Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “dependability modeling”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Simple model to investigate jet quenching and correlated errors for centrality-dependent nuclear modification factors in relativistic heavy-ion collisions

Here, we apply Bayesian techniques to compare a simple, empirical model for jet quenching in heavy-ion collisions to centrality-dependent jet R AA measured by ATLAS for Pb + Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV. We find that the R AA values for central collisions are adequately described with a model for the mean p T -dependent jet energy loss using only two parameters. This model is extended by incorporating two-dimensional initial geometry information from TRENTo and compared to centrality-dependent R AA values. We find that the results are sensitive to the value of the jet-quenching formation time, τ ƒ , and that the optimal value of τ ƒ varies with the assumed path-length dependence of the energy loss. We construct a covariance error matrix for the data from the p T -dependent contributions to the ATLAS systematic errors and perform Bayesian calibrations for several different assumptions for the systematic error correlations. We show that the most-probable functions and $χ^2_d$ values are sensitive to assumptions made when fitting to correlated errors. This work demonstrates the utility of a simple model that can quickly demonstrate the constraining power of jet-quenching observables with corresponding uncertainties and guide future studies using more sophisticated models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Evaluation of semi-empirical soot models for nonpremixed flames with increased stoichiometric mixture fraction and strain

Due to the size and complexity of industrial-scale systems and computing limitations, semi-empirical soot models are often employed in CFD simulations rather than computationally-expensive detailed models. Many of these models were developed for specific applications with unique characteristic timescales and/or validated only under fuel-air combustion conditions. Hence, their use in different contexts, such as oxy-combustion, could lead to inaccurate predictions. In this study, twelve semi-empirical models (1-step or 2-step) are evaluated on their ability to respond to changes in stoichiometric mixture fraction (Z st ) and strain in a series of ethylene counterflow flames which span the experimental sooting-to-non-sooting (yellow to blue) transition. A unique approach of plotting soot formation rate in normalized, local equivalence ratio space is introduced to aid in the analysis. Results show that no existing model is able to predict a blue flame when Z st is increased beyond the experimentally-measured sooting limit. Many models give the counter-intuitive result of increasing peak and/or integrated soot volume fraction (svf) as Z st increased, contrary to experimental observations. Two models are able to successfully predict a blue flame upon increase of strain to the sooting limit value. For two-step models, the strong dependence of growth rate on surface area results in high sensitivity of svf to changing flame boundary conditions, but with mixed accuracy. There is still a significant need for a robust semi-empirical model which can accurately predict soot fraction in systems with oxygen-enrichment or variable strain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spin-lattice model for cubic crystals

In this work, we present a methodology based on the Néel model to build a classical spin-lattice Hamiltonian for cubic crystals capable of describing magnetic properties induced by the spin-orbit coupling like magnetocrystalline anisotropy and anisotropic magnetostriction, as well as exchange magnetostriction. Taking advantage of the analytical solutions of the Néel model, we derive theoretical expressions for the parametrization of the exchange integrals and Néel dipole and quadrupole terms that link them to the magnetic properties of the material. This approach allows us to build accurate spin-lattice models with the desired magnetoelastic properties. We also explore a possible way to model the volume dependence of magnetic moment based on the Landau energy. This feature allows us to consider the effects of hydrostatic pressure on the saturation magnetization. We apply this method to develop a spin-lattice model for BCC Fe and FCC Ni, and we show that it accurately reproduces the experimental elastic tensor, magnetocrystalline anisotropy under pressure, anisotropic magnetostrictive coefficients, volume magnetostriction, and saturation magnetization under pressure at zero temperature. This work could constitute a step towards large-scale modeling of magnetoelastic phenomena.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

How Does Feedback Affect the Star Formation Histories of Galaxies?

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at z ∼ 0 across the IllustrisTNG, SIMBA, and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affect the efficiency of baryon cycling and the rates at which central black holes grow, whereas the effects of varying active galactic nucleus (AGN) feedback depend on model-specific implementations of black hole seeding, accretion, and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.

