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In modern composite design, superior macroscopic mechanical properties can be achieved by optimizing the microstructures of materials. However, the direct prediction of the microstructure–property relationship under nonlinear conditions through numerical methods can be inefficient or imprecise. In this study, a transfer learning strategy based on the reduced order model (ROM) was proposed, offering an enhanced and rapid prediction of the mechanical responses of composite materials under nonlinear conditions. Initially, an extensive training dataset was generated by applying the ROM, used to pre-train the neural network model. Following this, a few data calculated from full-order finite element models were leveraged to fine-tune the network parameters. This approach exploits the efficient data generation capability of the ROM, with its potential computational inaccuracies in nonlinear scenarios mitigated, leading to an improvement in the accuracy and efficiency of the surrogate model. Numerical examples of a nonlinear hyper-elastic material with inclusions were examined, revealing that the computational cost in the offline stage of the transfer learning method is only half that of traditional neural network models, and it enable near real-time predictions in the online stage. Notably, it was shown that the accuracy loss of the developed surrogate model in scenarios of strong non-linearity is significantly less than that of the ROM. This method presents an innovative pathway for the swift and accurate evaluation of the effective mechanical properties of composite structures, with the potential to offer valuable insights for related methodological research. [Display omitted] [ABSTRACT FROM AUTHOR]

Principal component transport-based data-driven reduced-order models (PC-transport ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to mitigate the computational cost associated with incorporating detailed chemical kinetics. Previous studies were mainly limited to replicating relatively-simple chemistry in canonical configurations. The objective of the present study, therefore, is to further explore the accuracy of PC-transport ROM on more complex combustion phenomenon where, for example, large hydrocarbon fuel chemistry spanning a broad range of thermochemical space governs sequential multi-stage compression ignition processes. The cumulative error of PC-transport for this problem, and for others that depend upon sequential highly nonlinear physics, has to be minimal as the combustion phasing and heat release rate in internal combustion engines depends upon accurate predictions of minor ignition species whose concentrations start from ashes and grow orders of magnitude over the course of low- and high-temperature autoignition. Specifically, the PC-transport ROM is applied to predict the compression ignition characteristics of lean n -heptane/air and primary reference fuel (PRF)/air mixtures in a two-dimensional (2-D) constant volume computational domain initialized with a two-dimensional isotropic turbulence spectrum and temperature inhomogeneities. PCA is used to define the low-dimensional manifold that represents the original thermochemical state vector, and artificial neural network (ANN) models are adopted to tabulate chemical kinetics, transport, and thermodynamic properties. A series of 2-D pseudo-turbulent simulations are performed at engine pressures by varying the initial mean and r.m.s. of temperature, turbulence intensity, and the composition of fuel/air mixture. The results show that the PC-transport ROM accurately reproduces the instantaneous and statistical ignition characteristics of the fuel/air mixture, aided by pre-processing techniques including species subsetting, data clustering, and data transformation. It is found that PCs are not properly scaled with a power transformer if reactants are included in the species subset, which leads to a decrease in the accuracy of the PC-transport ROM. A separation of the reactants from the species subset ensures that the temporal evolution of the PCs starts from zero and spans orders of magnitude with time, and as such, this approach is found to effectively redistribute both PCs and their source terms with a power transformer. The computational speed-up factor of the PC-transport ROM ranges between 5.1 and 15.0 for the cases with n -heptane/air mixture and PRF/air mixture, respectively. Moreover, a potential further speed-up is anticipated through a combination of reduction in grid resolution requirements and in the stiffness of the chemical system. As an example, many of the pre-processing methods for inhomogeneous compression ignition may also apply to other complex intermittent combustion phenomena. Novelty and significance statement • The PCA-based reduced-order model (PC-transport ROM) has been applied to the multi-stage compression ignition of large hydrocarbon fuels under HCCI-relevant conditions. The present work presents a systematic procedure to accurately capture the two-stage ignition behavior of lean n -heptane/air or PRF50/air mixture. • The present work demonstrates an advantage of the PC-transport ROM in terms of computational speed-up. The computational speed-up factor for the ROM is up to 15, and moreover, a potential additional speed-up is anticipated through the reduction in the spatial and temporal resolution required. • A series of 2-D PC-transport ROMs are conducted to demonstrate the robustness of the ROM. A limitation of the ROM against different operating conditions is also discussed. [ABSTRACT FROM AUTHOR]

This study presents data-driven reduced-order models (ROMs) of a lunar orbiter based on principal component analysis (PCA) and artificial neural networks (ANNs) for a ground thermal vacuum test to simulate space thermal environments. We employed a radial basis function network (RBFN) and deep neural network (DNN) from among the various types of ANNs. PCA extracts features from high-dimensional data, such as thermal analysis data. It is utilized in machine learning algorithms as a preprocessing step before inputting the data into neural networks. This process improves the convergence speed and training performances compared to using neural networks alone. The coefficients of the extracted principal component modes were regressed using the RBFN and DNN. Twenty thermal design parameters comprising infrared emissivity, effective thermal conductivity, thermal contact conductance coefficients, and thermal conductance were used to train the ROMs. We conducted training and test of the proposed models during the cold and hot balance phases of the ground test. Consequently, the temperature map can be estimated in seconds for the new design parameters, and the model results are consistent with thermal analysis and measurement data. [ABSTRACT FROM AUTHOR]

