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To compensate for thermal errors in machine tools, strategic sensor placement and rapid error calculation are crucial. This study addresses these challenges using model order reduction. A transfer function matrix and a sensitivity function were defined to optimize the number and locations of sensors without physically attaching them to the machine tool. This methodology was validated through experiments on a 3-axis machining center. The number of sensors was reduced by 50 %, and the calculations were performed instantaneously on a laptop. These results demonstrate the approach's effectiveness in advancing sensor placement strategies and real-time thermal displacement estimation. [Display omitted] • Developed sensor placement strategy for machine tools. • Reduced computation time for thermal error estimation. • Used transfer function matrix and sensitivity function. • Transfer function matrix was consistent with machine tool structure. • Thermal displacement could be estimated instantaneously without loss of accuracy. [ABSTRACT FROM AUTHOR]

There has been extensive research on the partial differential equations governing the theory of gas flow in coal mines. However, the traditional Proper Orthogonal Decomposition–Radial Basis Function (POD-RBF) reduced-order algorithm requires significant computational resources and is inefficient when calculating high-dimensional data for coal mine gas pressure fields. To achieve the rapid computation of gas extraction pressure fields, this paper proposes a model reduction method based on deep neural networks (DNNs) and convolutional autoencoders (CAEs). The CAE is used to compress and reconstruct full-order numerical solutions for coal mine gas extraction, while the DNN is employed to establish the nonlinear mapping between the physical parameters of gas extraction and the latent space parameters of the reduced-order model. The DNN-CAE model is applied to the reduced-order modeling of gas extraction flow–solid coupling mathematical models in coal mines. A full-order model pressure field numerical dataset for gas extraction was constructed, and optimal hyperparameters for the pressure field reconstruction model and latent space parameter prediction model were determined through hyperparameter testing. The performance of the DNN-CAE model order reduction algorithm was compared to the POD-RBF model order reduction algorithm. The results indicate that the DNN-CAE method has certain advantages over the traditional POD-RBF method in terms of pressure field reconstruction accuracy, overall structure retention, extremum capture, and computational efficiency. [ABSTRACT FROM AUTHOR]

This study proposes a high computationally efficient data-driven predictive entry guidance method for hypersonic vehicles under multiple no-fly zones. The method uses a reduced-order motion-model-based semianalytic guidance framework to obtain a trained neural network that only requires two-dimensional input. First, the sixth-order entry dynamic motion model is simplified to a third-order model by considering height as the independent variable. Second, based on the reduced-order motion model, a novel exponential function is introduced to yield a semianalytic range-to-go expression in longitudinal guidance. Third, to generate sample trajectory data for training the neural network, the semianalytic guidance framework is supported by the reduced-order motion model with the semianalytic range-to-go expression. Then, a new dynamic lateral guidance reversal logic based on a chain mode strategy is employed to avoid no-fly zones with different configurations and numbers. Finally, to obtain real-time trajectory online, a data-driven online predictive guidance method is proposed based on a back propagation neural network trained by sample trajectory data generated by the semianalytic guidance framework. The proposed method overcomes the drawbacks of most predictor–corrector guidance methods; i.e., the corrected guidance parameters are heavily dependent on the initial values of each iteration in each guidance cycle. Advantageously, the proposed method greatly reduces the online command calculation time in one guidance cycle and only requires two input data to train the neural network, i.e., height and range-to-go, thus yielding results that are close to the engineering reality. The effectiveness of the proposed method is verified through simulations. [ABSTRACT FROM AUTHOR]

To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms. [ABSTRACT FROM AUTHOR]

Real‐time hybrid simulation (RTHS) technique significantly streamlines experimental procedures by allowing researchers to study a substantial portion of the structure through numerical analysis. For effective real‐time interconnectivity between the investigated substructures, the numerical component must be solved within an extremely tight time frame. However, achieving a real‐time solution for large numerical substructures presents a major challenge. Hence, this paper proposes the Proper Orthogonal Decomposition (POD) method to reduce computational burden in RTHS and shows its implementation. The merits of the approach are shown by comparisons between the full‐order and reduced‐order numerical substructures, including nonlinearities. A shear frame retrofitted with superelastic shape memory alloy dampers is investigated as a numerical model. The soil‐structure interaction is also included using a finite element half‐space model with an artificial viscous‐spring boundary. Furthermore, the numerical substructure is coupled with shaking table experiments of a tuned liquid column damper to prove the feasibility of the method. With POD, the studied nonlinear numerical substructure can simulate up to 2655 degrees‐of‐freedom (DOFs) with a given hardware setup, while the full‐order model is limited to 135 DOF, underscoring the significance of the POD method in RTHS. [ABSTRACT FROM AUTHOR]

