Reduction In Computation Time Research Articles

Fatigue assessment of a FSAE car rear upright by a closed form solution of the critical plane method

Material fatigue is extensively discussed and researched within scientific and industrial communities. Fatigue damage poses a significant challenge for both metallic and non-metallic components, often resulting in unexpected failures of in-service parts. Within multiaxial fatigue assessment, critical plane methods have gained importance due to their ability to characterize a component's critical location and detect early crack propagation. However, the conventional approach to calculate critical plane factors is time-consuming, making it primarily suitable for research purposes or when critical regions are already known. In many real-world scenarios, identifying the critical area of a component is difficult due to complex geometries, varying loads, or time limitations. This challenge becomes particularly crucial after topological optimization of components and in the context of lightweight design. Recently, the authors proposed an efficient method for evaluating critical plane factors in closed form, applicable to all cases that necessitate the maximization of specific parameters based on stress and strain components or their combination. This paper presents and validates the proposed methodology, with reference to a rear upright of a FSAE car, which is characterized by a complex geometry and is subjected to non-proportional loading conditions. The efficient algorithm demonstrated a substantial reduction in computation time compared to the standard plane scanning method, while maintaining solution accuracy.

AN EFFICIENT METHOD TO SIMULATE THE VARYING STATIC RECEPTANCE OF A THIN-WALLED WORKPIECE

A new method to simulate the varying static receptance of a thin-walled workpiece based on the Cholesky decomposition is presented in this paper. In this method, the system stiffness matrix is updated by subtracting the elemental stiffness matrices of the removed elements from the initial system stiffness matrix. The updated system stiffness matrix is permuted by using a novel method based on element removing sequence and considering fill-in reduction. The Cholesky factor of the permuted system stiffness matrix is reused to compute the static receptance of the workpiece for multiple cutter locations. The reduction of computation time by using this new method is proved by a numerical test and the accuracy of the simulation is demonstrated with a machining test.

Spectral CT image reconstruction using a constrained optimization approach-An algorithm for AAPM 2022 spectral CT grand challenge and beyond.

CT reconstruction is of essential importance in medical imaging. In 2022, the American Association of Physicists in Medicine (AAPM) sponsored a Grand Challenge to investigate the challenging inverse problem of spectral CT reconstruction, with the aim of achieving the most accurate reconstruction results. The authors of this paper participated in the challenge and won as a runner-upteam. This paper reports details of our PROSPECT algorithm (Prior-based Restricted-variable Optimization for SPEctral CT) and follow-up studies regarding the algorithm's accuracy and enhancement of its convergencespeed. We formulated the reconstruction task as an optimization problem. PROSPECT employed a one-step backward iterative scheme to solve this optimization problem by allowing estimation of and correction for the difference between the actual polychromatic projection model and the monochromatic model used in the optimization problem. PROSPECT incorporated various forms of prior information derived by analyzing training data provided by the Grand Challenge to reduce the number of unknown variables. We investigated the impact of projection data precision on the resulting solution accuracy and improved convergence speed of the PROSPECT algorithm by incorporating a beam-hardening correction (BHC) step in the iterative process. We also studied the algorithm's performance under noisy projectiondata. Prior knowledge allowed a reduction of the number of unknown variables by . PROSPECT algorithm achieved the average root of mean square error (RMSE) of in the test data set provided by the Grand Challenge. Performing the reconstruction with the same algorithm but using double-precision projection data reduced RMSE to . Including the BHC step in the PROSPECT algorithm accelerated the iteration process with a 40% reduction in computationtime. PROSPECT algorithm achieved a high degree of accuracy and computationalefficiency.