This study presents a systematic numerical approach for the design and optimization of high aspect ratio composite wings subjected to aerodynamic loads. The primary objective is to develop a multi-objective, multi-disciplinary optimization framework that considers aerostructural constraints, such as subsonic aeroelasticity and geometrical nonlinearity. The incorporation of anisotropic properties of composite materials is emphasized to construct lightweight aerospace structures. Aeroelastic tailoring, a technique leveraging these properties, is employed in the optimization process. The proposed methodology integrates three analysis tools, Finite Element software for structural behavior simulation, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses with tailoring capabilities, and Particle Swarm Optimization (PSO) as a population-based stochastic optimization method. This integration enables the development of a powerful numerical approach, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, for designing composite wings with optimized aeroelastic and structural performance. The proposed methodology has broad applicability in aerospace engineering, encompassing aircraft and unmanned aerial vehicles, offering significant potential to enhance their design and overall performance. [ABSTRACT FROM AUTHOR]

Acute Ischaemic Stroke (AIS), a significant global health concern, results from occlusions in cerebral arteries, causing irreversible brain damage. Different type of treatments exist depending on the size and location of the occlusion. Challenges persist in achieving faster diagnosis and treatment, which needs to happen in the first hours after the onset of symptoms to maximize the chances of patient recovery. The current diagnostic pipeline, i.e. "drip and ship", involves diagnostic via advanced imaging tools, only available in large clinical facilities, which poses important delays. This study investigates the feasibility of developing a machine learning model to diagnose and locate occluding blood clots from velocity waveforms, which can be easily be obtained with portable devices such as Doppler Ultrasound. The goal is to explore this approach as a cost-effective and time-efficient alternative to advanced imaging techniques typically available only in large hospitals. Simulated haemodynamic data is used to conduct blood flow simulations representing healthy and different AIS scenarios using a population-based database. A Machine Learning classification model is trained to solve the inverse problem, this is, detect and locate a potentially occluding thrombus from measured waveforms. The classification process involves two steps. First, the region where the thrombus is located is classified into nine groups, including healthy, left or right large vessel occlusion, left or right anterior cerebral artery, and left or right posterior cerebral artery. In a second step, the bifurcation generation of the thrombus location is classified as small, medium, or large vessel occlusion. The proposed methodology is evaluated for data without noise, achieving a true prediction rate exceeding 95% for both classification steps mentioned above. The inclusion of up to 20% noise reduces the true prediction rate to 80% for region detection and 70% for bifurcation generation detection. This study demonstrates the potential effectiveness and efficiency of using haemodynamic data and machine learning to detect and locate occluding thrombi in AIS patients. Although the geometric and topological data used in this study are idealized, the results suggest that this approach could be applicable in real-world situations with appropriate adjustments. Source code is available in https://github.com/ahmetsenemse/Acute-Ischaemic-Stroke-screening-tool-. • AIS screening tool to detect and locate occlusions from haemodynamic waveforms. • ML classifiers trained from a virtual patient database and 1D blood flow modelling. • A two-step classification process facilitates the detection and localization. • Left and right common carotid arteries velocity profiles used as input. • High prediction rates as compared to other screening tools. [ABSTRACT FROM AUTHOR]

Optimization of functionally graded metamaterial arrays with a high dimensional and continuous geometric design space is cumbersome and could be accelerated via machine learning tools. Mechanical metamaterials can manipulate acoustic or ultrasonic waves by introducing significant dispersive and attenuative effects near their natural frequency. In this work, functionally graded structures are designed and optimized to combine the energy attenuation performance of multiple unit cells with varying frequency responses and to reduce the interlayer mismatch effects. Optimization through genetic algorithms avoids many local minima related to high dimensionality of the design space, but requires many iterations. A reduced order model (ROM) is applied, which can reproduce the transmission response traditionally calculated with FEM in a fraction of the time. Pairing GA and the ROM together, an array of 6 unit cells (with a total of 18 independent geometric design variables) is optimized to have stop bands with extended width and sharper boundaries. Symmetric functionally graded structures are determined to have optimal geometric configurations. Measured 3D printed features are projected onto the ROM solutions to quantify the effect of printing uncertainty on array performance. Repeatability error of ± 20 μ m is determined to reduce the mean depth of the transmission stop band by a factor of 1 0 2 and introduce small shifts in center frequency and band width. Proposed methods to improve the resolution of accessible points in the ROM space, reduce sensitivity to geometric uncertainty, and add design freedom include introducing out-of-plane perforations and varying constituent materials using tunable filled resin systems. [Display omitted] • The transfer matrix based reduced order scattering model matches well with FEM. • Applying unique transfer matrices for boundary cells resolves finite array effects. • Optimized arrays with wider, deeper, and square-shaped stop bands were identified. • Arrays with symmetric cell arrangements provided better insertion loss performance. • With manufacturing tolerances of ±20 μ m, insertion loss decreases from 1 0 6 to 1 0 4 . [ABSTRACT FROM AUTHOR]