Matrix (including interface) failure is a typical form of failure in the composite laminates for cryogenic storage tanks causing functionally useless of the structure. In this work, a low-cost multiscale model based on the Self-consistent Clustering Analysis (SCA) method is developed to predict the matrix failure process of the composite storage tanks. First, a reduced order model modeling method with fiber/matrix interfaces is proposed. Then, in conjunction with Progressive Failure Analysis (PFA), a reduced-order model is used to predict matrix failure and interface failure of the composites, mesh sensitivity analyses were carried out, and strength damage envelopes were obtained for unidirectional composites subjected to a combination of transverse stresses and in-plane shear. Compared with the prediction results of the Puck criterion, the errors in predicting damage strength for compressive-shear load and tensile-shear load do not exceed 10% and 30%, respectively. And finally, the thermal-mechanical load analysis of the composite storage tanks is carried out and the effect of homogenous temperature field and heterogenous temperature field on the load-bearing performance of the composite structure is analyzed. The method is proved to have good accuracy and to be very efficient. Its main feature is the ability to rapidly predict the stiffness and strength properties of composite structures under different environmental conditions, in agreement with experimental results, showing the potential to reduce the time and cost required for structural design. [ABSTRACT FROM AUTHOR]

The advent of advanced productive forces has catalyzed the ongoing evolution of intelligent and digital technologies within the pump-turbine sector. The integration of digital technologies has not only enhanced production efficiency but also facilitated intelligent operation management, offering novel solutions for pump-turbine design and operation, thereby accelerating the digital transformation in fluid machinery. This study first established the theoretical framework of the digital twin system for pump-turbines, followed by the development of a mathematical model and the construction of a twin virtual model utilizing Proper Orthogonal Decomposition (POD) reduction theory. The model's accuracy was validated through real-time pressure pulsation data, and exploratory investigations into cavitation prediction were subsequently carried out. To enable the visualization of the digital twin system, this study incorporated Open3D (Three-dimensional computer graphics tools) point cloud technology, effectively rendering the state cloud map of the pump-turbine. Finally, by synthesizing the proposed theories and technologies, the digital twin system's visualization and monitoring for pump-turbines were successfully implemented through the Unity3D simulation platform. This study offers novel insights into intelligent monitoring and prediction of pump-turbines, holding significant implications for the modernization and intelligent advancement of the hydropower industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors' previous research. [ABSTRACT FROM AUTHOR]

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is to take advantage not only of the drag coefficients but also physical fields such as velocity, pressure, and kinetic energy evaluated on a cutting plane in the wake of the vehicle and perpendicular to the road. This additional "augmented" information provides a more accurate and robust prediction of the drag force compared to a standard surface response methodology. As a first step, an original reparametrization of the shape based on combination coefficients of shape principal components is proposed, leading to a low-dimensional representation of the shape space. The second step consists in determining principal components of the x-direction momentum flux through a cutting plane behind the car. The final step is to find the mapping between the reduced shape description and the momentum flux formula to achieve an accurate drag estimation. The resulting surrogate model is a space-parameter separated representation with shape principal component coefficients and spatial modes dedicated to drag-force evaluation. The algorithm can deal with shapes of variable mesh by using an optimal transport procedure that interpolates the fields on a shared reference mesh. The Machine Learning algorithm is challenged on a car concept with a three-dimensional shape design space. With only two well-chosen samples, the numerical algorithm is able to return a drag surrogate model with reasonable uniform error over the validation dataset. An incremental learning approach involving additional high-fidelity computations is also proposed. The leading algorithm is shown to improve the model accuracy. The study also shows the sensitivity of the results with respect to the initial experimental design. As feedback, we discuss and suggest what appear to be the correct choices of experimental designs for the best results. [ABSTRACT FROM AUTHOR]

Over the past two decades, data-driven reduced-order modeling (ROM) strategies have gained significant traction in the nonlinear dynamics community. Currently, several challenges in physical interpretation and data availability remain overlooked in current methodologies. This work proposes a novel ROM methodology based on a newly proposed generalized characteristic value decomposition (GCVD) to address these obstacles. The GCVD-ROM approach proposes a new perspective toward data-driven ROMs via characterization of the dynamics before any ROM considerations are made. In doing so, a significant degree of versatility is inherited in the GCVD-ROM strategy, allowing our models to reproduce the full-scale dynamics in different regions of the parameter space at the cost of a single training data set. Our approach utilizes computationally efficient free-decay data sets alongside a windowed-decomposition scheme, allowing us to extract energy-dependent modal structures for use in model-order reduction. This is accomplished using the physically insightful characteristic values provided by the GCVD, which are shown to be directly related to the system poles at a particular response amplitude. This natural metric, paired with a resonance tracking scheme, allows us to address the difficulties associated with physical interpretation and data availability without sacrificing the convenient aspects of linear projection-based model order reduction. A computational framework for the continuation and bifurcation analysis using linear projection-based ROMs is also presented, permitting us to deploy rigorous analysis and bifurcation studies to verify that our ROMs reproduce the intrinsic complexity of full-scale systems. A detailed walk-through of the GCVD-ROM approach is demonstrated on a simple system where important practical considerations and implementation details are discussed using a concrete example. The discretized von Kármán beam and shallow arch partial differential equations are also used to explore complicated scenarios involving modal coupling across disparate time scales and internal resonances. [ABSTRACT FROM AUTHOR]

