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The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.

This work is devoted to the vibroacoustics of complex systems over a broad-frequency band of analysis. The considered system is composed of a complex structure coupled with an internal acoustic cavity. On one hand, the global displacements are associated with the main stiff part and on the other hand, the local displacements are associated with the preponderant vibrations of the flexible subparts. Such complex structures induce interweaving of these two types of displacements, which introduce an overlap of the usual three frequency bands (low-, medium- and high-frequency bands (LF, MF, and HF). A reduced-order computational vibroacoustic model is constructed by using a classical modal analysis with the elastic and acoustic modes. Nevertheless, the dimension of such reduced-order model (ROM) is still important when there is an overlap for each one of the three frequency bands. A multi-frequency reduced-order model is then constructed for the structure over the LF, MF, and HF bands. The strategy is based on a multilevel projection consisting in introducing three reduced-order bases that are obtained by using a spatial filtering methodology. To filter out the local displacements in the structure, a set of global shape functions is introduced. In addition, a classical ROM using acoustic modes is carried out for the acoustic cavity. Then, the coupling between the multilevel ROM and the acoustic ROM is presented. A nonparametric probabilistic modeling is then proposed to take into account the model uncertainties induced by modeling errors that increase with the frequency. The proposed approach is applied to a large-scale computational vibroacoustic model of a car. [ABSTRACT FROM AUTHOR]

Scientific research and engineering practice often require the modeling and decomposition of nonlinear systems. The dynamic mode decomposition (DMD) is a novel Koopman-based technique that effectively dissects high-dimensional nonlinear systems into periodically distinct constituents on reduced-order subspaces. As a novel mathematical hatchling, the DMD bears vast potentials yet an equal degree of unknown. This effort investigates the nuances of DMD sampling with an engineering-oriented emphasis. It aimed at elucidating how sampling range and resolution affect the convergence of DMD modes. We employed the most classical nonlinear system in fluid mechanics as the test subject—the turbulent free-shear flow over a prism—for optimal pertinency. We numerically simulated the flow by the dynamic-stress Large-Eddies Simulation with Near-Wall Resolution. With the large-quantity, high-fidelity data, we parametrized and identified four global convergence states: Initialization, Transition, Stabilization, and Divergence with increasing sampling range. Results showed that Stabilization is the optimal state for modal convergence, in which DMD output becomes independent of the sampling range. The Initialization state also yields sufficient accuracy for most system reconstruction tasks. Moreover, defying popular beliefs, over-sampling causes algorithmic instability: as the temporal dimension, n, approaches and transcends the spatial dimension, m (i.e., m < n), the output diverges and becomes meaningless. Additionally, the convergence of the sampling resolution depends on the mode-specific dynamics, such that the resolution of 15 frames per cycle for target activities is suggested for most engineering implementations. Finally, a bi-parametric study revealed that the convergence of the sampling range and resolution are mutually independent. [ABSTRACT FROM AUTHOR]

In particle-laden flows, a turbulent field can be produced in the carrier phase by the movement of the particle/spray cloud. In this study, the intensity and the integral length scale of the particle-induced turbulence are studied using a simple mechanistic model with comparison to experimental data and numerical simulations for large-scale numerical applications. The experimental results of DynAsp are investigated with numerical simulation results. Out of the spray nozzle, two regions can be distinguished for the spray dynamics: an inertial zone and an equilibrium zone. It is found that the initial injection velocity of the cloud has little effect on the terminal slip-velocity of the particles in the equilibrium zone far from the injection region. The turbulent kinetic energy is closely related to the particle slip-velocity and shows a maximal value when particles reach their terminal velocity inside the equilibrium zone. The integral length scale depends mainly on three parameters: particle slip-velocity, particle size and volume fraction. Combined with the terminal slip-velocity correlation, the reduced-order mechanistic model can give a reasonable estimation of the turbulent kinetic energy as well as the integral length scale of the particle-laden flow in large-scale configurations. [ABSTRACT FROM AUTHOR]

The fractional flow reserve index (FFR) is currently used as a gold standard to quantify coronary stenosis's functional relevance. Due to its highly invasive nature, the development of noninvasive surrogates based on simulations has drawn much attention in recent years, emphasizing efficient strategies that enable translational research. The focus of this work is twofold. First, to assess the feasibility of using a mid-fidelity numerical strategy (transversally enriched pipe element method, TEPEM), placed between low- and high-fidelity models, for the estimation of flow-related quantities, such as FFR and wall shear stress (WSS). Low-fidelity models, as zero- or one-dimensional models, are computationally inexpensive but in detriment of poorer spatially detailed predictions. On the other hand, high-fidelity models, such as classical three-dimensional numerical approximations, can provide detailed predictions but their transition to clinical application is prohibitive due to high computational costs. As a second goal, we quantify the impact of the length of lateral branches in the blood flow through the interrogated vessel of interest to further reduce the computational burden. Both studies are addressed considering a cohort of 17 coronary geometries. A total of 20 locations were selected to estimate the FFR index for a wide range of Coronary Flow Reserve (CFR) scenarios. Numerical results suggest that the mid-fidelity TEPEM model is a reliable approach for the efficient estimation of the FFR index and WSS, with an error in the order of 1 % and 5 % , respectively, when compared to the high-fidelity prediction. Moreover, such mid-fidelity models require much less computational resources, in compliance with infrastructure frequently available in the clinic, by achieving a speedup between 30 and 60 times compared to a conventional finite element approach. Also, we show that shortening peripheral branches does not introduce considerable perturbations either in the flow patterns, in the wall shear stress, or the pressure drop. Comparing the different geometric models, the error in the estimation of FFR index and WSS is reduced to less than 0.1 % and 2 % , respectively. [ABSTRACT FROM AUTHOR]

