language models in computational fluid dynamics: Leveraging, learning and creating knowledge

Long Wang, Lei Zhang, Guowei He

Theoretical and Applied Mechanics Letters 15 (2025) 100597. doi: 10.1016/j.taml.2025.100597

[Abstract] (1) [PDF 4937KB] (0)

Abstract:
This paper investigates the capabilities of large language models (LLMs) to leverage, learn and create knowledge in solving computational fluid dynamics (CFD) problems through three categories of baseline problems. These categories include (1) conventional CFD problems that can be solved using existing numerical methods in LLMs, such as lid-driven cavity flow and the Sod shock tube problem; (2) problems that require new numerical methods beyond those available in LLMs, such as the recently developed Chien-physics-informed neural networks for singularly perturbed convection–diffusion equations; and (3) problems that cannot be solved using existing numerical methods in LLMs, such as the ill-conditioned Hilbert linear algebraic systems. The evaluations indicate that reasoning LLMs overall outperform non-reasoning models in four test cases. Reasoning LLMs show excellent performance for CFD problems according to the tailored prompts, but their current capability in autonomous knowledge exploration and creation needs to be enhanced.

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