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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Radius of PSR J0740+6620 from NICER with NICER Background Estimates

We report a revised analysis for the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740+6620, studied previously with joint fits to NICER and XMM-Newton data by Riley et al. (2021) and Miller et al. (2021). We perform a similar Bayesian estimation for the pulse-profile model parameters, except that instead of fitting simultaneously the XMM-Newton data, we use the best available NICER background estimates to constrain the number of photons detected from the source. This approach eliminates any potential issues in the cross-calibration between these two instruments, providing thus an independent check of the robustness of the analysis. The obtained neutron star parameter constraints are compatible with the already published results, with a slight dependence on how conservative the imposed background limits are. A tighter lower limit causes the inferred radius to increase, and a tighter upper limit causes it to decrease. We also extend the study of the inferred emission geometry to examine the degree of deviation from antipodality of the hot regions. We show that there is a significant offset to an antipodal spot configuration, mainly due to the non-half-cycle azimuthal separation of the two emitting spots. The offset angle from the antipode is inferred to be above 25° with 84% probability. This seems to exclude a centered-dipolar magnetic field in PSR J0740+6620.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification

Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.

Bayesian parameter estimation↗
DOE OSTI2022

Parameter Estimation for Electrode Degradation: Learning in the Face of Model-Experiment Discrepancies

Use of physics-based models to interpret battery degradation data over the course of cycling can provide deeper physical insight into the internal states of the system and how they evolve. We present a neural network trained on simulations generated by a previously published physics-based model for a lithium trivanadate (LVO) cathode to estimate parameters that evolve over the course of cycling. We focus on the robustness of the neural network through two case studies that probe different kinds of discrepancies between model and experiment: nonideal data and imperfect model. In the former, the experimental protocols do not meet the assumption made in the training data generated by the physics-based model, while in the latter, the physics-based model fails to describe all of the measured cathode behavior even under ideal conditions. When there is total model-experiment agreement, a neural network estimates parameters with improved accuracy compared to a maximum likelihood analysis using the same set of simulations. However, in both types of model-experiment discrepancy, the neural network returned biased parameter estimates. We introduce a data augmentation procedure into the neural network training to mitigate these effects and improve robustness, and employ it to estimate parameters for a cycling LVO cathode.

Mayilvahanan, Karthik S. (ORCID:0000000316282332)↗
DOE OSTI2025

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

copolymers↗
DOE OSTI2018

Mapping Soil Cation‐Exchange Capacity using Bayesian Modeling and Proximal Sensors at the Field Scale

Core Ideas A Bayesian inference approach (INLA‐SPDE) was used to map topsoil and subsoil CEC. DEM, gamma‐ray spectrometer and EM induction data were combined to map CEC. Posterior marginal distributions of the model parameters and responses were estimated. Gamma‐ray data performed best in the topsoil, followed by DUALEM421S and elevation. Elevation data performed best in the subsoil, followed by gamma‐ray and DUALEM421S. Cation exchange capacity (CEC) affects soil fertility, acidity, and structural resilience. This is particularly the case in sugarcane growing areas of Australia because the soil there is sandy (>60%), strongly acidic (pH < 5.5), and strongly sodic (exchangeable sodium percentage [ESP] > 15%). Unfortunately, obtaining information on CEC at the field extent is time‐consuming and expensive. Here, we used a digital soil mapping approach to add value to limited (40) topsoil (0–0.3 m) and subsoil (0.6–0.9 m) CEC information. We first collected proximally sensed ancillary data from three sources, including a digital elevation model (DEM), γ‐ray (γ‐ray) spectrometer (RS700) and electromagnetic (EM) induction instruments. We then use a Bayesian inference approach (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA‐SPDE) implemented in R software to model the CEC and ancillary data. Accuracy (RMSE), bias (ME), and concordance (Lin's) of models were also generated from the different sources of ancillary data, either in combination or alone. We concluded, overall, that the INLA–SPDE approach could provide estimations of the posterior marginal distributions of the model parameters as well as the model responses as reported by other researchers. We also concluded that using the ancillary data sources in combination was most accurate (e.g., RMSE = 0.72) to predict CEC, least biased (e.g., ME = 0.07) and had the highest concordance (e.g., Lin's = 0.69) in both the topsoil and subsoil than using the ancillary data alone. The best ancillary data, when used alone for mapping CEC in the topsoil, was γ‐ray spectrometry, followed by EM data and elevation. For subsoil CEC, it was elevation, followed by γ‐ray spectrometry and then soil electrical conductivity (EC a ) data. The maps of the credibility interval (CI) indicated that better predictions were achieved in the topsoil and indicated where improvements in prediction could be achieved in the subsoil.