This article classifies, describes, and critically compares different modeling techniques and control methods for dual-active-bridge (DAB) dc–dc converters and provides explicit guidance about the DAB controller design to practicing engineers and researchers. First, available modeling methods for DAB including reduced-order model, generalized average model, and discrete-time model are classified and quantitatively compared using simulation results. Based on this comparison, recommendations for suitable DAB modeling method are given. Then, we comprehensively review the available control methods including feedback-only control, linearization control, feedforward plus feedback, disturbance-observer-based control, feedforward current control, model predictive current control, sliding mode control, and moving discretized control set model predictive control. Frequency responses of the closed-loop control-to-output and output impedance are selected as the metrics of the ability in voltage tracking and the load disturbance rejection performance. The frequency response plots of the closed-loop control-to-output transfer function and output impedance of each control method are theoretically derived or swept using simulation software PLECS and MATLAB. Based on these plots, remarks on each control method are drawn. Some practical control issues for DAB including dead-time effect, phase drift, and dc magnetic flux bias are also reviewed. This article is accompanied by PLECS simulation files of the reviewed control methods. [ABSTRACT FROM AUTHOR]

Precise model personalization is a key step towards the application of cardiovascular physical models. In this manuscript, we propose to use deep learning (DL) to solve the parameter estimation problem in cardiovascular hemodynamics. Based on the convolutional neural network (CNN) and fully connected neural network (FCNN), a multi‐input deep neural network (DNN) model is developed to map the nonlinear relationship between measurements and the parameters to be estimated. In this model, two separate network structures are designed to extract the features of two types of measurement data, including pressure waveforms and a vector composed of heart rate (HR) and pulse transit time (PTT), and a shared structure is used to extract their combined dependencies on the parameters. Besides, we try to use the transfer learning (TL) technology to further strengthen the personalized characteristics of a trained‐well network. For assessing the proposed method, we conducted the parameter estimation using synthetic data and in vitro data respectively, and in the test with synthetic data, we evaluated the performance of the TL algorithm through two individuals with different characteristics. A series of estimation results show that the estimated parameters are in good agreement with the true values. Furthermore, it is also found that the estimation accuracy can be significantly improved by a multicycle combination strategy. Therefore, we think that the proposed method has the potential to be used for parameter estimation in cardiovascular hemodynamics, which can provide an immediate, accurate, and sustainable personalization process, and deserves more attention in the future. [ABSTRACT FROM AUTHOR]

Abstract Using traditional high‐fidelity numerical simulation to simulate fluid flow in fractured porous media in a real field remains challenging. It involves a large number of degrees of freedom when matrix and fracture equations are solved. To address this challenge, we propose a Galerkin‐free framework to construct a reduced‐order model (ROM) based on the proper orthogonal decomposition (POD). Compared with the typical POD‐based modeling process commonly used in previous studies, the POD‐ROM can be built without performing the Galerkin projection of flow equations onto the low‐dimensional space spanned by the POD basis functions. The numerical integration method was incorporated to obtain the POD time coefficients based on the flow equations solved by the conventional finite volume method. Two complex fracture cases reflecting high‐contrast porous media in a two‐dimensional domain were designed to verify the accuracy and efficiency of the established Galerkin‐free POD‐ROM. Sensitivity analysis of parameters was conducted to examine the adaptability of the ROM. The results illustrate that, compared with the fine‐scale model, the ROM can significantly reduce the CPU time without compromising the quality of the numerical solutions.

Applying bionic airfoils is essential in enlightening the design of rotating machinery and flow control. Dynamic mode decomposition was used to reveal the low dimensional flow structure of Riblets, Seagull, and Teal bionic airfoils at low Reynolds numbers 1 × 105 and is compared with NACA4412 airfoils. The attack angle of the two-dimensional airfoil is 19°, and the SST k-ω turbulence model and ANSYS fluent were used to obtain the transient flow field data. The sparse identification of nonlinear dynamics reveals the nonlinear correlation between modal coefficients and establishes manifold dynamics. The results show that the bionic airfoil and NACA4412 airfoil have the same type of nonlinear correlation, and the dimension and form of the minimum reduced-order model are consistent. The modal coefficients always appear in the manifold equation in pairs with a phase difference of 90°. The dimension of the manifold equation is two-dimensional, and the absolute value of the coefficient corresponds to the fundamental frequency of airfoil vortex shedding. The reconstructed flow field based on the manifold equation is highly consistent with the numerical simulation flow field, which reveals the accuracy of the manifold equation. The relevant conclusions of this study emphasize the unity of the nonlinear correlation of bionic airfoils.

Reduced-order stick models are frequently employed to obtain dynamic amplification factors of self-elevating units (SEU), while the full-order finite element (FE) models are used for quasi-static analyses. This paper develops an efficient framework to create structural digital twins for SEUs by combining both stick models and full-order FE models. A stick model and a detailed FE model of an in-house developed generic SEU are established, respectively, following the standard industry guideline. Dynamic analyses are performed for the stick model based on the modal superposition method. Assuming that the stick model contains the key dynamic characteristics of the full-order FE model, the modal participation factors are multiplied by the corresponding mode shapes of the full FE model to derive the global dynamic responses of the entire SEU. The derived nodal displacements are imposed on the full-order model to obtain the member stresses. The global responses and member stresses are benchmarked with the results from a direct full-order dynamic FE analysis for various environmental conditions. The presented framework is found to significantly increase the efficiency of the simulation while retaining a similar accuracy, and it forms a critical step for the ongoing development of digital twins of fixed offshore structures.

