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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Potential of Data Center Controls in Grid Services

The rapid proliferation of large data centers brings both challenges and opportunities for grid reliability. The data center resources and their potential flexibility have the potential to contribute resources to grid operations. Through capabilities like energy shifting and resource coordination, data centers can help reduce their net demand on the transmission network, as well as provide additional grid services to support reliable operation on the grid. While transient and long-term grid planning and operations are the scenarios that draw most attention, the quasi-steady state timeseries (QSTS) operation of data centers and grid bring interesting scenarios that can help evaluate the data center controls to aid grid services. This work is focused on modeling data centers for QSTS applications – incorporating the AI data center load profiles and building on the PNNL digital twin model for the thermal management loads to enable simulation studies to reveal the impact of data center controls on grid performance. This includes the integration of a QSTS battery and natural gas generator model to incorporate local resource impacts to the system. The simulation study is performed with a modified IEEE 24-Bus transmission system. Scenarios are focused on evaluating the data center load impacts on the transmission system and leveraging both data center and local generation controls to mitigate those impacts and provide additional grid services. The data center controls revealed the ability to contribute to two main kinds of grid services: preventing congestion on a weak grid by coordinating the data center resources with the collocated BESS and onsite generation; and the ability to help the grid operations during stressed times of operation like during a contingency. Leveraging these and other capabilities has the potential to help data centers become grid responsive assets, aiding in both their integration into the power system and grid reliability.

power grid simulation↗
DOE OSTI2026

Dataset for the paper titled "Investigating the relationship between bolide entry angle and apparent direction of infrasound signal arrivals"

This dataset includes outputs generated for the journal publication titled: "Investigating the relationship between bolide entry angle and apparent direction of infrasound signal arrivals". The outputs include .csv files with model-generated synthetic trajectories of asteroids entering Earth at a variety of impact and approach (azimuthal) angles. All outputs are based on hypothetical but realistic scenarios.

Herrera, Natalie [Sandia National Laboratories (SN↗
NASA NTRS1978

Uncertainty propagation in a stratospheric model. I - Development of a concise stratospheric model. II - Monte Carlo analysis of imprecisions due to reaction rates

The paper discusses the development of a concise stratospheric model which uses iteration to obtain coupling between interacting species. The one-dimensional, steady-state, diurnally-averaged model generates diffusion equations with appropriate sources and sinks for species odd oxygen, H2O, H2, CO, N2O, odd nitrogen, CH4, CH3Cl, CCl4, CF2Cl2, CFCl3, and odd chlorine. The model evaluates steady-state perturbations caused by injections of chlorine and NO(x) and may be used to predict ozone depletion. The model is used in a Monte Carlo study of the propagation of reaction-rate imprecisions by calculating an ozone perturbation caused by the addition of chlorine. Since the model is sensitive to only 10 of the more than 50 reaction rates considered, only about 1000 Monte Carlo cases are required to span the space of possible results.

Rundel, R. D.↗
NASA NTRS1978

Recent advances in thermal-structural analysis and design

A continuing effort to develop a comprehensive capability for thermal-structural analysis and automated design (sizing) is reviewed. A principal role in the activity is played by the finite element program SPAR which contains both an efficient structural and thermal analysis capability. The benefit of having thermal and structural analyses in the same finite element program is illustrated by the application of SPAR to design calculations for the National Transonic Facility - a cryogenic wind tunnel under construction at Langley. Some experience with large-scale thermal structural analysis problems, particularly the space shuttle orbiter, has led to the identification of some analysis needs. Those needs include automated model generation and data output for lumped parameter thermal analysis, faster solution methods for nonlinear transient heat transfer, automated interpolation of temperature data from a thermal finite element model to a dissimilar structural finite model, and automated techniques to identify the times at which the critical combinations of transient heating and loads occur on a structure.

Adelman, H. M.↗
NASA NTRS1987

Integration of geometric modeling and advanced finite element preprocessing

The structure to a geometry based finite element preprocessing system is presented. The key features of the system are the use of geometric operators to support all geometric calculations required for analysis model generation, and the use of a hierarchic boundary based data structure for the major data sets within the system. The approach presented can support the finite element modeling procedures used today as well as the fully automated procedures under development.