Variable-density groundwater flow (VDGF) is jointly driven by hydraulic and density gradient, leading to strong nonlinearity, large computational burden of numerical models, and therefore huge computational cost of Monte Carlo simulation for uncertainty analysis. This study developed the reduced-order model (ROM) for VDGF and built the Gaussian process (GP) for simulating the numerical error of the ROM. The coupled model can obtain solutions of head and salinity across the study domain while GP simulates observation information at limited locations. Moreover, the coupled model can provide higher solution accuracies of head and salinity at the observation locations than the ROM. A two-dimensional (cross-section) VDGF test case was considered, where hydraulic conductivity was taken as a spatially random field. MC simulations were performed using three models, including the full-system model, the ROM, and the coupled model, with corresponding MC strategies denoted as FSMC, ROMC, and GP-ROMC, respectively. The results show that ROMC can be an alternative to FSMC for conducting uncertainty quantification. The relationship between head (or salinity) and the dimensional of ROM can be characterized using power functions with determinate coefficients larger than 0.99. GP-ROMC has higher solution accuracy than ROMC, which indicates that GP is capable for simulating the numerical error of ROM. The results in this study are significant for performing simulation, uncertainty quantification, risk assessment, and parameter estimate in the context of groundwater.

Thin-walled structures, i.e. the wing-upper-skin panels, are prone to buckling accompanied by a significantly large out-of-plane deflection. The computational efficiency of the conventional finite element based full-order method is not satisfactory for nonlinear buckling problems of the structure. In this work, the skin panels on the upper surface of the wing butt box are selected using a sub-modeling technique based on the nonlinear structural analysis. A reduced-order method is proposed to trace the geometrically nonlinear response of the single and double-curved skin panels in a stepwise manner. A predictor-corrector strategy is developed using the Koiter-perturbation-theory based reduced-order model and Newton iterations. The high-efficiency of the proposed method is validated by comparing the number of path-following steps and iterations with the full-order method. A fairly large step size is achieved to significantly reduce the computational cost in a nonlinear buckling analysis. The influence of the number of closely-spaced modes on the numerical accuracy and efficiency of the proposed reduced-order method is also studied. Different boundary condition, location, and thickness of the wing-upper-skin panel, are considered to further demonstrate the favorable performance of the proposed method. • Nonlinear buckling analysis is applied to skin panels with single and double-curved shapes on the wing upper surface. • A reduced-order method is adopted to trace geometrically nonlinear responses in predictor-corrector based stepwise manner. • Influence of the number of closely-spaced modes on numerical accuracy and efficiency of the proposed method is investigated. • Numerical accuracy and high-efficiency of the proposed method are validated for skin panels con- sidering various parameters. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractThe resonance spectrum expansion (RSE) self-shielding method was recently proposed by Nagoya and Osaka universities as a powerful alternative to existing approaches. First investigations of the RSE at Polytechnique Montreal show that it can effectively replace the actual subgroup method used for production calculations in DRAGON5. The Japanese implementation of the RSE method is limited to a solution of the Boltzmann transport equation (BTE) with the method of characteristics. We are proposing a new implementation of the RSE method compatible with various types of solutions for the BTE, including the collision probability and the interface current methods. We based our validation study on a subset made up of eight Rowlands pin cell benchmark cases. The absorption rates obtained after self-shielding are compared with exact values obtained using an elastic slowing-down calculation where each resonance is modeled individually in the resolved energy domain. Validation of Rowlands benchmark with effective multiplication factor calculations was also conducted with respect of the SERPENT2 Monte Carlo code. It is shown that the RSE method is compatible with both advanced and legacy energy meshes and performs slightly better than the production subgroup methods actually used. [ABSTRACT FROM AUTHOR]

A reduced-order model was generated from aerodynamic simulation data for the optimal design and operation management of an air-jet ejector to remove contaminated materials in manufacturing plants. Three significant design parameters of the nozzle structure after studying the computational fluid dynamics (CFD) of the preliminary design cases were determined. Polynomial type and deep neural feedforward network-based meta-models were established based on parametric CFD simulations considering three design variables, which were used to predict the suction performance; the models were successfully validated by comparing the results with those of the complete CFD. Finally, a proper orthogonal decomposition was adopted to reconstruct an internal flow field within a few seconds to facilitate real-time monitoring of the air flow without complete CFD simulations, which was realized as a digital twin for the air-jet ejector system. [ABSTRACT FROM AUTHOR]

