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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Impact of petrophysical uncertainty on Bayesian hydrogeophysical inversion and model selection

Quantitative hydrogeophysical studies rely heavily on petrophysical relationships that link geophysical properties to hydrogeological properties and state variables. Coupled inversion studies are frequently based on the questionable assumption that these relationships are perfect (i.e., no scatter). Using synthetic examples and crosshole ground-penetrating radar (GPR) data from the South Oyster Bacterial Transport Site in Virginia, USA, we investigate the impact of spatially-correlated petrophysical uncertainty on inferred posterior porosity and hydraulic conductivity distributions and on Bayes factors used in Bayesian model selection. Our study shows that accounting for petrophysical uncertainty in the inversion (I) decreases bias of the inferred variance of hydrogeological subsurface properties, (II) provides more realistic uncertainty assessment and (III) reduces the overconfidence in the ability of geophysical data to falsify conceptual hydrogeological models.

58 GEOSCIENCES↗
DOE OSTI2021

Pressurizer dynamic model and emulated programmable logic controllers for nuclear power plants cybersecurity investigations

This work demonstrates the functionality of the pressurizer using a fast-running three regions, non-equilibrium model and control programs of emulated PLCs. The state variables from an integrated model of primary loop in a representative PWR plant are communicated to the pressurizer’s emulated PLCs using a synchronized data transfer function. In turn, the PLCs communicate back instructions to the pressurizer model to adjust the pressure and water level to remain within preprogramed setpoints. Pressurizer model simultaneously solves the coupled mass and energy conservation equations in the saturated vapor and saturated and subcooled liquid regions using fixed step solver. Results demonstrate the response of the pressurizer model linked to an emulated pressure and water level PLCs in a simulated transient involving surge-in and surge-out events. The 50 ms response delay time for the emulated PLC insignificantly affects operation and predictions of the pressurizer model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Bifurcations analysis of turbulent energy cascade

This note studies the mechanism of turbulent energy cascade through an opportune bifurcations analysis of the Navier–Stokes equations, and furnishes explanations on the more significant characteristics of the turbulence. A statistical bifurcations property of the Navier–Stokes equations in fully developed turbulence is proposed, and a spatial representation of the bifurcations is presented, which is based on a proper definition of the fixed points of the velocity field. The analysis first shows that the local deformation can be much more rapid than the fluid state variables, then explains the mechanism of energy cascade through the aforementioned property of the bifurcations, and gives reasonable argumentation of the fact that the bifurcations cascade can be expressed in terms of length scales. Furthermore, the study analyzes the characteristic length scales at the transition through global properties of the bifurcations, and estimates the order of magnitude of the critical Taylor-scale Reynolds number and the number of bifurcations at the onset of turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Degenerate limit thermodynamics beyond leading order for models of dense matter

Analytical formulas for next-to-leading order temperature corrections to the thermal state variables of interacting nucleons in bulk matter are derived in the degenerate limit. The formalism developed is applicable to a wide class of non-relativistic and relativistic models of hot and dense matter currently used in nuclear physics and astrophysics (supernovae, proto-neutron stars and neutron star mergers) as well as in condensed matter physics. We consider the general case of arbitrary dimensionality of momentum space and an arbitrary degree of relativity (for relativistic models). For non-relativistic zero-range interactions, knowledge of the Landau effective mass suffices to compute next-to-leading order effects, but for finite-range interactions, momentum derivatives of the Landau effective mass function up to second order are required. Results from our analytical formulas are compared with the exact results for zero- and finite-range potential and relativistic mean-field theoretical models. In all cases, inclusion of next-to-leading order temperature effects substantially extends the ranges of partial degeneracy for which the analytical treatment remains valid. Effects of many-body correlations that deserve further investigation are highlighted.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Digital memcomputing: From logic to dynamics to topology

Digital memcomputing machines (DMMs) are a class of computational machines designed to solve combinatorial optimization problems. A practical realization of DMMs can be accomplished via electrical circuits of highly non-linear, point-dissipative dynamical systems engineered so that periodic orbits and chaos can be avoided. A given logic problem is first mapped into this type of dynamical system whose point attractors represent the solutions of the original problem. A DMM then finds the solution via a succession of elementary instantons whose role is to eliminate solitonic configurations of logical inconsistency (“logical defects”) from the circuit. By employing a supersymmetric theory of dynamics, a DMM can be described by a cohomological field theory that allows for computation of certain topological matrix elements on instantons that have the mathematical meaning of intersection numbers on instantons. We discuss the “dynamical” meaning of these matrix elements, and argue that the number of elementary instantons needed to reach the solution cannot exceed the number of state variables of DMMs, which in turn can only grow at most polynomially with the size of the problem. These results shed further light on the relation between logic, dynamics and topology in digital memcomputing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Electromechanical memcapacitor model offering biologically plausible spiking.

