Search NASA⌕ Search

SEARCH · Search NASA

Results for “Collective models”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Space plasma contactor research, 1987

A simple model describing the process of electron collection from a low pressure ambient plasma in the absence of magnetic field and contactor velocity effects is presented. Experimental measurments of the plasma surrounding the contactor are used to demonstrate that a double-sheath generally develops and separates the ambient plasma from a higher density, anode plasma located adjacent to the contactor. Agreement between the predictions of the model and experimental measurements obtained at the electron collection current levels ranging to 1 A suggests the surface area at the ambient plasma boundary of the double-sheath is equal to the electron current being collected divided by the ambient plasma random electron current density; the surface area of the higher density anode plasma boundary of the double-sheath is equal to the ion current being emitted across this boundary divided by the ion current density required to sustain a stable sheath; and the voltage drop across the sheath is determined by the requirement that the ion and electron currents counterflowing across the boundaries be at space-charge limited levels. The efficiency of contactor operation is shown to improve when significant ionization and excitation is induced by electrons that stream from the ambient plasma through the double-sheath and collide with neutral atoms being supplied through the hollow cathode.

Wilbur, Paul J.↗
DOE OSTI2025

FY25 Theory and Simulation Performance Target: Development of an integrated modeling framework for fusion reactor design and assessment (Final Report)

This report documents the FY25 Theory and Simulation Performance Target (TSPT) of developing an integrated modeling framework for fusion reactor design and assessment (FREDA). Over Q1-Q4, new capabilities were developed across both plasma and engineering domains and demonstrated on an example representation of a Compact Advanced Tokamak with a Dual Cooled Lead Lithium blanket. This represents a first-of-a-kind demonstration of coupled core-to-wall-to-engineering for a reactor. Self-consistent CESOL workflows were applied to provide core, pedestal, and SOL prediction; new modules were developed for energetic particle stability (FAR3D) and transport (TGLF-EP) analysis; and boundary plasma modeling (SOLPS-ITER, BOUT++/Hermes-3) was expanded to evaluate wall and divertor heat fluxes and interface with engineering thermal analysis. A parameterized CAD tool, TRACER, was expanded to generate medium-fidelity divertor, blanket, and coil geometries; OpenFOAM and Diablo workflows were applied for first-wall and divertor thermal analyses with helium cooling; and reduced-order models were created for high-mass-flux divertor cooling. Magnet multiphysics capabilities were verified between Elmer, Diablo, and a new MFEM-based solver, and workflows enable stress, thermal, and neutron-fluence analysis of TF coils with neutronics-driven heating. Nuclear and blanket analysis workflows were demonstrated, including tritium breeding, transport, and CFD-informed thermo-mechanical assessment. Preliminary multi-fidelity uncertainty quantification workflows were applied to boundary modeling codes and shown to achieve variance reductions with fewer high-fidelity boundary simulations. Key findings highlight the challenges of resolving the ITEP gap to find suitable balance between wall and divertor loads, neutron heating, and practical limits of PFC cooling. Next step priorities are to develop automated workflows to check boundary code convergence and detachment, implement tighter physics-engineering CAD provenance tracking, and inclusion of plasma-material interface models for SLAG and tungsten cracking behavior. Collectively, these developments establish sophisticated capabilities for predictive, multi-fidelity, whole-device modeling that integrates plasma physics, materials, magnets, and nuclear engineering to guide pathways to viable Fusion Pilot Plant design points.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2003