• Digital twin solution is demonstrated for a CHS X-joint under fatigue loading. • Digital twin is capable of accurately identifying, locating and quantifying cracks. • Relationship between crack size and nearby strains is revealed via substructuring. • Crack diagnosis is performed with affordable strain sensors. • Physics-informed model is validated through strain-interfaced physical twins. This paper aims to introduce a strain-interfaced local digital twin solution for a welded circular hollow section (CHS) X-joint subjected to brace axial loading. The solution comprises a series of machine learning algorithms to (1) identify the presence of cracks, (2) locate the cracks and (3) quantify the extent of cracking. These algorithms make use of strain readings in the vicinity of the crack to perform the diagnosis, representing a remote sensing methodology, thereby eliminating physical inspections. The validation of the proposed methodology includes two experiments – one each in the high and low cycle fatigue regime – demonstrating its wide scale applicability. The success of these experiments highlights the strong potential of affordable strain sensors in crack diagnosis assessments for CHS joints. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven approach to improve the efficiency of computational homogenization for nonlinear hyperelastic materials with different microstructures in a small strain context. By combining clustering analysis and Proper Orthogonal Decomposition (POD) with efficient sampling, a reduced order model is established to accurately predict elastoplasticity under monotonic loadings. The microscopic RVE is spatially divided into multiple clusters using the k-means clustering algorithm during the offline phase. As suggested in Kunc and Fritzen (2019a), the reduced order model is constructed using reduced bases of deformation gradient fluctuations on the microscale. In contrast to the conventional displacement-based approach, deformation gradient fluctuations are employed to generate the POD snapshots. To improve the prediction accuracy and reduce the cost of offline computation, the energy minimum point set generation method proposed by Kunc and Fritzen (2019b) is employed. Numerical results show a acceleration factor in the order of 10-100 compared to a purely POD-based model can be archived, which significantly improves the applicability for structural analysis, while maintaining a sufficient accuracy level. • A new data-driven method combines k-means clustering and POD. • Generate snapshots data by deformation gradient fluctuations. • Acceleration factor in the order of 10–100 while maintain accuracy. [ABSTRACT FROM AUTHOR]

Mechanical metamaterials (MMs) are micro-structured systems that have long attracted research as well as application interests due to their exotic dynamic functionalities and properties not seen in ordinary materials. Graded MM arrays further enrich the scope of achievable dynamic behavior by employing non-periodic media. Designing such systems is particularly challenging due to the unclear design-performance relationship as well as the heavy computational burden. In this work, we aim to control the time domain response patterns of finite MM arrays and introduce a data-driven design approach that addresses the current challenges. A high-fidelity reduced order modeling (ROM) method is incorporated with statistical learning approaches to realize data generation and physics validation with minimal effort. A variational autoencoder (VAE) is trained to learn the design-performance relation and is used to retrieve classes of design configurations associated with a desired performance. An example application for impact mitigation is shown, and the designed MM arrays exhibit superior protection performance. The combined ROM-VAE approach presents a systematic toolset for designing graded MM arrays with modulated responses, capable of a broad spectrum of tasks such as fast prototyping, inverse generation, and design principle identification. • A deep generative approach is shown for designing metamaterials, allowing for efficient exploration in a vast design space. • Reduced order models expedite data preparation and validation in the machine-learning-based design process. • Metamaterials targeting diverse performances can be promptly retrieved and show advanced wave control capabilities. • Statistical analysis on the generated designs reveals important features and identifies the underlying design principles. [ABSTRACT FROM AUTHOR]

This study investigates modelling of doubly-fed induction generator (DFIG)-based wind turbines for low-frequency oscillation analysis of power systems with high penetration of distributed energy. A reduced order model of wind turbine with DFIG is proposed for small signal stability analysis in electromechanical time scale for low-frequency oscillation analysis. Furthermore, a Thévenin equivalent model of DFIG with internal voltage is also presented. Base on the derived model, the effect of phase-locked loop is emphasised, and explained by using the internal voltage derived. The model proposed is well verified by modal analysis and time-domain simulations.