In this article, we introduce a new nanoscale electromechanical device - a leaky memcapacitor - and show that it may be useful for the hardware implementation of spiking neurons. The leaky memcapacitor is a movableplate capacitor that becomes quite conductive when the plates come close to each other. The equivalent circuit of the leaky memcapacitor involves a memcapacitive and memristive system connected in parallel. In the leaky memcapacitor, resistance and capacitance depend on the same internal state variable, which is the displacement of the movable plate. We have performed a comprehensive analysis showing that several types of spiking observed in biological neurons can be implemented with the leaky memcapacitor. Significant attention is paid to the dynamic properties of the model. As in leaky memcapacitors the capacitive, leaking resistive, and reset functionalities are implemented naturally within the same device structure, their use will simplify the creation of spiking neural networks.

Zhang, Zixi↗
DOE OSTI2020

SO(3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

This work examines the frame-invariance (and the lack thereof) exhibited in simulated anisotropic elasto-plastic responses generated from supervised machine learning of classical multi-layer and informed-graph-based neural networks, and proposes different remedies to fix this drawback. The inherent hierarchical relations among physical quantities and state variables in an elasto-plasticity model are first represented as informed, directed graphs, where three variations of the graph are tested. While feed-forward neural networks are used to train path-independent constitutive relations (e.g., elasticity), recurrent neural networks are used to replicate responses that depends on the deformation history, i.e. or path dependent. In dealing with the objectivity deficiency, we use the spectral form to represent tensors and, subsequently, three metrics, the Euclidean distance between the Euler Angles, the distance from the identity matrix, and geodesic on the unit sphere in Lie algebra, can be employed to constitute objective functions for the supervised machine learning. In this, the aim is to minimize the measured distance between the true and the predicted 3D rotation entities. Following this, we conduct numerical experiments on how these metrics, which are theoretically equivalent, may lead to differences in the efficiency of the supervised machine learning as well as the accuracy and robustness of the resultant models. Neural network models trained with tensors represented in component form for a given Cartesian coordinate system are used as a benchmark. Our numerical tests show that, even given the same amount of information and data, the quality of the anisotropic elasto-plasticity model is highly sensitive to the way tensors are represented and measured. The results reveal that using a loss function based on geodesic on the unit sphere in Lie algebra together with an informed, directed graph yield significantly more accurate rotation prediction than the other tested approaches.

42 ENGINEERING↗
DOE OSTI2021

Parallel projection—An improved return mapping algorithm for finite element modeling of shape memory alloys

Here, we present a novel finite element analysis of inelastic structures containing Shape Memory Alloys (SMAs). Phenomenological constitutive models for SMAs lead to material nonlinearities, that require substantial computational effort to resolve. Finite element analysis methods, which rely on Gauss quadrature integration schemes, must solve two sets of coupled differential equations: one at the global level and the other at the local, i.e. Gauss point level. In contrast to the conventional return mapping algorithm, which solves these two sets of coupled differential equations separately using a nested Newton procedure, we propose a scheme to solve the local and global differential equations simultaneously. In the process we also derive closed-form expressions used to update the internal/constitutive state variables, and unify the popular closest-point and cutting plane methods with our formulas. Numerical testing indicates that our method allows for larger thermomechanical loading steps and provides increased computational efficiency, over the standard return mapping algorithm.

