Data driven reduced modeling for fluidized bed with immersed tubes based on PCA and Bi-LSTM neural networksAuthor links open overlay panel

The fast and accurate reduced-order modeling of fluidized beds is a challenging task in the field of fluid dynamics,owing to their high dimensionality and nonlinear dynamic behavior.In this study,a nonintrusive reduced order modeling method,the reduced order model based on principal component analysis and bidirectional long short-term memory networks(PBLSTM ROM),was developed to capture complex spatio-temporal dynamics of fluidized beds.By combining principal component analysis and Bidirectional long-short-term memory networks,the PBLSTM ROM effectively extracted dynamic evolution information without any prior knowledge of governing equations,enabling reduced-order modeling of unsteady flow fields.The PBLSTM ROM was validated using the solid volume fraction and gas velocity flow fields of a fluidized bed with immersed tubes,showing superior performance over both the PLSTM and PANN ROMs in accurately capturing temporal changes in the fluidization fields,especially in the region near immersed tubes where severe fluctuations appear.Moreover,the PBLSTM ROM improved the simulation speed by five orders of magnitude compared to traditional computational fluid dynamics simulations.These findings suggest that the PBLSTM ROM presents a promising approach for analyzing the complex fluid flows in engineering practice.

Meta-Auto-Decoder:a Meta-Learning-Based Reduced Order Model for Solving Parametric Partial Differential Equations

Many important problems in science and engineering require solving the so-called parametric partial differential equations(PDEs),i.e.,PDEs with different physical parameters,boundary conditions,shapes of computational domains,etc.Typical reduced order modeling techniques accelerate the solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the ofline stage.These methods often need a predefined mesh as well as a series of precomputed solution snapshots,and may struggle to balance between the efficiency and accuracy due to the limitation of the linear ansatz.Utilizing the nonlinear representation of neural networks(NNs),we propose the Meta-Auto-Decoder(MAD)to construct a nonlinear trial manifold,whose best possible performance is measured theoretically by the decoder width.Based on the meta-learning concept,the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage.Fast adaptation to new(possibly heterogeneous)PDE parameters is enabled by searching on this trial manifold,and optionally fine-tuning the trial manifold at the same time.Extensive numerical experiments show that the MAD method exhibits a faster convergence speed without losing the accuracy than other deep learning-based methods.

Adding-Point Strategy for Reduced-Order Hypersonic Aerothermodynamics Modeling Based on Fuzzy Clustering

Reduced order models（ROMs） based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

PRICING CATASTROPHE OPTIONS WITH COUNTERPARTY CREDIT RISK IN A REDUCED FORM MODEL

In this paper, we study the price of catastrophe Options with counterparty credit risk in a reduced form model. We assume that the loss process is generated by a doubly stochastic Poisson process, the share price process is modeled through a jump-diffusion process which is correlated to the loss process, the interest rate process and the default intensity process are modeled through the Vasicek model： We derive the closed form formulae for pricing catastrophe options in a reduced form model. Furthermore, we make some numerical analysis on the explicit formulae.

Sliding Mode Control Design via Reduced Order Model Approach

This paper presents a design of continuous-time sliding mode control for the higher order systems via reduced order model. It is shown that a continuous-time sliding mode control designed for the reduced order model gives similar performance for thc higher order system. The method is illustrated by numerical examples. The paper also introduces a technique for design of a sliding surface such that the system satisfies a cost-optimality condition when on the sliding surface.

Reduced combustion time model for methane in gas turbine flow fields

Computational fluid dynamics （CFD） modeling of the complex processes that occur within the burner of a gas turbine engine has become a critical step in the design process. However, due to computer limitations, it is very difficult to completely couple the fluid mechanics solver with the full combustion chemistry. Therefore, simplified chemistry models are required, and the topic of this research was to provide reduced chemistry models for CH4/O2 gas turbine flow fields to be integrated into CFD codes for the simulation of flow fields of natural gas-fueled burners. The reduction procedure for the CH4/O2 model utilized a response modeling technique wherein the full mechanism was solved over a range of temperatures, pressures, and mixture ratios to establish the response of a particular variable, namely the chemical reaction time. The conditions covered were between 1000 and 2500 K for temperature, 0.1 and 2 for equivalence ratio in air, and 0.1 and 50 atm for pressure. The kinetic time models in the form of ignition time correlations are given in Arrhenius-type formulas as functions of equivalence ratio, temperature, and pressure; or fuel-to-air ratio, temperature, and pressure. A single ignition time model was obtained for the entire range of conditions, and separate models for the low-temperature and high-temperature regions as well as for fuel-lean and rich cases were also derived. Predictions using the reduced model were verified using results from the full mechanism and empirical correlations from experiments. The models are intended for （but not limited to） use in CFD codes for flow field simulations of gas turbine combustors in which initial conditions and degree of mixedness of the fuel and air are key factors in achieving stable and robust combustion processes and acceptable emission levels. The chemical time model was utilized successfully in CFD simulations of a generic gas turbine combustor with four different cases with various levels of fuel-air premixing.

