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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Franck–Condon Models for Simulating the Band Shape of Electronic Absorption Spectra

Band shape is an essential ingredient in the simulation of electronic absorption spectra. The excitation of multiple series of vibrational levels during an electronic excitation is a main contributor to band shapes. Here we present two simple models based on the Franck–Condon displaced-harmonic-oscillator model. The models are both derived from the time-dependent formulation of electronic spectroscopy. They assume that the transition dipoles do not depend on geometry and that the potential energy surfaces are locally quadratic; one model is second order in time and is called LQ2, and the other is third order in time and is called LQ3. These models are suitable for simulating the unresolved vibronic band shapes of electronic spectra that involve many vibrational modes. The models are straightforward and can be easily applied to simulate absorption spectra that are composed of many electronic transitions. As compared to carrying out molecular dynamics simulations, they require relatively few electronic structure calculations, and the additional cost for constructing the spectra is negligible. Therefore, the models are suitable for simulating the spectra of complex systems such as transition-metal complexes.

UV/Vis spectroscopy↗
DOE OSTI2020

Functional Requirements for the Modeling and Simulation of Advanced (Non-LWR) Reactor Mechanistic Source Term

A number of industry vendors are leading a resurgence in advanced reactor development. As the assurance of public safety from the accidental release of radionuclides to the environment is central to regulatory licensing and the reactor design process, the development of a mechanistic source term (MST) assessment has been identified by the advanced reactor industry as a foremost priority. In contrast to historic light-water reactor (LWR) source term analysis efforts, an MST attempts to realistically model the release and transport of radionuclides from the source to the environment for specific scenarios, while accounting for retention or transmutation phenomena and associated uncertainties. This work seeks to aid in the development of MST modeling and simulation tools by performing a preliminary identification of functional requirements for the major advanced reactor types.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Characterizing Turbulence at a Forest Edge: Comparing Sub-Filter Scale Turbulence Models in Simulations of Flow over a Canopy

In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e. pine needles, grass). Large eddy simulations (LES) are a common way of numerically representing turbulence. The Smagorinsky model (1963) serves as one of the most studied sub-grid scale representations in LES. In this investigation, the Smagorinsky model was implemented in HIGRAD/FIRETEC, LANL’s coupled fire-atmosphere model. This study was motivated by the need to quantitatively investigate the vorticity budget equation in HIGRAD/FIRETEC. The Smagorinsky turbulent kinetic energy (TKE) was compared to FIRETEC’s 1.5-order TKE eddy-viscosity subgrid-scale model, known as the Linn turbulence model. This was done in simulations of flow over flat terrain with a homogeneous, cuboidal canopy in the center of the domain. Examinations of the modeled vertical TKE profile and turbulent statistics at the leading edge, and throughout the canopy, show that the Smagorinsky model provides comparable results to that of the original closure model posed in FIRETEC.

58 GEOSCIENCES↗
DOE OSTI2017

NUISANCE-GENIE Validation and Sample Additions

aThe objective for this project was to use MINERvA data to tune the simulation models in order to obtain the precision needed for current and future neutrino experiments. In order to do this, the current models need to be validated and then improved. $\#10146$; Validation was done by recreating figures that have been used in previous publications $\#61553$; This was done by comparing data from the detector and the simulation model (GENIE) $\#10146$; Additionally, a newer version of GENIE was compared to the GENIE used for the publications to validate the new version as well as to note any improvements Another objective was to add new samples into the NUISANCE framework, which was used to compare data from the detector and simulation models. $\#10146$; Specifically, the added sample was the two dimensional histogram of the double differential cross section as a function of the transversal and z-direction momentum for Numu and Numubar $\#61553$; Was also used for validation

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

On approaches to analyze the sensitivity of simulated hydrologic fluxes to model parameters in the community land model

Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

58 GEOSCIENCES↗
DOE OSTI2021

Transport and chemistry of isoprene and its oxidation products in deep convective clouds

