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An overview of mathematical programming solvers:Theory,development,and recent advances 认领 引用
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作者 Lei HUANG Fan XIAO +2 位作者 Dongdong GE Wotao YIN Zhe LIANG 《ENGINEERING Management》 CSCD 2026年第1期1-16,共16页
Mathematical programming solvers are software tools designed to solve real‑world problems using mathematical programming algorithms.This survey explores the evolution of optimization technologies,from traditional meth... Mathematical programming solvers are software tools designed to solve real‑world problems using mathematical programming algorithms.This survey explores the evolution of optimization technologies,from traditional methods such as the simplex algorithm and branch‑and‑bound techniques to modern advancements that are facilitated by parallel computing,GPU acceleration,and AI algorithms.We also emphasize the recent emergence of mathematical programming solvers developed by research institutes and companies headquartered in China as major players,who have achieved remarkable success in benchmarks when compared to established solvers.This article provides a comprehensive overview of the theoretical foundations,historical progress,and emerging trends in mathematical programming solvers,offering valuable insights for both researchers and practitioners in the field. 展开更多
关键词 survey mathematical programming solver linear programming mixed‑integer programming nonlinear programming
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Sparse Approximations of the Schur Complement for Parallel Algebraic Hybrid Solvers in 3D 认领 引用 被引量：1
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作者 L.Giraud A.Haidar Y.Saad 《Numerical Mathematics(Theory,Methods and Applications)》 SCIE 2010年第3期276-294,共19页
In this paper we study the computational performance of variants of an algebraic additive Schwarz preconditioner for the Schur complement for the solution of large sparse linear systems.In earlier works,the local Schu... In this paper we study the computational performance of variants of an algebraic additive Schwarz preconditioner for the Schur complement for the solution of large sparse linear systems.In earlier works,the local Schur complements were computed exactly using a sparse direct solver.The robustness of the preconditioner comes at the price of this memory and time intensive computation that is the main bottleneck of the approach for tackling huge problems.In this work we investigate the use of sparse approximation of the dense local Schur complements.These approximations are computed using a partial incomplete LU factorization.Such a numerical calculation is the core of the multi-level incomplete factorization such as the one implemented in pARMS. The numerical and computing performance of the new numerical scheme is illustrated on a set of large 3D convection-diffusion problems;preliminary experiments on linear systems arising from structural mechanics are also reported. 展开更多
关键词 Hybrid direct/iterative solver domain decomposition incomplete/partial factorization Schur approximation scalable preconditioner convection-diffusion large 3D problems parallelscientific computing High Performance Computing.
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Embedding Physics into Machine Learning:A Review of Physics Informed Neural Networks as Partial Differential Equation Forward Solvers 认领 引用
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作者 Wenhui Fan Xujia Chen 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第3期1326-1364,共39页
Partial Differential Equations(PDEs)are a fundamental class of mathematical models widely used for modeling continuous systems across scientific and engineering disciplines.Physics Informed Machine Learning(PIML),whic... Partial Differential Equations(PDEs)are a fundamental class of mathematical models widely used for modeling continuous systems across scientific and engineering disciplines.Physics Informed Machine Learning(PIML),which utilises both data and scientific knowledge,provides a powerful framework that bridges Artificial Intelligence(Al)and PDEs.Among PIML methods,Physics Informed Neural Networks(PINNs)have emerged as a representative and widely adopted approach.This paper offers a structured,problem-oriented review of recent developments in the use of PiNNs as PDE forward solvers.We aim to help readers grasp the key trends and interrelations across methodological advances and practical applications.From a methodological perspective,we review existing approaches with a focus on Machine Learning(ML)models and representations,optimization objectives and strategies,as well as datasets and training procedures.From an application perspective,we examine the task characteristics and the applicability of PiNNs in domains such as fluid dynamics,heat transfer,solid mechanics,magnetism,and highlight several practical software toolkits and benchmarks.Despite the remarkable progress made in PiNN research,significant challenges remain in addressing complex real-world problems.Accordingly,we discuss current limitations in generalization capability,training efficiency,and optimization difficulty,and outline promising directions for future improvements. 展开更多