For reduced order models (ROMs) of fluid flows, we investigate theoretically and computationally whether differentiation and ROM spatial filtering commute, i.e., whether the commutation error (CE) is nonzero. We study the CE for the Laplacian and two ROM filters: the ROM projection and the ROM differential filter. Furthermore, when the CE is nonzero, we investigate whether it has any significant effect on ROMs that are constructed by using spatial filtering. As numerical tests, we use the Burgers equation with viscosities ν = 10− 1 and ν = 10− 3 and a 2D flow past a circular cylinder at Reynolds numbers Re = 100 and Re = 500. Our investigation (i) measures the size of the CE in these test problems and (ii) shows that the CE has a significant effect on ROM development for high viscosities, but not so much for low viscosities. [ABSTRACT FROM AUTHOR]

An effective reduced-order model is presented in this paper for the vibration analysis of a mistuned blade–disc–shaft assembly considering the flexibility of the shaft and the rotordynamic effects. For the sake of accurate modeling and quantitative analysis, three-dimensional (3D) finite element models were employed in obtaining the governing equations of motion with the Coriolis force, centrifugal stiffening, and spin softening effects taken into account. Then, an efficient model order reduction technique based on the coordinate projection by normal modes of tuned assembly and cyclic symmetry analysis was developed for mistuned blade–disc–shaft assembly. The criterion of whether one matrix could be incorporated in cyclic symmetry analysis is presented. During the modeling, the mistuning in blade and disc was taken into account and dealt with independently. In mistuning projection, the blade and disc parts were both projected onto their tuned counterparts of the sector model, where the boundary conditions were set to be fixed and free, respectively. Finally, an example of a blade–disc–shaft assembly was employed to validate the effectiveness of the presented method in free and forced vibration analysis. [ABSTRACT FROM AUTHOR]

A new technique for frequency limited model order reduction of discrete time second-order systems is presented. Discrete time frequency limited Gramians (DFLGs) and corresponding discrete algebraic Lyapunov equations are developed. An efficient technique for the computation of DFLGs and their Cholesky factors is presented. Computed DFLGs are partitioned to obtain position and velocity Gramians. These Gramians are balanced with different combinations to obtain various balanced transformations that yield Hankel singular values (HSVs) for order reduction. Frequency limited discrete time balanced truncation framework is proposed and truncation based on magnitudes of HSVs is applied to obtain the reduced order model. Moreover, stability conditions for reduced order models are stated. Results of the proposed technique are compared with infinite Gramians balancing scheme in order to certify the usefulness of the presented technique for frequency limited applications. [ABSTRACT FROM AUTHOR]

Highly varying sloshing loads are a superposition of load components resulting from a sequence of different physical phenomena. However, not all features of spatial and temporal variations of sloshing loads and associated phenomena are equally important when failure of structure is considered. Therefore, the prediction of sloshing loads should be focused on those load components which lead to failure. These components can be found by employing a structural model, which should be fast computationally considering the huge number of possible sloshing loads. This paper presents a reduced order model based on the beam-foundation model which is derived for the Mark-III cargo containment system. The model is validated against a detailed finite element model and it conservatively predicts the stresses at failure locations. The calculation time using the model is approximately two orders smaller in comparison to a finite element model computation, which allows the model to be applied for finding governing load components and associated physical phenomena. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of the influence of variabilities and uncertainties when homogenizing the effective behavior of elastic heterogeneous media. A new non-intrusive approach is proposed connecting computational homogenization schemes and reduced order models. The effect of the local material variabilities and uncertainties on the overall behavior is studied using a high dimensional parametric approach. [ABSTRACT FROM AUTHOR]

Compared with the traditional phenomenological method, the multiscale simulation has significant advantages. This paper presents a methodology and computational homogenization framework to predict the macroscale behavior of woven composites based on fiber and matrix in microscale. The major challenge conducting multiscale analysis is the huge computational cost. To improve the efficiency, one of the reduced order models, which is called the data-driven self-consistent clustering analysis (SCA), is introduced and the multiscale framework is proposed by integrating two SCA solvers from different scales. The macroscale performance of 4-H satin weave carbon/carbon composites is investigated using the proposed framework. In order to reconstruct a real microstructure representative volume element (RVE), both microscale and mesoscale architectures are observed using scanning electron microscopy (SEM) and optical microscopy, and statistical geometry features are obtained. In addition, the SCA method is also verified by comparing the results with the finite element method (FEM). The uniaxial tension process is simulated using the multiscale approach, and strain/stress fields in both mesoscale and microscale can be captured simultaneously. Moreover, the uniaxial tensile experiments are also carried out to validate this framework, which shows high efficiency and great accuracy. [ABSTRACT FROM AUTHOR]

• Enable current methods to accommodate the use of multiple meshes. • Numerical issues due to the presence of multiple meshes are solved efficiently. • Improve efficiency and accuracy in the creation of ROMs. • Reduce computational cost in the calculation of normal modes. A technique is proposed to overcome computational issues caused by the use of multiple meshes in reduced order models (ROMs) for the structural dynamics of mistuned blisks. Due to the need for repairs (blends) or geometric changes during design iterations, models often require the use of distinct meshes for the same component. Most ROMs for such cases start from pristine modal information, which must be obtained for every mesh involved. Due to the nature of normal modes in cyclic structures, a first challenge arises with respect to their correct clocking, or alignment. Modes have arbitrary clocking for cyclic symmetric systems, and hence modes are potentially different for different meshes. In addition to this, imperfect clocking and cyclic interface compatibility can strongly affect the accuracy of the predicted response. This paper presents a method to preserve the accuracy of ROMs and at the same time reduce the computational overhead associated with the presence of multiple morphed meshes. Numerical issues associated with the presence of multiple meshes and sets of modes are investigated. [ABSTRACT FROM AUTHOR]