Deep convective clouds can transport trace gases from the planetary boundary layer into the upper troposphere where subsequent chemistry may impact aerosol particle formation and growth. In this modelling study, we investigate processes that affect isoprene and its oxidation products injected into the upper troposphere by an isolated deep convective cloud in the Amazon. We run a photochemical box model with coupled cloud microphysics along hundreds of individual air parcel trajectories sampled from a cloud-resolving model simulation of a convective event. The box model simulates gas-phase chemical reactions, gas scavenging by liquid and ice hydrometeors, and turbulent dilution inside a deep convective cloud. The results illustrate the potential importance of gas uptake to anvil ice in regulating the intensity of the isoprene oxidation and associated low volatility organic vapour concentrations in the outflow. Isoprene transport and fate also depends on the abundance of lightning-generated nitrogen oxide radicals (NO x = NO + NO 2 ). If gas uptake on ice is efficient and lightning activity is low, around 30% of the boundary layer isoprene will survive to the cloud outflow after approximately one hour of transport, while all the low volatile oxidation products will be scavenged by the cloud hydrometeors. If lightning NO x is abundant and gas uptake by ice is inefficient, then all isoprene will be oxidised during transport or in the immediate outflow region, while several low volatility isoprene oxidation products will have elevated concentrations in the cloud outflow. Reducing uncertainties associated with the uptake of vapours on ice hydrometeors, especially HO 2 and oxygenated organics, is essential to improve predictions of isoprene and its oxidation products in deep convective outflows and their potential contribution to new particle formation and growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Equation-based object-oriented modeling and simulation for data center cooling: A case study

© 2019 Elsevier B.V. Data center cooling accounts for about 1% of electricity usage in the United States. Computer models are pivotal in designing and operating energy-efficient cooling systems. Compared to conventional building performance simulation programs, the equation-based object-oriented modeling language Modelica is an emerging approach that can enable fast prototyping and dynamic simulation of cooling systems. In this case study, we first modeled the cooling and control systems of an actual data center located in Massachusetts using the open-source Modelica Buildings library, and then calibrated a baseline model based on measurement data. The simulation of the baseline model identified several operation-related issues in the cooling and control systems, such as degraded cooling coils, improper dead band in control settings, and simultaneous cooling and heating in air handlers. Afterwards, we used a sequential search technique as well as an optimization scheme to investigate the energy saving potentials for different energy efficiency measures aiming to address the abovementioned issues. Simulation results show potential energy savings up to 24% by resolving identified control-related issues and optimizing the supply air temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Experimental investigation of abnormal transverse flow enhancement of α particles in heavy-ion collisions

The mass dependence of the transverse flow for Z = 1–5 fragments from the collisions of 40 Ar + 27 Al, 40 Ar + 48 Ti, and 40 Ar + 58 Ni at 47 MeV/nucleon is investigated experimentally in this article. The transverse flow values are determined using the in-plane components of the fragment transverse momenta, where three conventional methods, i.e., the kinetic flow tensor method, the transverse momentum analysis method, and the azimuthal correlation method, are applied to reconstruct the reaction plane in an event-by-event basis. It is demonstrated from the comparison of the present experimental mass dependent flow measurements and the model simulations using an improved antisymmetrized molecular dynamics model that the experimentally observed abnormal α transverse flow enhancement is closely related to the reaction plane reconstruction procedure in the flow extraction. We further investigate the physical existence of the abnormal α flow behavior using a two-particle azimuthal correlation method, which allows us to provide the relative flow magnitude information with an identification of fragment charge number without the knowledge of the reaction plane differing from the three conventional methods. It is found that the relative flow magnitudes deduced from the two-particle azimuthal correlation functions with an identification of Z, with the correction for the recoil effect imposed by the momentum conservation, show a monotonically increasing trend as a function of fragment charge number, with no exception of the α flow enhancement. These results, in addition to those from the improved antisymmetrized molecular dynamics model simulations, definitely provide experimental evidences for the inexistence of the abnormal α flow behavior in the heavy-ion collisions at the present incident energy region in nature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