关键词 Physics Informed Neural Networks(PINN) Machine Learning(ML) Partial Differential Equations(PDEs) forward solver Artificial Intelligence(AI)for science
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Fast and High-Order Approximation of Parabolic Equations Using Hierarchical Direct Solvers and Implicit Runge-Kutta Methods 认领 引用
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作者 Ke Chen Daniel Appelö +1 位作者 Tracy Babb Per-Gunnar Martinsson 《Communications on Applied Mathematics and Computation》 EI CSCD 2026年第1期248-268,共21页
A stable and high-order accurate solver for linear and nonlinear parabolic equations is presented.An additive Runge-Kutta method is used for the time stepping,which integrates the linear stiff terms by an explicit sin... A stable and high-order accurate solver for linear and nonlinear parabolic equations is presented.An additive Runge-Kutta method is used for the time stepping,which integrates the linear stiff terms by an explicit singly diagonally implicit Runge-Kutta(ESDIRK)method and the nonlinear terms by an explicit Runge-Kutta(ERK)method.In each time step,the implicit solution is performed by the recently developed Hierarchical Poincaré-Steklov(HPS)method.This is a fast direct solver for elliptic equations that decomposes the space domain into a hierarchical tree of subdomains and builds spectral collocation solvers locally on the subdomains.These ideas are naturally combined in the presented method since the singly diagonal coefficient in ESDIRK and a fixed time step ensures that the coefficient matrix in the implicit solution of HPS remains the same for all time stages.This means that the precomputed inverse can be efficiently reused,leading to a scheme with complexity(in two dimensions)O(N1.5)for the precomputation where the solution operator to the elliptic problems is built,and then O(N log N)for the solution in each time step.The stability of the method is proved for first order in time and any order in space,and numerical evidence substantiates a claim of the stability for a much broader class of time discretization methods.Numerical experiments supporting the accuracy of the efficiency of the method in one and two dimensions are presented. 展开更多
关键词 Explicit singly diagonally implicit Runge-Kutta(ESDIRK) Parabolic Direct solver High-order Hierarchical
Online learning to accelerate nonlinear PDE solvers:Applied to multiphase porous media flow 认领 引用
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作者 Vinicius L.S.Silva Pablo Salinas +2 位作者 Claire E.Heaney Matthew D.Jackson Christopher C.Pain 《Artificial Intelligence in Geosciences》 EI CSCD 2025年第2期161-176,共16页
We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations(PDEs)that is based on online/adaptive learning.It is applied in the context of multiphase flow in porous... We propose a novel type of nonlinear solver acceleration for systems of nonlinear partial differential equations(PDEs)that is based on online/adaptive learning.It is applied in the context of multiphase flow in porous media.The proposed method rely on four pillars:(i)dimensionless numbers as input parameters for the machine learning model,(ii)simplified numerical model(two-dimensional)for the offline training,(iii)dynamic control of a nonlinear solver tuning parameter(numerical relaxation),(iv)and online learning for time real-improvement of the machine learning model.This strategy decreases the number of nonlinear iterations by dynamically modifying a single global parameter,the relaxation factor,and by adaptively learning the attributes of each numerical model on-the-run.Furthermore,this work performs a sensitivity study in the dimensionless parameters(machine learning features),assess the efficacy of various machine learning models,demonstrate a decrease in nonlinear iterations using our method in more intricate,realistic three-dimensional models,and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85%reduction in computational time. 展开更多
关键词 Nonlinear PDE solver Machine learning Online learning Numerical relaxation Multiphase flows Porous media
