Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model validation”

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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-T-664: Radiobiological Modeling of Prophylactic Cranial Irradiation in Mice

Purpose: Prophylactic cranial irradiation (PCI) is a clinical technique used to reduce the incidence of brain metastasis and improve overall survival in select patients with ALL and SCLC, and we have shown the potential of PCI in select breast cancer patients through a mouse model (manuscript in preparation). We developed a computational model using our experimental results to demonstrate the advantage of treating brain micro-metastases early. Methods: MATLAB was used to develop the computational model of brain metastasis and PCI in mice. The number of metastases per mouse and the volume of metastases from four- and eight-week endpoints were fit to normal and log-normal distributions, respectively. Model input parameters were optimized so that model output would match the experimental number of metastases per mouse. A limiting dilution assay was performed to validate the model. The effect of radiation at different time points was computationally evaluated through the endpoints of incidence, number of metastases, and tumor burden. Results: The correlation between experimental number of metastases per mouse and the Gaussian fit was 87% and 66% at the two endpoints. The experimental volumes and the log-normal fit had correlations of 99% and 97%. In the optimized model, the correlation between number of metastases per mouse and the Gaussian fit was 96% and 98%. The log-normal volume fit and the model agree 100%. The model was validated by a limiting dilution assay, where the correlation was 100%. The model demonstrates that cells are very sensitive to radiation at early time points, and delaying treatment introduces a threshold dose at which point the incidence and number of metastases decline. Conclusion: We have developed a computational model of brain metastasis and PCI in mice that is highly correlated to our experimental data. The model shows that early treatment of subclinical disease is highly advantageous.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

An asynchronous parallel high-throughput model calibration framework for crystal plasticity finite element constitutive models

Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.

304L stainless steel↗
DOE OSTI2018

High Initial Sputter Rate Found for Vaccinia Virions Using Isotopic Labeling, NanoSIMS, and AFM

High-lateral-resolution secondary ion mass spectrometry (SIMS) has the potential to provide functional and depth resolved information from small biological structures, such as viral particles (virions) and phage, but sputter rate and sensitivity are not characterized at shallow depths relevant to these structures. Here we combine stable isotope labeling of the DNA of vaccinia virions with correlated SIMS imaging depth profiling and atomic force microscopy (AFM) to develop a nonlinear, nonequilibrium sputter rate model for the virions and validate the model on the basis of reconstructing the location of the DNA within individual virions. Our experiments with a Cs+ beam show an unexpectedly high initial sputter rate (~100 um 2 ·nm·pA –1 ·s –1 ) with a rapid decline to an asymptotic rate of 0.7 um 2 ·nm·pA –1 ·s –1 at an approximate depth of 70 nm. Correlated experiments were also conducted with glutaraldehyde-fixed virions, as well as O – and Ga + beams, yielding similar results. Based on our Cs + sputter rate model, the labeled DNA in the virion was between 50 and 90 nm depth in the virion core, consistent with expectations, supporting our conclusions. Virion densification was found to be a secondary effect. In conclusion, accurate isotopic ratios were obtained from the initiation of sputtering, suggesting that isotopic tracers could be successfully used for smaller virions and phage.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Coverage-Dependent Adsorption of Phenol on Pt(111) from First Principles

In this work, we quantify the coverage dependence of the adsorbate-adsorbate and metal-adsorbate interactions for phenol on Pt(111) using density functional theory. For the four most favorable adsorption sites, we find that the adsorption energy of phenol decreases linearly as a function of coverage. As such, the repulsive phenol-phenol lateral interactions are strongest near saturation when the inter-molecular distances are less than ~4.5 Å, manifesting in a decrease of the C-O dihedral bond angle of ~4°. The linear dependence of the adsorption energy on coverage allows for the construction of a mean-field model. We validate our mean-field model for phenol adsorption by comparing the theoretically predicted differential heat of adsorption to the experimental data, where a maximum deviation of 0.11 eV is found. This compares well to earlier reported results that used a similar mean-field approach for the adsorption of benzene on Pt(111), suggesting that lateral interactions between functionalized aromatics can be parameterized using a simpler approximation. Overall, this work demonstrates a simple method toward the improvement in current state-of-the-art hydrodeoxygenation modeling at surfaces by allowing for the incorporation of coverage effects in systems with large, aromatic compounds without resorting to overly complex models.

