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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Simulations of Cyclone Sidr in the Bay of Bengal with a High-Resolution Model: Sensitivity to Large-Scale Boundary Forcing

The predictability of Cyclone Sidr in the Bay of Bengal was explored in terms of track and intensity using the Advanced Research Hurricane Weather Research Forecast (AHW) model. This constitutes the first application of the AHW over an area that lies outside the region of the North Atlantic for which this model was developed and tested. Several experiments were conducted to understand the possible contributing factors that affected Sidr s intensity and track simulation by varying the initial start time and domain size. Results show that Sidr s track was strongly controlled by the synoptic flow at the 500-hPa level, seen especially due to the strong mid-latitude westerly over north-central India. A 96-h forecast produced westerly winds over north-central India at the 500-hPa level that were notably weaker; this likely caused the modeled cyclone track to drift from the observed actual track. Reducing the model domain size reduced model error in the synoptic-scale winds at 500 hPa and produced an improved cyclone track. Specifically, the cyclone track appeared to be sensitive to the upstream synoptic flow, and was, therefore, sensitive to the location of the western boundary of the domain. However, cyclone intensity remained largely unaffected by this synoptic wind error at the 500-hPa level. Comparison of the high resolution, moving nested domain with a single coarser resolution domain showed little difference in tracks, but resulted in significantly different intensities. Experiments on the domain size with regard to the total precipitation simulated by the model showed that precipitation patterns and 10-m surface winds were also different. This was mainly due to the mid-latitude westerly flow across the west side of the model domain. The analysis also suggested that the total precipitation pattern and track was unchanged when the domain was extended toward the east, north, and south. Furthermore, this highlights our conclusion that Sidr was influenced from the west side of the domain. The displacement error was significantly reduced after the domain size from the western model boundary was decreased. Study results demonstrate the capability and need of a high-resolution mesoscale modeling framework for simulating the complex interactions that contribute to the formation of tropical cyclones over the Bay of Bengal region

Kumar, Anil↗
DOE OSTI2020

Development of a Time-Height Profile Assimilation Technique for Large-Eddy Simulation

Mesoscale-to-microscale coupling (MMC) aims to address the limited scope of traditional large-eddy simulations by driving the microscale flow with information concerning large-scale weather patterns provided by mesoscale models. We present a new offline MMC technique for horizontally homogeneous microscale flow conditions, in which internal forcing terms are computed based on mesoscale time–height profiles of mean-flow quantities. The advantage of such an approach is that it can be used to drive a microscale simulation with either mesoscale or observational data, and that it does not rely on specific terms in the mesoscale budget equations, which are typically not part of the default output of a mesoscale solver. The performance of the proposed profile assimilation technique is assessed based on the simulation of a typical diurnal cycle over the Scaled Wind Farm Technology site in west Texas. Results indicate that simple data assimilation techniques lead to unphysically high levels of shear and turbulence caused by the algorithm’s inability to cope with inaccuracies in the mesoscale time–height profiles. Modifying the algorithm to account for vertical coherence in the mesoscale source terms gives the microscale solver a greater ability to correct the provided mesoscale time–height profiles, leading to improved predictions of shear and turbulence statistics. The resulting turbulence statistics are in good agreement with meteorological tower observations and simulation results obtained with state-of-the-art coupling techniques using mesoscale budget components.

data assimilation↗
DOE OSTI2020

Multiscale-Informed Modeling of High Temperature Component Response with Uncertainty Quantification

This report summarizes a joint effort between Argonne National Laboratory, Idaho National Laboratory, and Los Alamos National Laboratory to develop and deploy constitutive models targeted at predicting the life of Grade 91 alloy components subjected to high temperature environments typical of those that structural components in advanced nuclear reactors would experience. Two distinct, but complementary constitutive modeling approaches have been taken here. The first employs a phenomenological viscoplastic model for which parameters have been calibrated based on experimental data for a wide range of Grade 91 alloy that has undergone a variety of processing. A Bayesian approach was used to derive distributions of uncertain parameters for this model based on this data set. The second approach is a reduced order model suitable for engineering-scale analysis that is based on the results of a large set of mesoscale simulations. Mesoscale models allow for the microstructure and composition of a particular alloy to be directly taken into account in the computation of the viscoplastic response, but are computationally expensive, which makes it impractical to directly call those models for the material constitutive response in an engineering-scale simulation. The reduced-order representation of the response of the underlying model used here allows for an engineering-scale model to take into account the characteristics of the underlying microstructure, while only incurring a reasonable computational expense. Both of these approaches have different strengths, and are applicable for different parts of the design/analysis process. The phenomenological models can be readily parameterized based on a set of experimental data for a given class of materials and used for scoping calculations. Once a specific material is chosen and adequately characterized, the reduced order models can accurately predict the response of that specific alloy, and because the models are based on predictive models of the underlying microstructure, they can be used to more confidently predict the response under conditions in regions where there is limited experimental data. Both of these models have been integrated in the Grizzly code, which is used here to perform proof-of-concept uncertainty quantification analyses of a simple component under prototypical conditions. The built- in stochastic analysis capabilities in the MOOSE framework that Grizzly is built on are used here to run large sets of simulations for this uncertainty quantification analysis. As would be expected, because the reduced order models are developed for a much more tightly defined alloy, they predict tighter distributions of the time to failure than the phenomenological models, which are calibrated to a broader set of data. Also important is that these simulations demonstrate that a reduced order modeling approach can be successfully deployed to propagate uncertainties from the material scale to practical engineering-scale component simulations.