The Effects of Spatial Reconstruction and Flux Solver on the Performance of High-Order Finite-Volume Compressible Flow Solvers 认领 引用
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作者 Chengxiang Li Xing Ji +1 位作者 Kun Xu Lian-Ping Wang 《Communications in Computational Physics》 SCIE CSCD 2025年第6期37-73,共37页
Most high-order computational fluid dynamics methods for compressible flows are based on the Riemann solver for the flux evaluation and high-order interpolation or reconstruction such as the Weighted Essential Non-Osc... Most high-order computational fluid dynamics methods for compressible flows are based on the Riemann solver for the flux evaluation and high-order interpolation or reconstruction such as the Weighted Essential Non-Oscillatory(WENO)scheme for spatial accuracy.The advantage of this kind of combination is the easy implementation and the ability to achieve the required spatial accuracy.However,despite the extensive research on high-order spatial reconstruction in the past,solvers coupling high-order space and time schemes have not been systematically evaluated.In this paper,based on the same fifth-order finite volume method(FVM),comparisons of the performance of the same flux solver with different reconstructions and the same reconstruction but different flux solvers are carried out on a structured mesh.For reconstruction,the TENO scheme and classic WENO-Z reconstruction have been chosen as representative methods.Meanwhile,for the flux solver,Lax-Friedrichs(LF)Riemann solver,HLLC solver,and GKS are considered.Through a series of simulated comparison cases,the unique characteristics of GKS and TENO have been demonstrated.Overall,the comparisons suggest that proper spatial and temporal coupling is important for accurate shock and vortex capturing. 展开更多
关键词 TENO reconstruction Gas-kinetic scheme(GKS) Lax-Friedrichs Riemann solver high-order scheme
ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers 认领 引用 被引量：5
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作者 Opeoluwa Owoyele Pinaki Pal 《Energy and AI》 2022年第1期142-148,共7页
Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural ne... Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach.This has motivated the use of neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system.However,due to the nonlinearities and multi-scale nature of combustion,the predicted solution often diverges from the true solution when these machine learning models are coupled with a computational fluid dynamics solver.This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers.In the present work,a novel neural ordinary differential equations approach to modeling chemical kinetics,termed as ChemNODE,is developed.In this machine learning framework,the chemical source terms predicted by the neural networks are integrated during training,and by computing the required derivatives,the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution.A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions.It is shown that ChemNODE accurately captures the chemical kinetic behavior and reproduces the results obtained using the detailed kinetic mechanism at a fraction of the computational cost. 展开更多
关键词 Machine learning Neural ordinary differential equations Artificial neural networks Deep learning Chemical kinetics Artificial neural network Chemistry solvers
Three-dimensional inversion of metallic minerals electromagnetic data using efficient multigrid solver 认领 引用
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作者 Yong-fei WANG Rong-wen GUO +2 位作者 Zhuo LIU Ding-hui YANG Deng-kang WANG 《Transactions of Nonferrous Metals Society of China》 SCIE EI CAS CSCD 2026年第3期943-955,共13页
A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion e... A three-dimensional(3D)electromagnetic(EM)inversion algorithm based on the nonlinear conjugate gradient(NLCG)method and a two-color plane Gauss-Seidel(GS)multigrid(MG)forward solver is developed to improve inversion efficiency.The results indicate that the computational efficiency of each inversion can be improved by approximately a factor of three by using the proposed MG solver.First,the accuracy of the MG solver is validated through a test on a synthetic model.Next,the numerical performance of the inversion algorithm is evaluated using this model.Finally,the inversion algorithm is applied to a field EM data collected at the Beiya gold polymetallic ore district.A 3D resistivity model is obtained,and the formation process of the metal ore is analyzed. 展开更多
关键词 metallic minerals electromagnetic data gradient inversion method multigrid solver
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3D finite-difference numerical simulation of the gravitational field using a preconditioned GMRES iterative solver 认领 引用