PYOED: AN ETENSIBLE SUITE FOR DATA ASSIMILATION AND MODEL-CONSTRAINED OPTIMAL DESIGN OF EXPERIMENTS

SF-23-005 PyOED is a highly extensible scientific package that enables developing and testing model-constrained optimal experimental design (OED) for inverse problems. Specifically, PyOED aims to be a comprehensive Python toolkit for model-constrained OED. The package targets scientists and researchers interested in understanding the details of OED formulations and approaches. It is also meant to enable researchers to experiment with standard and innovative OED technologies with a wide range of test problems (e.g., simulation models). OED, inverse problems (e.g., Bayesian inversion), and data assimilation (DA) are closely related research fields, and their formulations overlap significantly. Thus, PyOED is continuously being expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators. These pieces are added such that they can be permuted to enable testing OED methods in various settings of varying complexities. The PyOED core is completely written in Python and utilizes the inherent object-oriented capabilities; however, PyOED is meant to be extensible rather than scalable. Specifically, PyOED is developed to ``enable rapid development and benchmarking of OED methods with minimal coding effort and to maximize code reutilization.'' PyOED will be continuously expanded with a plethora of Bayesian inversion, DA, and OED methods as well as new scientific simulation models, observation error models, and observation operators.

ATTIA, AHMEDMOHAMED↗
DOE OSTI2012

Intrusive analysis for NEK5000: development of intrusive uncertainty quantification for high-dimensional, high-fidelity codes.

We study the use of lower-fidelity training data for uncertainty quantification of complex simulation models. In our approach, computationally expensive full-model outputs are approximated applying proper orthogonal-decomposition-based dimensionality reduction to the full model. A Gaussian-processes-based machine learning approach is then used to model the difference (or, rather, the correspondence) between the higher-fidelity and the lower-fidelity data. This stochastic model can be constructed by using few additional full code evaluations; it is used to calibrate arbitrarily many lower-quality outputs in order to create additional training data for sampling-based analysis. In effect, an adequate approximation of the simulation model’s response to uncertainty can be constructed at a modest computational cost. In fact, we aim at the number of full-model evaluations comparable to that required for a linear approximation (as opposed to numbers traditionally associated with sampling in high-dimensional spaces). In this report, we explain the basic algorithm, suggest some performance tuning options, and give a first characterization for the class of simulation models of nuclear engineering for which the suggested report is effective.

Roderick, O.↗
DOE OSTI2021

Next Generation Co-Molded One-Piece Automotive Parts

The purpose of this project was to determine the feasibility of co-molding Class A Sheet Molding Compound (SMC), structural SMC, and continuous fiber prepreg materials to produce a single piece co-molded automotive part, such as a hood. The combination of the three molding materials and the incorporation of selective design features (ribs, flanges, corrugations) was expected to eliminate the need for inner reinforcement panels, significantly reducing tooling costs and simplifying the manufacturing process. A multi-material solution would also result in significant weight savings. The scope of this project included the material characterization of the three materials, development of cure and flow simulation models, and validation of the models against parts molded with different combinations of the materials on an 11”x11” plaque tool with rib features. The intent of this project was to apply the learnings obtained from co-molding a part with simplified geometry to a Phase 2 project that would produce a single piece hood, co-molded with the same materials. Resins from INEOS Composites were provided to IDI to produce SMC and continuous fiber prepregs. Purdue University and INEOS Composites characterized the rheological and curing behavior of the different materials as well as the mechanical properties of the co-molded parts. This data was used by Purdue University to create simulation models. Models were created to predict both flow patterns and predict mechanical properties of different laminate constructions in and around the ribs. Michigan State University - Corktown validated Purdue’s models by co-molding the three materials in different combinations on a tool containing rib features provided by Century Tool. The co-molded parts were evaluated against the simulation models by Purdue, Corktown and INEOS. The project team demonstrated the following: • The ability to obtain a cohesive co-molded structure made up of Class A SMC, Structural SMC, and continuous fiber prepreg. • The ability to obtain a co-molded part with a Class A surface • Modeling of flow and fiber orientation • Modeling to predict mechanical properties of multi-material co-molded structures The predicted flow behavior and fiber orientation from simulations were then compared with the molded samples. Exact local orientation state was difficult to compare between microscopy and flow simulation, but captured general trends. Multi-material flow behavior was generally modeled well with SMC materials, but the introduction of woven material sheets requires further model development. Purdue compared the predicted versus actual mechanical properties, and INEOS Composites evaluated the surface appearance of unpainted and painted co-molded parts. The models developed by Purdue demonstrated that the mechanical properties can be predicted for parts with varying material configurations. The resulting models can be applied to future co-molded part design, tooling design, and molding conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Comparisons between nonlinear kinetic modelings of simulated Raman scattering using envelope equations