Droop control has been well adopted for multi-source multi-load DC microgrids (MGs) due to its inherent modularity and reliability by only using local measurements, especially with the most commonly-used voltage-current droop (V-I) mode. While another implementation named current-voltage droop (I-V) mode also can be adopted. With the same value of droop gain in these two modes, a DC MG will have the same steady-state feature, but its dynamic performance of DC voltage control may be different. Our primary motivation is to investigate this phenomenon from the perspective of equivalent RLC circuits. This paper proposes a generic reduced-order modeling method suitable for exploring the dynamic stability of DC voltage control with these two modes. By ignoring fast inner current control dynamic, each droop based DC voltage control unit can be modeled as a RLC parallel circuit in these two modes, which is convenient for modular modeling and extension. With these models, the essential cause of system dynamic stability difference and the physical meaning of key control parameters in these two modes can be revealed in an intuitive way. In addition, if the inner current control with slow dynamic cannot be ignored in some specific scenarios, a modified RLC model can be still obtained to analyze its influence. Moreover, based on reduced-order models, analytical solutions of dynamic performance indexes have been obtained, through which the impact of control parameters on dynamic performance of DC bus voltage can be characterized. Finally, the effectiveness of the proposed reduced-order modeling method has been verified by detailed simulation and experiment results. [ABSTRACT FROM AUTHOR]

The bronchial tree plays a main role in the human respiratory system because the air distribution throughout the lungs and gas exchange with blood occur in the airways whose dimensions vary from several centimeters to micrometers. Organization of about 60,000 conducting airways and 33 million respiratory airways in a limited space results in a complex structure. Due to this inherent complexity and a high number of airways, using target-oriented dimensional reduction is inevitable. In addition, there is no general reduced-order model for various types of problems. This necessitates coming up with an appropriate model from a variety of different reduced-order models to solve the desired problem. Lumped formulation, trumpet, or typical path model of whole or parts of bronchial tree are frequently used reduced-order models. On the other hand, using any of these models results in underestimation of flow heterogeneity leading to inaccurate prediction of the systems whose mechanisms depend on the fluid heterogeneity. In this study, a simple robust model combining mechanistic and non-mechanistic modeling approaches of the bronchial tree is proposed which overcomes the limitations of the previous reduced-order models and gives the same results of a detailed mechanistic model for the first time. This model starts from an accurate multi-branching model of conducting and respiratory airways (i.e., the base model) and suggests a proxy model of conducting airway and reduced-order model of respiratory airways based on the base model to significantly reduce computational cost while retaining the accuracy. The combination of these models suggests various reduced-order surrogate models of the human bronchial tree for different problems. The applications and limitations of each reduced-order model are also discussed. The accuracy of the proposed model in the prediction of fluid heterogeneity has been examined by the simulation of multi-breath inert gas washout because the alveolar slope is the reflection of fluid heterogeneity where the computational time decreases from 121 h (using the base model) to 4.8 s (using the reduced-order model). A parallel strategy for solving the equations is also proposed which decreases run time by 0.18 s making the model suitable for real-time applications. Furthermore, the ability of the model has been evaluated in the modeling of asthmatic lung as an instance of abnormal lungs, and in the modeling of O2–CO2 exchange as an instance of nonlinear reacting systems. The results indicate that the proposed model outperforms previous models based on accuracy, robustness, and run time. [ABSTRACT FROM AUTHOR]

The basis generation in reduced order modeling usually requires multiple high-fidelity large-scale simulations that could take a huge computational cost. In order to accelerate these numerical simulations, we introduce a FOM/ROM hybrid approach in this paper. It is developed based on an a posteriori error estimation for the output approximation of the dynamical system. By controlling the estimated error, the method dynamically switches between the full-order model and the reduced-oder model generated on the fly. Therefore, it reduces the computational cost of a high-fidelity simulation while achieving a prescribed accuracy level. Numerical tests on the non-parametric and parametric PDEs illustrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Conventional methods to estimate the static formation temperature (SFT) require borehole temperature data measured during thermal recovery periods. This can be both economically and technically prohibitive under real operational conditions, especially for high-temperature boreholes. This study investigates the use of temperature logs obtained under injection conditions to determine SFT through inverse modelling. An adaptive sampling approach based on machine-learning techniques is applied to explore the model space efficiently by iteratively proposing samples based on the results of previous runs. Synthetic case studies are conducted with rigorous evaluation of factors affecting the quality of SFT estimates for deep hot wells. The results show that using temperature data measured at higher flow rates or after longer injection times could lead to less-reliable results. Furthermore, the estimation error exhibits an almost linear dependency on the standard error of the measured borehole temperatures. In addition, potential flow loss zones in the borehole would lead to increased uncertainties in the SFT estimates. Consequently, any prior knowledge about the amount of flow loss could improve the estimation accuracy considerably. For formations with thermal gradients varying with depth, prior information on the depth of the gradient change is necessary to avoid spurious results. The inversion scheme presented is demonstrated as an efficient tool for quantifying uncertainty in the interpretation of borehole data. Although only temperature data are considered in this work, other types of data such as flow and transport measurements can also be included in this method for geophysical and rock physics studies. [ABSTRACT FROM AUTHOR]