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作者 TONG Xiao-zhong XIE Wei +3 位作者 MA Hui-ying WEN Xin-yue ZHU Wen-di ZHANG Chen 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第2期847-860,共14页
With the evolution of geophysical surveys from traditional two-dimensional(2 D)to three-dimensional(3 D)models,the resulting large data volumes pose significant challenges to inversion,particularly when resolving larg... With the evolution of geophysical surveys from traditional two-dimensional(2 D)to three-dimensional(3 D)models,the resulting large data volumes pose significant challenges to inversion,particularly when resolving large-scale 3 D structures.A direct solver for solving an ill-conditioned linear system resulting from the finite-difference approximation of a boundary value problem requires more memory and time than iterative solvers.To overcome this limitation,an efficient iterative solver for 3 D finite-difference approach is introduced to calculate the 3 D gravitational potential and the associated gravitational field.Firstly,the boundary value problem associated with 3 D gravitational potential is discretized using central finite-difference technique based on right rectangular prismatic grids.The resulting large unsymmetric sparse systems are then solved using the generalized minimal residual algorithm(GMRES)iterative solver in combination with incomplete LU factorization.Secondly,to obtain high-accuracy partial derivatives of gravitational potential,a high-degree Lagrange interpolation scheme is employed.Finally,three density models are applied to test the accuracy,reliability,and flexibility of our 3 D finite-difference algorithm.All computational results demonstrate that our method provides an accurate approximation of the gravitational field and is applicable to 3 D forward modeling. 展开更多
关键词 3 D gravitational potential boundary value problem numerical simulation finite-difference algorithm GMRES iterative solver
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Massively parallel and programmable photonic differential equation solver 认领 引用
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作者 Jiahao Wang Wen Chen +8 位作者 Zhou Zhou Dongyu Hu Zile Li Peng Chen Yan-qing Lu Shuang Zhang Cheng-Wei Qiu Shaohua Yu Guoxing Zheng 《Opto-Electronic Advances》 SCIE EI CAS CSCD 2026年第5期3-13,共11页
Calculus equations are fundamental mathematical tools,whose numerical solution is crucial.Existing solvers with optical analog computing struggle to simultaneously integrate programmability and parallel processing,thu... Calculus equations are fundamental mathematical tools,whose numerical solution is crucial.Existing solvers with optical analog computing struggle to simultaneously integrate programmability and parallel processing,thus constraining computational speed and density.Herein,we propose a reconfigurable all-optical platform capable of solving variable-coefficient first-order ordinary differential equations in parallel.We utilize the electrically tunable liquid crystals(LCs)as computing kernels to address these equations.The solver's applicability to canonical scientific problems,such as heat conduction and resistor-capacitor circuit dynamics,is further showcased with simultaneous solving of 158 equations with only one single forward propagation of light.Experimental results confirm the efficacy of the platform in solving equations in an ultra-fast,reconfigurable,broadband,and parallel manner. 展开更多
关键词 photonics computing ordinary differential equations photonic differential equation solver
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A Fast Calculation Method for Dynamic Carbon Emission Factors Based on ILU Decomposition and BiCGSTABs 认领 引用
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作者 Lihua Zhong Feng Pan +4 位作者 Yuyao Yang Lei Feng Jinghe Jiang Guo Lin Xiaoshun Zhang 《Energy Engineering》 EI 2026年第6期81-97,共17页
This paper addresses the challenge of efficiently calculating dynamic carbon emission factors(CEFs)in large-scale power systems.Traditional methods that rely on direct matrix inversion are computationally intensive an... This paper addresses the challenge of efficiently calculating dynamic carbon emission factors(CEFs)in large-scale power systems.Traditional methods that rely on direct matrix inversion are computationally intensive and become impractical for networks with thousands of nodes.To overcome this limitation,a fast and scalable computational framework is proposed based on the incomplete LU(ILU)preconditioned biconjugate gradient stabilized(BiCGSTAB)iterative solver.The proposed approach formulates the nodal CEF model as a