Complicated deformation simulating on temperature-driven 4D printed bilayer structures based on reduced bilayer plate model

The four-dimensional(4D) printing technology, as a combination of additive manufacturing and smart materials, has attracted increasing research interest in recent years. The bilayer structures printed with smart materials using this technology can realize complicated deformation under some special stimuli due to the material properties.The deformation prediction of bilayer structures can make the design process more rapid and thus is of great importance. However, the previous works on deformation prediction of bilayer structures rarely study the complicated deformations or the influence of the printing process on deformation. Thus, this paper proposes a new method to predict the complicated deformations of temperature-sensitive 4D printed bilayer structures,in particular to the bilayer structures based on temperature-driven shape-memory polymers(SMPs) and fabricated using the fused deposition modeling(FDM) technology. The programming process to the material during printing is revealed and considered in the simulation model. Simulation results are compared with experiments to verify the validity of the method. The advantages of this method are stable convergence and high efficiency,as the three-dimensional(3D) problem is converted to a two-dimensional(2D) problem.The simulation parameters in the model can be further associated with the printing parameters, which shows good application prospect in 4D printed bilayer structure design.

Reduced EGFR signaling enhances cartilage destruction in a mouse osteoarthritis model

Osteoarthritis （OA） is a degenerative joint disease and a major cause of pain and disability in older adults. We have previously identified epidermal growth factor receptor （EGFR） signaling as an important regulator of cartilage matrix degradation during epiphyseal cartilage development. To study its function in OA progression, we performed surgical destabilization of the medial meniscus （DMM） to induce OA in two mouse models with reduced EGFR activity, one with genetic modification （, was/＋ mice） and the other one with pharmacological inhibition （gefitinib treatment）. Histological analyses and scoring at 3 months post-surgery revealed increased cartilage destruction and accelerated OA progression in both mouse models. TUNEL staining demonstrated that EGFR signaling protects chondrocytes from OA-induced apoptosis, which was further confirmed in primary chondrocyte culture. Immunohistochemistry showed increased aggrecan degradation in these mouse models, which coincides with elevated amounts of ADAMTS5 and matrix metalloproteinase 13 （MMP13）, the principle proteinases responsible for aggrecan degradation, in the articular cartilage after DMM surgery. Furthermore, hypoxia-inducible factor 2α （HIF2α）, a critical catabolic transcription factor stimulating MMP13 expression during OA, was also upregulated in mice with reduced EGFR signaling. Taken together, our findings demonstrate a primarily protective role of EGFR during OA progression by regulating chondrocyte survival and cartilage degradation.

OPTIMIZATION OF A REDUCED CHEMICAL KINETIC MODEL FOR HCCI ENGINE SIMULATIONS BY MICRO-GENETIC ALGORITHM

A reduced chemical kinetic model （44 species and 72 reactions） for the homogeneous charge compression ignition （HCCI） combustion of n-heptane was optimized to improve its autoignition predictions under different engine operating conditions. The seven kinetic parameters of the optimized model were determined by using the combination of a micro-genetic algorithm optimization methodology and the SENKIN program of CHEMKIN chemical kinetics software package. The optimization was performed within the range of equivalence ratios 0.2-1.2, initial temperature 310- 375 K and initial pressure 0, 1-0.3 MPa, The engine simulations show that the optimized model agrees better with the detailed chemical kinetic model （544 species and 2 446 reactions） than the original model does.

Reduced Life Expectancy Model Analyses of Exposure Time Effects of Endocrine Disruptors to Teleost Fishes Based on Effect Concentration of Hepatic Biomarkers

In this current paper, the exposure time effects on four endocrine disruptors and teleost fishes were evaluated using the reduced life expectancy (RLE) model based on the effect concentration (EC50) of available literature published. The result on the regression analysis over different exposure times has demonstrated that the EC50 of hepatic biomarkers falls with increasing exposure times in a predictable manner. The slopes of the regression equations reflect the strength of the toxic effects on the various teleost fish. The EC50 reduction over time can be interpreted based on the bioconcentration process, which can be used to understand transfer routes of the compounds from water to fish body. RLE model also provides useful information in assessing the toxic effects on fish life expectancy as a result of the occurrence of compounds.