Model‐based prediction of fractional flow reserve (FFR) in the context of stable coronary artery disease (CAD) diagnosis requires a number of modelling assumptions. One of these assumptions is the definition of a baseline coronary flow, ie, total coronary flow at rest prior to the administration of drugs needed to perform invasive measurements. Here we explore the impact of several methods available in the literature to estimate and distribute baseline coronary flow on FFR predictions obtained with a reduced‐order model. We consider 63 patients with suspected stable CAD, for a total of 105 invasive FFR measurements. First, we improve a reduced‐order model with respect to previous results and validate its performance versus results obtained with a 3D model. Next, we assess the impact of a wide range of methods to impose and distribute baseline coronary flow on FFR prediction, which proved to have a significant impact on diagnostic performance. However, none of the proposed methods resulted in a significant improvement of prediction error standard deviation. Finally, we show that intrinsic uncertainties related to stenosis geometry and the effect of hyperemic inducing drugs have to be addressed in order to improve FFR prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: The disc-blade-shaft integration rotor is a new type of structural component used to improve the performance of aeroengine. It usually suffer from severe vibration problems due to the low stiffness and low damping capacity. Therefore, the dynamic behavior is an important issue to be researched in the design process. Purpose: The main purpose of this work is to investigate the coupling vibration characteristics of multi-component and the influence of blade mistuning on the vibration localization of the integrated rotor. Methods: To solve the large-scale calculation problem in dynamic analysis, the improved prestress hybrid interface component mode synthesis method is adopted to obtain reduced-order model of the complex rotor. By combining with nodal diameter spectrum (NDS) method, the natural characteristics and the forced vibration response of the integration rotor system are analyzed. Then, deterministic mistuning mode is introduced into the reduction model and vibration localization characteristics is investigated. Results: Compared with the full finite element model, the error of frequency calculation with the reduced-order model of the integration rotor is relatively small. After considering prestress effects, the frequency of the integration rotor increases. The vibration response of the integrated rotor changes obviously when the parameters such as excitation order, coupling stiffness changed. Conclusion: The results of frequency and mode shapes verified the validity of the improved prestress hybrid interface component mode synthesis method. For the integration rotor, the multi-stage and multi-component coupled mode appears mainly with low nodal diameters, with the increase of ND number, the mode coupling degree among the dominated vibration stage blisk and other stage blisk decreases. Besides, the mistuning pattern, the excitation order and the inter-stage coupling stiffness have great influence on the vibration localization of the rotor system. [ABSTRACT FROM AUTHOR]

Digital Twins, which tend to intervene over the entire life cycle of products from early design phase to predictive maintenance through optimization processes, are increasingly emerging as an essential component in the future of industries. To reduce the computational time reduced-order modeling (ROM) methods can be useful. However, the spread of ROM methods at an industrial level is currently hampered by the difficulty of introducing them into commercial finite element software, due to the strong intrusiveness of the associated algorithms, preventing from getting robust and reliable tools all integrated in a certified product. This work tries to circumvent this issue by introducing a weakly-invasive reformulation of the LATIN-PGD method which is intended to be directly embedded into Simcenter Samcef TM finite element software. The originality of this approach lies in the remarkably general way of doing, allowing PGD method to deal with not only a particular application but with all facilities already included in such softwares—any non-linearities, any element types, any boundary conditions...—and thus providing a new high-performance all-inclusive non-linear solver. [ABSTRACT FROM AUTHOR]

A reduced-order model (ROM) is proposed for efficient drag prediction on a streamlined body with surface imperfections that emulate leading-edge roughness or erosion-induced damage. Surface imperfections are idealised as forward-facing step(s) for which the chordwise position, spanwise length, and distribution of steps are varied. It is hypothesised that superposed a bilinear dependencies on the chordwise location and spanwise length of individual steps comprising the damage provide for reasonable ROM predictions of the corresponding change in total drag on the streamlined body. Direct numerical simulations are applied to test the ROM hypotheses and to study interactions between the three-dimensional steps and the separated near-wall turbulent flow fields, justifying the underlying terms and form of the ROM. Insights into the flow physics influencing both form and friction contributions to total drag are revealed, and satisfactory model performance is demonstrated for complex damage idealisations that emulate fracture of laminated wind turbine blades. [ABSTRACT FROM AUTHOR]

It remains an open question whether the state of knowledge on bend hydrodynamics applies to sharp bends in the largest river systems on Earth. This paper reports a field investigation into the hydrodynamics in two consecutive sharp bends of the Middle Yangtze River (MYR), and demonstrates that the reduced‐order model of Blanckaert and de Vriend is able to resolve the secondary flow strength and continuous flow redistribution in rivers of this size to high accuracy. Consequently, the model has been validated over the entire range of scales. These two bends show similarities to those of smaller size in terms of bathymetry, flow redistribution, and secondary flow evolution. The transverse location of the core of maximum velocity along the bends lags behind the thalweg by approximately half a width. A center‐region cell of secondary flow develops over the outer bank near the apex, and gradually decays downstream until it completely vanishes at the bend exit. Both bends are mildly curved from the perspective of hydrodynamic modeling, with a weak interaction between the secondary flow and primary flow, although they are sharply curved from the perspective of geomorphology (width/radius > 0.5). A term‐by‐term analysis of the model indicates that the flow redistribution is primarily controlled by planimetric changes in curvature and by topographic steering, and is slightly influenced by the secondary flow. However, the secondary flow may indirectly affect the flow redistribution by conditioning the bed topography. Important knowledge gaps remain with respect to the coupling between the hydrodynamic and morphological processes. Key Points: Field measurements of 3D flow field in very large sharp bendsPatterns of mean and secondary flows are similar as in smaller systemsThe reduced‐order model can resolve the flow field over the entire range of scales [ABSTRACT FROM AUTHOR]

Reduced order models (ROMs) are constructed by proper orthogonal decomposition (POD) and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe tangentially fired pulverized coal boiler. POD is performed to extract low-dimensional basis vectors to reproduce 3-D distribution of reacting scalars with respect to the operation parameters of total secondary air (TSA) and burner zone stoichiometric ratio (BSR). The ROMs by Kriging and RBFN both reproduce the scalar fields within 6% averaged relative L2 norm error at three validation points in the parameter space. It is possible to reproduce a 3-D scalar field at any unexplored operation condition within a few seconds through parallel computation of the ROM. It allows fast evaluation of the effects of varying operation parameters in the design stage and real time response of a digital twin based on the ROM for smart operation and maintenance of industrial combustion facilities.