Cloud Modeling Using Field Project Data for the Study of Precipitation Processes

The use of cloud-resolving models (CRMs) in the study of precipitation process and their relation to the large-scale environment can be generally categorized into two approaches. The first approach is so called "cloud ensemble modeling". In this approach, many clouds of different size in various stages of their lifecycles can be present at any model simulation time. Large-scale effects are derived from observations and imposed into the model as the main forcing. The advantage of this approach is that the modeled convection will be forced to have the same intensity, thermodynamic budget and organization as the obserations.This approach will also allow CRMs to perform multi-day or multi-week time integrations. The second approach usually requires initial temperature and water vapor profiles that have a medium to large CAPE, and open lateral boundary conditions are used. The modeled clouds could be termed "self-forced convection". Model improvements, such as in the microphysics, are achieved using the second approach. In cloud ensemble modeling, accurate large-scale advective tendencies for temperature and water vapor are the main forcing for the CRMs. We found that the large-scale advective terms for temperature and water vapor are not always consistent, For example, large-scale forcing could indicate strong drying which would produce cooling in the model through evaporation but not contain large-scale advective heating to compensate. This discrepancy in forcing would cause differences between the observed and modeled latent heating profiles. Good measurements of other quantities (i.e., surface fluxes and radiation) are also required to perform variational objective analysis that computes and minimizes a "cost function" that constrains the difference between the large-scale advective forcing in temperature and water vapor. With self-forced convection, accurate vertical distributions of temperature, moisture (water vapor), and horizontal winds are required. The timing of the measurements relative to cloud development is crucial (i.e., prior to cloud triggering). Microphysical measurements (i.e., the cloud number concentration and size distribution) can also be used in this second approach but are of secondary importance with cloud ensemble modeling. In this paper, data collected during TRMM field campaigns (FCs; i.e., SCSMEX, LBA and KWAJEX) which were aimed at validating TRMM products (i.e., rainfall and the vertical distribution of latent heating) will be used to examine the impact of errors in the initial conditions (e.g.., soundings an large-scale forcing) on simulated rainfall distributions and brightness. Rainfall and precipitations simulated from a CRM will also be compared with those estimated by a schocastic model.

Tao, W.-K.↗
NASA NTRS2016

Architecture for Integrated Medical Model Dynamic Probabilistic Risk Assessment

Probabilistic Risk Assessment (PRA) is a modeling tool used to predict potential outcomes of a complex system based on a statistical understanding of many initiating events. Utilizing a Monte Carlo method, thousands of instances of the model are considered and outcomes are collected. PRA is considered static, utilizing probabilities alone to calculate outcomes. Dynamic Probabilistic Risk Assessment (dPRA) is an advanced concept where modeling predicts the outcomes of a complex system based not only on the probabilities of many initiating events, but also on a progression of dependencies brought about by progressing down a time line. Events are placed in a single time line, adding each event to a queue, as managed by a planner. Progression down the time line is guided by rules, as managed by a scheduler. The recently developed Integrated Medical Model (IMM) summarizes astronaut health as governed by the probabilities of medical events and mitigation strategies. Managing the software architecture process provides a systematic means of creating, documenting, and communicating a software design early in the development process. The software architecture process begins with establishing requirements and the design is then derived from the requirements.

Probability Theory↗
DOE OSTI2026

Decoding THz‐Driven Dynamic Fingerprints of Ferroelectric Nanotwin Networks

Ultrafast polarization dynamics in ferroelectrics are of considerable interest for high-speed tunable dielectrics and electro-optics. Extended domain wall networks formed in ferroelectric twin nanodomains can support collective dynamics in the terahertz regime but require techniques that track polarization and strain evolution driven by ultrafast stimulus. Here, we use multi-modal probing of THz-pulse-driven excitations in PbTiO 3 /SrTiO 3 superlattices by combining X-ray free electron laser measurements that directly tracks lattice changes, with optical second harmonic generation that tracks the electronic potential coupled with the lattice potential. Dynamical phase-field modeling enables fingerprinting of these collective modes as superpositions of domain “breathing” through wall oscillations and polarization “rotations” with still walls. Ultrafast domain wall motion at 0.1–0.5 THz is observed at practical fields of 100 kV/cm with wall velocities of >4000 m/s, approaching typical speed of sound in PbTiO 3 . A unique “charging” mode is discovered that can electrically charge and discharge domain walls on ∼4 ps time scale thus dynamically tuning wall conductivity. Integrated experimental and theoretical fingerprinting of the dynamical landscape presented here enables ultrafast control of ferroics for high-speed microelectronics and optical applications.