Shephard, Mark S.↗
NASA NTRS1997

Evaluation of the 29-km Eta Model for Weather Support to the United States Space Program

The Applied Meteorology Unit (AMU) conducted a year-long evaluation of NCEP's 29-km mesoscale Eta (meso-eta) weather prediction model in order to identify added value to forecast operations in support of the United States space program. The evaluation was stratified over warm and cool seasons and considered both objective and subjective verification methodologies. Objective verification results generally indicate that meso-eta model point forecasts at selected stations exhibit minimal error growth in terms of RMS errors and are reasonably unbiased. Conversely, results from the subjective verification demonstrate that model forecasts of developing weather events such as thunderstorms, sea breezes, and cold fronts, are not always as accurate as implied by the seasonal error statistics. Sea-breeze case studies reveal that the model generates a dynamically-consistent thermally direct circulation over the Florida peninsula, although at a larger scale than observed. Thunderstorm verification reveals that the meso-eta model is capable of predicting areas of organized convection, particularly during the late afternoon hours but is not capable of forecasting individual thunderstorms. Verification of cold fronts during the cool season reveals that the model is capable of forecasting a majority of cold frontal passages through east central Florida to within +1-h of observed frontal passage.

Manobianco, John↗
NASA NTRS2024

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Daniel Bruder↗
NASA NTRS1981

An optimization model for energy generation and distribution in a dynamic facility

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Lansing, F. L.↗
DOE OSTI2025

High‐Asymmetry Metasurface: A New Solution for Terahertz Resonance via Active Learning‐Augmented Diffusion Model

Terahertz (THz) metamaterials with high‐figure‐of‐merit (high‐FoM) performance resonance are essential for advancing sensors, detectors, and imagers. Conventional designs focus on symmetric or low‐asymmetry geometric structures, leaving high‐asymmetry designs largely unexplored due to the inefficiency of trial‐and‐error‐based rational design. Recent deep learning techniques offer automation and acceleration but are constrained by the need for large datasets inherent to their data‐driven nature. Here, a novel prior knowledge‐guided generative model augmented by a physics‐constrained active learning mechanism to design high‐asymmetry metamaterials. An advanced diffusion model learns features from a small set of classical structures with high‐FoM THz resonance and generates new high‐asymmetry structures. To mitigate the limited number of classical structures, the generated high‐asymmetry structures are actively selected and integrated into the initial training dataset based on their physical characteristics. Experimental results demonstrate the superior resonance performance of the generated high‐asymmetry metamaterials over classical designs, exhibiting improvements exceeding 30% in key resonance metrics. Remarkably, this performance is attained using only 68 classical structures as the initial training dataset, significantly reducing the data requirements for deep learning‐based metamaterial design. The proposed scheme for generating high‐asymmetry structures provides a new effective and efficient solution for high‐FoM resonance, expanding applications in high‐sensitivity THz metadevices.

diffusion model↗
NASA NTRS2012

Antenna Calibration and Measurement Equipment

A document describes the Antenna Calibration & Measurement Equipment (ACME) system that will provide the Deep Space Network (DSN) with instrumentation enabling a trained RF engineer at each complex to perform antenna calibration measurements and to generate antenna calibration data. This data includes continuous-scan auto-bore-based data acquisition with all-sky data gathering in support of 4th order pointing model generation requirements. Other data includes antenna subreflector focus, system noise temperature and tipping curves, antenna efficiency, reports system linearity, and instrument calibration. The ACME system design is based on the on-the-fly (OTF) mapping technique and architecture. ACME has contributed to the improved RF performance of the DSN by approximately a factor of two. It improved the pointing performances of the DSN antennas and productivity of its personnel and calibration engineers.

Rochblatt, David J.↗
NASA NTRS1993

Extensions to the CIRA reference models for middle atmosphere ozone

The recent ozone reference models generated for the new COSPAR CIRA include ozone vertical structure from 25 to 90 km as a function of month and latitude based on five satellite experiments. The new model provided here extends the ozone vertical structure climatology from 20 mb (about 25 km) to 70 mb (about 18 km) based on three years of recently reprocessed AEM-2 SAGE I (sunset) data. In addition, model refinements are made at altitudes above 25 km based on the reprocessed data. Comparisons are made between the ozone reference models and nonsatellite data sets. The model extensions to lower altitudes are in excellent agreement with in situ measurements both at mid latitudes and in the tropics. Annual mean models of ozone are also provided as a function of latitude from 100 mb (about 16 km) to 0.003 mb (about 90 km).

Keating, G. M.↗
NASA NTRS1990

Diabatic forcing and initialization with assimilation of cloud and rain water in a forecast model: Methodology