Optimizing vertical ground heat exchanger modelling through GPU-accelerated computation strategies

Enhancing Vertical Ground Heat Exchanger (VGHE) modelling for efficient Vertical Ground-Coupled Heat Pump (VGCHP) systems, renowned for their eco-friendly operation, has garnered significant attention. Optimally designing and sustaining the long-term efficiency of VGCHP systems necessitates rigorous, protracted VGHE modelling, a process fraught with computational intensity. To surmount this challenge, this contribution introduces an accelerated VGHE modelling paradigm grounded in Graphics Processing Unit (GPU) processing. This entails employing the Finite Line Source (FLS) model based on Fast Fourier Transform (FFT). To ameliorate computational demands, the processing capabilities of both Central Processing Units (CPUs) and GPUs are harnessed, leveraging CPU- and GPU-based FFT and inverse FFT (IFFT). Hybrid and non-hybrid processing modes are explored, differentiating between CPU- and GPU-based FFT in non-hybrid scenarios, while hybrid approaches combine both for distinct simulation periods. By scrutinizing two processing systems with varying specifications, diverse CPU- and GPU-based FFT combinations are assessed. Findings unequivocally underscore the superiority of CPU-based FFT for initial years, succeeded by GPU-based FFT for ensuing years, in terms of computational efficiency. The efficacy of the hybrid computation approach is demonstrated through a reduction of total computational time in 20-year VGCHP operation simulations by up to 33.5 % compared to conventional CPU-based FFT methods.

Dynamic stiffness matrix perturbation theory for time-varying structural analysis

Matrix perturbation theory (MPT) is widely used in sensitivity analysis and reanalysis of structural eigenvalue problems when small changes are imposed on parameters. However, the conventional MPT based on the finite element method is only applicable to the cases with small parameter variations, and it is difficult to avoid cumulative errors caused by multi-step perturbations. A key issue for the application of MPT is how to evaluate the error of perturbation solution and correct the results during the process of multi-step perturbation solution. By invoking the dynamic stiffness method (DSM), Wittrick-Williams algorithm, and the precise integration method (PIM), in this paper, we propose an improved method with controllable accuracy, self-checking and elimination of accumulated errors, referred to as the dynamic stiffness matrix perturbation theory (DSMPT). In present method, the rationality of the results can be estimated preliminarily according to dynamic stiffness equation, which is not present in the original MPT, can greatly enhance the accuracy of the perturbation results and evaluate the errors. Case studies prove that the DSMPT combined with PIM provides a convenient way to realize accuracy self-inspection and rectification of the perturbation results, and the maximum relative error of eigenvalue and response can be controlled to be smaller than 0.2% and 0.025%, respectively. Besides, DSMPT can also be applied to the analysis of coupled and nonlinear dynamic system with time-varying modal characteristics. Finally, a thermally-induced vibration analysis of tensegrity modules during space deployment is taken as an example to further illustrate the advancements of the DSMPT in solving time-varying dynamic problems. Results show that the DSMPT effectuate an almost sixfold reduction in computational time.

Subgrid modeling of urban flooding incorporating buildings' effects

Coastal cities at low-lying elevations face extensive inundation risks when attacked by extreme storm surges or tsunamis. Efficient and accurate modeling of flooding is critical for reducing losses in economic and lives. Therefore, the paradox arises that the computational cost is usually too high for rapid forecasts when considering the complex topography and buildings. The present contribution presents a subgrid-based approach, which combines the subgrid model and a drag force module, to realize efficient modeling of urban flooding at a high resolution. The volume-occupation effects of buildings are incorporated naturally in the subgrid treatment, and the blockage, sheltering, and frictional effects of buildings acting on the flows are considered through a simple, bulk parameterization in the form of drag forces. The model is validated with a laboratory experiment and then applied to an ideal numerical model at a real city scale. The proposed model is compared to three other models using coarse grids, with high-resolution simulation results serving as the benchmark for evaluation. The results demonstrate that the presence of buildings induces noticeable alterations in the distribution of water level and velocity fields within the urban area. The proposed model yields comparable predictions to the benchmark in terms of inundation extent, elevation, and velocity. Furthermore, the model exhibits minor sensitivity to grid size, allowing for a significant reduction in computational time compared to high-resolution simulations. The model achieves a well-balanced combination of high resolution, accuracy, and efficiency for large-scale simulations of coastal urban flooding.