In this article, Particle Swarm Optimization (PSO) based closed loop control of Bridgeless Cuk converter operating in Continuous Conduction Mode (CCM) to achieve unity power factor and high efficiency are presented. Absence of the diode bridge rectifier in the proposed converter reduces losses and thereby increases the efficiency. The state space modeling of Bridgeless Cuk converter operating in CCM mode is derived using state space averaging technique. Hankel matrix approach is used for obtaining the third order model from the original seventh order model of the converter to simplify the controller design. The cascade control strategy is implemented using PSO based Proportional Integral (PI) controller for source current shaping and load voltage regulation. Extensive Simulation studies have been carried out using Matlab/Simulink software. Laboratory prototype for 300 watts is developed and the proposed control algorithm is realized using 32 bit C2000 DSP microcontroller. Simulation and experimental results verify that the proposed system is able to regulate the output voltage with high efficiency, less % Total Harmonic Distortion (THD) and achieves a power factor close to unity for wide load, line and set point variations to improve the power quality at ac mains as suggested by international power quality standards, such as IEC-61000-3-2. [ABSTRACT FROM AUTHOR]

A two-surface, pressure-insensitive plasticity model is further developed to represent the mechanical response of hexagonal close packed metals. The model describes the 3D plastic anisotropy of a material, the tension–compression asymmetry, and the consistent evolution upon straining of both the net anisotropy and the asymmetry. The model may be viewed as a reduced order quasi-crystal plasticity model whereby the two activation surfaces represent glide- and twinning-dominated flow. The two-surface formulation enables to represent independent, yet coupled, hardening laws in terms of effective plastic strains accumulated on either generic deformation system. Application of the model to a discriminating data set assembled for a magnesium alloy thick plate illustrates the capabilities and versatility of the modeling approach. • Two-surface constitutive model developed to describe elasto-plastic behavior of HCP materials. • Symmetric glide-dominated surface and asymmetric twinning-affected surface used. • Plastic flow anisotropy characterized in full 3D for Mg alloy AZ31. • Model assessed against experiments for principal and off-axes orientations. • Model shown to be effective in describing evolving plastic anisotropy and yield asymmetry. [ABSTRACT FROM AUTHOR]

This paper analyzes the effect of the operation frequency of an inductive charging system on the heat generation and its impact on the overall system efficiency. This is done considering the change of the material properties due to both frequency and temperature changes. The analysis is achieved using FEA modeling software ANSYS workbench. For faster subsequent optimization, an order-reduced model is implemented in a system-level simulation. Various tests show the good agreement of the results between the reduced order model and those of dynamically coupled-field solvers. Our implementation allows for precise calculation of the losses of the different system parts. Furthermore, an experiment has been conducted to validate the simulation results. [ABSTRACT FROM AUTHOR]

Highlights • Coarse-mesh CFD snapshots are used for Model reduction of CVD reactor. • CPU time reduction of 60% for fine-mesh computations with complex chemistry. • Candidate models for deposition of Al from DMEAA evaluated. Abstract A computational methodology is introduced for the development of a Reduced Order Model (ROM) using data from low-fidelity, in terms of the spatial discretization, Computational Fluid Dynamics (CFD) simulations. The methodology is applied in the context of investigating efficiently, new chemistry pathways in Chemical Vapor Deposition (CVD) processes by comparing experimental findings with simulation results. The proposed approach involves building a very coarse, yet three-dimensional CFD model of the process that does not include chemical reactions, which provides "snapshots", i.e. time instances, of the dynamic behavior of the system. This bundle of low-fidelity data is used for generating a reduced order model by implementing Proper Orthogonal Decomposition (POD), for the time-invariant subspace approximation of the data and Artificial Neural Networks (ANN) for the time-dependent coefficients. This last step circumvents the need for Galerkin projection of the equations on the subspace, rendering therefore the approach equation-free and purely data-driven. The ultimate goal addressed in this work is to produce fast approximations of the temperature, pressure and flow field in a CVD reactor, which will then be used as starting points of high-fidelity, in terms of the computational mesh, CFD simulations, that include multiple surface and gas phase reactions. Given the quality of approximations, the model corrects for the species distributions after only a few time-steps, providing thus quick and accurate deposition-rate and uniformity results. This allows for efficient and easy fine-tuning adjustments of the chemistry model diminishing the need to solve the high-fidelity model many times. [ABSTRACT FROM AUTHOR]