Iyer, Kartheik G. [Columbia Univ., New York, NY (U↗
DOE OSTI2025

Electron-Ion Dynamics with Time-Dependent Density Functional Theory: Towards Predictive Solar Cell Modeling

This project focussed on two aspects of computational modeling with a view toward application for photovoltaic design: (i) increased reliability of exchange-correlation functionals in time-dependent density functional theory (TDDFT) (a method of choice of the calculation of electronic spectra and dynamics), especially for time-resolved non-perturbative dynamics, and (ii) the development of a practical but rigorously-based method for coupling electronic and nuclear motion via the exact-factorization (EF) approach (a relatively new framework for developing approximations).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A deformation-dependent coupled Lagrangian/semi-Lagrangian meshfree hydromechanical formulation for landslide modeling

Abstract The numerical modelling of natural disasters such as landslides presents several challenges for conventional mesh-based methods such as the finite element method (FEM) due to the presence of numerically challenging phenomena such as severe material deformation and fragmentation. In contrast, meshfree methods such as the reproducing kernel particle method (RKPM) possess unique features conducive to modelling extreme events such as the absence of a structured mesh and the ease of adaptive refinement, among others. While the semi-Lagrangian reproducing kernel (SL-RK) shape functions of RKPM defined in the current configuration have proven to be effective in extreme event modelling, the computational cost for the re-evaluation of the shape functions at every time step is costly. In this work, a deformation-dependent coupling of the Lagrangian reproducing kernel (L-RK) and SL-RK approximations is proposed for the solution of a hydro-mechanical formulation for effective simulations of landslides. The ramp function is constructed based on an equivalent plastic strain as a deformation-dependent transition from L-RK shape functions to SL-RK ones as the deformation progresses. The particular focus of the paper will be on modelling seepage-induced landslides with a mixed $$u$$ u – $$p$$ p formulation to couple the solid and fluid phases. Examples are presented to examine the effectiveness of this coupled Lagrangian/semi-Lagrangian reproducing kernel (L–SL RK) formulation and to highlight its performance in landslide modelling.

Baek, Jonghyuk↗
DOE OSTI2021

A New Method for Aerosol Measurement Using Wide-field Photometry

We present a new method to measure the vertical aerosol optical depth (VAOD) during clear nights using a wide-field imager—a CCD camera with a photographic lens on an equatorial mount. A series of 30 s exposures taken at different altitudes above the horizon can be used to measure the VAOD with a precision better than 0.008 optical depths within a few minutes. Such a measurement does not produce any light and is thus suitable for use at sites where other astronomical instruments are located. The precision of the VAOD measurement depends on the laboratory calibration of spectral properties of the system and of the response of the camera electronics to varying illumination levels, as well as careful considerations of the details of stellar photometry and modeling of the dependence of the measured stellar flux, the star color, and the position within the field of view. The results obtained with robotic setups at the future sites of the Cerenkov Telescope Array show good internal consistency and agreement with simultaneous measurements from a Sun/Moon photometer located at the same sites.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

TokaMaker: An open-source time-dependent Grad-Shafranov tool for the design and modeling of axisymmetric fusion devices

Here, in this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial degree for analytic and cross-code test cases.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A remapping scheme for history-dependent material state variables

Here, we present an interpolation scheme for use with material models involving history-dependent state variables. The interpolation scheme is based on the solution of a constrained optimization problem, to identify the smoothest monotone curve which produces an admissible solution. Unlike classical schemes used for transport equations, the proposed methodology accounts for the interdependence of the history variables and produces admissible interpolations of the material's state variables. After introducing a general solution scheme for the problem, a simplified and more computationally efficient method is derived. Finally, as a validation problem for the proposed methods, we consider the advection of internal state variables belonging to material models with highly nonlinear yield surfaces.