The focus of this part of the investigation is to find one or more general modeling techniques that will help reduce the time taken by numerical forecast models to initiate or spin-up precipitation processes and enhance storm intensity. If the conventional data base could explain the atmospheric mesoscale flow in detail, then much of our problem would be eliminated. But the data base is primarily synoptic scale, requiring that a solution must be sought either in nonconventional data, in methods to initialize mesoscale circulations, or in ways of retaining between forecasts the model generated mesoscale dynamics and precipitation fields. All three methods are investigated. The initialization and assimilation of explicit cloud and rainwater quantities computed from conservation equations in a mesoscale regional model are examined. The physical processes include condensation, evaporation, autoconversion, accretion, and the removal of rainwater by fallout. The question of how to initialize the explicit liquid water calculations in numerical models and how to retain information about precipitation processes during the 4-D assimilation cycle are important issues that are addressed. The explicit cloud calculations were purposely kept simple so that different initialization techniques can be easily and economically tested. Precipitation spin-up processes associated with three different types of weather phenomena are examined. Our findings show that diabatic initialization, or diabatic initialization in combination with a new diabatic forcing procedure, work effectively to enhance the spin-up of precipitation in a mesoscale numerical weather prediction forecast. Also, the retention of cloud and rain water during the analysis phase of the 4-D data assimilation procedure is shown to be valuable. Without detailed observations, the vertical placement of the diabatic heating remains a critical problem.

Raymond, William H.↗
NASA NTRS2018

Optical Performance Prediction of the Thirty Meter Telescope after Initial Alignment Using Optical Modeling

We present an estimate of the optical performance of the Thirty Meter Telescope (TMT) after execution of the full telescope alignment plan. The TMT alignment is performed by the Global Metrology System (GMS) and the Alignment and Phasing System (APS). The GMS first measures the locations of the telescope optics and instruments as a function of elevation angle. These initial measurements will be used to adjust the optics positions and build initial elevation look-up tables. Then the telescope is aligned using starlight as the input for the APS at multiple elevation angles. APS measurements are used to refine the telescope alignment to build elevation and temperature dependent look-up tables. Due to the number of degrees of freedom in the telescope (over 10,000), the ability of the primary mirror to correct aberrations on other optics, the tight optical performance requirements and the multiple instrument locations, it is challenging to develop, test and validate these alignment procedures. In this paper, we consider several GMS and APS operational scenarios. We apply the alignment procedures to the model-generated TMT, which consists of various quasi-static errors such as polishing errors, passive supports errors, thermal and gravity deformations and installation position errors. Using an integrated optical model and Monte-Carlo framework, we evaluate the TMT’s aligned states using optical performance metrics at multiple instrument and field of view locations. The optical performance metrics include the Normalized Point Source Sensitivity (PSSN), RMS wavefront error before and after Adaptive Optics (AO) correction, pupil position change, and plate scale distortion.

Rogers, John↗
DOE OSTI2025

Method to simultaneously facilitate all jet physics tasks

Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system ($pp$ versus $ep$), for generative models, for likelihood ratio estimation, and for anomaly detection. We consider our omnilearn approach thus as a jet-physics foundation model. It is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗
NASA NTRS1977

The July climate and a comparison of the January and July climates simulated by the GISS general circulation model

Results are presented for a study directed to evaluate the ability of the global general circulation model of the Goddard Institute for Space Studies (GISS) in simulating seasonal differences as related to an experiment simulating the July climatology which parallels the January simulation presented by Somerville et al. (1974). The July and January simulations are compared with each other and with climatological data on seasonal changes, mainly for the Northern Hemisphere troposphere. The comparison shows that the model-generated energy cycle, distribution of winds, temperature, humidity and pressure, dynamical transports, diabatic heating, evaporation, precipitation and cloud cover are all realistic for the Northern Hemisphere troposphere in July. The model's simulation of seasonal differences is generally quite realistic since the systematic quantitative errors do not affect the simulation of relative changes, to first order. Defects that could seriously bias the model's performance in particular climate experiments are identified and discussed.

Stone, P. H.↗
NASA NTRS1998

Sarnoff JND Vision Model for Flat-Panel Design

This document describes adaptation of the basic Sarnoff JND Vision Model created in response to the NASA/ARPA need for a general-purpose model to predict the perceived image quality attained by flat-panel displays. The JND model predicts the perceptual ratings that humans will assign to a degraded color-image sequence relative to its nondegraded counterpart. Substantial flexibility is incorporated into this version of the model so it may be used to model displays at the sub-pixel and sub-frame level. To model a display (e.g., an LCD), the input-image data can be sampled at many times the pixel resolution and at many times the digital frame rate. The first stage of the model downsamples each sequence in time and in space to physiologically reasonable rates, but with minimum interpolative artifacts and aliasing. Luma and chroma parts of the model generate (through multi-resolution pyramid representation) a map of differences-between test and reference called the JND map, from which a summary rating predictor is derived. The latest model extensions have done well in calibration against psychophysical data and against image-rating data given a CRT-based front-end. THe software was delivered to NASA Ames and is being integrated with LCD display models at that facility,

Brill, Michael H.↗
NASA NTRS2021

Climate Predicts Geographic and Temporal Variation in Mosquito-Borne Disease Dynamics on Two Continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88%for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

Climate-change ecology↗