Abstract A physics-based, reduced order model was developed to describe the capacity degradation in LiNiMnCoO 2 -graphite cells. By starting from fundamental principles, the model captures the effects of four degradation mechanisms: (i) SEI growth on the anode, (ii) electrolyte oxidation on the cathode, (iii) anode active material loss, and (iv) cathode active material loss, the last two due to chemomechanical fracture. The model is computationally efficient (∼1 ms/cycle) and enables physical, real-time, capacity loss calculations for automotive applications. Results demonstrate that under storage conditions, SEI growth and electrolyte oxidation are the major degradation mechanisms, in agreement with experiments. In contrast, batteries subjected to electric currents of a wide amplitude, close to the upper cutoff voltage, electrolyte oxidation contributes ∼50% of all the degradation mechanisms, consistent with recent experiments in the literature. Chemomechanically induced active material losses are maximal in the anode at high states of charge and maximal in the cathode at low states of charge. Results quantify the contribution to degradation from each individual mechanism, highlighting, for the first time, the need of physics-based, on-the-fly descriptions that go beyond traditional coulomb counting approaches. Finally, the identification of the individual degradation contributions enables the possibility of tailoring the charge/discharge sequence to extend battery life. Highlights • An electrochemical, chemomechanical framework for NMC-graphite LIBs was developed. • Experimentally reported capacity fading mechanisms are theoretically explained. • SEI-, electrolyte oxidation-, and chemomechanical fracture- mechanisms are identified. • Storage and current density-dependent conditions are identified. • The first on-the-fly, physics-based description approach is presented. [ABSTRACT FROM AUTHOR]

• An angular reduced order model is derived for radiative transfer in non grey media. • A Proper Orthogonal Decomposition method is used to extract optimal angular basis functions from high order SN reference data. • The truncation of the POD basis set is allowed to vary with the absorption coefficient class in order to optimally compute the radiative source term. • The method is applied to solve the radiation field associated to an air/H2O mixture flowing in a square differentially heated cavity. This paper investigates a reduced order model for the angular discretisation of the radiative transfer equation (RTE) when considering non grey participating gases. The key idea is to use a global model for the gas radiative properties and to derive an angular reduced order model, based on the Proper Orthogonal Decomposition (POD) method, for each absorption coefficient class independently. Angular POD basis functions are extracted from high order S N reference solutions. A finite element approach is used to discretised the RTE in space and angle and the POD angular matrices of the reduced system are easily constructed from the S N angular matrices of the reference solutions. The angular POD basis sets are truncated at different levels depending on the absorption coefficient class in order to optimally compute the total radiative power. The method is applied to solve the radiation field associated to an air/H 2 O mixture flowing in a square differentially heated cavity, with black isothermal walls and diffuse reflecting adiabatic walls. Results show that the POD model is very accurate and efficient for treating the thick classes but it suffers from a low convergence rate for the thin classes. For computing the radiative power, the reduced order model allows to reduce the averaged number of angular basis functions of an order of magnitude and to reduce the CPU time by a factor 2 to 3 to reach a given level of accuracy, compared to a standard S N method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a reduced order model is developed to simulate the dynamics of a bladed disk or blisk with nonlinear damping coatings adhered to its blades. The nonlinear forces exerted by these coatings on the underlying linear blisk structure are a function of the local strain. It is known that coatings modify the stiffness and damping of each blade depending on its amplitude. Blisks, which are designed as perfectly cyclic symmetric structures with identical blades, never behave as such in practice due to various uncertainties encountered during their manufacturing. This asymmetry in the structure is also referred to as mistuning. Mistuning in the linear blisk structure, which causes different blades to respond with non-identical amplitudes, interacts with the coating nonlinearity to yield a mistuning pattern which depends on the blade amplitudes. Additional stiffness and damping parameters that are dependent on the blade amplitude are introduced into a reduced linear model to formulate the nonlinear reduced order model. It is found that this model captures the nonlinear amplitude dependent mistuning effect and predicts the nonlinear coated blisk responses accurately near isolated blisk mode families in blade-dominated frequency regions where these coating effects are likely to be dominant. Significant reductions in the computational effort are achieved through this reduction. [ABSTRACT FROM AUTHOR]

Abstract Spent nuclear fuel elements contain a significant amount of fissile materials that gradually decompose generating heat and radiation. This decomposition occurs inside of deep water pools, where spent elements are cooled through natural convection. CFD calculation of all necessary cases is not feasible. Nevertheless, a light, fast and accurate model of this convection problem can be obtained utilizing Proper Orthogonal Decomposition (POD) and Galerkin Projection methods. Thus, we carry out our modeling as follows: i) Firstly, the high fidelity solver Star-CCM+ is utilized to resolve the incompressible Boussinesq formulation of the pool. The results obtained constitute a set of solutions available at discrete times for each variable. ii) Subsequently, these solutions are utilized to build a special basis, which constitutes the POD. This is built considering an optimal linear combination of the solutions at different times. Notably, each reduced set of components of the basis allows for the reproduction of a maximum of the dynamics of the variables. iii) Thirdly, the system of equations is projected in this basis, Galerkin Projection. A Reduced Order Model (ROM) can be created using a small amount of the components, sufficient for the required accuracy. This simplified model is thus mathematically sound, and derived from first principles. Finally, the results of the model are compared with high fidelity solutions. The assessment includes the capabilities of the model to reproduce transients and to approach the final steady state. Additionally, we evaluate the performance of the MPI-parallelized software generated. [ABSTRACT FROM AUTHOR]