Geomaterial mechanical parameters are critical to implementing construction design and evaluating stability through feedback analysis in geotechnical engineering. The back analysis is widely utilized to identify and calibrate the geomaterial mechanical properties in geotechnical engineering. This study developed a novel back-analysis framework by combining a reduced-order model (ROM), grey wolf optimization (GWO), and numerical technology. The ROM was adopted to evaluate the response of the geotechnical structure based on a numerical model. GWO was used to search and identify the geomaterials properties based on the ROM. The developed back analysis framework was applied to a circular tunnel and a practical tunnel for determining the mechanical property of the surrounding rock mass. The results showed that the ROM could be an excellent surrogated model and replaced it with the numerical model. The obtained geomaterial properties were in excellent agreement with the actual properties. The deformation behavior captured by the developed framework was consistent with the theoretical solution in a circular rock tunnel. The developed framework provides a practical, accurate, and convenient approach for calibrating the geomaterial properties based on field monitoring data in practical geotechnical engineering applications.

This study develops a data-driven reduced-order model based on a deep convolutional neural network (CNN) for real-time and accurate prediction of the drug trajectory and concentration field in transarterial chemoembolization therapy to assist in directing the drug to the tumor site. The convolutional and deconvoluational layers are used as the encoder and the decoder, respectively. The input of the network model is designed to contain the information of drug injection location and the blood vessel geometry and the output consists of the drug trajectory and the concentration field. We studied drug delivery in two-dimensional straight, bifurcated blood vessels and the human hepatic artery system and showed that the proposed model can quickly and accurately predict the spatial–temporal drug concentration field. For the human hepatic artery system, the most complex case, the average prediction accuracy was 99.9% compared with the CFD prediction. Further, the prediction time for each concentration field was less than 0.07 s, which is four orders faster than the corresponding CFD simulation. The high performance, accuracy and speed of the CNN model shows the potential for effectively assisting physicians in directing chemoembolization drugs to tumor-bearing segments, thus improving its efficacy in real-time.

This study presents a data reconstruction-based two-step non-intrusive reduced-order modeling (ROM) based on discrete Fourier transformation (DFT) and proper orthogonal decomposition-radial basis function (POD-RBF) interpolation. To efficiently approximate a system for various parametric inputs, two offline and one online stage are proposed. The first offline stage adjusts and reconstructs sampled data using a scaling factor. During the adjusting procedure, the fast Fourier transform operation is used to transform a domain between the time and frequency, and the POD-RBF interpolation method efficiently generates adjusted data. The second offline stage constructs multiple ROMs in the frequency domain for interpolation with respect to the parameter. Finally, in the online stage, the solution field depending on the changes in input parameters, is approximated using the POD-RBF interpolation and the inverse Fourier transformation. The accuracy and efficiency of the proposed method are verified using the 2-D unsteady incompressible Newtonian fluid problems and are compared to the OpenFOAM software program showing remarkable efficiencies in computing approximated solutions.

This work presents the development of a multi-fidelity, parametric and non-intrusive reduced-order modelling method to tackle the problem of achieving an acceptable predictive accuracy under a limited computational budget, i.e. with expensive simulations and sparse training data. Traditional multi-fidelity surrogate models that predict scalar quantities address this issue by leveraging auxiliary data generated by a computationally cheaper lower fidelity code. However, for the prediction of field quantities, simulations of different fidelities may produce responses with inconsistent representations, rendering the direct application of common multi-fidelity techniques challenging. The proposed approach uses manifold alignment to fuse inconsistent fields from high- and low-fidelity simulations by individually projecting their solution onto a common latent space. Hence, simulations using incompatible grids or geometries can be combined into a single multi-fidelity reduced-order model without additional manipulation of the data. This method is applied to a variety of multi-fidelity scenarios using a transonic airfoil problem. In most cases, the new multi-fidelity reduced-order model achieves comparable predictive accuracy at a lower computational cost. Furthermore, it is demonstrated that the proposed method can combine disparate fields without any adverse effect on predictive performance. [ABSTRACT FROM AUTHOR]

Blade crack will cause severe mistuning of hard-coated blisks, which will lead to vibration localization. To identify crack mistuning and analyze influence factors, in this study, a mistuning identification method of blade cracks in hard-coated blisks is presented based on modified component mode mistuning reduced-order model, in which the hard-coated blisk with blade crack is decomposed into a substructure of tuned hard-coated blisk and a substructure of coated blade with cracks. Crack mistuning of each coated blade can be obtained by a single identification calculation. After verifying the rationality of this identification method, the influence factors of blade crack mistuning are analyzed. The influence factors include the crack location on the coated blade (cracks occurring only in coating or only in blade substrate or both in blade substrate and coating), crack length, crack position in the radial direction of the blisk, and modal data type of coated blisk used for mistuning identification calculation. The research results show that, with the increase of crack length, the mistuning of crack occurring only in the coating does not increase continuously but decreases firstly and then increases. For the first bending modes, the closer the blade crack is to the blade root, the larger the mistuning is. For the second bending modes, the blade crack located at the position of maximum modal displacement will produce large mistuning. For hard-coated blisk with blade crack, these crack mistuning variation rules are of great significance to the dynamic analysis and the determination of the crack location. [ABSTRACT FROM AUTHOR]

Frequency-domain model reduction is a crucial concern in applying the prevailing impedance method for the stability analysis of complex systems, e.g., the modular multilevel converter (MMC). Recently, it has been shown that under symmetric conditions, a 2 × 2 matrix-based impedance model characterizing the two coupled frequencies of MMC are sufficient for its stability analysis. However, when the asymmetry occurs, principally, a much higher number of frequency couplings will appear in the MMC and thereby leads to a significant rise in the model dimension. Enlighted by this issue, there is an urgent need of finding a suitable frequency-domain method that can serve as a general criterion for model reduction. To this end, this article proposes a block diagonal dominance (BDD)-based model reduction method and applied it to the asymmetric MMC. Basically, the BDD can decompose an N-dimensional task to N one-dimensional tasks, via which a significant reduction in model dimension can be realized. It is shown that by properly shifting the impedance model from one domain to another (e.g., α-β domain to d-q domain), the BDD property can be achieved for most asymmetric scenarios. Finally, various case studies considering different asymmetry degrees are conducted to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The ramjet/scramjet engines require the control-oriented model to predict the inlet flow field in less than a few seconds. However, it is challenging for these kinds of inlets which utilize curved shock waves to compress the air flow. In this paper, a reduced-order model based on the computational fluid dynamics, the proper orthogonal decomposition theory, and the radial basis function interpolation method is developed. After that, the curved shock waves dominated flow fields of a ramjet inlet under different angles of attack and free stream Mach numbers are predicted with this reduced-order model and compared to the full order computational fluid dynamics solutions. The results show that this reduced-order model can successfully predict the curved shock waves, the curved shock wave/boundary layer interactions, and the shock trains caused by a back pressure with high accuracies. The consumed time is only 0.11 s. The performance parameters are also predicted with the relative errors no more than 2%. • A RBF-POD method is studied for the curved shock compression inlet. • The method can predict the flow field accurately and rapidly. • The predictive accuracy is improved by increasing the snapshot number. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