In order to save the time and material costs associated with refrigeration system performance evaluations, a reduced-order model (ROM) using highly accurate numerical analysis results and some experimental values was developed. To solve the shortcomings of these traditional methods in monitoring complex systems, a simplified reduced-order system model was developed. To evaluate the performance of the refrigeration system compressor, the temperature of several points in the system where the compressor actually operates was measured, and the measured values were used as input values for ROM development. A lot of raw data to develop a highly accurate ROM were acquired from a VRF system installed in a building for one year, and in this study, specific operating conditions were selected and used as input values. In this study, the ROM development process can predict the performance of compressors used in air conditioning systems, and the research results on optimizing input data required for ROM generation were observed. The input data are arranged according to the design of experiments (DOE), and the accuracy of ROM according to data arrangement is compared through the experiment results. [ABSTRACT FROM AUTHOR]

The analysis of soil–structure interaction (SSI) problems has been established successfully in recent decades. In particular, the solution in the frequency domain provides an exact and efficient method for computing the response of the coupled system. Despite this, the state of practice as a first attempt to incentivize time domain analyses compatible with standard finite element packages introduces the so‐called dimensionless flexible‐base frequency. This frequency, which depends solely on the structure‐to‐soil‐period ratio, allows transforming the frequency domain analyses into time domain analyses using frequency‐independent soil impedance values. However, if such frequency exists for the combined system, it must depend on several physical variables. In this work, we propose a supervised approach to obtain the flexible‐base dimensionless frequency at which the frequency‐independent soil impedance should be used. The analysis is carried out using five dimensionless parameters, and the importance of each one to the estimation of the dimensionless flexible‐base frequency is investigated. We use an inverse problem based on ensemble Kalman inversion (EnKI) to obtain the optimal frequency of the interaction. The data obtained are then employed in a machine‐learning framework to map a set of dimensionless parameters to such a frequency. The generated mapping is finally verified, and a significant improvement in time‐domain simulations is shown compared to the state of practice. [ABSTRACT FROM AUTHOR]

Used extensively in structural dynamics analysis, the energy method is advantageous for transforming boundary value problems of differential equations into variational extremum problems. Recent applications include analyzing the wave and vibration characteristics of track structures. However, traditional energy methods for dynamic modeling and analysis of ballastless track structures require obtaining the global stiffness matrix and mass matrix of the entire coupled system, which decreases computational efficiency. The EM-CMS algorithm for frequency domain dynamic response analysis of ballastless track structures has been proposed to address this issue. The core of EM-CMS is the development of model reduction strategies within the framework of the energy method to reduce matrix dimensions and thereby improve computational efficiency. Specifically, the steel spring floating slab track is the focus of this research. Utilizing the energy functional variational method, the modal properties of the rail and floating slab structures are obtained, and truncation is performed. A reduced-order model for the steel spring floating slab track is established by considering the boundary conditions between the rail-floating slab and the floating slab foundation (connected through fastener springs and steel springs, respectively, and considering the springs' elastic potential energy). Comparatively, the computational efficiency of EM-CMS is approximately ten times greater than that of the energy method. The method's accuracy is also carefully validated against finite element simulations. [ABSTRACT FROM AUTHOR]

Some of the existent frequency-limited model reduction techniques result in unstable reduced-order models (ROMs). Further, the ROMs of a few methods differ significantly from the actual model, resulting in a significant approximation error. Therefore, this work presents an innovative structure of confined frequency-intervals Gramians for continuous-time systems. The developed ROM ensures stability and minimal inaccuracy in approximation error over the desired frequency intervals. Also, the developed technique provides an easy-to-calculate error-bound. Compared to conventional approaches, the suggested approach offers consistent results, illustrating its usefulness. Further, the numerical cases support the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind and solar energy as primary energy sources, their impact on the power generation system cannot be ignored due to the high uncertainty of their changes. Traditional time-domain simulation methods are insufficient to capture the system’s dynamic behavior under nearly infinite scenarios. This paper proposes the reduced-order model-based reachability analysis to obtain the dynamic trajectories of critical state variables after introducing primary energy disturbances into the proposed hybrid wind-solar microgrids. The small-signal model of the hybrid wind-solar microgrid is established and its order is reduced based on the singular perturbation theory. Moreover, the zonotope-based primary energy disturbance model is developed, which expresses the primary energy disturbances in the form of a set and enables the primary energy disturbances to participate in reachability analysis, thereby reducing the need for multiple simulations and improving computational efficiency. By comparing the full-order and reduced-order models' dynamic responses, it is evident that the maximum error between them during the dynamic process is only 1.7%, validating the accuracy of the reduced-order model. From the simulation results, it can be observed that the proposed reduced-order model-based reachability analysis can effectively improve the calculation speed while achieving almost the same results as the full-order model. Furthermore, utilizing the proposed method for computing reachable sets with small time steps has reduced the computation time by up to almost 5 times, confirming the efficiency and feasibility of the proposed method.© 2017 Elsevier Inc. All rights reserved.

Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in critical-size fractures. The differentiation of the cells on a scaffold is impacted among other factors by the surface deformation of the scaffold due to mechanical loading and the wall shear stresses imposed by the interstitial fluid flow. These factors are in turn significantly affected by the material properties, the geometry of the scaffold, as well as the loading and flow conditions. In this work, a numerical framework is proposed to study the influence of these factors on the expected osteochondral cell differentiation. The considered scaffold is rectangular with a 0/90 lay-down pattern and a four-layered strut made of polylactic acid with a 5% steel particle content. The distribution of the different types of cells on the scaffold surface is estimated through a scalar stimulus, calculated by using a mechanobioregulatory model. To reduce the simulation time for the computation of the stimulus, a probabilistic machine learning (ML)-based reduced-order model (ROM) is proposed. Then, a sensitivity analysis is performed using the Shapley additive explanations to examine the contribution of the various parameters to the framework stimulus predictions. In a final step, a multiobjective optimization procedure is implemented using genetic algorithms and the ROM, aiming to identify the material parameters and loading conditions that maximize the percentage of surface area populated by bone cells while minimizing the area corresponding to the other types of cells and the resorption condition. The results of the performed analysis highlight the potential of using ROMs for the scaffold design, by dramatically reducing the simulation time while enabling the efficient implementation of sensitivity analysis and optimization procedures. [ABSTRACT FROM AUTHOR]

The rapid acquisition of flow field characterization information is crucial for closed-loop active flow control. The proper orthogonal decomposition (POD) method is a widely used flow field downscaling modeling method to obtain flow characteristics effectively. Based on the POD method, a flow field reduced-order model (ROM) is constructed in this paper for the flow field control of a hydrofoil of a blended-wing-body underwater glider (BWB-UG) with stabilized suction and blowing forces. Compared with the computational fluid dynamics (CFD) simulation, the computational time required to predict the target flow field using the established POD-ROM is only about 0.1 s, which is significantly less than the CFD simulation time. The average relative error of the predicted surface pressure is not more than 6.9%. These results confirm the accuracy and efficiency of the POD-ROM in reconstructing flow characteristics. The timeliness problem of fast flow field prediction in BWB-UG active flow control is solved by establishing a fast prediction model in an innovative way. [ABSTRACT FROM AUTHOR]

Purpose: Aortic dissection is associated with a high mortality rate. Although computational approaches have shed light on many aspects of the disease, a sensitivity analysis is required to determine the significance of different factors. Because of its complex geometry and high computational expense, the three-dimensional (3D) fluid-structure interaction (FSI) simulation is not a suitable approach for sensitivity analysis. Methods: We performed a Monte Carlo simulation (MCS) to investigate the sensitivity of hemodynamic quantities to the lumped parameters of our zero-dimensional (0D) model with numerically calculated lumped parameters. We performed local and global analyses on the effect of the model parameters on important hemodynamic quantities. Results: The MCS showed that a larger lumped resistance value for the false lumen and the tears result in a higher retrograde flow rate in the false lumen (the coefficient of variation, c v , i = 0.0183 , the sensitivity S X i σ = 0.54 , Spearman's coefficient, ρ s = 0.464 ). For the intraluminal pressure, our results show a significant role in the resistance and inertance of the true lumen (the coefficient of variation, c v , i = 0.0640 , the sensitivity S X i σ = 0.85 , and Spearman's coefficient, ρ s = 0.855 for the inertance of the true lumen). Conclusion: This study highlights the necessity of comparing the results of the local and global sensitivity analyses to understand the significance of multiple lumped parameters. Because of the efficiency of the method, our approach is potentially useful to investigate and analyze medical planning. [ABSTRACT FROM AUTHOR]

The licensing of new reactors implies the use of verified and validated neutronic codes. Numerical validation can rely on sensitivity and uncertainty studies, but they require repeated execution of time-consuming neutron flux and depletion calculations. The computational costs can be reduced by using perturbation theories. However, the uncoupled Depletion Perturbation Theory is restricted to single integral values such as nuclide density. Relying on reduced-basis approaches, which reconstruct all nuclide densities at once, is one way to get around this restriction. Furthermore, the adjoint-based reduced-order model uses the direct and adjoint equations for projection. For diffusion or transport calculations, the Exact-to-Precision Generalized Perturbation Theory was developed. Still, no models for depletion calculations are readily available. Therefore, this paper describes a novel adjoint-based reduced-order model for the Bateman Equation. It uses a range-finding algorithm to create the basis and the uncoupled Depletion Perturbation Theory for the reconstruction of the first order replaced by with a first order formulation. Our paper shows that for several perturbed cases, the depletion reduced-order model successfully reconstructs the nuclide densities. As a result, this serves as a proof of concept for our adjoint-based reduced-order model, which can perform sensitivity and uncertainty burn-up analysis in a shorter time. [ABSTRACT FROM AUTHOR]

