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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Calibration of the ARID robot

The author has formulated a new, general model for specifying the kinematic properties of serial manipulators. The new model kinematic parameters do not suffer discontinuities when nominally parallel adjacent axes deviate from exact parallelism. From this new theory the author develops a first-order, lumped-parameter, calibration-model for the ARID manipulator. Next, the author develops a calibration methodology for the ARID based on visual and acoustic sensing. A sensor platform, consisting of a camera and four sonars attached to the ARID end frame, performs calibration measurements. A calibration measurement consists of processing one visual frame of an accurately placed calibration image and recording four acoustic range measurements. A minimum of two measurement protocols determine the kinematics calibration-model of the ARID for a particular region: assuming the joint displacements are accurately measured, the calibration surface is planar, and the kinematic parameters do not vary rapidly in the region. No theoretical or practical limitations appear to contra-indicate the feasibility of the calibration method developed here.

Doty, Keith L↗
DOE OSTI2021

Experiment and non-local crystal plasticity finite element study of nanoindentation on Al-8Ce-10Mg alloy

Cerium and magnesium strengthened aluminum alloys, or Al-Ce-Mg, is a recently developed alloy family that exhibits good mechanical properties at elevated temperatures (~300 °C). To examine the single-crystal properties of Al-Ce-Mg alloys, nanoindentation experiments are conducted in this study. A crystal plasticity finite element model (CPFEM) with the evolution of geometrically necessary dislocations (GNDs) is applied to simulate the indentation in individual grains. A parametric study is carried out to investigate the sensitivity of each crystal plasticity model parameter to the indentation behavior. The highly sensitive parameters are calibrated by matching the indentation load-depth curves, while the rest parameters are obtained from bulk polycrystal uniaxial tension tests. Overall, satisfactory matching between experiment and simulation is obtained for each individual grain. The calculated hardness, as determined from the experiment, shows the dependence on indent depth, which is captured by the GND model. Furthermore, the effect of grain orientation and neighboring grains to nanoindentation behavior have been discussed with the comparison between the simulation and experiments.

36 MATERIALS SCIENCE↗
NASA NTRS1982

Contrast reduction by the atmosphere and retrieval of nonuniform surface reflectance

A radiative transfer model is developed which gives the upward radiance at nadir for any 1-D Lambertian surface reflectance. This model is used to depict the atmospheric effect on the transmittance of contrast for any 1-D surface reflectance. Here by contrast we mean a general variation of the radiation field across the image. With the aid of this model an inversion algorithm is developed for retrieval of true surface reflectance from high resolution satellite data (e.g., Landsat). This inversion technique can be a useful tool for extraction of surface reflectance from satellite data in the case of a surface reflectance variable in one dimension only (e.g., seashore or near borders of big fields). A sensitivity study of the inversion procedure on the knowledge of atmospheric parameters and sensor calibration was performed. It is shown that this inversion technique is stable even in the presence of errors in the sensor calibration and the atmospheric parameters. The method was applied to Landsat data in two wavelengths. The results show reasonable dependence of the derived surface reflectance on the distance from the seashore.

Mekler, Y.↗
NASA NTRS2010

Real-Time Visualization of Spacecraft Telemetry for the GLAST and LRO Missions

GlastCam and LROCam are closely-related tools developed at NASA Goddard Space Flight Center for real-time visualization of spacecraft telemetry, developed for the Gamma-Ray Large Area Space Telescope (GLAST) and Lunar Reconnaissance Orbiter (LRO) missions, respectively. Derived from a common simulation tool, they use related but different architectures to ingest real-time spacecraft telemetry and ground predicted ephemerides, and to compute and display features of special interest to each mission in its operational environment. We describe the architectures of GlastCam and LROCam, the customizations required to fit into the mission operations environment, and the features that were found to be especially useful in early operations for their respective missions. Both tools have a primary window depicting a three-dimensional Cam view of the spacecraft that may be freely manipulated by the user. The scene is augmented with fields of view, pointing constraints, and other features which enhance situational awareness. Each tool also has another "Map" window showing the spacecraft's groundtrack projected onto a map of the Earth or Moon, along with useful features such as the Sun, eclipse regions, and TDRS satellite locations. Additional windows support specialized checkout tasks. One such window shows the star tracker fields of view, with tracking window locations and the mission star catalog. This view was instrumental for GLAST in quickly resolving a star tracker mounting polarity issue; visualization made the 180-deg mismatch immediately obvious. Full access to GlastCam's source code also made possible a rapid coarse star tracker mounting calibration with some on the fly code adjustments; adding a fine grid to measure alignment offsets, and introducing a calibration quaternion which could be adjusted within GlastCam without perturbing the flight parameters. This calibration, from concept to completion, took less than half an hour. Both GlastCam and LROCam were developed in the C language, with non-proprietary support libraries, for ease of customization and portability. This no-blackboxes aspect enables engineers to adapt quickly to unforeseen circumstances in the intense operations environment. GlastCam and LROCam were installed on multiple workstations in the operations support rooms, allowing independent use by multiple subsystems, systems engineers and managers, with negligible draw on telemetry system resources.