This paper aims to investigate the application of Harris hawks optimization (HHO) optimization for the solution of model order reduction (MOR) problem of power system application. The proposed approach is implemented to determine the reduced order equivalent model of large-scale power system model. The obligations encountered by the higher-scale model like stability, calculative effort and problem into local optima can be enhanced by this propounded methodology. These yielded reduced models have been tried in addition to the existing algorithms and the obtained results are contrasted considering various technical parameters to accomplish its effectiveness, reliability and robustness of the adopted strategy. Thus, superior performance of the proposed method is demonstrated. The validation of the methodology in terms of error index, time and frequency domain outputs, convergence curves with scalability of the approach are outlined in the investigation of the system. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of wind turbine wake characteristics is vital, particularly in the context of expanding large offshore wind farms. Existing wake measurement techniques provide only spatially sparse wake measurement data, limiting their utility in precise wind turbine design and control. This paper introduces a data-driven approach that combines proper orthogonal decomposition (POD) with machine learning (ML) techniques, designing a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. This framework establishes a nonlinear mapping between sensor measurements and low-dimensional POD coefficients. Two distinct sensor placements, wall-mounted and wake-mounted, are investigated for real measurement scenarios. The results highlight the effectiveness of the proposed wake flow estimation method in reconstructing a complete flow field from exceptionally sparse sensor data, with both wall-mounted and wake-mounted strategies, exhibiting promising results with maximum relative errors of 6.37% and 4.51%, respectively. From the reliability assessments considering various configurations of POD modes and sensor numbers, the ROM-WFE framework demonstrates its capability to estimate wake flow effectively, offering a cost-effective tool for practical applications. Furthermore, the framework maintains accuracy even with high-noise and low-frequency data, demonstrating robustness and generalization. This method significantly contributes to wind turbine wake prediction controller design, promising accurate and robust wake flow field estimation, potentially revolutionizing active wake control and enhancing wind farm operational efficiency. • Combining POD and ML techniques to establish a Reduced Order Modeling-based Wake Flow Estimation (ROM-WFE) framework. • Exploring the effectiveness of ROM-WFE in wall- and wake-mounted sensor placement strategies. • Utilizing fewer than three sensors enhances practicality for real-world applications and improves wind farm efficiency. • Demonstrating resilience and adaptability, maintaining accuracy even with high-noise, low-frequency data. [ABSTRACT FROM AUTHOR]

Thanks to their low computational cost, reduced-order models (ROMs) are indispensable in ensemble-based simulations used, e.g., for uncertainty quantification, inverse modeling, and optimization. Since data used to train a ROM are typically obtained by running a high-fidelity model (HFM) multiple times, a ROM's efficiency rests on the computational cost associated with the data generation and training phase. One such ROM, a polynomial chaos expansion (PCE), often provides a robust description of an HFM's response surface in the space of model parameters. To reduce the data-generation cost, we propose to train a PCE on multi-fidelity data, part of which come from the dynamic HFM and the remainder from dynamic mode decomposition (DMD); the latter is used to interpolate the HFM data in time. Our numerical experiments demonstrate the accuracy of the proposed method and provide guidelines for the optimal use of DMD for interpolation purposes. • We introduce a new method to extend the use of PCE as a surrogate of dynamic models. • Accuracy of DMD to interpolate high-fidelity data in time and feed PCE is assessed. • We test our approach on two-dimensional multiphase flow in heterogeneous media. [ABSTRACT FROM AUTHOR]

This study conducts an extensive theoretical-experimental investigation into the nonlinear dynamical response of a base-excited initially curved panel clamped at two ends via bolted joints. A new distributed nonlinear joint stiffness model is proposed capable of exhibiting interconnected effects at various states of the contact interface. More specifically, the proposed model allows the control of the nonlinear softening behaviour of the panel through controlling the displacement threshold for micro-slip and new stick states, as well as the rate at which the micro-slip region develops. Due to the inclined angle of the clamping supports, the panel is slightly curved in its fastened arrangement. Hence, the panel is modelled as a shallow shell using Donnell's nonlinear shallow shell theory, taking into account von Kármán strain nonlinearities, while retaining all in-plane and out-of-plane displacements. Structural damping is considered via use of the Kelvin–Voigt model. Taking into account the work of the nonlinear joint stiffness model, the partial differential equations governing the in-plane and out-of-plane motions of the panel are derived using the generalised Hamilton's principle, which are then discretised into a reduced-order model using a two-dimensional Galerkin modal decomposition approach. For the experimental part, two nominally identical panels are considered and several tests are conducted through base excitation in the primary resonance region and the backbone curves are obtained directly via the Phase Lock Loop method. Extensive comparisons are conducted between experimental results and theoretical predictions for both backbone curves and time histories of the panel midpoint velocity and displacement. It is shown that the theoretical predictions are in very good agreement with the experimental results, with the proposed joint stiffness model being capable of predicting the significant variability in the experimental results. [Display omitted] [ABSTRACT FROM AUTHOR]