sparse linear system and employs Krylov subspace acceleration with ILU preconditioning to enhance convergence and numerical stability.The method is applied to synthetic power grid test cases ranging from 200 to 10,000 nodes to evaluate its accuracy and efficiency.Results show that the ILU-preconditioned BiCGSTAB algorithm achieves convergence within seven iterations,reducing computation time by more than two orders of magnitude compared with conventional direct matrix inversion.The method accurately tracks both local and imported carbon emissions at each node,providing fine-grained temporal and spatial emission profiles.Moreover,the ILU decomposition can be reused across time steps,further improving computational efficiency for dynamic and real-time scenarios.Overall,the proposed method demonstrates excellent scalability and robustness,making it well suited for high-frequency,real-time carbon emission monitoring in large power systems.The findings provide a computational foundation for carbon-aware dispatch,emission accounting,and policy-oriented applications in future low-carbon power grids. 展开更多
关键词 Dynamic carbon emission factors BiCGSTAB ILU preconditioning iterative solver large-scale power systems
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THE SCHWARZ CHAOTIC RELAXATION METHOD WITH INEXACT SOLVERS ON THE SUBDOMAINS 认领 引用 被引量：1
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作者 Jian-guo Huang 《Journal of Computational Mathematics》 SCIE EI 1999年第2期125-132,共8页
In this paper, a S-CR method with inexact solvers on the subdomains is presented, and then its convergence property is proved under very general conditions. This result is important because it guarantees the effective... In this paper, a S-CR method with inexact solvers on the subdomains is presented, and then its convergence property is proved under very general conditions. This result is important because it guarantees the effectiveness of the Schwarz alternating method when executed on message-passing distributed memory multiprocessor system. 展开更多
关键词 S-CR method chaotic algorithm inexact solvers
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The probability of success in solving linear systems of equations using quantum singular value transformation 认领 引用 被引量：1
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作者 Leigh Lapworth 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期138-151,共14页
Quantum linear equation solvers generate a solution vector that sits in a subspace of the quantum computer’s larger state space.Accessing the solution vector relies on applying measurement projectors that remove comp... Quantum linear equation solvers generate a solution vector that sits in a subspace of the quantum computer’s larger state space.Accessing the solution vector relies on applying measurement projectors that remove components in the orthogonal subspace.This study examines the probability that the projectors are successful and how the success probability is influenced by the properties of the linear system,e.g.,the matrix condition number,and approximations made in the quantum solver.The analysis is performed within a non-linear computational fluid dynamics code where,at each iteration,a linearised system is passed to an emulated quantum solver.The linear systems are solved using quantum singular value transformation,for which the accuracy of the matrix inversion polynomial can be controlled via user input.The results show that success probabilities vary between 10−6and 10−2for the cases considered.More accurate approximations have lower success probabilities and require longer circuits.Less accurate approximations are analysed and show that variations during the non-linear iterations are related to the eigen content of the right-hand side vector.The use of amplitude amplification is shown to be able to increase success probabilities from 10−6to 10−2,in accordance with theory,but at a cost of a 100 times increase in circuit depth.Whilst these results are specific to the fluid flow test cases,they are generalisable to other types of linear solvers. 展开更多
关键词 Quantum linear equation solver Quantum singular value transformation Success probability Computational fluid dynamics
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KSSOLV-GPU 2.0:A Lightweight MATLAB Toolbox for GPU-Accelerated Plane-Wave Hybrid Functional and Spin-Polarized Density Functional Theory Calculations 认领 引用
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作者 Yehao Zeng Zhenlin Zhang +10 位作者 Zhaolong Luo Jun Gao Sheng Chen Wentiao Wu Yexuan Lin Yihong Zhang Xinming Qin Weiyi Wang Zhiwen Zhuo Shizhe Jiao Wei Hu 《Chinese Journal of Chemical Physics》 SCIE EI CAS CSCD 2026年第2期182-196,I0169,共15页