Consideration of transient effects is important for industrial applications of heat transfer structure optimisation studies; however, the huge computational cost associated with transient problems is a pressing concern. This paper proposes an extension of a previous reduced-dimensional model to transient conjugate heat transfer between a fluid flow and solid-heated plates in a plate heat exchanger. The extended reduced-dimensional model introduces the temperature field of the plate governed by the heat conduction equation, which is coupled to the temperature field of the fluid, governed by the convection-diffusion equation, through the heat flux balance equation at the contact surface. The model is based on assumptions of fully developed flow and constant temperature profile, reducing the three-dimensional problem to a planar problem and significantly reducing computational costs. The accuracy of the model for the simulation of transient heat transfer is verified by comparison with a three-dimensional model. In this paper, the topography of the heat exchanger plate is optimised for both steady-state and transient conditions by applying the reduced-dimensional model. The effectiveness of the optimised design was demonstrated by the cross-check of both the reduced-dimensional and full three-dimensional models. Furthermore, this work considers the effect of time-independent boundary conditions and time-dependent boundary conditions on transient optimisation. The transient and steady-state optimised designs are analysed and compared for both conditions, and the necessity of transient optimisation is discussed. [ABSTRACT FROM AUTHOR]

The thermal protection system (TPS) represents one of the most critical subsystems for vehicle re-entry. However, due to uncertainties in thermal loads, material properties, and manufacturing deviations, the thermal response of the TPS exhibits significant randomness, posing considerable challenges in engineering design and reliability assessment. Given that uncertain aerodynamic heating loads manifest as a stochastic field over time, conventional surrogate models, typically accepting scalar random variables as inputs, face limitations in modeling them. Consequently, this paper introduces an effective characterization approach utilizing proper orthogonal decomposition (POD) to represent the uncertainties of aerodynamic heating. The augmented snapshots matrix is used to reduce the dimension of the random field by the decoupling method of independently spatial and temporal bases. The random variables describing material properties and geometric thickness are also employed as inputs for probabilistic analyses. An uncoupled POD Gaussian process regression (UPOD-GPR) model is then established to achieve highly accurate solutions for transient heat conduction. The model takes random heat flux fields as inputs and thermal response fields as outputs. Using a typical multi-layer TPS and thermal structure as two examples, probabilistic analyses are conducted. The mean square relative error of a typical multi-layer TPS is less than 4%. For the thermal structure, the averaged absolute error of the radiation and insulation layer is less than 25 °C and 6 °C when the maximum reaches 1200 °C and 150 °C, respectively. This approach can provide accurate and rapid predictions of thermal responses for TPS and thermal structures throughout their entire operating time when furnished with input heat flux fields and structural parameters. [ABSTRACT FROM AUTHOR]

In this paper, a modified bidirectional CLLC DC/DC converter is proposed to improve the efficiency in wide voltage range applications. The proposed converter adopts a LC series resonant branch parallel with the transformer, so that the equivalent inductance of the parallel branch and the equivalent transformer magnetizing inductor varies with the switching frequency. When the required gain is small and the operating frequency is high, the switch’s turn-off current and the circulating power can be reduced through the large impedance of the LC resonant circuit; When the required gain is large, the switching frequency decreases and the auxiliary branch impedance decreases to obtain a higher voltage conversion ratio. The steady state operation and characteristics of the presented converter are analyzed, a design flowchart and a design example of the modified CLLC are also presented. Furthermore, the model of modified CLLC is established and is reduced for simple analysis. A 48V/400V/800W prototype was built and the experimental results verified the correctness of the proposed converter and the feasibility of the modeling results. Compared with the traditional CLLC resonant converter, the efficiency under wide voltage range application is significantly improved.

To address the inefficiencies and time-consuming nature of traditional hot-air anti-icing system designs, reduced-order models (ROMs) and machine learning techniques are introduced to predict anti-icing surface temperature distributions. Two models, AlexNet combined with Proper Orthogonal Decomposition (POD-AlexNet) and multi-CNNs with GRU (MCG), are proposed by comparing several classic neural networks. Design variables of the hot-air anti-icing cavity are used as inputs of the two models, and the corresponding surface temperature distribution data serve as outputs, and then the performance of these models is evaluated on the test set. The POD-AlexNet model achieves a mean prediction accuracy of over 95%, while the MCG model reaches 96.97%. Furthermore, the proposed model demonstrates a prediction time of no more than 5.5 ms for individual temperature samples. The proposed models not only provide faster predictions of anti-icing surface temperature distributions than traditional numerical simulation methods but also ensure acceptable accuracy, which supports the design of aircraft hot-air anti-icing systems based on optimization methods such as genetic algorithms.