A helmet is the main protective equipment for a child pillion passenger. A safe helmet must be able to mitigate head and brain injuries resulting from high head impact loading. A lightweight helmet is preferable, especially for children. This paper proposed to study the effect of materials, liner thickness, and friction at the head–helmet interface on linear and rotational accelerations using reduced-order modelling. A child head–helmet finite element model was developed and validated against an experiment. Finite element simulations were conducted to generate training data for the establishment of reduced-order models which were subsequently used to predict the linear and rotational accelerations for various helmet parameters. The prediction could be performed in a very short time compared to its corresponding finite element simulation. The use of aluminium foam enhanced mitigation of the linear and rotational accelerations as well as weight reduction. This study also revealed that the head–helmet friction coefficient had a strong effect on the rotational acceleration, while the liner thickness predominantly affected the linear acceleration. However, the liner thickness had less influence on the rotational acceleration when the head–helmet friction was low. The risk of brain concussion as well as diffusional injury could be reduced by enabling low friction at head–helmet surface.

Flange wrinkling is often seen in deep-drawing process when the applied blankholding force is too small. This paper investigates the plastic wrinkling of flange under a constant blankholding force. A series of deep-drawing experiments of AA1100-O blanks are conducted with different blankholding forces. The critical cup height and wrinkling wave numbers for each case is established. A reduced-order model of flange wrinkling is developed using the energy method, which is implemented to predict the flange wrinkling of AA1100-O sheet by incrementally updating the flange geometry and material hardening parameters during the drawing process. A deep-drawing finite element model is developed in ABAQUS/standard to simulate the flange wrinkling of AA1100-O blanks under constant blankholding force. The predicted cup height and wave numbers from the finite element model and reduced-order model are compared with the experimental results, which demonstrates the accuracy of the reduced-order model, and its potential application in fast prediction of wrinkling in deep-drawing process.

Phase change material (PCM)-based thermal energy storage (TES) systems are widely used for repeated intermittent heating and cooling applications. However, such systems typically face some challenges due to the low thermal conductivity and expensive encapsulation process of PCMs. The present study overcomes these challenges by proposing a lightweight, low-cost, and low thermal resistance TES system that realizes a fluid-to-PCM additively manufactured metal-polymer composite heat exchanger (HX), based on our previously developed cross-media approach. A robust and simplified, analytical-based, 1D reduced-order model (ROM) was developed to compute the TES system performance, saving computational time compared to modeling the entire TES system using PCM-related transient CFD modeling. The TES model was reduced to a segment-level model comprising a single PCM-wire cylindrical domain based on the tube-bank geometry formed by the metal fin-wires. A detailed study on the geometric behavior of the cylindrical domain and the effect of overlapped areas, where the overlapped areas represent a deviation from 1D assumption on the TES performance, was conducted. An optimum geometric range of wire-spacings and size was identified. The 1D ROM assumes 1D radial conduction inside the PCM and analytically computes latent energy stored in the single PCM-wire cylindrical domain using thermal resistance and energy conservation principles. The latent energy is then time-integrated for the entire TES, making the 1D ROM computationally efficient. The 1D ROM neglects sensible thermal capacity and is thus applicable for the low Stefan number applications in the present study. The performance parameters of the 1D ROM were then validated with a 2D axisymmetric model, typically used in the literature, using commercially available CFD tools. For validation, a parametric study of a wide range of non-dimensionalized parameters, depending on applications ranging from pulsed-power cooling to peak-load shifting for building cooling application, is included in this paper. The 1D ROM appears to correlate well with the 2D axisymmetric model to within 10%, except at some extreme ranges of a few of the non-dimensional parameters, which lead to the condition of axial conduction inside the PCM, deviating from the 1D ROM.

Organoids are three-dimensional multicellular tissue constructs. When cultured in vitro, they recapitulate the structure, heterogeneity, and function of their in vivo counterparts. As awareness of the multiple uses of organoids has grown, e.g. in drug discovery and personalised medicine, demand has increased for low-cost and efficient methods of producing them in a reproducible manner and at scale. Here we focus on a bioreactor technology for organoid production, which exploits fluid flow to enhance mass transport to and from the organoids. To ensure large numbers of organoids can be grown within the bioreactor in a reproducible manner, nutrient delivery to, and waste product removal from, the organoids must be carefully controlled. We develop a continuum mathematical model to investigate how mass transport within the bioreactor depends on the inlet flow rate and cell seeding density, focusing on the transport of two key metabolites: glucose and lactate. We exploit the thin geometry of the bioreactor to systematically simplify our model. This significantly reduces the computational cost of generating model solutions, and provides insight into the dominant mass transport mechanisms. We test the validity of the reduced models by comparison with simulations of the full model. We then exploit our reduced mathematical model to determine, for a given inlet flow rate and cell seeding density, the evolution of the spatial metabolite distributions throughout the bioreactor. To assess the bioreactor transport characteristics, we introduce metrics quantifying glucose conversion (the ratio between the total amounts of consumed and supplied glucose), the maximum lactate concentration, the proportion of the bioreactor with intolerable lactate concentrations, and the time when intolerable lactate concentrations are first experienced within the bioreactor. We determine the dependence of these metrics on organoid-line characteristics such as proliferation rate and rate of glucose consumption per cell. Finally, for a given organoid line, we determine how the distribution of metabolites and the associated metrics depend on the inlet flow rate. Insights from this study can be used to inform bioreactor operating conditions, ultimately improving the quality and number of bioreactor-expanded organoids.