Efficient inverse-designed structural infill for complex engineering structures

Inverse design of high-resolution and fine-detailed 3D lightweight mechanical structures is notoriously expensive due to the need for vast computational resources and the use of very fine-scaled complex meshes. Furthermore, in designing for additive manufacturing, infill is often neglected as a component of the optimized structure. In this paper, both concerns are addressed using a so-called de-homogenization topology optimization procedure on complex engineering structures discretized by 3D unstructured hexahedrals.Using a rectangular-hole microstructure (reminiscent to the stiffness optimal orthogonal rank-3 multi-scale) as a base material for the multi-scale optimization, a coarse-scale optimized geometry can be obtained using homogenization-based topology optimization. Due to the microstructure periodicity, this coarse-scale geometry can be up-sampled to a fine single-scale physical geometry with optimized infill, with only a minor loss in structural performance and at a fraction of the cost of a fine-scale solution. The upsampling on 3D unstructured grids is achieved through stream surface tracing, aligning with the optimized local orientations. The periodicity of the physical geometry can be tuned, such that the material serves as a structural component and also as an efficient infill for additive manufacturing designs.The method is demonstrated through three examples of varying geometrical complexity. It achieves comparable structural performance to “brute force” state-of-the-art methods but stands out for its significant computational time reduction. By allowing multiple active layers, the mapped solution becomes more mechanically stable, leading to an increased critical buckling load factor without additional computational expense. The control of active layers also provides direct control over the internal structure, i.e., infill, ensuring that the infill is incorporated as a structural component and enhancing the manufacturability of the de-homogenization procedure. Furthermore, the proposed approach exhibits promising results, achieving volume fractions and weighted compliance values within 5% of the large-scale SIMP model, while demonstrating a computational efficiency improvement ranging from 10 times to over 250 times.

3-D induction log modelling with integral equation method and domain decomposition pre-conditioning

SUMMARY The deployment of electromagnetic (EM) induction tools while drilling is one of the standard routines for assisting the geosteering decision-making process. The conductivity distribution obtained through the inversion of the EM induction log can provide important information about the geological structure around the borehole. To image the 3-D geological structure in the subsurface, 3-D inversion of the EM induction log is required. Because the inversion process is mainly dependent on forward modelling, the use of a fast and accurate forward modelling tool is essential. In this paper, we present an improved version of the integral equation (IE) based modelling technique for general anisotropic media with domain decomposition pre-conditioning. The discretized IE after domain decomposition equals a fixed-point equation that is solved iteratively with either the block Gauss–Seidel or Jacobi pre-conditioning. Within each iteration, the inverse of the block matrix is computed using a Krylov subspace method instead of a direct solver. An additional reduction in computational time is obtained by using an adaptive relative residual stopping criterion in the iterative solver. Using this domain decomposition scheme, numerical experiments show computation time reductions by factors of 1.97–2.84 compared to solving the full-domain IE with a GMRES solver and a contraction IE pre-conditioner. Additionally, the reduction of memory requirement for covering a large area of the induction tool sensitivity enables acceleration with limited GPU memory. Hence, we conclude that the domain decomposition method is improving the efficiency of the IE method by reducing the computation time and memory requirement.

A novel subject-wise dictionary learning approach using multi-subject fMRI spatial and temporal components

The conventional dictionary learning (DL) algorithms aim to adapt the dictionary/sparse code to individual functional magnetic resonance imaging (fMRI) data. Thus, lacking the capability to consolidate the spatiotemporal diversities offered by other subjects. Considering that subject-wise (sw) data matrix can be decomposed into the sparse linear combination of multi-subject (MS) time courses and MS spatial maps, two new algorithms, sw sequential DL (swsDL) and sw block DL (swbDL), have been proposed. They are based on the novel framework, defined by the mixing model, where base matrices prepared by operating a computationally fast sparse spatiotemporal blind source separation method over multiple subjects are employed to adapt the mixing matrices to sw training data. They solve the optimization models formulated using l_0/l_1-norm penalization/constraints through dictionary/sparse code pair update and alternating minimization approach. They are unique because no existing sparse DL method can incorporate MS spatiotemporal components while updating sw atoms/sparse codes, which can eventually be assembled using neuroscience knowledge to extract group-level dynamics. Various fMRI datasets are used to evaluate and compare the performance of the proposed algorithms with existing state-of-the-art algorithms. Specifically, overall, a 14% increase in the mean correlation value and 39% reduction in the mean computation time exhibited by swsDL and swbDL, respectively, over the adaptive consistent sequential dictionary algorithm.

