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Results for “computer model calibration”

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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dealing with Measurement Uncertainties as Nuisance Parameters in Bayesian Model Calibration

In the presence of model discrepancy, the calibration of physics-based models for physical parameter inference is a challenging problem. Lack of identifiability between calibration parameters and model discrepancy requires additional identifiability constraints to be placed on the model discrepancy to obtain unique physical parameter estimates. If these assumptions are violated, the inference for the calibration parameters can be systematically biased. In many applications, such as in dynamic material property experiments, many of the calibration inputs refer to measurement uncertainties. Here, we develop a metric for identifying overfitting of these measurement uncertainties, propose a prior capable of reducing this overfitting, and show how this leads to a diagnostic tool for validation of physical parameter inference. The approach is demonstrated for a benchmark example and applied for a material property application to perform inference on the equation of state parameters of tantalum.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

An experimental study of transmission, reflection and scattering of sound in a free-jet flight simulation facility and comparison with theory

Acoustic time delays across a free-jet shear layer are measured and compared with predictions based on (1) ray paths refracted abruptly across a cylindrical vortex sheet and (2) ray paths traced through a more realistic diverging flow model. The close agreement between measurement and theory confirms that Snell's law provides an accurate prediction of wavefront refraction or angle changes across a diverging shear layer. Microphones are placed on calculated ray paths to determine the coherent transmission and internal reflection characteristics of the shear layer and also the scattering of sound by the shear-layer turbulence. The transmission data essentially verify the proposed, theoretical calibration factor which forms part of a computational procedure that is being developed to convert model jet data from a free-jet facility to inflight conditions.

Ahuja, K. K.↗
DOE OSTI2025

Simultaneous inference of equation of state parameters and unknown data errors with uncertainty quantification via hierarchical Bayesian posterior maximization

Equations of state (EOSs) are a key component in running hydrodynamic simulations as they relate the thermodynamic states for the material. The Davis reactants EOS is commonly used for modeling high explosives (HEs), and the EOS model parameters are calibrated using material specific data. The calibrations are often performed with uncertainty quantification via Bayesian inference to account for uncertainty in the data and generate ensembles of likely parameters. However, there are relatively few HE data sets to use for calibration and many are historical and lack error information. In this work, we simultaneously calibrate the Davis reactants EOS model parameters and unknown data error terms for the high explosive PBX 9501. To quantify the uncertainty in the models and the data, we use a Bayesian framework for the calibration and compute the hierarchical Bayesian posterior distribution with both a posteriori maximization approach and Markov Chain Monte Carlo. In general, we find that, given our assumptions, the two approaches result in similar calibrated parameters, posterior covariance matrices, and insights about the parameters but that the posterior maximization requires far less computational resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

domain of applicability↗
DOE OSTI2021

TP89 - SIRZ Decomposition Spectral Estimation (V.1.0)

The purpose of this test plan is to generate and evaluate calibrated system spectral response models for a dual-energy x-ray computed tomography (DECT) scanner. These spectral response models, which represent the energy distribution of photons observed by the detector with no test articles in the beam path, are necessary for spectral decomposition reconstruction techniques such as SIRZ, which are used for precise material characterization.

42 ENGINEERING↗
NASA NTRS1973

Minitrack tracking function description, volume 2

The minitrack tracking function is described and specific operations are identified. The subjects discussed are: (1) preprocessor listing, (2) minitrack hardware, (3) system calibration, (4) quadratic listing, and (5) quadratic flow diagram. Detailed information is provided on the construction of the tracking system and its operation. The calibration procedures are supported by mathematical models to show the application of the computer programs.

Englar, T. S.↗
NASA NTRS1974

Atmospheric effects on remote sensing of non-uniform temperature sources

The effects are considered of an absorbing, emitting, and scattering atmosphere upon the remote sensing of surface areas having non-uniform intensity. These atmospheric effects may be significant in determination, by remote sensing, of non-uniform surface temperature distributions, and the results of the investigation are applicable in such cases. Analytical methods and a digital computational program are presented, expressing the results in terms of contrast and contrast transmittance between two adjacent emitting areas having unequal intensities, in the presence of a additional disturbing emitters. In the computational procedure, emitting areas are replaced by point-source emitters, each assigned and effective intensity based upon the intensity of the area it replaces. Absorbing, emitting, and scattering behavior of the atmosphere may be specified in the computational procedure either by means of analytical atmospheric models or by means of calibrating ground level emitters.