Highlights • The global-nonlinear dynamics of heterogeneous tubular reactors is modeled. • An adjustable-order ODE model is built by PDE spatial discretization. • Multiplicity is assessed with bifurcation analysis through continuation with respect to order. • The methodology is applied to a 13-profile gasification reactor with experimental data. • The gasification reactor is robuslty bistable and is modeled with order 30. Abstract The problem of efficiently describing the nonlinear dynamics of spatially distributed tubular heterogeneous reactors is addressed, including multiplicity, stability, and transient behavior. An adjustable-order model is generated with a convergent partial differential equation (PDE)-to-ordinary differential equation (ODE) discretization. Efficiency means the ability to describe the PDE dynamics quantitatively, up to kinetics-transport (KT) parameter error propagation and with the smallest possible order. The problem is solved by combining notions and tools from nonlinear dynamics (bifurcation analysis and structural stability), numerical methods (error propagation analysis and continuation), and chemical reactor engineering. Solvability requires the existence of an order below the critical one for the onset of excessive error propagation. The approach is applied to a 13-profile gasification reactor with experimental data, unknown multiplicity, and finite difference (FD) discretization. It is found that the reactor is robustly bistable, and can be described by a 30th-order model with considerably less equations than in previous related studies. [ABSTRACT FROM AUTHOR]

Abstract The present work proposes an original reduced order model for prediction of passive reduction of structural vibration by means of shunted piezoelectric patches. The problem consists of an elastic structure with surface-mounted piezoelectric patches. The piezoelectric elements are connected to a resonant shunt circuits in order to damp specific resonant frequencies of the structure. An efficient electromechanical finite element formulation for the dynamic analysis of the problem is first presented. The classical modal superposition techniques using the system eigenvectors with all patches short-circuited or open-circuited are then recalled. An advanced reduced order models using two new modal projection bases able to solve the problem at lower cost are developed: (i) the combined basis formed by both the short-circuited and open-circuited modes, and (ii) the coupled basis formed by the electromechanical modes that take into account the effect of the inductances of the electrical shunt circuits. Various numerical and experimental results are presented in order to validate and illustrate the efficiency of the proposed new finite element reduced order formulations. [ABSTRACT FROM AUTHOR]

This study investigates modelling of doubly-fed induction generator (DFIG)-based wind turbines for low-frequency oscillation analysis of power systems with high penetration of distributed energy. A reduced order model of wind turbine with DFIG is proposed for small signal stability analysis in electromechanical time scale for low-frequency oscillation analysis. Furthermore, a Thévenin equivalent model of DFIG with internal voltage is also presented. Base on the derived model, the effect of phase-locked loop is emphasised, and explained by using the internal voltage derived. The model proposed is well verified by modal analysis and time-domain simulations. [ABSTRACT FROM AUTHOR]

A multiscale model is built to understand how microscale characteristics and the thermo‐chemical and electrochemical phenomena, occurring in the electrode and electrolyte assembly, may affect the overall performance of a solid oxide fuel cell (SOFC) stack. This study presents the integration of two‐dimensional finite volume models: a 1D microscale model and a 1D (or 2D) macroscale (channel/cell) model. The new tool is calibrated against the experimental data of a short‐stack via a numerical procedure aiming at the minimisation of the mean square deviation of the model from the measured data. Subsequently, the distribution of electrochemical active thickness in a state‐of‐the‐art solid oxide cell channel is calculated; the result is limited between 3% and 7% of the electrode thickness. An axially graded electrode is studied by changing the particle radii in order to locally control the triple phase boundary length distribution along the cell channel. The performances of a four‐section graded electrode is estimated in comparison to a reference non‐graded electrode. The average current density increases by approximately 6% in the short‐stack. If such a graded design was introduced into a state‐of‐the‐art cogeneration system, the extrapolation of these results suggests that a power output increase up to 13.5% is attainable. [ABSTRACT FROM AUTHOR]

Abstract Microstructure evolution in the electrodes of solid oxide fuel cell is an important degradation mechanism which reduces active sites for redox reaction and the electric conductivity. Phase field models for microstructure evolution simulation are usually expensive for large scale simulations. In this work, a reduced-order coarsening model is developed using dynamic discrepancy reduced modeling, which reduces the model order by inserting Gaussian process stochastic functions into the dynamic equations of Ostwald ripening. The reduced order model has been calibrated on a dataset generated by a phase field model that has been well validated to experiments. A validating dataset has also been generated with which the model prediction show good agreement. This model is further applied to predict long term microstructure evolution in different SOFC electrodes. This work is the first attempt of building a degradation model of SOFC using data science techniques. Graphical abstract Image 1 Highlights • A reduced order coarsening model for microstructure evolution prediction in SOFC electrode. • Dynamic discrepancy reduced modeling is used to enhance the Ostwald ripening model. • Phase field simulations used in model training and validation. • First attempt of applying data science technique in the research of SOFC degradation. [ABSTRACT FROM AUTHOR]