KSSOLV(Kohn−Sham solver)is a MAT-LAB(Matrix Laboratory)toolbox de-signed for solving the Kohn-Sham density functional theory(DFT)equations by us-ing the plane-wave basis set.Leveraging the powerful capabilities of MAT... KSSOLV(Kohn−Sham solver)is a MAT-LAB(Matrix Laboratory)toolbox de-signed for solving the Kohn-Sham density functional theory(DFT)equations by us-ing the plane-wave basis set.Leveraging the powerful capabilities of MATLAB’s parallel computing toolbox and an ad-vanced,optimized calculation workflow,KSSOLV uniquely enables efficient graph-ics processing unit(GPU)acceleration,making DFT calculations accessible on standard personal computing hardware.Here,KSSOLV-GPU 2.0,as the latest release,demonstrates substantial computational gains.In benchmarks,particularly involving calculations such as hybrid functionals and spin-polar-ized systems for complex band structure analysis,KSSOLV-GPU 2.0 achieves a speedup of more than an order of magnitude compared to conventional central processing unit based im-plementations.This significant acceleration marks a pivotal advancement in performing com-plex materials simulations,making KS-DFT increasingly accessible on personal computing platforms. 展开更多
关键词 Kohn-Sham solver Density functional theory Matrix Laboratory(MATLAB) Graphics processing unit(GPU) Hybrid functional k-point sampling Spin polarization
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Simulation on plasma discharge and transport in large and complex geometric space 认领 引用
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作者 Shiyi Tang Mengran Xiao +7 位作者 Ziqi Ma Dongjie Yang Xiaokai An Liangliang Liu Suihan Cui Ricky K.Y.Fu Paul K.Chu Zhongzhen Wu 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第3期387-398,共12页
The plasma discharge and transport properties in the vacuum systems is critical for film deposition controlling.However,industrial-scale vacuum systems usually exhibit large and complex geometries,leading to boundary ... The plasma discharge and transport properties in the vacuum systems is critical for film deposition controlling.However,industrial-scale vacuum systems usually exhibit large and complex geometries,leading to boundary distortion and convergence difficulty in the conventional simulation techniques.In this work,a PIC/MCC model with FEM solver for non-uniform grids is established to precisely construct a large simulation domain with complex boundaries using the fluid model,and tracks the charged particle movements in non-uniform electromagnetic fields by the PIC/MCC method.The discharge process in a large cylindrical vacuum chamber shows the obvious interaction between the spatial electromagnetic field and plasma.The distribution of deposited ions is consistent with the potential gradient of the sheath.Besides,the ion deposition proportion is increased by more than 3 times and the average ion energy is increased by over 45.0 eV compared with the constant potential,indicating that the background electric field plays a significant role.When the spatial potential is steady,the plasma leads to stable accumulation with the peak density of 1015m-3achieving convergence at 0.3μs,thus demonstrating the excellent operation speed and convergence compared to the individual fluid model and PIC/MCC method.The density of the computational grids modified further according to the Debye length reveals a significantly improved computational performance with the convergence process compressed into 0.26μs and the total runtime reduced by 40%. 展开更多
关键词 large and complex geometric space PIC/MCC model with FEM solver for non-uniform grids discharge and transport properties computation performance
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Spectral Elliptic Solvers in a Finite Cylinder 认领 引用
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作者 F.Auteri L.Quartapelle 《Communications in Computational Physics》 SCIE 2009年第2期426-441,共16页
New direct spectral solvers for the 3D Helmholtz equation in a finite cylindrical region are presented.A purely variational(no collocation)formulation of the problem is adopted,based on Fourier series expansion of the... New direct spectral solvers for the 3D Helmholtz equation in a finite cylindrical region are presented.A purely variational(no collocation)formulation of the problem is adopted,based on Fourier series expansion of the angular dependence and Legendre polynomials for the axial dependence.A new Jacobi basis is proposed for the radial direction overcoming the main disadvantages of previously developed bases for the Dirichlet problem.Nonhomogeneous Dirichlet boundary conditions are enforced by a discrete lifting and the vector problem is solved by means of a classical uncoupling technique.In the considered formulation,boundary conditions on the axis of the cylindrical domain are never mentioned,by construction.The solution algorithms for the scalar equations are based on double diagonalization along the radial and axial directions.The spectral accuracy of the proposed algorithms is verified by numerical tests. 展开更多