Stoneking, Eric T.↗
NASA NTRS2021

Understanding the Impacts of Land Surface and PBL Observations on the Terrestrial and Atmospheric Legs of Land-Atmosphere Coupling

Accurately representing land-atmosphere (L-A) interactions and coupling in NWP systems remains a challenge. New observations, incorporated into models via assimilation or calibration, hold the promise of improved forecast skill, but erroneous model coupling can hinder the benefits of such activities. To better understand model representation of coupled interactions and feedbacks, this study demonstrates a novel framework for coupled calibration of the Single Column Model (SCM) capability of the NASA Unified Weather Research and Forecasting (NU-WRF) system coupled to NASA’s Land Information System (LIS). The local land-atmosphere coupling (LoCo) process chain paradigm is used to assess the processes and connections revealed by calibration experiments. Two summer case studies in the U. S. Southern Great Plains are simulated in which LSM parameters are calibrated to diurnal observations of LoCo process chain components including 2-meter temperature, 2-meter humidity, surface fluxes (Bowen ratio), and PBL height. Results show a wide range of soil moisture and hydraulic parameter solutions depending on which L-A variable (i.e. observation) is used for calibration, highlighting that improvement in either SHP or ISM when not in tandem with the other can provide undesirable results. Overall, this work demonstrates that a process chain calibration approach can be used to assess L-A connections, feedbacks, strengths, and deficiencies in coupled models, as well as quantify the potential impact of new sources of observations of land-PBL variables on coupled prediction.

Patricia Lawston-Parker↗
NASA NTRS2018

Determination of Ballistic Limit for IM7/8552 Using LS-DYNA MAT 261

The goal of the NASA ACC High Energy Dynamic Impact Project is to determine the state of the art of dynamic fracture simulations for high velocity impact for composite fuselage shielding applications. Using a building block approach, several computational models considered under NASA ACC are being validated against test data, starting at unconfigured panels and progressing to configured panels under combined out-of-plane and in-plane loading due to ballistic impact. The computational models being evaluated in this project include MAT 162, MAT 213, MAT 261, SPG, and Peridynamics. In this paper, the simulation results using LS-Dyna Material MAT 261 are presented. In particular, a series of blind predictions for unconfigured panels were performed to determine the ballistic limit or V50 velocity. MAT 261 employs failure approach that is generally physically-based using fracture toughness criteria. The overall material model relies on typical ply-level stiffness properties, similar to MAT 162 and other composite continuum damage material models. The fracture toughness values are based on standard tests, and thus are not subject to extensive calibration. This approach is more efficient than performing extensive optimization studies for calibration of parameters. Also, this approach of relying on physical properties reduces the uncertainty of results, as questions concerning the quality and extent of the calibration studies is no longer relevant. However, it was found that carefully controlled coupon-level tests are needed to accurately obtain the required fracture toughness values. Additionally, it should be noted that there is one significant parameter in MAT 261 that does appear to require calibration, and that is the overall failure strain. This is the strain at which the element is deleted, and is not the same as the strain at which damage begins to accumulate. This failure strain is termed EFS (Effective Failure Strain), and is the maximum effective stain for element failure. Simulations have shown that this value will significantly affect impact response and failure. The paper presents the effect of this element failure strain parameter, along with possible uncertainties in fracture toughness values. With an adjusted appropriate value for EFS, it is seen that simulation results compare well with impact test data for predicted penetration velocity.