THz dynamics↗
NASA NTRSDate not supplied

Better Constraining Supercooled Clouds Could Reduce Projected Warming Spread

The increase of climate sensitivity to rising greenhouse gas concentrations in the coupled model intercomparison project phase 6 (CMIP6) Earth system models (ESMs) compared to CMIP5 ESMs is primarily attributed to a larger extratropical cloud response to climate change, referred to as cloud feedback. The ratio of supercooled liquid cloud water relative to all cloud water, termed liquid phase ratio (LPR), which has also notably increased in many recent ESMs, is thought to be a primary driver of the extratropical cloud feedback increase. Unlike the preponderance of previous studies that compare native model LPR directly with observations, here we evaluate LPR over three ESM generations against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) using an instrument simulator approach, which mimics instrument limitations and uses consistent cloud definitions and resolutions. We find that current coupled model intercomparison project (CMIP) ESMs collectively simulate greater LPR than previous CMIP generations and overestimate LPR compared to observations, contrary to previous findings, likely driven by past inconsistent comparisons of ESM outputs with CALIPSO observations. We further show that greater LPR in ESM present-day climate is unexpectedly correlated with a smaller extratropical cloud feedback, attributable to a decrease of cloud amount exceeding the increase of cloud optical depth. Finally, our results suggest that improving constraints on model LPR using our evaluation framework would likely reduce the spread in CMIP6 climate sensitivities, owing to its effect on extratropical cloud feedback from supercooled clouds.

greenhouse gas concentrations↗
NASA NTRS1981

Modeling of the aircraft in-trail-following task during profile descent

The cockpit display of traffic information (CDTI) system concepts enable the pilot to observe the surrounding air traffic pattern. The impact of such a system is far reaching in terms of improved safety, pilot and controller workload, and aircraft fuel efficiency. One direct payoff is the ability to distribute the ATC workload to the pilot in such tasks as merging and spacing. The CDTI application of spacing approach aircraft in the terminal area is addressed. In-trail-following/CDTI experiments were performed using realistic cockpit simulators and profile descent approach scenarios. Based on collected experimental simulator data, pilot models were developed which include state estimation, decision making and flight control aspects. These models were coupled with models of aircraft and CDTI equipment to study the dynamic phenomena and stability of strings of aircraft along various approach patterns.

Goka, T.↗
DOE OSTI2024

Road Lidar Dataset for the TxDOT Austin District

This is a road lidar data collection for developing road elevation models and road inundation mapping methodologies, a joint work between ORNL and The University of Texas at Austin. This dataset is generated as part of the flood transportation infrastructure, partly funded by the NOAA CIROH project. ORNL is a project partner for high-performance computing-empowered flood inundation mapping methodology R&D. The dataset is computed using a GPU-accelerated lidar data processing workflow developed at ORNL. The lidar data source is from TxGIO, the state lidar data collection site. The output dataset is in two formats: laz and copc. It is organized by TxDOT's maintenance sections, covering the Austin District. Data size: 3.86 billion road lidar points, 1.67% of the entire lidar data input Projection: EPSG:32614 (WGS84/UTM zone 14N) Website: https://web.corral.tacc.utexas.edu/nfiedata/road3d/austin_district/AustinMaintenanceSections_H_epsg6343_V_epsg5703/ LICENSE FOR USE -- MAPS AND DATA DISCLAIMER This resource is shared under the Creative Commons Attribution CC BY, http://creativecommons.org/licenses/by/4.0/ MAPS AND DATA DISCLAIMER The Oak Ridge National Laboratory (ORNL) shall not be held liable for improper or incorrect use of the data described or information contained on this map or associated series of maps. The data and related map graphics are not legal, land survey or engineering documents and are not intended to be used as such. ORNL gives no warranty, express or implied, as to the accuracy, reliability, utility or completeness of this information. The user of these maps and data assumes all responsibility and risk for the use of the maps and data. ORNL disclaims all warranties, representations or endorsements either express or implied, with regard to the information contained in this map product, including, but not limited to, all implied warranties of merchantability, fitness for a particular purpose or non-infringement. This preliminary map product is for research and review purposes only. It is not intended to be used for emergency management operational or life safety decisions at the local or regional governmental level or by the general public. Users requiring information regarding hazardous conditions or meteorological conditions for specific geographic areas should consult directly with their city or county emergency management office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Random field estimation approach to robot dynamics