In this paper, we compare two recent models [N. A. Yampolsky and N. J. Fisch, Phys. Plasmas 16, 072104 (2009); D. Benisti, D. J. Strozzi, L. Gremillet, and O. Morice, Phys. Rev. Lett. 103, 155002 (2009)] introduced to predict the nonlinear growth of stimulated Raman scattering in the kinetic regime, and providing moreover a nonlinear description of the collisionless, Landau-like, damping rate of the driven electron plasma wave. We first recall the general theoretical framework common to these two models, based on the derivation of the imaginary part of the electron susceptibility, {chi}{sub i}, and then discuss in detail why the two approaches differ. By comparing the theoretical predictions for {chi}{sub i} to those derived from test particle or Vlasov simulations, we moreover discuss the range of validity of the two models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A statistical and simulation-informed model for estimating permeability from pore size distribution in saturated geomaterials

Accurate permeability estimation is essential across subsurface engineering applications but remains challenging due to the complex pore structures of natural geomaterials. Traditional empirical methods and simplified theoretical models often inadequately capture the role of pore size distribution and connectivity. Here, this study develops a statistical and simulation-informed permeability model that collapses pore-scale complexity into a compact scaling of the form k = αϕμ d 2 , where ϕ is porosity, μ d is mean pore size, and α is a weakly varying coefficient. By combining pore network simulations with statistical analysis of unimodal and bimodal pore size distributions, we identify three key findings: (i) permeability is much more sensitive to mean pore size than to porosity; (ii) across extensive datasets, the ratio σ d /μ d (standard deviation to mean) clusters around a characteristic value ∼0.4, allowing the effects of the full pore size distribution to be represented by μ d and a narrowly varying α ≈ 0.05; and (iii) for bimodal systems, there exists a critical fraction of small pores ∼0.78 above which flow becomes small-pore dominated, enabling the definition of an effective flow-controlling pore population and facilitating simplified permeability estimation for such systems. The resulting model, which requires only porosity and a representative mean pore size as inputs, is validated against comprehensive experimental datasets (>1700 samples) spanning diverse soils and rocks and achieves good predictive accuracy. Overall, this work provides a physically grounded yet practically simple permeability estimator suitable for subsurface engineering, environmental protection, and resource management applications.

Permeability↗
DOE OSTI2017

Modeling and Simulation of Membrane-Based Dehumidification and Energy Recovery Process

This paper introduces a first-order physics-based model that accounts for the fundamental heat and mass transfer between a humid-air vapor stream on feed side to another flow stream on permeate side. The model comprises a few optional submodels for membrane mass transport; and it adopts a segment-by-segment method for discretizing heat and mass transfer governing equations for flow streams on feed and permeate sides. The model is able to simulate both dehumidifiers and energy recovery ventilators in parallel-flow, cross-flow, and counter-flow configurations. The predicted tresults are compared reasonably well with the measurements. The open-source codes are written in C++. The model and open-source codes are expected to become a fundament tool for the analysis of membrane-based dehumidification in the future.

Gao, Zhiming↗
DOE OSTI2023

INL Report: Multiphysics Modeling and Simulation of Deimos, an Advanced Reactor Experiment

When designing a novel reactor, nuclear experiments are essential to validate the predictive capability of modeling/simulation tools and to justify the investment of a full-scale prototype. Being able to accurately predict temperature reactivity coefficients is of high importance to reactor designers as it impacts the safety and performance of the system. Deimos is a proposed graphite-moderated, beryllium-reflected, high assay low-enriched uranium (HALEU) tri-structural isotropic (TRISO) fueled experiment for NCERC. Deimos is a valuable experiment for validating the predictive performance of modeling and simulation tools since it comprises a relatively thermal energy spectrum with 84% thermal neutrons causing fission, 15% epithermal neutrons causing fission, and 1% fast neutrons causing fission. Additionally, by electrically heating the experiment, reactivity can be validated at various temperatures. Deimos will serve as a testbed for future advanced reactor concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Low Frequency Modulation of Extreme Temperature Regimes in a Changing Climate