42 ENGINEERING↗
NASA NTRS2009

Alongfront Variability of Precipitation Associated with a Midlatitude Frontal Zone: TRMM Observations and MM5 Simulation

On 19 February 2001, the Tropical Rainfall Measuring Mission (TRMM) satellite observed complex alongfront variability in the precipitation structure of an intense cold-frontal rainband. The TRMM Microwave Imager brightness temperatures suggested that, compared to the northern and southern ends of the rainband, a greater amount of precipitation ice was concentrated in the middle portion of the rainband where the front bowed out. A model simulation conducted using the fifth-generation Pennsylvania State University National Center for Atmospheric Research (PSU NCAR) Mesoscale Model (MM5) is examined to explain the distribution of precipitation associated with the cold-frontal rainband. The simulation reveals that the enhanced precipitation ice production and the implied mean ascent along the central part of the front were associated with a synergistic interaction between a low-level front and an upper-level front associated with an intrusion of high-PV stratospheric air. The low-level front contributed to an intense bow-shaped narrow cold-frontal rainband (NCFR). The upper-level front was dynamically active only along the central to northern portion of the NCFR, where the upper-level PV advection and Q-vector convergence were most prominent. The enhanced mean ascent associated with the upper-level front contributed to a wide cold-frontal rainband (WCFR) that trailed or overlapped with the NCFR along its central to northern segments. Because of the combination of the forcing from both lower- and upper-level fronts, the ascent was deepest and most intense along the central portion of the front. Thus, a large concentration of precipitation ice, attributed to both the NCFR and WCFR, was produced.

Han, Mei↗
NASA NTRS1988

Minimum Energy Routing through Interactive Techniques (MERIT) modeling

The MERIT program is designed to demonstrate the feasibility of fuel savings by airlines through improved route selection using wind observations from their own fleet. After a discussion of weather and aircraft data, manually correcting wind fields, automatic corrections to wind fields, and short-range prediction models, it is concluded that improvements in wind information are possible if a system is developed for analyzing wind observations and correcting the forecasts made by the major models. One data handling system, McIDAS, can easily collect and display wind observations and model forecasts. Changing the wind forecasts beyond the time of the most recent observations is more difficult; an Australian Mesoscale Model was tested with promising but not definitive results.

Wylie, Donald P.↗
NASA NTRS2009

Weather Research and Forecasting Model Wind Sensitivity Study at Edwards Air Force Base, CA

This abstract describes work that will be done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting "wind cycling" cases at Edwards Air Force Base, CA (EAFB), in which the wind speeds and directions oscillate among towers near the EAFB runway. The Weather Research and Forecasting (WRF) model allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. The goal of this project is to assess the different configurations available and determine which configuration will best predict surface wind speed and direction at EAFB.

Watson, Leela R.↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
NASA NTRS1995

The importance of mesoscale circulations generated by subgrid-scale landscape heterogeneities in general circulation models

A mesoscale atmospheric model was used to evaluate the impact of subgrid-scale landscape discontinuities on the vertical profiles of resolved temperature, moisture, and moist static energy in the planetary boundary layer (PBL) of general circulation models (GCMs). These profiles were produced with a 3D version of the model (using a horizontal grid resolution of 7.5 km and 13 vertical layers in the PBL) by averaging horizontally the various atmospheric variables over a 180 x 180 sq km domain-about the size of the horizontal domain represented by a single grid element in a GCM. They were compared to corresponding vertical profiles produced with a 1D version of the model, which simulates the PBL, as in a GCM, over a single horizontal grid element. Differences obtained between the horizontally averaged atmospheric variables produced with the 3D simulations and the 1D simulations emphasize the impact of subgrid-scale landscape discontinuities on GCM-resolved variables. Various types of landscape discontinuities, characterized by horizontal contrasts of surface wetness and size of land patches, were simulated under various background-wind conditions. Differences of temperature, specific humidity, and moist static energy as large as 4 K, 6 g/kg, and 10 kJ/kg were obtained in some cases. These differences were not affected significantly by moderate winds but were sensitive to the spatial distribution of surface wetness. These results emphasize the need to parameterize mesoscale processes induced by landscape discontinuities in GCMss.