Super-resolution-assisted rapid high-fidelity CFD modeling of data centers

Data center thermal management requires a good understanding of critical cooling airflow path. While CFD modeling excels at portraying airflow and temperature fields, it is often computationally intensive. Recent advancement in deep learning offers a rapid prediction of key parameters at several points of interest based on operation conditions of data centers. However, these models fail to deliver the comprehensive flow physics of an entire domain that CFD provides. This paper presents an innovative super-resolution approach, originating from computer vision, to model the air flow and temperature field in the cold aisle of a realistic data center. The proposed model reconstructs a high-fidelity CFD-generated flow field that typically takes several hours by using a low-fidelity CFD-generated flow field which only takes several minutes. The drastic reduction in computational time makes real-time prediction feasible, while providing detailed information for data center engineers to understand the flow field. Two variations of the framework are proposed and compared with the ground truth generated by the high-fidelity CFD model. A Mean Absolute Error less than 0.5 °C is achieved for air temperature prediction across all test scenarios, and less than 0.1 m/s for air velocity prediction. A sensitivity study is also conducted to determine the importance of input features.

Physics-informed neural network combined with characteristic-based split for solving Navier–Stokes equations

This paper presents a novel approach for solving the shallow-water transport equation using a physics-informed neural network (PINN) combined with characteristic-based split (CBS). Our method separates the variables, so it is no need to consider the weight coefficients between different loss functions. As not all partial derivatives are involved in the gradient backpropagation, our method can save half of the memory occupation without losing accuracy, and resulting in a significant reduction in computation time compared to traditional PINN. We solving the progress of the dispersion of hot water under known flow fields. Furthermore, we propose a boundary condition that accounts for the second-order partial derivative term, which is more appropriate for solving the diffusion equation with open domains than the commonly used assumption of zero boundary values. Our numerical results demonstrate that this boundary condition leads to improved convergence of the network. In addition, we introduce a parameter estimation method to estimate the diffusion coefficient of hot water flow, which requires information from the field at only two different times. We observe that excessive participation of variables in gradient backpropagation can lead to neural networks getting trapped in local optima. We use PINN combined with CBS method to solve 3-D incompressible flow. As the number of variables involved in gradient backpropagation increases, the accuracy of the solution decreases, which can partially support our viewpoint. The source codes for the numerical examples in this work are available at https://github.com/double110/PINN-cbs-.git.

Bayesian backcalculation of pavement properties using parallel transitional Markov chain Monte Carlo

AbstractThis paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight deflectometer (FWD) data. The backcalculation procedure yields estimates and uncertainties for each pavement property of interest. As a by‐product of the Bayesian procedure, information about measurement error is recovered. The Bayesian method is tested on simulated FWD backcalculations and compared with a state‐of‐the‐art trust‐region optimization algorithm, and it achieves estimation errors that are nearly an order of magnitude lower than the trust‐region solver. Confidence intervals are computed from thousands of simulated backcalculations and are shown to quantify uncertainty in estimated pavement properties. To cope with the computational expense of backcalculation, a fully parallel transitional Markov chain Monte Carlo procedure is developed. The fully parallel algorithm scales well to computation with many processor cores, and it yields up to a 50% reduction in computation time when compared to existing parallel implementations.

Innovative Automated Diabetic Retinopathy Severity Grading for Advanced Clinical Insights

Diabetic retinopathy (DR) is a hazardous eye disorder that affects the retina and may lead to vision loss and blindness, especially in diabetics. Early identification is critical for a good outcome, however diabetic retinopathy can only be diagnosed through time-consuming and labor-intensive colour fundus pictures. In order to overcome this challenge, this study proposes a Deep Learning-based strategy that useIntelligent Diabetic Retinopathy Grading through Enhanced Neural Networks (IDR-ENN)to classify retinal pictures into distinct stages of diabetic retinopathy. The proposed approach was trained on a dataset that included 2200 photos from the testing set and 11000 coloured retinal images from the training set. The simulation results suggest that the IDR-ENNbased algorithm can achieve excellent levels of accuracy, sensitivity, and specificity.In this study, we propose a method to significantly reduce the computational time for diabetic retinopathy detection. A novelIDR-ENN approach achieves a remarkable 85% reduction in training computational time for diabetic retinopathy detection. The paper's overall conclusion underlines the potential of deep learning to improve the diagnosis and grading of diabetic retinopathy, which might have a significant impact on the prevention of blindness caused by this disease.