Kinetic models of natural gas combustion in an internal combustion engine

In this study, combustion of methane was simulated using four kinetic models of methane in CHEMKIN 4.1.1 for 0-D closed internal combustion （IC） engine reactor. Two detailed （GRIMECH3.0 ＆ UBC MECH2.0） and two reduced （One step ＆ Four steps） models were examined for various IC engine designs. The detailed models （GRIMECH3.0, ＆ UBC MECH2.0） and 4-step models successfully predicted the combustion while global model was unable to predict any combustion reaction. This study illustrated that the detailed model showed good concordances in the prediction of chamber pressure, temperature and major combustion species profiles. The detailed models also exhibited the capabilities to predict the pollutants formation in an IC engine while the reduced schemes showed failure in the prediction of pollutants emissions. Although, there are discrepancies among the profiles of four considered model, the detailed models （GRIMECH3.0 ＆ UBC MECH2.0） produced the acceptable agreement in the species prediction and formation of pollutants.

Estimates of required impurity fraction for EAST divertor detachment

During the EAST radiative divertor experiments,one of the key challenges was how to avoid the occurrence of disruptive events caused by excessive impurity seeding.To estimate the required impurity fraction for divertor detachment,we introduce a reduced edge plasma radiation model.In the model,based on the momentum conservation along the magnetic field line,the upstream pressure is determined by the plasma density and temperature at the divertor target,and then the impurity radiation loss is obtained by the balance of the heat and particle fluxes.It is found that the required impurity fraction shows a non-monotonic variation with divertor electron temperature(T_(d))when 0.1 eV<T_(d)<10 eV.In the range of 0.1 eV<T_(d)<1 e V,the position near the valley of required impurity fraction corresponds to strong plasma recombination.Due to the dependence of the volumetric momentum loss effect on the T_(d)in the range of 1 eV<T_(d)<10 eV,the required impurity fraction peaks and then decreases as T_(d)is increased.Compared to neon,the usage of argon reduces the impurity fraction by about twice.In addition,for the various fitting parameters in the pressure-momentum loss model,it is shown that the tendency of required impurity fraction with T_(d)always increases first and then decreases in the range of 1 eV<T_(d)<10 eV,but the required impurity fraction decreases when the model that characterizes the strong loss in pressure momentum is used.

Assessment of a two-surface plasticity model for hexagonal materials

A computationally efficient two-surface plasticity model is assessed against crystal plasticity. Focus is laid on the mechanical behavior of magnesium alloys in the presence of ductility-limiting defects, such as voids. The two surfaces separately account for slip and twinning such that the constitutive formulation captures the evolving plastic anisotropy and evolving tension-compression asymmetry. For model identification, a procedure is proposed whereby the initial guess is based on a combination of experimental data and computationally intensive polycrystal calculations from the literature. In drawing direct comparisons with crystal plasticity, of which the proposed model constitutes a heuristically derived reduced-order model, the available crystal plasticity simulations are grouped in two datasets. A calibration set contains minimal data for both pristine and porous material subjected to one loading path. Then the two-surface model is assessed against a broader set of crystal plasticity simulations for voided unit cells under various stress states and two loading orientations. The assessment also includes microstructure evolution(rate of growth of porosity and void distortion). The ability of the two-surface model to capture essential features of crystal plasticity is analyzed along with an evaluation of computational cost. The prospects of using the model in guiding the development of physically sound damage models in Mg alloys are put forth in the context of high-throughput simulations.

Convective drying of wood chips:Accelerating coupled DEM-CFD simulations with parametrized reduced single particle models

The simulation of industry-scale reactive bulks is challenging due to the complex interaction between fluid and particles.The particles in the bulk and their interaction with the fluid flow can be described by combined Discrete Element Method-Computational Fluid Dynamics(DEM-CFD)models.However,the computational cost of the Finite Volume(FV)methods deployed in these models can become prohibi-tively expensive,especially for high inner-particle resolution.Single particle Reduced Models(RMs)can be used to achieve both fast and accurate descriptions of the processes in each particle.As an example of bulk systems comprising heat and mass transfer,we compared FV and RM simulations for the drying of wood chips in a bulk reactor.A manifold-based nonlinear interpolation was applied to resolve changing boundary conditions for the RM.Our simulations showed that RMs provide accurate values for the thermodynamic state variables of the particles.Furthermore,the time required for the bulk simulation was reduced by 67%with the RMs.It is evident that simulations with high inner-particle resolution can be accelerated by RMs if manifold-based nonlinear interpolation is used to address changing boundary conditions.

Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation

Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices.

Regularized dynamic mode decomposition algorithm for time sequence predictions

Dynamic mode decomposition(DMD) aims at extracting intrinsic mechanisms in a time sequence via linear recurrence relation of its observables, thereby predicting later terms in the sequence. Stability is a major concern in DMD predictions. We adopt a regularized form and propose a Regularized DMD(Re DMD) algorithm to determine the regularization parameter. This leverages stability and accuracy. Numerical tests for Burgers' equation demonstrate that Re DMD effectively stabilizes the DMD prediction while maintaining accuracy. Comparisons are made with the truncated DMD algorithm.

Bionic Optimisation of the Earthquake Resistance of High Buildings by Tuned Mass Dampers

The optimisation of earthquake resistance of high buildings by multi-tuned mass dampers was investigated using bionic algorithms. In bionic or evolutionary optimisation studies the properties of parents are crossed and mutated to produce a new generation with slightly different properties. The kids which best satisfy the object of the study, become the parents of the next generation. After a series of generations essential improvements of the object may be observed. Tuned mass dampers are widely used to reduce the impact of dynamic excitations on structures. A single mass system and multi-mass oscillators help to explain the mechanics of the dampers. To apply the bionic optimisation strategy to high buildings with passive tuned mass dampers subject to seismic loading a special beam element has been developed. In addition to the 6 degrees of freedom of a conventional beam element, it has 2 degrees of freedom for the displacements of the dampers. It allows for fast studies of many variants. As central result, efficient designs for damping systems along the height of an edifice are found. The impact on the structure may be reduced essentially by the use of such dampers designed to interact in an optimal way.

A Discontinuous Galerkin Method for Blood Flow and Solute Transport in One-Dimensional Vessel Networks

This paper formulates an efficient numerical method for solving the convection diffusion solute transport equations coupled to blood flow equations in vessel networks.The reduced coupled model describes the variations of vessel cross-sectional area,radially averaged blood momentum and solute concentration in large vessel networks.For the discretization of the reduced transport equation,we combine an interior penalty discontinuous Galerkin method in space with a novel locally implicit time stepping scheme.The stability and the convergence are proved.Numerical results show the impact of the choice for the steady-state axial velocity profile on the numerical solutions in a fifty-five vessel network with physiological boundary data.

The Space-Time CE/SE Method for Solving Reduced Two-Fluid Flow Model

The space-time conservation element and solution element(CE/SE)method is proposed for solving a conservative interface-capturing reducedmodel of compressible two-fluid flows.The flow equations are the bulk equations,combined with mass and energy equations for one of the two fluids.The latter equation contains a source term for accounting the energy exchange.The one and two-dimensional flow models are numerically investigated in this manuscript.The CE/SE method is capable to accurately capture the sharp propagating wavefronts of the fluids without excessive numerical diffusion or spurious oscillations.In contrast to the existing upwind finite volume schemes,the Riemann solver and reconstruction procedure are not the building block of the suggested method.The method differs from the previous techniques because of global and local flux conservation in a space-time domain without resorting to interpolation or extrapolation.In order to reveal the efficiency and performance of the approach,several numerical test cases are presented.For validation,the results of the current method are compared with other finite volume schemes.

A reduced order aerothermodynamic modeling framework for hypersonic vehicles based on surrogate and POD

Aerothermoelasticity is one of the key technologies for hypersonic vehicles. Accurate and efficient computation of the aerothermodynamics is one of the primary challenges for hypersonic aerothermoelastic analysis. Aimed at solving the shortcomings of engineering calculation, compu- tation fluid dynamics （CFD） and experimental investigation, a reduced order modeling （ROM） framework for aerothermodynamics based on CFD predictions using an enhanced algorithm of fast maximin Latin hypercube design is developed. Both proper orthogonal decomposition （POD） and surrogate are considered and compared to construct ROMs. Two surrogate approaches named Kriging and optimized radial basis function （ORBF） are utilized to construct ROMs. Furthermore, an enhanced algorithm of fast maximin Latin hypercube design is proposed, which proves to be helpful to improve the precisions of ROMs. Test results for the three-dimensional aerothermody- namic over a hypersonic surface indicate that： the ROMs precision based on Kriging is better than that by ORBF, ROMs based on Kriging are marginally more accurate than ROMs based on POD- Kriging. In a word, the ROM framework for hypersonic aerothermodynamics has good precision and efficiency.