Lynn, Barry H.↗
DOE OSTI2025

Mesoscale Linear Elastic Modeling and Homogenization of Marine Energy Composites

The design of fiber-reinforced composite (FRC)-based components for marine energy applications necessitates a fundamental understanding of material properties and the resulting geometry to predict long-term performance. In this work, we present a modeling workflow to predict linear elastic and diffusive bulk properties at the mesoscale for an idealized geometry based on knowledge of fiber and resin properties. A parametric study was performed to identify the key model input parameters that influence bulk properties. Furthermore, we demonstrate how bulk properties can be leveraged in high-fidelity image-based simulations, where imperfections in tow geometry and voids captured during X-ray computed tomography imaging are explicitly represented within the simulation. Bulk properties of interest include moduli, Poisson’s ratios, hygroscopic swelling, diffusivity, and moisture uptake, which are key parameters for characterizing FRC performance within marine environments. Modeling predictions agreed well with experimental data, except for estimating swelling coefficients, likely due to crack accumulation as a function of moisture uptake. The mesoscale modeling workflow ultimately highlights a versatile framework for understanding the influence of material and geometric properties, which can be leveraged to rapidly assess new FRC-based components.

computational mechanics↗
NASA NTRS2002

Land Surface Data Assimilation and the Northern Gulf Coast Land/Sea Breeze

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSU/NCAR MM5 V3-4 and applied on a 4-km domain for this particular application. It is recognized that a 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Lapenta, William M.↗
NASA NTRS2003

Application of Land Surface Data Assimilation to Simulations of Sea Breeze Circulations

A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite- observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The sea/land breeze is a well-documented mesoscale circulation that affects many coastal areas of the world including the northern Gulf Coast of the United States. The focus of this paper is to examine how the satellite assimilation technique impacts the simulation of a sea breeze circulation observed along the Mississippi/Alabama coast in the spring of 2001. The technique is implemented within the PSUNCAR MM5 V3-5 and applied at spatial resolutions of 12- and 4-km. It is recognized that even 4-km grid spacing is too coarse to explicitly resolve the detailed, mesoscale structure of sea breezes. Nevertheless, the model can forecast certain characteristics of the observed sea breeze including a thermally direct circulation that results from differential low-level heating across the land-sea interface. Our intent is to determine the sensitivity of the circulation to the differential land surface forcing produced via the assimilation of GOES skin temperature tendencies. Results will be quantified through statistical verification techniques.

Mackaro, Scott↗
DOE OSTI2022

Marmot V2

MARMOT is a robust numerical tool for mesoscale modeling of fuel performance developed under the NEAMS Fuels technical area to predict the coevolution of microstructure and properties in fuel and cladding materials. MARMOT accomplishes this using the phase field method coupled with finite strain mechanics and heat conduction. MARMOT is based on the open source Multiphysics Object-Oriented Simulation Environment (MOOSE) and solves the coupled partial differential equations defining the physics using the finite element method. MARMOT is being developed in order to facilitate the development of improved materials models for fuel performance, but it is also being developed as a powerful tool in and of itself for the simulation of mesoscale fuel performance.

Aagesen, LarryK.↗
NASA NTRS1986

Coupled ice-ocean dynamics in the marginal ice zones Upwelling/downwelling and eddy generation

This study is aimed at modeling mesoscale processes such as upwelling/downwelling and ice edge eddies in the marginal ice zones. A two-dimensional coupled ice-ocean model is used for the study. The ice model is coupled to the reduced gravity ocean model through interfacial stresses. The parameters of the ocean model were chosen so that the dynamics would be nonlinear. The model was tested by studying the dynamics of upwelling. Wings parallel to the ice edge with the ice on the right produce upwelling because the air-ice momentum flux is much greater than air-ocean momentum flux; thus the Ekman transport is greater than the ice than in the open water. The stability of the upwelling and downwelling jets is discussed. The downwelling jet is found to be far more unstable than the upwelling jet because the upwelling jet is stabilized by the divergence. The constant wind field exerted on a varying ice cover will generate vorticity leading to enhanced upwelling/downwelling regions, i.e., wind-forced vortices. Steepening and strengthening of vortices are provided by the nonlinear terms. When forcing is time-varying, the advection terms will also redistribute the vorticity. The wind reversals will separate the vortices from the ice edge, so that the upwelling enhancements are pushed to the open ocean and the downwelling enhancements are pushed underneath the ice.