The project examines long-term changes in extreme temperature episodes (ETE) associated with planetary climate modes (PCMs) in both the real atmospheric and climate model simulations. The focus is on cold air outbreaks (CAOs) and warm waves (WWs) occurring over the continental US during the past 60 winters. No significant long-term trends in either WWs or CAOs are observed over the US. The annual frequency of CAOs is affected by the (i) North Atlantic Oscillation (NAO) over the Southeast US and (ii) Pacific–North American (PNA) pattern over the Northwest US. WW frequency is influenced by the (i) NAO over the eastern US and (ii) combined influence of PNA, Pacific decadal oscillation (PDO), and ENSO over the southern US. The collective influence of PCMs accounts for as much as 50% of the regional variability in ETE frequency. During CAO (WW) events occurring over the southeast US, there are low (high) pressure anomalies at higher atmospheric levels over the southeast US with oppositely-signed pressure anomalies in the lower atmosphere over the central US. These patterns lead to anomalous northerly (for CAOs) or southerly (for WWs) flow into the southeast leading to cold or warm surface air temperature anomalies, respectively. One distinction is that CAOs involve substantial air mass transport while WW formation is more local in nature. The primary differences among event categories are in the origin and nature of the pressure anomaly features linked to ETE onset. In some cases, PCMs help to provide a favorable environment for event onset. Heat budget analyses indicate that latitudinal transport in the lower atmosphere is the main contributor to regional cooling during CAO onset. This is partly offset by adiabatic warming associated with subsiding air. Additional diagnoses reveal that this latitudinal transport is partly due to the remote physical influence of a shallow cold pool of air trapped along the east side of the Rocky Mountains. ETE and PCM behavior is also studied in (CMIP5) climate model simulations. Although the climate models considered are able to represent the overall behavior of ETEs, the frequency of WWs (CAOs) is too high (low) in many models. While all models qualitatively replicate the overall structure of the PNA pattern, a small minority of models fails to properly simulate the NAO pattern. Model shortcomings in representing the NAO and PNA patterns have important consequences for simulating associated regional variability in surface air temperature and storm track behavior. The influence of PCMs on ETEs is underestimated in most CMIP5 models. In particular, none of the models are able to accurately simulate observed linkages between ETEs and the PDO, due to a gross misrepresentation of the PDO pattern in most models. Our results indicate that predictions of future CAO and WW behavior are currently limited by the ability of climate models to accurately represent PCM characteristics. Our study also considers the behavior of PCMs known as annular modes. It is determined that north-south movements in the stratospheric jet stream (related to the Polar Annular Mode) result in long-lasting impacts upon surface weather conditions including regional air temperature anomalies. The structure and dynamics of the stratospheric northern annular mode (or SNAM, related to changes in the strength of the stratospheric jet stream) was studied in CMIP5 models. In models with poorly-resolved stratospheres, the amplitude of SNAM at stratospheric altitudes is typically too weak, consistent with weaker stratospheric jet variability. However, this distinction does not carry over to the associated tropospheric signature of SNAM. A regional analysis illustrates that most CMIP5 models (regardless of whether the stratosphere is well-resolved) have anomalously weak and eastward shifted (compared to observed SNAM events) storm track and sea level pressure anomaly patterns during SNAM events. Analyses of stratosphere–troposphere coupling reveal that large-scale wave activity in the stratosphere is anomalously weak in CMIP5 models having poorly-resolved stratospheres, due to correspondingly weak upward propagation of wave activity from the troposphere. This suggests that such models have anomalously weak stratosphere-troposphere coupling in association with SNAM events. Paradoxically, however, there is little apparent impact upon the attendant tropospheric variability.

54 ENVIRONMENTAL SCIENCES↗