42 ENGINEERING↗
DOE OSTI2022

Gaussian Mixture Model Solvers for the Boltzmann Equation

This report documents our experience constructing a numerical method for the collisional Boltzmann equation that is capable of accurately capturing the collisionless through strongly collisional limits. We explore three different functional representations and present a detailed account of a numerical method based on a spatially dependent Gaussian mixture model (GMM). The Kullback-Leibler divergence is used as a closeness measure and various expectation maximization (EM) solution algorithms are implemented to find a compact representation in velocity space for distribution functions that exhibit significant non-Maxwellian character. We discuss issues that appear with this representation over a range of Knudsen numbers for a prototypical test problem and demonstrate that the strongly collisional limit recovers a solution to Euler's equations. Looking forward, this approach is broadly applicable to the non-relativistic and relativistic collisional Vlasov equations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Sensitivity Study of Multiscale and Phenomenological Elasto-Viscoplastic Grade 91 Material Models for Component-Scale Response

Many advanced nuclear reactor concepts currently being developed are targeting higher operating temperatures relative to the current fleet of light water nuclear reactors, for efficiency gains and other operational considerations. The design of high temperature structural components with reliable long-term operational performance will depend on material models that accurately capture the inelastic deformation mechanisms active in these environments. In this work, we perform a detailed parameter sensitivity analysis of two unified elasto-viscoplastic Grade 91 material models capable of capturing long term high temperature creep deformation. The first model is a phenomelogical material model from the Nuclear Engineering Material Library (NEML) developed at Argonne National Lab. The NEML model parameters and their uncertainty were fit to a range of Grade 91 experimental data using Bayesian Markov Chain Monte Carlo analysis. The second model is a LAROMance data-driven surrogate material model developed at Los Alamos National Lab. The LAROMance model is fit to a large database of responses produced by a mechanistic crystal plasticity based polycrystal model. Parameters for the LAROMance surrogate material model reflect the pedigree of the Grade 91 microstructure. Both material models have been integrated into the Grizzly code, based on the open-source MOOSE multiphysics simulation framework, to simulate both the progression of aging mechanisms and the effects of that aging on nuclear power plant structures. Grizzly is used analyze a three-dimensional Grade 91 piping system to compare the long-term inelastic response predicted by these two fundamentally different models and assess the sensitivity of the material model input parameters on this quantity of interest.

42 ENGINEERING↗
DOE OSTI2025

Advancing Multi-Hazard Risk and Safety Considerations for Aging Nuclear Facilities (Revision 1)

This project will demonstrate a multi-hazard time-dependent probabilistic risk assessment (PRA) approach for nuclear facilities considering aging-related deterioration of structures. A generic pressurized water reactor (PWR) reactor subjected to seismic mainshock-aftershock sequences considering the aging of the containment structure will be used as a case study to demonstrate the multi-hazard PRA approach. Using advanced modeling and simulation, seismic mainshock-aftershock fragility functions will be simulated for the containment structure considering aging effects. A multi-hazard PRA model for a generic PWR reactor will be built to quantify the multi-hazard core damage frequency (CDF) and large early release frequency (LERF) with explicit time-dependent modeling of event sequences. To date, the cascading impacts of multi-hazards are not adequately accounted for in the PRA models for nuclear facilities. In addition, for both initial and periodic evaluation of facilities to withstand natural phenomena hazards (NPH), deterioration of the SSCs due to aging and other effects is not adequately considered. By advancing multi-hazard considerations accounting for aging effects, this project will contribute to improved understanding of the safety of aging nuclear facilities. Project outcomes such as the multi-hazard CDF and LERF will also allow facility owners to optimize upgrade and retrofit protocols. This project is divided into two components: (1) advanced modeling and simulations; and (2) multi-hazard PRA. For the first component, Idaho National Laboratory’s (INL) Multi-hazard Analysis for STOchastic time-DOmaiN phenomena (MASTODON) and BlackBear codes will be used to simulate the seismic response and damage of a containment structure under cascading mainshock-aftershock sequences with aging effects. Uncertainties related to the seismic inputs, material parameters, and environmental factors such as temperature and humidity will be identified and propagated to the fragility functions. These fragility functions, which consider aging effects, will be time dependent. The second component will take the fragility information from the first component to build a multi-hazard time-dependent PRA model starting from a generic PWR model to evaluate the multi-hazard CDF and LERF and characterize the associated consequences. For this component, OpenPRA Web Application and SAPHIRE code will be used. Events such as the Fukushima Daiichi accident have highlighted the importance of considering the cascading impacts of multi-hazards for PRA. Moreover, many of the reactors in the current nuclear fleet in the United States (US) are already operating well beyond their initially planned design life, and applications for further extensions to operating licenses are being considered. Therefore, considering multi-hazard effects and aging deterioration in the NPH risk assessment process will contribute to the safety of both existing and future nuclear facilities. Outcomes of this project will thus directly benefit the standards DOE-STD-1020-16 and DOE-HDBK-1224-18.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