There has been extensive research on the partial differential equations governing the theory of gas flow in coal mines. However, the traditional Proper Orthogonal Decomposition–Radial Basis Function (POD-RBF) reduced-order algorithm requires significant computational resources and is inefficient when calculating high-dimensional data for coal mine gas pressure fields. To achieve the rapid computation of gas extraction pressure fields, this paper proposes a model reduction method based on deep neural networks (DNNs) and convolutional autoencoders (CAEs). The CAE is used to compress and reconstruct full-order numerical solutions for coal mine gas extraction, while the DNN is employed to establish the nonlinear mapping between the physical parameters of gas extraction and the latent space parameters of the reduced-order model. The DNN-CAE model is applied to the reduced-order modeling of gas extraction flow–solid coupling mathematical models in coal mines. A full-order model pressure field numerical dataset for gas extraction was constructed, and optimal hyperparameters for the pressure field reconstruction model and latent space parameter prediction model were determined through hyperparameter testing. The performance of the DNN-CAE model order reduction algorithm was compared to the POD-RBF model order reduction algorithm. The results indicate that the DNN-CAE method has certain advantages over the traditional POD-RBF method in terms of pressure field reconstruction accuracy, overall structure retention, extremum capture, and computational efficiency.

Advanced Aerial Mobility (AAM) platforms are poised to begin high-density operations in urban areas nationwide. This new category of aviation platforms spans a broad range of sizes, from small package delivery drones to passenger-carrying vehicles. Unlike traditional aircraft, AAM vehicles operate within the urban boundary layer, where large structures, such as buildings, interrupt the flow. This study examines the response of a package delivery drone, a general aviation aircraft, and a passenger-carrying urban air mobility aircraft through an urban wind field generated using Large Eddy Simulations (LES). Since it is burdensome to simulate flight dynamics in real-time using the full-order solution, reduced-order wind models are created. Comparing trajectories for each aircraft platform using full-order or reduced-order solutions reveals little difference; reduced-order wind representations appear sufficient to replicate trajectories as long as the spatiotemporal wind field is represented. However, examining control usage statistics and time histories creates a stark difference between the wind fields, especially for the lower wing-loading package delivery drone where control saturation was encountered. The control saturation occurrences were inconsistent across the full-order and reduced-order winds, advising caution when using reduced-order models for lightly wing-loaded aircraft. The results presented demonstrate the effectiveness of using a simulation environment to evaluate reduced-order models by directly comparing their trajectories and control activity metrics with the full-order model. This evaluation provides designers valuable insights for making informed decisions for disturbance rejection systems. Additionally, the results indicate that using Reynolds-averaged Navier–Stokes (RANS) solutions to represent urban wind fields is inappropriate. It was observed that the mean wind field trajectories fall outside the 95% confidence intervals, a finding consistent with the authors’ previous research.

In this paper, a novel surrogate model for shape-parametrized vehicle drag force prediction is proposed. It is assumed that only a limited dataset of high-fidelity CFD results is available, typically less than ten high-fidelity CFD solutions for different shape samples. The idea is to take advantage not only of the drag coefficients but also physical fields such as velocity, pressure, and kinetic energy evaluated on a cutting plane in the wake of the vehicle and perpendicular to the road. This additional “augmented” information provides a more accurate and robust prediction of the drag force compared to a standard surface response methodology. As a first step, an original reparametrization of the shape based on combination coefficients of shape principal components is proposed, leading to a low-dimensional representation of the shape space. The second step consists in determining principal components of the x-direction momentum flux through a cutting plane behind the car. The final step is to find the mapping between the reduced shape description and the momentum flux formula to achieve an accurate drag estimation. The resulting surrogate model is a space-parameter separated representation with shape principal component coefficients and spatial modes dedicated to drag-force evaluation. The algorithm can deal with shapes of variable mesh by using an optimal transport procedure that interpolates the fields on a shared reference mesh. The Machine Learning algorithm is challenged on a car concept with a three-dimensional shape design space. With only two well-chosen samples, the numerical algorithm is able to return a drag surrogate model with reasonable uniform error over the validation dataset. An incremental learning approach involving additional high-fidelity computations is also proposed. The leading algorithm is shown to improve the model accuracy. The study also shows the sensitivity of the results with respect to the initial experimental design. As feedback, we discuss and suggest what appear to be the correct choices of experimental designs for the best results.