Abstract For transient analysis of structural dynamics with reduced order model (ROM), data recovery procedures that use modal methods such as the classical mode-acceleration (MA) and modal-displacement (MD) methods are an important step in order to increase the convergence and accuracy of the solution. In this work, we propose novel MA and MD methods for highly accurate transient analysis with a reduced order model based on enhanced Craig–Bampton (ECB) formulation, which is an extension of the classical Craig–Bampton (CB) method that is a way to reduce the size of a finite element (FE) model. The performance of the proposed data recovery approach is demonstrated with two numerical examples. We also investigate the relation between the proposed and classical MA and MD methods. [ABSTRACT FROM AUTHOR]

Abstract A Novel Nodalized Reduced Order Model (NNROM) is developed in this paper to analyze the linear stability phenomena in a heated channel with supercritical water as a coolant. The existing models are based on finite volume approach, leading to a large number of non-linear time-dependent ODEs, making linear stability analysis (for infinitesimally perturbation) computationally expensive and tedious. Moreover, the non-linear stability analysis considers the effect of small but finite perturbations which becomes even more difficult. It is pointed out that the accuracy of the reduced order model developed here is not compromised, as the comparisons of the model results, with existing studies show good agreement. In ordered to develop the NNROM, the heated channel is divided into N number of nodes. The one-dimensional mass, energy and momentum conservation partial differential equations are converted into the corresponding time-dependent non-linear ordinary differential equations (ODEs) by applying the weighted residual method. The linear stability threshold of the system is determined by analyzing the eigenvalues of the Jacobian matrix at the steady states of the set of ODEs. Moreover, the linear stability boundary (Hopf bifurcation line) is represented in terms of trans-pseudo-critical phase change number ( N t p c ) , and pseudo-subcooling number ( N s p c ). A parametric study is done to identify the change in linear stability behavior of the system with the design parameters. Furthermore, non-linear stability analysis is carried out to identify Generalized Hopf (GH) bifurcation points in the N t p c − N s p c space. The GH points divide the stability boundary into sub-critical Hopf and super-critical Hopf parts, which is further varify by the numerical simulations. The identification of sub-critical region is quite important as it shows unstable limit cycles in the (linearly) stable region. Highlights • Novel Nodalized Reduced Order Model (NNROM) is developed for Supercritical water. • Single heated channel is nodalized into N numbers of node. • Non-linear stability analysis is carried out. • Sub-critical, super-critical and generalized hopf bifurcations is detected. • Qualitative change in stability behavior for design parameters is explained. [ABSTRACT FROM AUTHOR]

Abstract In recent years, the interest and demand for small satellites have grown exponentially. While in the past the end-of-life design for this type of spacecraft was often approximated or totally neglected, it has recently become increasingly important. Indeed, small spacecraft able to achieve advanced mission objectives are more frequently on the worldwide space agenda. They may contain components which might withstand the re-entry conditions and reach the ground. In addition, these spacecraft are usually limited to shallow re-entries which are more sensitive to atmospheric model uncertainties and thus have larger debris fields. The objective of this work is to provide a reliable and efficient statistical analysis to estimate the risk to aeronautic and maritime traffic as well as to ground based populations. A simple geometric safety assessment is proposed, based on the safety boxes concept introduced in the ESA Space Debris Mitigation Compliance Verification Guidelines. Correctly estimating the dimensions of a safety box and locating it over uninhabited regions, such as the oceans, guarantees a casualty risk below a prescribed value. Furthermore, by estimating the probability of debris landing outside the largest possible safety box within which there is a zero casualty risk, the maximum probability of control failure admissible for the mission can be estimated. This proposed safety analysis is achieved using two re-entry models of differing complexity. The high fidelity model includes both the aerodynamic and aerothermodynamic effects that occur during re-entry and is used to statistically characterize "high level" uncertain variables such as the ballistic coefficient and the demise altitude. The reduced order model is based on these high level variables and captures the spacecraft fragmentation behavior and its re-entry dynamics with significantly less computation time than the high fidelity model. Coupled with advanced statistical techniques designed to estimate very low probabilities such as the Inputs' Statistics Method, a reliable safety analysis can be conducted with a limited overall computational burden. The proposed safety analysis is applied to a fictitious 2U CubeSat mission that performs a controlled re-entry using the Drag De-orbit Device developed by the ADAMUS laboratory at the University of Florida. Highlights • Novel application of the inputs' statistics method to re-entry of small satellites. • Computation of safety box for CubeSat parts impact greatly reduced. • Alternative to tightly controlled state of the art tools from ESA and NASA. [ABSTRACT FROM AUTHOR]