In this paper, the internal resonance phenomena of a composite shaft-disk system with multi-degrees-of-freedom are analyzed. The force caused by the unbalanced mass of the disk is considered as an external excitation force. The shaft is simply supported. Shear deformation and gyroscopic effects are considered. The strain–displacement relationship of the shaft element is expressed using the Timoshenko beam theory. Each node has 5 degrees of freedom. SHBT (simplified homogenized beam theory) is applied to calculate the stiffness of the composite shaft. WQEM (weak form quadrature element method) is used to construct the element matrices, and the system matrices are established using the element matrix assembly rule of the FEM (finite element method). The reduced-order model is applied to reduce the calculation time. IHB (incremental harmonic balance) method is utilized to solve the nonlinear equations of motion of the composite shaft-disk system. The nonlinear vibration characteristics of the Jeffcott rotor are analyzed using the proposed method and compared with the results of previous researches, and the results are very similar. Based on these considerations, the nonlinear vibration phenomena of the composite shaft-disk system with multi-degrees-of-freedom are considered at the several resonance points. [ABSTRACT FROM AUTHOR]

• Physics-based modeling of the dynamics of arch resonators for temperature and pressure sensing applications. • Experimental verification of physics-based model of arch microbeams under electric actuation. • Deep learning for simultaneous measurements of temperature and pressure. • Data and network training for the estimation of temperature and pressure from the dynamics of arch resonators. The ability to measure pressure and temperature using a MEMS sensor constitutes a major interest for several engineering applications. In this paper, we present a method and system for simultaneous measurements of pressure and temperature using electrically-actuated arch resonators. The sensor design is selected so that the arch microbeam is sensitive to temperature variations of the surrounding via the inherent thermal stress and to pressure change via the squeeze-film damping resulting from the air flow between the microbeam and the fixed underneath electrode (substrate). A physics-based model is formulated and validated by comparing the static deflection of the microbeam and its natural frequencies under varying temperature to experimental data reported in the literature. We use deep learning to estimate the pressure and temperature from the natural frequencies, quality factors and static deflection of the microbeam. Results show accurate prediction of the temperature and pressure from the quality factors of the arch resonator based on the first three vibration modes. Further improvement is achieved by adding the natural frequencies to the input data. The robustness of the deep learning approach to noise is demonstrated by the small errors obtained using different loss functions when introducing different noise levels to the training data. The proposed approach allows, for the first time, the combination of arch beams dynamics and deep learning techniques for simultaneous sensing of pressure and temperature. [ABSTRACT FROM AUTHOR]

In this paper, we mainly study a reduced‐order finite difference (FD) method with an extra modified term for the Allen–Cahn equation with a small parameter perturbation and a nonlinear term concerned with the energy function. First, using a proper orthogonal decomposition (POD) technique, we construct a set of optimal POD basis and establish a reduced‐order modified finite difference (ROMFD) scheme. Second, we prove the discrete maximum‐bound‐principle (DMBP) preserving and discrete energy‐stability (DES) preserving of the ROMFD solutions under some restrictions on the coefficient of the modified term and the time step size, in particular when the coefficient of modified term is large enough, the ROMFD scheme satisfies unconditional DMP and unconditional DES for any time step. And we analyze the error estimate of the ROMFD solution for the Allen–Cahn equation. Finally, we give some numerical tests to verify all the theoretical results of our ROMFD method, including (unconditional) DMBP‐preserving and (unconditional) DES‐preserving, and to show that the CPU running time of our ROMFD method with very few degrees of freedom is much less than that of the FD method. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

We investigate the potential of electrostatic initially curved microbeams to serve as bifurcation gas sensors. Toward that end, we develop static and dynamic reduced-order models of those beams and investigate their nonlinear response. Unlike many models, ours takes into account naturally occurring asymmetries present in fabricated microbeams. We conduct a detailed analysis of the nonlinear dynamics of arch beams focused on their exploitation for inertial sensing applications. The static response reveals that accounting for asymmetry replaces the saddle-node bifurcation, where snap-back occurs, with a symmetry-breaking bifurcation and reduced the voltage range for bistability. The dynamic analysis shows that a symmetry-breaking bifurcation precludes dynamic snap-through in the vicinity of superharmonic resonance, thereby significantly reducing the amplitude of those oscillations. It also shows evidence of a period-doubling bifurcation route to chaos in the vicinity of primary resonance. Based on these findings, we present a novel phase-based bifurcation gas sensor. The proposed detection mechanism allows, for the first time, the use of transition from regular periodic to chaotic motions in inertial sensing of gases. The sensor operation point is set close to the cyclic-fold bifurcation in the vicinity of primary resonance. When mass added by gas immobilization on the detector layer exceeds a threshold, the sensor oscillations abruptly transition from regular periodic motions to chaotic motions. This change can be detected by monitoring the response phase angle as it undergoes a major shift from slow variation within a limited range to fast variation over the full range due to the stretching and folding of the chaotic attractor. The proposed detection mechanism allows the sensor to operate in binary (digital) and analog modes. This is achieved by evaluating the RMS of the response phase angle φ ¯ and using it to either detect a gas concentration in excess of a safe threshold as an abrupt jump in φ ¯ or via a calibration curve relating φ ¯ to the gas concentration. The minimum detectable mass of the sensor is found to be 120 pg. [ABSTRACT FROM AUTHOR]

A novel reduced‐order modeling method with two‐step strategy is proposed for nonlinear buckling analysis of axially compressed thin‐walled cylinder. The nonlinear response analysis up until the buckling point is achieved quickly by solving the small‐scale reduced‐order model, accounting for the geometrically nonlinear behavior. The buckling point is determined accurately using an efficient buckling detection algorithm, where the stiffness information of the reduced‐order model is extrapolated based on an eigenvalue analysis until a singular tangent stiffness is obtained. Then, the nonlinear buckling fiber angle optimization scheme of laminated composite cylinders is constructed in the MATLAB's MultiStart scheme with gradient‐based algorithms. The proposed two‐step reduced‐order modeling strategy is adopted to calculate the nonlinear buckling objection function at a much lower cost. Numerical results for axially compressed cylinders with various geometric imperfection fields demonstrate the good performance of the proposed strategy in both nonlinear buckling analyses and lamination optimizations. [ABSTRACT FROM AUTHOR]