The difference equations of Kalman filtering and smoothing recursively factor and invert the covariance of the output of a linear state-space system driven by a white-noise process. Here it is shown that similar recursive techniques factor and invert the inertia matrix of a multibody robot system. The random field models are based on the assumption that all of the inertial (D'Alembert) forces in the system are represented by a spatially distributed white-noise model. They are easier to describe than the models based on classical mechanics, which typically require extensive derivation and manipulation of equations of motion for complex mechanical systems. With the spatially random models, more primitive locally specified computations result in a global collective system behavior equivalent to that obtained with deterministic models. The primary goal of applying random field estimation is to provide a concise analytical foundation for solving robot control and motion planning problems.

Rodriguez, Guillermo↗
NASA NTRS2014

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

farm crops↗
NASA NTRS2018

Snow in the Changing Sea-Ice Systems

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

Snow↗
NASA NTRS2022

Mapping Firn Saturation over Greenland using NASA’s Soil Moisture Active Passive Satellite

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using L-band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization L-band brightness temperature (T^(B)_(V)) imagery (2015–2019) generated using observations collected over Greenland by NASA’s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer L-band brightness temperature model. We map water-saturated firn layers via a “firn saturation” parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA’s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.

Greenland ice sheet↗
NASA NTRSDate not supplied

Spacecraft Fire Safety Predictions using Verified Saffire Model

A model developed using Fire Dynamics Simulator (FDS) that aimed to determine the effect of a fire in a spacecraft was validated by data collected during the Saffire campaign. The model used inlet and outlet temperatures and CO 2 concentrations of the Saffire payload where fire spread was taking place to determine the amount of heat and combustion products that made it into Northrop Grumman’s Cygnus vehicle. The model was then validated using six remote sensors in various places in the vehicle, as well as a far field device (FFD) in the open zenith section that was representative of average vehicle values. The current work focuses on using the model to predict fire safety scenarios. One simulation aimed to determine the fate of HCl, which sticks to surfaces. The model prediction showed that the HCl was removed from the atmosphere rapidly. This compared well against the FFD data in the Saffire VI campaign event where a bottle of 5% HCl was released into the vehicle. An additional simulation where the Environmental Control and Life Support System (ECLSS) was shut off once the FFD reached 5 ppm of HCl showed that HCl stayed in the atmosphere considerably longer. Continuing the simulation with the ECLSS activated and after temperatures returned to their initial conditions, resulted in a rapid removal of HCL similar to that observed in the original HCl release scenario model. Finally, a simulation that used the heat release rate from a lithium-ion battery test to determine the effect it would have on a spacecraft was performed. This simulation used a heat addition rate that is considerably higher than what was determined from the burning of solid fuels in the Saffire campaign and hence produced a non-trivial temperature increase in more locations within the vehicle.

Fire Safety↗
DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗
NASA NTRS1977

A Markov model for X-band atmospheric antenna-noise temperatures

A five-state Markov model is suggested for the X-band antenna-noise temperatures based on data collected at Goldstone. The states of the model are determined by changes in the cloud and rain condition of the atmosphere so as to take advantage of the correlation observed between the antenna temperatures and changes in rain rates. Then an indication is given of how to obtain the estimates of the parameters of the model from the data.

Adeyemi, O. H.↗
NASA NTRS1985

Spectral estimation of green leaf area index of oats

Green leaf area index (LAI) is a measure of vegetative growth and development and is frequently used as an input parameter in yield estimation and evapotranspiration models. Extensive destructive sampling is usually required to achieve accurate estimates of green LAI in natural situations. In this investigation, a statistical modeling approach was used to predict the green LAI of oats from bidirectional reflectance data collected with multiband radiometers. Stepwise multiple regression models based on two sets of spectral reflectance factors accounted for 73 percent and 65 percent of the variance in green LAI of oats. Exponential models of spectral data transformations of greenness, normalized difference, and near-infrared/red ratio accounted for more of the variance in green LAI than the multiple regression models.

Best, R. G.↗