Byar, Alan↗
DOE OSTI2026

Xanthos-Lake Model Source Code

This repository contains the source code for Xanthos-Lake, a lake-modeling extension of the Xanthos framework that introduces a coupled lake component comprising the Xanthos-Lake Snow and Ice Model (xLSIM) and the Xanthos-Lake Water Balance Model (xLWBM). xLSIM is a basin-aware machine-learning model for lake snow, ice, and thermal conditions. It predicts monthly lake ice thickness, snow depth, snow-cover fraction, mixing-layer temperature, and lake ice fraction from meteorological forcing and lake surface-area information. It uses sequence-based deep-learning architectures, including Transformer and hybrid Long Short-Term Memory–Transformer (LSTM–Transformer) models, together with seasonal encoding, multi-lake learning, physical masking, and basin-level cryospheric and non-cryospheric classification. The training workflow uses Ray for scalable execution and includes optional Ray Tune hyperparameter optimization. Model predictions, observations, diagnostics, and feature-importance outputs are written in NetCDF. xLWBM is the water-balance component of the new lake framework. It simulates monthly lake storage, surface area, evaporation, inflow, outflow, and lake–groundwater exchange. It combines physical water-balance equations with calibrated bathymetric relationships, weir-based outlet flow, modified Penman open-water evaporation, groundwater head relaxation, Penman–Monteith snow and ice sublimation, and snow, ice, and thermal conditions supplied by xLSIM. The model calibrates lake parameters against satellite-derived surface-area data, using evaporation-based calibration where surface-area data are unavailable, and supports small, medium, and large lake classes. For large lakes, xLWBM is integrated with the managed-routing workflow so that lake storage and outflow interact directly with downstream river routing and reservoir operations. Together, xLSIM and xLWBM provide Xanthos with a coupled lake-modeling capability. xLSIM supplies the snow, ice, and thermal conditions that affect lake evaporation and snow- and ice-related water exchanges, while xLWBM translates those conditions into dynamic lake storage, surface area, evaporation, and discharge. In return, xLWBM supplies evolving lake surface area to xLSIM. This coupling enables Xanthos to represent lakes as active hydrologic components within basin-scale water-availability and routing simulations.

Machine Learning↗
DOE OSTI2026

Process‐Oriented Calibration of a Turbulence Scheme in the DOE's Global Storm‐Resolving Model Using Machine Learning

A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

58 GEOSCIENCES↗
DOE OSTI2021

Calibrating Microscopic Car-Following Models for Adaptive Cruise Control Vehicles: Multiobjective Approach

Adaptive cruise control (ACC) vehicles are the first step toward comprehensive vehicle automation. However, the impacts of such vehicles on the underlying traffic flow are not yet clear. Therefore, it is of interest to accurately model vehicle-level dynamics of commercially available ACC vehicles so that they may be used in further modeling efforts to quantify the impact of commercially available ACC vehicles on traffic flow. Importantly, not only model selection but also the calibration approach and error metric used for calibration are critical to accurately model ACC vehicle behavior. In this work, we explore the question of how to calibrate car following models to describe ACC vehicle dynamics. Specifically, we apply a multi-objective calibration approach to understand the tradeoff between calibrating model parameters to minimize speed error vs. spacing error. Three different car-following models are calibrated for data from six vehicles. The results are in line with recent literature and verify that targeting a low spacing error does not compromise the speed accuracy whether the opposite is not true for modeling ACC vehicle dynamics.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Calibration and uncertainty quantification for Davis Equation of State models for the High Explosive PBX 9501 products

Abstract This paper investigates the uncertainty in the parameters used in the calibration of an Davis Equation Of State (EOS) for the detonation products of the High Explosive PBX 9501. The procedure sought to make use of all available information about this HE to inform the best set of calibration parameters as well as the uncertainty in these parameters. The procedure made use of historical experimental data, the results from thermo‐chemical modeling as well as data on the best isentrope function fit to cylinder test experimental data. Combining all these heterogeneous data sources together in a Bayesian calibration, yielded a posterior mean and covariance. Sampling from the posterior distribution and evaluating an important Quantity Of Interest (QOI) in the EOS model, the detonation speed of a one‐inch rate stick, produced a distribution which showed variations which were in agreement with experiments. The uncertainty in the EOS was reported as eleven sets of model calibrations which spanned the range of this QOI.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS2020

An Observation‐Driven Approach to Improve Vegetation Phenology in a Global Land Surface Model