关键词 Spectral elliptic solvers Dirichlet and Neumann conditions cylindrical coordinates Legendre and Jacobi polynomials uncoupled vector problem
Numerical Analysis of Supercritical Fuel Cracking in Trapezoidal Rib Channels 认领 引用
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作者 Jiangbo Wu Ke Yang +2 位作者 Qincheng Bi Heyao Sun Xi Song 《Fluid Dynamics & Materials Processing》 EI 2026年第5期123-146,共24页
Background:Conjugate heat transfer in supercritical hydrocarbon fuels within microchannels is strongly influenced by sharp thermophysical property variations and chemical reactions,posing significant challenges for ac... Background:Conjugate heat transfer in supercritical hydrocarbon fuels within microchannels is strongly influenced by sharp thermophysical property variations and chemical reactions,posing significant challenges for accurate numerical prediction.To address this,a high-fidelity solver is developed within the OpenFOAM framework,incorporating detailed reaction mechanisms and demonstrating robust stability under steady supercritical conditions.In particular,to mitigate numerical oscillations and accuracy loss in the pseudo-critical region,a high-order variable-property transport model,based on an eight-segment,seventh-order polynomial formulation,is introduced and integrated in the solver.This model is tightly coupled with the Peng–Robinson equation of state and a simplified cracking mechanism,enhancing both stability and predictive capability for highly nonlinear supercritical reacting flows.The proposed approach is applied to compare the coupled thermal–hydraulic–chemical behavior of a baseline straight channel and a trapezoidal-ribbed configuration under supercritical pressure.Unlike conventional fully distributed rib arrangements,the proposed design achieves heat transfer enhancement using a limited number of rib elements.The ribs promote elevated turbulent kinetic energy in their wake and intensify near-wall mixing.As a result,peak wall temperature is reduced by 30–40 K under identical conditions,effectively suppressing localized hot spots.Although improved cooling slightly decreases the cracking conversion rate,the design markedly lowers the risk of fuel coking by eliminating high-temperature regions,thereby enhancing overall thermal management.The performance evaluation criterion(PEC)remains close to or slightly above unity across different mass flow rates,indicating a modest but meaningful thermo-hydraulic benefit and practical engineering potential. 展开更多
关键词 Supercritical hydrocarbon fuel conjugate heat transfer thermal cracking multi-physics solver variable physical properties trapezoidal rib
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Flow-Induced Transverse Vibration of Three Equal-Diameter Cylinders in an Equilateral Triangle Using the Immersed Boundary–Lattice Boltzmann Flux Solver 认领 引用 被引量：1
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作者 Xiaodi Wu Jiaqi Li +1 位作者 Shuo Huang Ruosi Zha 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第2期437-448,共12页
To explore the relationship between dynamic characteristics and wake patterns,numerical simulations were conducted on three equal-diameter cylinders arranged in an equilateral triangle.The simulations varied reduced v... To explore the relationship between dynamic characteristics and wake patterns,numerical simulations were conducted on three equal-diameter cylinders arranged in an equilateral triangle.The simulations varied reduced velocities and gap spacing to observe flow-induced vibrations(FIVs).The immersed boundary–lattice Boltzmann flux solver(IB–LBFS)was applied as a numerical solution method,allowing for straightforward application on a simple Cartesian mesh.The accuracy and rationality of this method have been verified through comparisons with previous numerical results,including studies on flow past three stationary circular cylinders arranged in a similar pattern and vortex-induced vibrations of a single cylinder across different reduced velocities.When examining the FIVs of three cylinders,numerical simulations were carried out across a range of reduced velocities(3.0≤Ur≤13.0)and gap spacing(L=3D,4D,and 5D).The observed vibration response included several regimes:the desynchronization regime,the initial branch,and