09 BIOMASS FUELS↗
DOE OSTI2015

The effect of non-uniform temperature and velocity fields on long range ultrasonic measurement systems in MYRRHA

SCK.CEN, the Belgian Nuclear Research Center, is developing MYRRHA, a generation IV liquid metal cooled nuclear research reactor. As the liquid metal coolant is opaque to light, normal visual feedback during fuel manipulations is not available and must therefore be replaced by a system that is not hindered by the opacity of the coolant. In this respect ultrasonic based instrumentation is under development at SCK.CEN to provide feedback during operations under liquid metal. One of the tasks that will be tackled using ultrasound is the detection and localization of a potentially lost fuel assembly. In this application, the distance between ultrasonic sensor and target may be as large as 2.5 m. At these distances, non uniform velocity and temperature fields in the liquid metal potentially influence the propagation of the ultrasonic signals, affecting the performance of the ultrasonic systems. In this paper, we investigate how relevant temperature and velocity gradients inside the liquid metal influence the propagation of ultrasonic waves. The effect of temperature and velocity gradients are simulated by means of a newly developed numerical ray-tracing model. The performance of the model is validated by dedicated water experiments. The setup is capable of creating velocity and temperature gradients representative for MYRRHA conditions. Once validated in water, the same model is used to make predictions for the effect of gradients in the MYRRHA liquid metal environment. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Models Predicting Void Swelling Incubation Dose as a function of Irradiation Conditions

This report documents the development status of a cluster dynamics model to predict microstructural evolution in high-energy particle irradiated ferritic martensitic steels, in which we have recently implemented a model for the biased absorption of self-interstitial defects at small, nanometer-sized cavities that can provide an intrinsic nucleation barrier for the onset of void swelling. Following a brief review of the cluster dynamics modeling approach, this report describes the atomistic modeling results that demonstrate this phenomenon, along with an investigation on the influence of helium content on modifying the intrinsic cavity bias. Subsequently, the report describes the initial results obtained from our cluster dynamics modeling of the incorporation of this cavity bias on the predicted swelling incubation dose as a function of irradiation temperature. Finally, the report concludes with a discussion of the remaining modeling research required to fully validate the model for application to high dose neutron or ion beam irradiation exposure conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Development of the Technical Basis of a Unified Viscoplastic Model of 316H Stainless Steel for Incorporation into ASME Division 5

This report describes the development of a unified viscoplastic model describing the deformation of 316 stainless steel. The model was developed for incorporation into Section III, Division 5 of the ASME Boiler and Pressure Vessel Code covering the design and fabrication of Class A high temperature nuclear reactor components. The current version of the Code does not provide reference inelastic models, leaving the responsibility of defining an adequate model up to either the plant owner or designer. This approach represents a substantial barrier to using the ASME design by inelastic analysis methods. As historical practice demonstrates these methods produce much more efficient designs, compared to the design by elastic analysis methods, eliminating this barrier could result in safer, more effective, and more efficient structural designs thereby lowering the cost of future advanced reactors. This report summarizes the process of gathering experimental data, calibrating the candidate inelastic model against that data, and validating the model. The report also summarizes work on a new method of calculating the sensitivities of history dependent nonlinear constitutive models that was used to calibrate the 316H model using gradient-based optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Testing, Characterization, and Modeling of the Resonant Plate Test Environment

The resonant plate shock test is a dynamic test of a mid-field pyroshock environment where a projectile is struck against a plate. The structure undergoing the simulated field shock is mounted to the plate. The plate resonates when struck and provides a two sided shock that is representative of the shock observed in the field. This test environment shock simulates a shock in a single coordinate direction for components looking to provide evidence that they will survive a similar or less shock when deployed in their operating environment. However, testing in one axis at a time provides many challenges. The true environment is a multi-axis environment. The test environment exhibits strong off-axis motion when only motion in one axis is desired. Multiple fixtures are needed for a single test series. It would be advantageous if a single test could be developed that tests the multi-axis environment simultaneously. In order to design such a test, a model must be developed and validated. The model can be iterated in design and configuration until the specified multi-axis environment is met. The test can then execute the model driven test design. This report discusses the resonant plate model needed to design future tests and the steps and methods used to obtain the model. This report also details aspects of the resonant plate test discovered during the process of model development that aids in our understanding of the test.

42 ENGINEERING↗
DOE OSTI2023

PV Module Operating Temperature - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has an extensive test site where PV modules and other system components are deployed and monitored for testing and evaluation. For this dataset PV Performance Labs has assembled one year of measurements from the Systems Long-Term Evaluation (SLTE) project (formerly known as PV Lifetime) providing the main variables needed to investigate and validate PV module operating temperature models: irradiance, ambient temperature, wind speed and back-of-module temperature. For use with more advanced thermal modeling, an estimate of down-welling long-wave radiation is also included.