A review on the applicability of machine learning techniques to the metamodeling of energy systems

The use of physics-based models for the development and optimization of energy systems is popular due to their versatility. However, their inherent complexity often makes these simulations computationally prohibitive, despite the current computational power available nowadays. This problem is aggravated when considering renewable energy systems, which are subjected to intermittent weather conditions and must be operated in connection with a grid of variable demand. In that sense, the use of metamodels has shown immense potential in the simulation, analysis, and optimization of energy systems. Therefore, this review presents a comprehensive analysis of the extensive application of metamodeling techniques in energy systems. After conducting a careful search using the Scopus database while targeting a well-defined group of keywords, 474 articles published from 2013 to 2023 were obtained in the present literature review. With a rigorous filtering and screening process, the first pool of articles was reduced to 126. The resulting articles were then separated in two main groups, reviews and research articles. Each of these groups was further organized in terms of the energy system studied, as well as the applicability, machine learning technique and error indicator. The results indicate a steady and significant growth in the number of publications associated with energy systems and different types of metamodeling techniques. The reasons are arguably twofold, reduction of computational time provided by the metamodels and their good accuracy. Nevertheless, and as reported by other studies, the review also identified the need to develop a standardized method for evaluating the error analysis of these models and a comparison between their actual efficiency, which includes accuracy and time. Furthermore, no clear relation was found between specific types of metamodels and specific energy systems.

Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecasting

Reference evapotranspiration is a critical hydrological measurement closely associated with agriculture. Accurate forecasting is vital in effective water management and crop planning in sustainable agriculture. In this study, the future values of reference evapotranspiration are forecasted by applying a recurrent long short-term memory neural network optimized using the Coronavirus Optimization Algorithm, a novel bioinspired metaheuristic based on the spread of COVID-19. The input data is sourced from the Sistema Agrometeorológico para a Gestão da Rega no Alentejo, in Portugal, with meteorological data such as air temperature or wind speed. Several baseline models are applied to the same problem to facilitate comparisons, including support vector machines, multi-layer perceptron, Lasso and decision tree. The results demonstrate the successful forecasting performance of the proposed model and its potential in this field. In turn, to gain deeper insights into the model’s inner workings, the SHapley Additive exPlanation tool is applied for explainability. Consequently, the study identifies the most relevant variables for reference evapotranspiration forecasting, including previously measured evapotranspiration values. Additionally, a univariable model is tested using historic evapotranspiration values as input, offering a comparable performance with a considerable reduction of computational time.

Enhanced artificial intelligence technique for soft fault localization and identification in complex aircraft microgrids

- In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios.

A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection

In recent years, convolutional neural networks (CNNs) have demonstrated promising results in detecting structural damage. However, their architectures often overlook spatial and temporal effects simultaneously. This limitation can result in the loss of valuable information and an incapability to fully capture the complexity of the data, ultimately leading to reduced accuracy and suboptimal performance. This study proposes an intuitive three-dimensional CNN architecture that takes into account vibration history along with sensor spatial relations based on their relative positions. Furthermore, a multi-task learning (MTL) approach is suggested, which is a powerful approach for performing multiple tasks with a single network. The proposed 3D CNN method has been employed to detect single and double damage cases in an experimental steel frame through conventional classification alongside the transfer learning (TL). Moreover, MTL is used to detect single and double damage scenarios with a single unified network, which evaluates damage presence in separate tasks. The 3D CNN fulfilled state-of-the-art performance and 100% accuracy in detecting structural damage in almost all experiments. Additionally, the MTL model achieved promising results even in the presence of severe imbalanced classes of data. Furthermore, it was observed that the utilization of TL resulted in a notable reduction of computation time by 68% and the number of trainable parameters by 90% with the same level of accuracy in double-damage cases.