Hakkinen, S.↗
NASA NTRS1985

Results from a limited area mesoscale numerical simulation for 10 April 1979

Results are presented from a nine-hour limited area fine mesh (35-km) mesoscale model simulation initialized with SESAME-AVE I radiosonde data for Apr. 10, 1979 at 2100 GMT. Emphasis is on the diagnosis of mesoscale structure in the mass and precipitation fields. Along the Texas/Oklahoma border, independent of the short wave, convective precipitation formed several hours into the simulation and was organized into a narrow band suggestive of the observed April 10 squall line.

Kalb, M. W.↗
NASA NTRS2007

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

This report describes the work done by the Applied Meteorology Unit (AMU) in assessing the success of different model configurations in predicting warm season convection over East-Central Florida. The Weather Research and Forecasting Environmental Modeling System (WRF EMS) software allows users to choose among two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). There are also data assimilation analysis packages available for the initialization of the WRF model - the Local Analysis and Prediction System (LAPS) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS). Besides model core and initialization options, the WRF model can be run with one- or two-way nesting. Having a series of initialization options and WRF cores, as well as many options within each core, creates challenges for local forecasters, such as determining which configuration options are best to address specific forecast concerns. This project assessed three different model intializations available to determine which configuration best predicts warm season convective initiation in East-Central Florida. The project also examined the use of one- and two-way nesting in predicting warm season convection.

Watson, Leela R.↗
NASA NTRS2015

Multiscale and Multiphysics Modeling of Additive Manufacturing of Advanced Materials

The objective of this proposed project is to research and develop a prediction tool for advanced additive manufacturing (AAM) processes for advanced materials and develop experimental methods to provide fundamental properties and establish validation data. Aircraft structures and engines demand materials that are stronger, useable at much higher temperatures, provide less acoustic transmission, and enable more aeroelastic tailoring than those currently used. Significant improvements in properties can only be achieved by processing the materials under nonequilibrium conditions, such as AAM processes. AAM processes encompass a class of processes that use a focused heat source to create a melt pool on a substrate. Examples include Electron Beam Freeform Fabrication and Direct Metal Deposition. These types of additive processes enable fabrication of parts directly from CAD drawings. To achieve the desired material properties and geometries of the final structure, assessing the impact of process parameters and predicting optimized conditions with numerical modeling as an effective prediction tool is necessary. The targets for the processing are multiple and at different spatial scales, and the physical phenomena associated occur in multiphysics and multiscale. In this project, the research work has been developed to model AAM processes in a multiscale and multiphysics approach. A macroscale model was developed to investigate the residual stresses and distortion in AAM processes. A sequentially coupled, thermomechanical, finite element model was developed and validated experimentally. The results showed the temperature distribution, residual stress, and deformation within the formed deposits and substrates. A mesoscale model was developed to include heat transfer, phase change with mushy zone, incompressible free surface flow, solute redistribution, and surface tension. Because of excessive computing time needed, a parallel computing approach was also tested. In addition, after investigating various methods, a Smoothed Particle Hydrodynamics Model (SPH Model) was developed to model wire feeding process. Its computational efficiency and simple architecture makes it more robust and flexible than other models. More research on material properties may be needed to realistically model the AAM processes. A microscale model was developed to investigate heterogeneous nucleation, dendritic grain growth, epitaxial growth of columnar grains, columnar-to-equiaxed transition, grain transport in melt, and other properties. The orientations of the columnar grains were almost perpendicular to the laser motion's direction. Compared to the similar studies in the literature, the multiple grain morphology modeling result is in the same order of magnitude as optical morphologies in the experiment. Experimental work was conducted to validate different models. An infrared camera was incorporated as a process monitoring and validating tool to identify the solidus and mushy zones during deposition. The images were successfully processed to identify these regions. This research project has investigated multiscale and multiphysics of the complex AAM processes thus leading to advanced understanding of these processes. The project has also developed several modeling tools and experimental validation tools that will be very critical in the future of AAM process qualification and certification.

Liou, Frank↗
DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