A Dynamic Risk Framework for the Physical Security of Nuclear Power Plants

This paper describes ongoing work within the Light Water Reactor Sustainability pathway at Idaho National Laboratory (INL) to optimize the security and cost of nuclear power plants. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). EMRALD is leveraged to optimize the security posture of a nuclear power plant by integrating force-on-force (FOF) simulations and operator mitigation actions, including dynamic and flexible coping strategies (FLEX). To illustrate the methodology, four attack scenarios are modeled in a commercially available FOF simulation tool using a hypothetical nuclear power plant facility. The simulation results provide valuable insights into possible attack outcomes, as well as the probabilistic risk of a core damage event given these outcomes. Safety mitigation procedures are modeled in EMRALD dependent on the attack outcomes by considering human operator uncertainties. The results demonstrate that the number of armed responders can be optimized, while still maintaining the same protection level as the initial security posture. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the nuclear power plants to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Assessing the impact of valence sd neutrons and protons on fusion

Experimental near-barrier fusion cross sections for 17 F + 12 C are compared to the fusion excitation functions for 16,17,18 O, 19 F, and 20 Ne ions on a carbon target. Normalized or reduced fusion cross sections are utilized in order to compare the different systems and account for the differing static size of the incident ions as well as changes in fusion barrier. Fusion excitation functions for the case of the mirror nuclei 17 F and 17 O with a single sd nucleon are compared. The 17 F data are also juxtaposed with nuclei involving multiple sd nucleons. Trends of the fusion cross section above the barrier beyond the expected systematic behavior are observed. These trends are interpreted as the interplay of the sd protons and neutrons. Furthermore, the experimental data are also compared to a widely used analytic model of near-barrier fusion, a time-dependent Hartree-Fock model, and coupled channels calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Dynamic Risk Framework for the Optimization of Physical Security Posture of Nuclear Power Plants

This paper describes an ongoing work within the Light Water Reactor Sustainability pathway at Idaho National Laboratory (INL) to optimize security and cost of nuclear power plants. It introduces the dynamic risk assessment tool developed at INL, Event Modeling Risk Assessment using Linked Diagrams (EMRALD). EMRALD was leveraged to optimize the security posture of a nuclear power plant by integrating force-on-force (FOF) simulations and operator mitigation actions including the dynamic and flexible coping strategies (FLEX). To illustrate the methodology, four attack scenarios were modeled in a commercially available FOF simulation tool using a hypothetical nuclear power plant facility. The simulation results provide valuable insights into possible attack outcomes, as well as the probabilistic risk of core damage event given these outcomes. Safety mitigation procedures were modeled in EMRALD dependent on the attack outcomes by considering human operator uncertainties. The results demonstrate that the number of armed responders can be optimized, while still maintaining the same protection level as the initial security posture. The proposed modeling and simulation framework of integrating FLEX equipment with FOF models enables the nuclear power plants to credit FLEX portable equipment in the plant security posture, resulting in an efficient and optimized physical security system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