Mcneill, W. A.↗
NASA NTRS1983

Transient thermal modeling of the nonscanning ERBE detector

A numerical model to predict the transient thermal response of the ERBE nonscanning wide field of view total radiometer channel was developed. The model, which uses Monte Carlo techniques to characterize the radiative component of heat transfer, is described and a listing of the computer program is provided. Application of the model to simulate the actual blackbody calibration procedure is discussed. The use of the model to establish a real time flight data interpretation strategy is recommended. Modification of the model to include a simulated Earth radiation source field and a filter dome is indicated.

Mahan, J. R.↗
NASA NTRS2005

Data Assimilation Technology Transfer from Planetary Exploration to Terrestrial Applications

Data assimilation can be a valuable tool for discovery and exploration. Planetary missions--principally polar orbiting spacecraft--are now capable of returning enough information to constrain global dynamical models. However, such missions operate under a number of severe constraints: there is no ground truth to calibrate and validate either the models or the data: and there are limited computational resources available, particularly onboard the spacecraft, for the near real-time processing that is required for aerobraking and other semi-autonomous maneuvers. A modified data assimilation approach, in which the prime analysis variable is the observed quantity (i.e., infrared radiances), has been found to be effective in deriving global meteorology from Mars Global Surveyor Thermal Emission Spectrometer data. It may also be useful in a number of terrestrial applications (e.g. aerosol assimilation).

Houben, Howard↗
NASA NTRS2016

Orion EFT-1 Cavity Heating Tile Experiments and Environment Reconstruction

Developing aerothermodynamic environments for deep cavities, such as those produced by micrometeoroids and orbital debris impacts, poses a great challenge for engineers. In order to assess existing cavity heating models, two one-inch diameter cavities were flown on the Orion Multi-Purpose Crew Vehicle during Exploration Flight Test 1 (EFT1). These cavities were manufactured with depths of 1.0 in and 1.4 in, and they were both instrumented. Instrumentation included surface thermocouples upstream, downstream and within the cavities, and additional thermocouples at the TPS-structure interface. This paper will present the data obtained, and comparisons with computational predictions will be shown. Additionally, the development of a 3D material thermal model will be described, which will be used to account for the three-dimensionality of the problem when interpreting the data. Furthermore, using a multi-dimensional inverse heat conduction approach, a reconstruction of a time- and space-dependent flight heating distribution during EFT1 will be presented. Additional discussions will focus on instrumentation challenges and calibration techniques specific to these experiments. The analysis shown will highlight the accuracies and/or deficiencies of current computational techniques to model cavity flows during hypersonic re-entry.

Salazar, Giovanni↗
DOE OSTI2021

SMBH seeds from dissipative dark matter

The existence of supermassive black holes (SMBHs) with masses greater than ~ 10 9 M ⊙ at high redshift ( z ≳ 7) is difficult to accommodate in standard astrophysical scenarios. We study the possibility that (nearly) totally dissipative self-interacting dark matter (tdSIDM)–in rare, high density dark matter fluctuations in the early Universe — produces SMBH seeds through catastrophic collapse. We use a semi-analytic model, tested and calibrated by a series of N-body simulations of isolated dark matter halos, to compute the collapse criteria and timescale of tdSIDM halos, where dark matter loses nearly all of its kinetic energy in a single collision in the center-of-momentum frame. Applying this model to halo merger trees, we empirically assign SMBH seeds to halos and trace the formation and evolution history of SMBHs. We make predictions for the quasar luminosity function, the M BH -σ v * relation, and cosmic SMBH mass density at high redshift and compare them to observations. We find that a dissipative dark matter interaction cross-section of σ/ m ~ 0.05 cm 2 /g is sufficient to produce the SMBHs observed in the early Universe while remaining consistent with ordinary SMBHs in the late Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A Causal Approach to Model Validation and Calibration

This poster presents a novel method for validation and verification that focuses on identifying causal relationships between data elements, moving beyond traditional statistical and machine learning approaches. These methods employ causal discovery techniques to reveal the underlying mechanisms of data generation. The research utilizes structural causal models and directed acyclic graphs to depict causal relationships. This approach assists in achieving alignment between simulation models and reality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Empirically-calibrated H100 node power models for accurate AI training energy estimation