42 ENGINEERING↗
DOE OSTI2023

Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data

Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

An artificial neural network framework for reduced order modeling of transient flows

Here, this paper proposes a supervised machine learning framework for the non-intrusive model order reduction of unsteady fluid flows to provide accurate predictions of non-stationary state variables when the control parameter values vary. Our approach utilizes a training process from full-order scale direct numerical simulation data projected on proper orthogonal decomposition (POD) modes to achieve an artificial neural network (ANN) model with reduced memory requirements. This data-driven ANN framework allows for a nonlinear time evolution of the modal coefficients without performing a Galerkin projection. Our POD-ANN framework can thus be considered an equation-free approach for latent space dynamics evolution of nonlinear transient systems and can be applied to a wide range of physical and engineering applications. Within this framework we introduce two architectures, namely sequential network (SN) and residual network (RN), to train the trajectory of modal coefficients. We perform a systematic analysis of the performance of the proposed reduced order modeling approaches on prediction of a nonlinear wave-propagation problem governed by the viscous Burgers equation, a simplified prototype setting for transient flows. We find that the POD-ANN-RN yields stable and accurate results for test problems assessed both within inside and outside of the database range and performs significantly better than the standard intrusive Galerkin projection model. Finally, our results show that the proposed framework provides a non-intrusive alternative to the evolution of transient physics in a POD basis spanned space, and can be used as a robust predictive model order reduction tool for nonlinear dynamical systems.

42 ENGINEERING↗
DOE OSTI2018

State space parameterization of explosive eigenvalues during autoignition

Explosive modes such as ignition and extinction are characterized by an eigenvalue of the chemical Jacobian matrix with positive real part, representing the transient instability of chain-branching chemistry and thermal feedback. Formation and eigen-decomposition of the Jacobian matrix are expensive operations whose cost increases cubically with chemical mechanism size. As an alternative to directly computing the eigenvalues of the Jacobian, we explore principal component analysis (PCA) along with nonlinear regression as a methodology to parameterize the eigenvalues by state variables (or linear combinations thereof). We evaluate this modeling strategy using homogeneous autoignition data on two different applications: pseudotransient continuation (Ψtc)-based ODE solvers and chemical explosive mode analysis (CEMA). Results indicate that the PCA-based parameterization of the eigenvalues appears feasible for Ψtc solvers in autoignition calculations over a range of temperatures and pressures. Our results also show that eigenvalue models are capable of tracking sharp discontinuities (such as ignition or extinction) in the eigenvalue for computational flame diagnostics such as CEMA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗
DOE OSTI2018

Effect of disturbances on MED-TVC plant characteristics: Dynamic modeling and simulation

This study describes a detailed steady-state analysis and transient operation of a multi-effect desalination plant with a thermal vapor compressor unit (MED-TVC). The model developed is based on solving the basic conservation equations of mass, energy, and salt. It can predict steady-state operation and transient behavior when the plant is subject to abrupt changes in the main input parameters including motive steam flow rate (m ms ), cooling seawater flow rate (m cw ) and cooling seawater temperature (T cw ). These sudden changes simulate the conditions a MED-TVC plant may be exposed to. Both the steady-state and dynamic aspects of the model are validated against published experimental data. Monitoring the variation of four state variables (vapor temperature, brine level, brine and vapor flow rates) in each effect shows that disturbances in the motive steam flow rate and the cooling seawater temperature have a significant effect on the plant performance. On the other hand, the disturbance in the cooling seawater mass flow rate has only a moderate effect. For all applied disturbances, the change in the brine level is the slowest compared to the changes in vapor temperature, and brine and vapor flow rates. Furthermore, the effect of changing seawater salinity shows a slight effect on the total distillate production and the specific heat transfer area.

42 ENGINEERING↗
DOE OSTI2022

Impacts of climate change on estuarine stratification and implications for hypoxia within a shallow subtropical system

Vertical density stratification often plays an important role in the formation and expansion of coastal hypoxic zones through its effect on near-bed circulation and vertical oxygen flux. However, the impact of future climate change on estuarine circulation is widely unknown. Here, we developed and calibrated a three-dimensional hydrodynamic model for Pensacola Bay, a shallow subtropical estuary in the northeastern Gulf of Mexico. Model simulations based on years 2013–2017 were applied to examine changes in salinity, temperature, and density under future climate scenarios, including increased radiative forcing (IR) and temperature (T), increased freshwater discharge (D), sea level rise (SLR), and wind intensification (W). Simulations showed that the impacts of climate change on modeled state variables varied over time with external forcing conditions. The model demonstrated the potential for sea level rise and increased freshwater discharge to episodically increase vertical density gradients in the Bay. However, increased wind forcing destabilized vertical gradients, at times reducing the spatial extent and duration of stable stratification. For time periods with low freshwater discharge, moderate increases in wind speed (10%) can destabilize density gradients strengthened by increased discharge (10%) and sea level rise (0.48 m). In contrast, destruction of strong density gradients that form near the mid-Bay channel following flood events requires stronger wind forcing. Furthermore, these results highlight the importance of considering natural variability in freshwater and wind forcing, as well as local phenomena that are generally unresolved by global climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