Testing wave energy converters in the ocean could be expensive and complex, which necessitates the use of numerical modeling. However, accurately modeling the response of wave energy converters with high-fidelity simulations can be computationally intensive in the design stage where different configurations must be considered. Reduced-order models based on simplified equations of motion can be very useful in the design, optimization, or control of wave energy converters. Given the complex dynamics of wave energy converters, accurate representation, and evaluation of relative contributions by different forces are required. This effort is concerned with a performance characterization of the hydrodynamic response of an oscillating surge wave energy converter that is based on a reduced-order model. A state-space model is used to represent the radiation damping term. Morison's representation of unsteady forces is used to account for the nonlinear damping. Wave tank tests are performed to validate simulations. A free response simulation is used to determine the coefficients of the state-space model. Torque-forced simulations are used to identify the coefficients of the nonlinear damping term for different amplitudes and wave frequencies. The impact of varying these coefficients on the response is investigated. An assessment of the capability of the model in predicting the hydrodynamic response under irregular forcing is performed. The results show that the maximum error is 3% when compared with high-fidelity simulations. It is determined that the nonlinear damping is proportional to the torque amplitude and its effects are more pronounced as the amplitude of the flap oscillations increases. [ABSTRACT FROM AUTHOR]

This paper introduces two enhanced model order reduction techniques designed for scenarios involving frequency-weighted and frequency-limited-interval Gramians in the continuous-time domain. The primary objective is to address the instability issue identified in existing approaches in the continuous-time domain, as formulated by Enns for frequency-weighted scenarios and Gawronski & Juang for frequency-limited-interval scenarios. Despite numerous solutions proposed in the literature to mitigate this problem, a persistent challenge remains the high approximation error between the original and reduced-order systems. To overcome this limitation, the proposed improved techniques focus on ensuring stability in reduced-order models while simultaneously minimising the approximation error between the original and reduced systems. Furthermore, these enhanced techniques provide a computationally straightforward, a priori error bound formula. Numerical findings underscore the correctness and efficiency of the proposed techniques in reducing the approximation error while maintaining stability, thereby substantiating their efficacy. [ABSTRACT FROM AUTHOR]

The interaction between streamwise vortices and fuel plumes was investigated through numerical simulations and experiments conducted in a supersonic combustion chamber. Different interactions between vortices and plumes were generated by varying the position of fuel injection on the alternating-wedge struts. This study also involved the development of a reduced-order model to predict the development of fuel plumes and elucidate the mixing mechanism of streamwise vortices. To validate the accuracy of the reduced-order model, Mie scattering and Acetone-PLIF measurements were employed to observe changes in plume morphology downstream of the struts. By utilizing this model, the influence of fuel injection on the development of streamwise vortices can be assessed effectively. Furthermore, the reduced-order model exhibits reasonable predictive capabilities concerning the evolution of streamwise vortices and fuel plumes, thus shedding light on the underlying interaction mechanism between fuel injection and streamwise vortices. • Study on fuel/air mixing mechanism downstream of alternating-wedge struts • Two approaches were employed: optical measurement experiments and a prediction model • The influence of vortex deformation is introduced into the prediction model • The predicted results showed excellent agreement with those measured by the experiment [ABSTRACT FROM AUTHOR]

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To optimize structures and monitor their health, it is essential to build an accurate dynamic analysis model. However, traditional modeling methods based solely on physical information or data-driven techniques may not suffice for many engineering applications. While physical models can accurately simulate complex equipment, they may also incur high computational time. On the other hand, data-driven models may improve computational efficiency but are subject to significant deviations due to the influence of training data. To address these challenges, the Physics-Informed Neural Network (PINN) has gained popularity for imposing physical constraints during the training process, leading to better generalization capabilities with fewer data samples. This paper proposes a physics-informed hybrid modeling (PIHM) approach that combines a reduced-order model, kernel functions, and dynamic equations to predict dynamic output with limited training data and physical information. The method integrates prior physics information into function approximation by incorporating the reduced dynamic equation into a surrogate modeling framework. The loss function considers inertial and damping effects, ensuring physical plausibility. Unlike traditional PINN applications, the proposed modeling method is more explainable, as the trained model can be expressed in function form with engineering interpretation. The approach is verified with a real-world engineering example (telehandler boom) under complex load conditions, demonstrating accuracy, efficiency, and physical plausibility. Overall, the proposed method offers promising capabilities in solving problems where high-fidelity simulation is challenging. [ABSTRACT FROM AUTHOR]

Real-time stress distribution data for implants and cortical bones can aid in determining appropriate implant placement plans and improving the post-placement success rate. This study aims to achieve these goals via a parametric reduced-order model (ROM) method based on stress distribution data obtained using finite element analysis. For the first time, the finite element analysis cases for six design variables related to implant placement were determined simultaneously via the design of experiments and a sensitivity analysis. The differences between the minimum and maximum stresses obtained for the six design variables confirm that the order of their influence is: Young's modulus of the cancellous bone > implant thickness > front–rear angle > left–right angle > implant length. Subsequently, a one-dimensional (1-D) CAE solver was created using the ROM with the highest coefficient of determination and prognosis accuracy. The proposed 1-D CAE solver was loaded into the Ondemand3D program and used to implement a digital twin that can aid with dentists' decision making by combining various tooth image data to evaluate and visualize the adequacy of the placement plan in real time. Because the proposed ROM method does not rely entirely on the doctor's judgment, it ensures objectivity. [ABSTRACT FROM AUTHOR]