Accurately quantifying the energy use of artificial intelligence (AI) training is critical for infrastructure planning, carbon accounting, and sustainable data center operation, but few studies have directly measured the power consumption of production workloads on contemporary hardware. By combining empirical measurements from Brookhaven National Laboratory during AI training on 8-graphics-processing-unit H100 systems with open-source benchmarking data, we develop statistical models relating computational intensity to node-level power consumption. We measure the gap between manufacturer-rated thermal design power (TDP) and actual power demand during AI training. Our analysis reveals that even computationally intensive workloads operate at only 76% of the 10.2 kW TDP rating. Our architecture-specific model, calibrated to floating-point operations, predicts energy consumption with 11.4% mean absolute percentage error, significantly outperforming TDP-based approaches (27%–37% error). We identified distinct power signatures between transformer and convolutional neural network architectures, with transformers showing characteristic fluctuations that may impact grid stability. These results provide a measurement-grounded basis for improving AI training energy estimates, enabling more reliable infrastructure sizing, cost projections, and environmental impact assessments.

Newkirk, Alex C↗
DOE OSTI2020

A hybrid calibration approach to Hertz-type contact parameters for discrete element models

This study aims at providing a hybrid calibration framework to estimate Hertz-type contact parameters (particle-scale shear modulus and Poisson ratio) for both two-dimensional and three-dimensional discrete element modelling (DEM). On the basis of statistically isotropic granular packings, a set of analytical formulae between macroscopic material parameters (Young modulus and Poisson ratio) and particle-scale Hertz-type contact parameters for granular systems are derived under small-strain isotropic stress conditions. However, the derived analytical solutions are only estimated values for general models. By viewing each DEM modelling as an implicit mathematical function taking the particle-level parameters as independent variables and employing the derived analytical solutions as the initial input parameters, an automatic iterative scheme is proposed to obtain the calibrated parameters with higher accuracies. Considering highly nonlinear features and discontinuities of the macro-micro relationship in Hertz-based discrete element models, the adaptive moment estimation algorithm is adopted in this study because of its capacity of dealing with noise gradients of cost functions. Here, the proposed method is validated with several numerical cases including randomly distributed monodisperse and polydisperse packings. Noticeable improvements in terms of calibration efficiency and accuracy have been made.

Constitutive law↗
DOE OSTI2020

Data-Driven Constitutive Model for the Inelastic Response of Metals: Application to 316H Steel

Here, predictions of the mechanical response of structural elements are conditioned by the accuracy of constitutive models used at the engineering length-scale. In this regard, a prospect of mechanistic crystal-plasticity-based constitutive models is that they could be used for extrapolation beyond regimes in which they are calibrated. However, their use for assessing the performance of a component is computationally onerous. To address this limitation, a new approach is proposed whereby a surrogate constitutive model (SM) of the inelastic response of 316H steel is derived from a mechanistic crystal plasticity-based polycrystal model tracking the evolution of dislocation densities on all slip systems. The latter is used to generate a database of the expected plastic response and dislocation content evolution associated with several instances of creep loading. From the database, a SM is developed. It relies on the use of orthogonal polynomial regression to describe the evolution of the dislocation content. The SM is then validated against predictions of the dead load creep response given by the polycrystal model across a range of temperatures and stresses. When the SM is used to predict the response of 316H during complex non monotonic loading, extrapolating to new loading conditions, it is found that predictions compare particularly well against those from the physics-based polycrystal model.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. Finally, the powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

36 MATERIALS SCIENCE↗
NASA NTRS1980

Applications of HCMM satellite data

Underflight data were radiometrically calibrated at several locations and surface water temperature measurements were made for several areas approximating the field of view of the HCMM sensor in a study of the thermal properties of Lake Ontario and Lake Erie and of the heat island problem in selected areas adjacent to these lakes. The temperatures obtained from the radiometrically collected imagery were then plotted against the apparent temperatures for these same locations obtained from HCMM computer compatible tapes. The sensor calibration provides a foundation for development of a refined model for radiometric correction of HCMM data to produce maps of thermal bar development in Lake Ontario. Thermal patterns of several urban areas were generated from HCMM tapes and scaled to corresponding land use and topographic maps. The resulting map overlays are being interpreted in terms of land use influence on the extend and severity of heat island indicators.

Source record↗