An offline unstructured biogeochemical model (UBM) for complex estuarine and coastal environments

Due to increased pollutant loads and water use from coastal development and population growth, occurrences of low-dissolved oxygen and "hypoxic zones" have increased. Reports of fish kills and water quality impairment are also becoming more frequent in many coastal waters. Water quality managers and regulatory agencies rely on numerical modeling tools to quantify the relative contributions of anthropogenic and "natural" pollutant loads (nutrients and biochemical oxygen demand) on dissolved oxygen levels and use the results for remedial activities and source control. The ability to conduct seasonlong simulations with sufficient nearshore resolution is therefore a key requirement. Mesh flexibility and the ability to increase site specific resolution without disturbing the larger domain setup and calibration are critical. The objective of this effort was to develop a robust biogeochemical model suitable for simulation of water quality dynamics including dissolved oxygen in complex coastal environments with multiple tidal channels, tidal flats, and density-driven circulation using unstructured-grid formulation. This paper presents an offline unstructured biogeochemical model that uses the Finite Volume Coastal Ocean Model (FVCOM) discretization of the study domain and the corresponding hydrodynamic solution to drive biogeochemical kinetics based on a water quality model CE-QUAL-ICM. In this paper, the linkage between selected hydrodynamic and water quality models is subjected to several scalar transport and biogeochemical module tests (plume transport and dilution, BOD/DO sag, and phytoplankton/nutrients reaction), and results are compared to their analytical solutions as part of model validation. A preliminary application of the biogeochemical model with a year-long simulation of Hood Canal basin in Puget Sound, USA, is presented as an example and a test of the tool in a real estuary setting. The model reproduced the dynamics and seasonal variations in the biogeochemical state variables and was used to test short-term wind-driven dynamics that could influence dissolved oxygen concentrations in Hood Canal.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A two-phase investment model for optimal allocation of phasor measurement units considering transmission switching

Ensuring the reliability of an electrical power system requires a wide-area monitoring and full observability of the state variables. Phasor measurement units (PMUs) collect in real time synchronized phasors of voltages and currents which are used for the observability of the power grid. Due to the considerable cost of installing PMUs, it is not possible to equip all buses with PMUs. In this paper, we propose an integer linear programming model to determine the optimal PMU placement plan in two investment phases. In the first phase, PMUs are installed to achieve full observability of the power grid whereas additional PMUs are installed in the second phase to guarantee the N - 1 observability of the power grid. The proposed model also accounts for transmission switching and single contingencies such as failure of a PMU or a transmission line. Results are provided on several IEEE test systems which show that our proposed approach is a promising enhancement to the methods available for the optimal placement of PMUs.

Phasor measurement unit↗
DOE OSTI2023

Granger Causality for prediction in Dynamic Mode Decomposition: Application to power systems

Here, the dynamic mode decomposition (DMD) technique extracts the dominant modes characterizing the innate dynamical behavior of the system within the measurement data. For appropriate identification of dominant modes from the measurement data, the DMD algorithm necessitates ensuring the quality of the input measurement data sequences. On that account, for validating the usability of the dataset for the DMD algorithm, the paper proposed two conditions: Persistence of excitation (PE) and the Granger Causality Test (GCT). The virtual data sequences are designed with the hankel matrix representation such that the dimensions of the subspace spanning the essential system modes are increased with the addition of new state variables. The PE condition provides the lower bound for the trajectory length, and the GCT provides the order of the model. Satisfying the PE condition enables estimating an approximate linear model, but the predictability with the identified model is only assured with the temporal causation among data searched with GCT. The proposed methodology is validated with the application for coherency identification (CI) in a multi-machine power system (MMPS), an essential phenomenon in transient stability analysis. The significance of PE condition and GCT is demonstrated through various case studies implemented on 22 bus six generator system.

24 POWER TRANSMISSION AND DISTRIBUTION↗