14 SOLAR ENERGY↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
DOE OSTI2015

Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. Thus, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth system models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth system models challenge validation and parameterization of hydrothermal models. A recently developed surface–subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to achieve the goals of constructing a process-rich model based on plausible parameters and to identify fine-scale controls of ALT in ice-wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze–thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g., troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Using field observations to inform thermal hydrology models of permafrost dynamics with ATS (v0.83)

Climate change is profoundly transforming the carbon-rich Arctic tundra landscape, potentially moving it from a carbon sink to a carbon source by increasing the thickness of soil that thaws on a seasonal basis. However, the modeling capability and precise parameterizations of the physical characteristics needed to estimate projected active layer thickness (ALT) are limited in Earth System Models (ESMs). In particular, discrepancies in spatial scale between field measurements and Earth System Models challenge validation and parameterization of hydrothermal models. A recently developed surface/subsurface model for permafrost thermal hydrology, the Advanced Terrestrial Simulator (ATS), is used in combination with field measurements to calibrate and identify fine scale controls of ALT in ice wedge polygon tundra in Barrow, Alaska. An iterative model refinement procedure that cycles between borehole temperature and snow cover measurements and simulations functions to evaluate and parameterize different model processes necessary to simulate freeze/thaw processes and ALT formation. After model refinement and calibration, reasonable matches between simulated and measured soil temperatures are obtained, with the largest errors occurring during early summer above ice wedges (e.g. troughs). The results suggest that properly constructed and calibrated one-dimensional thermal hydrology models have the potential to provide reasonable representation of the subsurface thermal response and can be used to infer model input parameters and process representations. The models for soil thermal conductivity and snow distribution were found to be the most sensitive process representations. However, information on lateral flow and snowpack evolution might be needed to constrain model representations of surface hydrology and snow depth.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A non-cooperative meta-modeling game for automated third-party calibrating, validating and falsifying constitutive laws with parallelized adversarial attacks

The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification. While there are numerous efforts to develop paradigms and standard procedures to validate models, difficulties may arise due to the sequential, manual, and often biased nature of the commonly adopted calibration and validation processes, thus slowing down data collections, hampering the progress towards discovering new physics, increasing expenses and possibly leading to misinterpretations of the credibility and application ranges of proposed models. This work attempts to introduce concepts from game theory and machine learning techniques to overcome many of these existing difficulties. Here, we introduce an automated meta-modeling game where two competing AI agents systematically generate experimental data to calibrate a given constitutive model and to explore its weakness such that the experiment design and model robustness can be improved through competitions. The two agents automatically search for the Nash equilibrium of the meta-modeling game in an adversarial reinforcement learning framework without human intervention. In particular, a protagonist agent seeks to find the more effective ways to generate data for model calibrations, while an adversary agent tries to find the most devastating test scenarios that expose the weaknesses of the constitutive model calibrated by the protagonist. By capturing all possible design options of the laboratory experiments into a single decision tree, we recast the design of experiments as a game of combinatorial moves that can be resolved through deep reinforcement learning by the two competing players. Our adversarial framework emulates idealized scientific collaborations and competitions among researchers to achieve a better understanding of the application range of the learned material laws and prevent misinterpretations caused by conventional AI-based third-party validation. Numerical examples are given to demonstrate the wide applicability of the proposed meta-modeling game with adversarial attacks on both human-crafted constitutive models and machine learning models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