An empirical model calibration approach is presented that aims to approximate missing biosphere processes in a global land surface model without the need for substantial model structural changes. The strategy is implemented here using the NASA Catchment‐CN land surface model and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of the fraction of absorbed photosynthetically active radiation (FPAR). Existing plant functional types (PFTs) of the Catchment‐CN model are divided into three subtypes, based on the bias between the model‐simulated and MODIS‐observed FPAR. Separate sets of vegetation parameters for each subtype are then calibrated at a small number of grid cells with homogeneous, single‐PFT land cover, using MODIS FPAR reference observations from 2003 to 2009. The effectiveness of the empirical approach at improving the realism of modeled vegetation dynamics is investigated with two global model simulations for the period 2010–2016, one using the newly calibrated parameter values and the other using the original values. Globally, the calibrated parameters reduce the root mean square error (RMSE) of the modeled FPAR with respect to MODIS by 0.029 (∼10%) on average. In some regions, substantially larger RMSE reductions are achieved. RMSE reductions are primarily driven by model bias reductions, with neutral effects on the temporal correlation skill. While the empirical approach is suitable for achieving consistent model improvements, it is shown to be sensitive to the characteristics of the model error, specifically a dominance of the bias component in the case of Catchment‐CN. Ultimately, more fundamental model structural changes may be required to achieve better improvements.

J. Kolassa↗
NASA NTRS2004

Bayesian Vision for Shape Recovery

We present a new Bayesian vision technique that aims at recovering a shape from two or more noisy observations taken under similar lighting conditions. The shape is parametrized by a piecewise linear height field, textured by a piecewise linear irradiance field, and we assume Gaussian Markovian priors for both shape vertices and irradiance variables. The observation process. also known as rendering, is modeled by a non-affine projection (e.g. perspective projection) followed by a convolution with a piecewise linear point spread function. and contamination by additive Gaussian noise. We assume that the observation parameters are calibrated beforehand. The major novelty of the proposed method consists of marginalizing out the irradiances considered as nuisance parameters, which is achieved by Laplace approximations. This reduces the inference to minimizing an energy that only depends on the shape vertices, and therefore allows an efficient Iterated Conditional Mode (ICM) optimization scheme to be implemented. A Gaussian approximation of the posterior shape density is computed, thus providing estimates both the geometry and its uncertainty. We illustrate the effectiveness of the new method by shape reconstruction results in a 2D case. A 3D version is currently under development and aims at recovering a surface from multiple images, reconstructing the topography by marginalizing out both albedo and shading.

Jalobeanu, Andre↗
DOE OSTI2024

Recommendations for harmonized reporting of radiation Dosimetry by adoption of Compatibility in Irradiation Research Protocols Expert Roundtable (CIRPER)

Here, in radiobiology, radiation physics, and related areas, there is growing interest in transitioning from cesium-based irradiators to X-ray technologies. Therefore, it makes it imperative for reproducibility purposes, for researchers to provide specifications regarding device parameters, machine calibration details, and experimental setup when innovative X-ray technology is being employed. Several groups have already identified a glaring lack in completeness of critical information on experimental setups, equipment, and physical parameters in published radiation biology studies. The Alternative Technologies Meta Study Report by Brookhaven National Laboratory (BNL) examined 456 manuscripts published between 2017-2021. Only 64 of the reviewed studies (14.0%) consistently reported methods that could allow for replication of their studies. However, 85% provided information on the manufacturer, the irradiator model and the absorbed radiation dose. Approximately 66% provided information on the energy spectra and the dose rate, but only one third disclosed the filter used. Less than 15% of the reviewed studies reported information on calibration and field geometry, specifically field size and distance from source, which are critical parameters in determining the absorbed radiation dose.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1992

Calibration of a helium-cooled infrared spatial radiometer and grating spectrometer

Methods used by the Space Dynamics Laboratory of Utah State University (SDL/USU) to calibrate infrared sensors are described, using the Infrared Background Signature Survey (IBSS) spatial radiometer and grating spectrometer as examples. A calibration equation and a radiometric model are given for each sensor to describe their responsivity in terms of individual radiometric parameters. The calibration equation terms include dark offset, linearity, absolute responsivity, and measurement uncertainty, and the radiometric model domains include spatial, spectral, and temporal domains. A portable calibration facility, designed and fabricated by SDL/USU, provided collimated, extended, diffuse scatter, and Jones sources in a single cryogenic dewar. This multi-function calibrator allowed calibration personnel to complete a full calibration of the IBSS infrared radiometer and spectrometer in two 15-day periods. A calibration data system was developed to control and monitor the calibration facility, and to record and analyze sensor data.

Jacobsen, Larry↗
DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

Abstract The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quarterly Research Performance Progress Report (Q8)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.

15 GEOTHERMAL ENERGY↗