Li, Nan↗
DOE OSTI2019

Modeling Sulfur Poisoning of Palladium Membranes Used for Hydrogen Separation

Hydrocarbons are the most important source for hydrogen production. A combined reaction-separation process using inorganic membranes can significantly increase the reaction conversion by shifting the equilibrium toward product formation. Sulfur poisoning is a significant problem as it deactivates the most commonly used metallic membranes. The relationship of the membrane activity and surface coverage with the surface structure has been recognized in the literature. A theoretical model to simulate hydrogen transport in the presence of sulfur compounds is presented. This model accounts for active site deactivation and permanent structural damage to the membrane. Transport and reaction rate parameters used in the model have been estimated from experimental data. Qualitatively, the model represents well the behavior of inorganic membranes, including partial membrane activity regeneration after the sulfur source is removed.

08 HYDROGEN↗
DOE OSTI2022

Parameter estimation from spontaneous imbibition into volcanic tuff

Two-phase fluid flow properties underlie quantitative prediction of water and gas movement, but constraining these properties typically requires multiple time-consuming laboratory methods. The estimation of two-phase flow properties (van Genuchten parameters, porosity, and intrinsic permeability) is illustrated in cores of vitric nonwelded volcanic tuff using Bayesian parameter estimation that fits numerical models to observations from spontaneous imbibition experiments. The uniqueness and correlation of the estimated parameters is explored using different modeling assumptions and subsets of the observed data. The resulting estimation process is sensitive to both moisture retention and relative permeability functions, thereby offering a comprehensive method for constraining both functions. The data collected during this relatively simple laboratory experiment, used in conjunction with a numerical model and a global optimizer, result in a viable approach for augmenting more traditional capillary pressure data obtained from hanging water column, membrane plate extractor, or mercury intrusion methods. This method may be useful when imbibition rather than drainage parameters are sought, when larger samples (e.g., including heterogeneity or fractures) need to be tested that cannot be accommodated in more traditional methods, or when in educational laboratory settings.

58 GEOSCIENCES↗
DOE OSTI2017

Leaf Photosynthetic Parameters Vcmax and Jmax and Supporting Gas Exchange Data, Barrow, Alaska, 2012-2016

Photosynthetic parameters Vcmax and Jmax estimated from CO2 response curves and from light saturated photosynthesis using the one point method. Data were collected in Barrow 2012-2016 on 7 species - Arctophila fulva, Arctagrostis latifolia, Carex aquatilis, Dupontia fisheri, Eriophorum angustifolium, Petasites frigidus, Salix pulchra. To allow for future reanalysis of our data, and to maximize further use of it by the modelling community, all our data including our raw gas exchange data is available. This dataset contains 3 csv, 3 xlsx, and 1 pdf files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Online Static Load Model Estimation in Distribution Systems: Preprint

The paper focuses on parameter identification for time varying voltage dependent polynomial load models, namely the constant impedance (Z), constant current (I), and constant power (P), in the distribution systems. Typically, parameter identification has been done for aggregated loads at substations for the transmission planning studies. Given the higher voltage volatility faced by active distribution systems, this paper takes the load model estimation to the distribution networks. The paper assumes the presence of measurements at the distribution nodes from various sources, each at varying temporal resolutions. Least squares estimation (LSE) method with bounded variables is used to convert the measurements into ZIP parameter estimations for loads at various locations of the feeder. The results and discussions focus on some of the estimation issues faced in a distribution system due to varying voltage sensitivities of loads along a feeder, measurement resolution, impact of window size and sampling rate of measurement data, and the use of dynamic simulations to create synthetic measurement data in the absence of real ones.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Time Variations of Observed H α Line Profiles and Precipitation Depths of Nonthermal Electrons in a Solar Flare

We compare time variations of the H α and X-ray emissions observed during the pre-impulsive and impulsive phases of the C1.1-class solar flare on 2013 June 21 with those of plasma parameters and synthesized X-ray emission from a 1D hydrodynamic numerical model of the flare. The numerical model was calculated assuming that the external energy is delivered to the flaring loop by nonthermal electrons (NTEs). The H α spectra and images were obtained using the Multi-channel Subtractive Double Pass spectrograph with a time resolution of 50 ms. The X-ray fluxes and spectra were recorded by RHESSI . Pre-flare geometric and thermodynamic parameters of the model and the delivered energy were estimated using RHESSI data. The time variations of the X-ray light curves in various energy bands and those of the H α intensities and line profiles were well correlated. The timescales of the observed variations agree with the calculated variations of the plasma parameters in the flaring loop footpoints, reflecting the time variations of the vertical extent of the energy deposition layer. Our result shows that the fast time variations of the H α emission of the flaring kernels can be explained by momentary changes of the deposited energy flux and the variations of the penetration depths of the NTEs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Here, our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

big data↗
DOE OSTI2021

A rigorous physics-based enhanced parameter estimation (EPE) methodology for calibration of building energy simulations