SU-C-201-07: Validation of a GATE Gamma Camera Model for the Siemens Symbia

Purpose: To develop a simulation model of a clinical gamma camera/SPECT system and to validate the model using experimental and published measurements from the clinical system. Methods: Geant4 Application for Tomographic Emission (GATE) was used to create a model of the Siemens Symbia gamma camera. A modular model was implemented that allows specifying combinations of crystal thickness (3/8”, 5/8”) and collimator (LEHR, MELP, HE). Shielding, energy resolution, intrinsic resolution, crystal thickness, and collimator properties were set based on manufacturer specifications. Validation of the model was performed by simulating NEMA 2007 gamma camera tests including spatial resolution and sensitivity for Tc99; these were compared with experimental and published data for the scanner. The simulated energy spectra of a Tc99 line source in acrylic blocks was visually compared with the corresponding experimental acquisition. For a 4 cm diameter sphere filled with Tc99, the attenuation maps were generated from simulation data, and the photopeak and scatter window were extracted from GATE output using ROOT to create DICOM files to use in the clinical reconstruction. Results: Simulated spatial resolutions for LEHR 3/8” crystal at 0, 10 cm, 10 cm (with scatter), and 30 cm were 4, 6.7, 7.9, and 14.5 mm FWHM; these were 9% less than published data. For 5/8” crystal the spatial resolutions were 4.5, 7.0, 8.5, and 14.7 mm FWHM; these were 4% to 10% less than published data. Simulated sensitivity was within 3.5% of published data for both LEHR 3/8” and 5/8”. The simulated energy spectra matched the photopeak and scatter window well, but did overestimate the counts below 90 keV. The simulated attenuation map and projection data were successfully reconstructed with the clinical software, and the passed visual inspection. Conclusions: Validation of a specific clinical scanner allows future studies of quantification accuracy for both planar and SPECT imaging. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA138986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Toward Understanding the Effect of Low-Activity Waste Glass Composition on Sulfur Solubility

The concentration of sulfur in nuclear waste glass melter feed must be maintained below the point where salt accumulates on the melt surface. The allowable concentrations may range from 0.37 to over 2.05 weight percent (of SO3 on a calcined oxide basis). If the amount of sulfur exceeds its tolerance level a molten salt will accumulate and upset melter operations and potentially shorten melter useful life. Therefore relatively conservative limits have been placed on sulfur loading in melter feed which in-turn significantly impacts the amount of glass that will be produced, in particular at the Hanford site. Crucible-scale sulfur solubility data and scaled melter sulfur tolerance data have been collected on simulated Hanford waste glasses over the last 15 years. These data were compiled and analyzed. A model was developed to predict the solubility of SO3 in glass based on 312 individual glass compositions. This model was shown to well represent the data, accounting for over 80% of the variation in data and was well validated. The model was also found to accurately predict the tolerance for sulfur in melter feed based on 19 scaled melter tests. The model is appropriate for control of waste glass processing which includes uncertainty quantification. The model also gives quantitative estimates of component concentration effects on sulfur solubility. The components that most increase sulfur solubility are Li2O > V2O5 ≈ TiO2 < CaO < P2O5 ≈ ZnO. The components that most decrease sulfur solubility are Cl > Cr2O3 > SiO2 ≈ ZrO2 > Al2O3.

Borosilicate↗
DOE OSTI2026

Experimental observation and integrated modelling of proton-beryllium fusion in He and D plasmas at JET

Validated integrated modelling of JET ITER-like wall experiments in which fusion performance is driven by reactions between fast ions and intrinsically present metal wall impurities is presented. A steady-state L-mode plasma with dominant proton-beryllium fusion and neutron yields of up to ≈ 6·10 13 s -1 is developed in He and D, via radiofrequency heating of a H minority. The fusion drive is unambiguously confirmed by the neutral particle analyser, fast ion loss detector, and γ-ray diagnostics. Experiments are analysed via an integrated modelling framework, developed to model the two-stage proton beryllium-fusion chain and produce high-fidelity fusion product source terms. The modelling chain comprises TRANSP and JETTO for plasma core modelling, LOCUST for full orbit product tracking and collisional slowing-down, DRESS to resolve two- and three-body fusion kinematics, and MCNP for neutron transport calculations. Modelling shows that the primary 9 Be(p,n) 9 B reaction is the dominant neutron emitter at naturally present concentrations of beryllium in these experiments. The yield contribution of secondary reactions between fusion products and beryllium, 9 Be(d,n) 10 B and 9 Be(α,n) 12 C, is found to be negligible. The proton-deuteron knock-on effect in D plasmas is modelled, which is calculated to contribute ≈ 25% to the total neutron yield. For both He and D discharges the total computed neutron rates match fission chamber (FC) measurements within the combined experimental and computational uncertainty, with an average discrepancy of ≈ ± 20%. Realistic proton-beryllium neutron sources are propagated through JET’s MCNP neutron transport model which shows that 235 U FCs’ response is sensitive to p–Be source changes, with up to ≈ 10% variation compared to a D–D neutron source. We show that the high-energy tail of the fast proton minority can be studied with multi-foil neutron activation. The framework is also applied to the study of interactions between fast protons and boron impurities, of relevance to ITER. We calculate that in JET conditions a significant alpha source with DT-like energies could be generated through 11 B(p, α)2α fusion, and detected via γ-emission in secondary interactions between fast alphas and boron. The work represents an important step towards validating predictive integrated modelling capabilities for non-standard fusion reactions.

JET↗