the lower branch.Notably,the transverse amplitude peaked,and a double vortex street formed in the wake when the reduced velocity reached the lower branch.This arrangement of three cylinders proved advantageous for energy capture as the upstream cylinder’s vibration response mirrored that of an isolated cylinder,while the response of each downstream cylinder was significantly enhanced.Compared to a single cylinder,the vibration and flow characteristics of this system are markedly more complex.The maximum transverse amplitudes of the downstream cylinders are nearly identical and exceed those observed in a single-cylinder set-up.Depending on the gap spacing,the flow pattern varied:it was in-phase for L=3D,antiphase for L=4D,and exhibited vortex shedding for L=5D.The wake configuration mainly featured double vortex streets for L=3D and evolved into two pairs of double vortex streets for L=5D.Consequently,it well illustrates the coupling mechanism that dynamics characteristics and wake vortex change with gap spacing and reduced velocities. 展开更多
关键词 Flow-induced vibration Equal-diameter cylinders Lattice boltzmann flux solver Immersed boundary method
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A low Mach number asymptotic analysis of dissipation-reducing methods for curing shock instability 认领 引用 被引量：1
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作者 Hongping GUO Xun WANG Zhijun SHEN 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2025年第4期723-744,共22页
We are intrigued by the issues of shock instability,with a particular emphasis on numerical schemes that address the carbuncle phenomenon by reducing dissipation rather than increasing it.For a specific class of plana... We are intrigued by the issues of shock instability,with a particular emphasis on numerical schemes that address the carbuncle phenomenon by reducing dissipation rather than increasing it.For a specific class of planar flow fields where the transverse direction exhibits vanishing but non-zero velocity components,such as a disturbed onedimensional(1D)steady shock wave,we conduct a formal asymptotic analysis for the Euler system and associated numerical methods.This analysis aims to illustrate the discrepancies among various low-dissipative numerical algorithms.Furthermore,a numerical stability analysis of steady shock is undertaken to identify the key factors underlying shock-stable algorithms.To verify the stability mechanism,a consistent,low-dissipation,and shock-stable HLLC-type Riemann solver is presented. 展开更多
关键词 Riemann solver numerical shock instability low Mach number HLLC
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Hybrid physics-informed and data-driven mode solver for optical fiber design 认领 引用
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作者 Xiao Luo Min Zhang +3 位作者 Zhuo Wang Xiaotian Jiang Yuchen Song Danshi Wang 《Advanced Photonics Nexus》 CSCD 2025年第6期149-164,共16页
An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers.By incorporating the governing Helmholtz equation into training,the working mechanism of the proposed o... An efficient neural mode-solving operator is proposed for evaluating the propagation properties of optical fibers.By incorporating the governing Helmholtz equation into training,the working mechanism of the proposed operator adheres to the physics essence of fiber analysis.The training of the mode-solving operator adopts a hybrid physics-informed and data-driven approach,providing the advantages of strong physical consistency,enhanced prediction accuracy,and reduced data dependency in comparison with purely datadriven methods.Benefiting from the improvements in network input-output mapping formulation,the proposed operator offers broader applicability to different fiber types and greater flexibility for property optimization.Combined with the particle swarm optimization and refractive index optimization,the operator demonstrates its capacity for the inverse design of multi-step-index fibers(MSIFs)and graded-index fibers(GRIFs).For MSIFs,to ensure a low mode crosstalk for short-distance transmission systems,optimized refractive index profiles(RIPs)of both three-ring and four-ring structures are obtained from large structure parameter search spaces.For GRIFs,to ensure a low receiving complexity for long-haul transmission systems,optimized RIP with low root mean square mode group delay is obtained through point-wise fine-tuning.Moreover,the operator is capable of analyzing the effect of dopant diffusion in manufacturing. 展开更多
关键词 inverse design few-mode fiber mode solver neural operator structure optimization hybrid-driven deep learning.
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