Buildings rarely perform as designed/simulated and there are numerous tangible benefits if this gap is reconciled. A new scientifically rigorous yet pragmatic methodology for calibrating building energy simulations - called Enhanced Parameter Estimation (EPE) - is proposed that allows physically relevant parameter estimation rather than a blind force-fit to energy use data. Starting with a rapidly created simulation model, calibration is performed in two stages: (a) building shell calibration with the HVAC system replaced by an ideal system that meets the loads (b) HVAC system calibration with the building shell and all internal loads replaced by a box with only process loads. In the first stage, EPE identifies a small number of high-level heat flows in the energy balance, calculates them with specifically tailored individual driving functions, introduces physically significant parameters to best accomplish energy balance, and, estimates the parameters and their uncertainty bounds. Calibration is thus done with corrective heat flows without any arbitrary tuning of input parameters. Calibration accuracy is enhanced by machine learning of the residual errors. The EPE methodology is demonstrated by means of: a synthetic building and an actual 75,000 sq. ft. building in Pennsylvania. Future work needed for widespread application is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Preliminary Results on Bayesian Inverse UQ for OECD/NEA WPNCS Subgroup 14 Benchmark Exercise for Error Recovery and Experimental Coverage

The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.

Bayesian Inference↗
DOE OSTI2026

Automated Signal Timing Plan Reconstruction Using High-Resolution Event-Based Controller Data for Digital Twins

Transportation digital twins are essential tools for evaluating emerging technologies such as connected and automated vehicles, adaptive traffic signal control, and mobility optimization strategies. Realistic digital twins require accurate emulation of real-world signal controllers and detailed signal timing plans. However, signal timing plans are often unavailable or difficult to access, forcing researchers and modelers to rely on assumed fixed timings or halt their analysis. To overcome this challenge, we present a method that directly estimates signal timing plan parameters using high-resolution, event-based data from traffic signal controllers. The proposed method extracts key parameters, including cycle length, offset, phase sequence, coordinated phases, phase-specific minimum and maximum green durations, vehicle extensions, and splits under coordination. A rule-based deterministic signal timing reconstruction algorithm based on traffic signal operation rules, such as those outlined in the Signal Timing Manual, is developed and validated. We evaluate this method, which uses high-resolution controller event logs and verified signal timing plans, on 94 signalized intersections in Nashville, Tennessee, demonstrating their ability to generate accurate, simulation-ready signal timing plans for tools such as SUMO and Vissim.

Saroj, Abhilasha [ORNL] (ORCID:0000000191178063)↗
DOE OSTI2019

Combining a land surface model with groundwater model calibration to assess the impacts of groundwater pumping in a mountainous desert basin

The quantification of recharge and trans-valley underflow is needed in arid regions to estimate the impacts of new water withdrawals on the water table. However, for mountainous desert areas, such estimates are highly challenging, due to data scarcity, heterogeneous soils, and long residence times. Conventional assessment employs isolated groundwater models configured with simplified uniform estimates of recharge. Here, we employed a data-constrained surface-subsurface process model to provide an ensemble of spatially distributed recharge and underflow estimates using perturbed parameters. Then, the Model-Independent Parameter Estimation and Uncertainty Quantification (PEST) package was used to calibrate the aquifer hydraulic conductivity field in MODFLOW for this ensemble and reject implausible recharge values. This novel dual-model approach, broadly applicable to mountainous arid regions, was designed to maximally exploit available data sources. It can assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns due to pumping. We applied this approach to the Chuckwalla basin in California, USA to determine natural recharge. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where run-off water infiltrates. If an evenly distributed recharge was employed as in conventional studies, it would result in regional biases in estimated drawdown and larger uncertainty bounds. Here, we also note that the speed of groundwater recovery does not guarantee sustainability: heavy pumping induces large hydraulic gradients that initially recover quickly when pumping is halted, but the system may not ultimately recover to pre-pumping levels.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Elastic functional principal component regression

We study regression using functional predictors in situations where these functions contains both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via preprocessing, that is, use an off–the–shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has a comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square–root slope function. These functions preserve the L 2 norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Furthermore, using both simulated and real–world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Convective Parameters Derived from Radiosonde Data (SONDEPARAM) Value-Added Product Report

Radiosondes provide fundamental observations of the vertical profile of atmospheric state (pressure, temperature, humidity, and winds), with important implications for subsequent studies on environmental controls on cloud conditions. Within convective cloud environments, there is an increasing demand for additional value-added products (VAPs) to facilitate the use of U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility radiosonde data sets. Such VAPs should provide quick and reliable estimates for several standard radiosonde parameters or quantities of interest using common assumptions, as well as open, flexible code for visualization and user interaction. The Convective Parameters Derived from Radiosonde Data (SONDEPARAM) VAP will apply several robust algorithms used in Wang et al. (2020) for the calculation of useful radiosonde convective cloud parameters, including the convective available potential energy (CAPE), convective inhibition (CIN), and other convective parameters, for several different assumptions regarding the initial parcel characteristics (i.e., surface-based, most unstable, mixed layer). These ARM VAP codes are developed in open, flexible Python formats, with the intention that these parameters/calculations will be incorporated into traditional ARM quick-look radiosonde plotting, yet associated with user-available codes for ease in user reproduction and assumption modification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