Development of a composite model for predicting urban surface temperature distribution in the context of GIS

In this paper, we present a new model for simulating radiation heat transfer and surface temperatures in urban environments, which is a prerequisite for simulating outdoor mean radiant temperature for pedestrians. We propose a new model whose novelty lies in a new algorithm for simulating diffuse radiation by combining the Nusselt unit sphere method and Monte Carlo ray tracing algorithm. This combined use of different methods is automatically activated depending on the complexity of the scene and, in particular, the arrangement of the facets facing each other. The model is implemented in a Python-based tool, t4gpd, which combines the capabilities of geographic information science and related technologies (GIS &T) with efficient ray-casting solutions. The model is tested on a theoretical mock-up of nine virtual buildings in Nantes under summer and winter weather conditions and compared with SOLENE-Microclimat results. The view factor and solar radiation flux simulation results agree well with the reference solution, with a significant reduction in simulation computation time. However, the model has limitations due to the exclusion of vegetation evapotranspiration characteristics, wind profile model, and soil with complete stratigraphy. Overall, this new model provides an efficient tool for simulating outdoor pedestrian thermal comfort in urban environments and has potential for further development and validation.

Improved Medium Baseline RTK Positioning Performance Based on BDS/Galileo/GPS Triple-Frequency-Only Observations

With the global service of the BeiDou Navigation Satellite System (BDS), the Galileo Navigation Satellite System (Galileo), and the modernization of the Global Positioning System (GPS), achieving high-precision positioning through triple-frequency-only observations in medium baseline real-time kinematics (RTK) is anticipated. This study investigates the impacts of double-difference (DD) troposphere delay and ionosphere delay on ambiguity resolution (AR) based on six medium baselines at a latitude of 30°. Additionally, it evaluates positioning accuracy, fixing rate, convergence time, and computational time using triple-frequency-only (B1I/B2a/B3I, E1/E5a/E5b, L1/L2/L5) data, comparing these results to those obtained from dual-frequency (B1I/B2a, E1/E5a, L1/L2) and combined dual-frequency and triple-frequency data. The experimental findings suggest that, for geometry-based wide-lane (WL) AR, the DD troposphere delay and ionosphere delay can be disregarded. However, they cannot be overlooked when aiming to resolve the raw ambiguity. Triple-frequency-only RTK exhibits comparable positioning accuracy to dual-frequency RTK, with its primary advantage lying in faster convergence. The probability of achieving convergence within 180 s is approximately 8.0% higher for triple-frequency-only RTK compared to dual-frequency RTK. In terms of computational time, the use of triple-frequency-only data reduces the required time by 8.26 s compared to the approach that simultaneously employs both dual-frequency and triple-frequency data, resulting in a computational time reduction of approximately 20%. Therefore, when conducting medium baseline RTK positioning, it is recommended to adopt the ambiguity resolution method proposed in this paper based on triple-frequency-only observations.

Experimental and numerical validation of a hybrid method for modelling the wake flow of two in-line wind turbines

To forecast the wake flow and power reduction affecting downstream turbines reliably and accurately, a hybrid wake model CFD(ALM)-IDWM was improved, in which the forecasting of Vθmax is improved from linear to cubic. Then, a partially overlapping computational domain is added behind the far-wake domain of upstream wind turbine to calculate the aerodynamic performance of a downstream wind turbine, and the numerical model of full CFD(ALM) and CFD(ALM)-IDWM for two in-line wind turbines are developed. Subsequently, a wind tunnel model test on two in-line wind turbines was carried out to validate the full CFD(ALM) model firstly. Subsequently, the hybrid model of CFD(ALM)-IDWM is numerically validated by the full CFD(ALM) simulations. The results show that CFD(ALM)-IDWM cannot only predict the wake characteristics such as vortices and wakes in the wake region more accurately, but can also accurately simulate the average values of the downstream turbine thrust and torque. By ignoring certain flow field details including acceleration, turbulent viscosity, and Reynolds stress during the simulation process, the computational time is reduced. Base on the same CFD(ALM), the computation time of CFD(ALM)-IDWM was approximately 60% of that of full CFD(ALM). The longer the computational domain of IDWM, the greater reduction in computational time.