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DOE OSTI2020

Statistical Treatment of Inverse Problems Constrained by Differential Equations-based Models with Stochastic Terms

This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution represented numerically by an ensemble of simulations. The goal is to formulate the inverse problem, in particular the objective function, to find the closest forward distribution (i.e., the output of the stochastic forward problem) that best explains the distribution of the observations in a certain metric. We use proper scoring rules, a concept employed in statistical forecast verification, namely energy, variogram, and hybrid (i.e., combination of the two) scores. We study the performance of the proposed formulation in the context of two applications: a coefficient field inversion for subsurface flow governed by an elliptic partial differential equation with a stochastic source and a parameter inversion for power grid governed by differential-algebraic equations. In both cases we show that the variogram and the hybrid scores produce better parameter inversion results than does the energy score, whereas the energy score leads to better probabilistic predictions.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fusion RF Modeling Machine Learning (FusionML_RF) v1.0

FusionML_RF consists of multiple codes and trained machine learning (ML) models that perform low-cost output modeling from the Genray-CQL3D. Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. For example, completing a single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time. On the other hand, these ML models achieve ~ms of inference time with high accuracy across the input parameter space. This software collection consists of multiple components. (1) codes that use ML methods and precomputed Genray-CQL3D simulation output to build regression models that enable approximate computations of Genray-CLQ3D outputs from arbitrary but physically meaningful input parameters (surrogate modeling); (2) three trained models created by the team, using a database of 16,000+ GENRAY/CQL3D simulations, to study the performance of ML models for surrogate modeling; (3) codes that load the trained models and simulation data, and then compute mean squared error between the models' predictions and the ground truth of simulation output data. This collection is being made available in conjunction with a scientific publication about the work to promote reusability and provide an artifact of the scientific work.

Bai, Zhe↗

DOE OSTI2021

Robust Importance Sampling for Bayesian Model Calibration with Spatio-Temporal Data

This paper addresses two challenges in Bayesian calibration: 1) computational speed of existing sampling algorithms, and 2) calibration with spatio-temporal responses. The commonly used Markov Chain Monte Carlo (MCMC) approaches require many sequential model evaluations making the computational expense prohibitive. This paper proposes an efficient sampling algorithm: iterative importance sampling with genetic algorithm (IISGA). While iterative importance sampling enables computational efficiency, the genetic algorithm enables robustness by preventing sample degeneration and avoids getting stuck in multimodal search spaces. An inflated likelihood further enables robustness in high-dimensional parameter spaces by enlarging the target distribution. Spatio-temporal data complicate both surrogate modeling, which is necessary for expensive computational models, and the likelihood estimation. In this work, singular value decomposition is investigated for reducing the high-dimensional field data to a lower-dimensional space prior to Bayesian calibration. Then the likelihood is formulated and Bayesian inference is performed in the lower-dimension, latent space. An illustrative example is provided to demonstrate IISGA relative to existing sampling methods, and then IISGA is employed to calibrate a thermal battery model with 26 uncertain calibration parameters and spatio-temporal response data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Weak lensing in DES Y3: redshift distributions, shape catalogue, and mass mapping

The size of galaxy surveys have rapidly escalated with time, growing from thousands of galaxies of the first pioneering surveys to current and future surveys (DES, KiDS, HSC, LSST, Euclid, WFIRST), which map (or plan to map) a few hundreds millions of galaxies. These ambitious programs have the potential to answer many open questions in Cosmology (or, depending on their results, to open new exciting scenarios, involving new physics beyond the standard cosmological model and the standard model of particle physics). In particular, this thesis focuses on the first three years of data from the Dark Energy Survey (DES Y3), which comprises 100 million galaxies observed in 5 different optical bands over 5000 square degrees of the southern hemisphere. DES and the data set used in this thesis are discussed in detail in Chapter 3, which together with Chapter 1 and 2 constitutes Part I of this thesis. The scientifically positive outcome of DES and other photometric galaxy surveys goes h and in h and with a supreme understanding of the data, of the observational systematics affecting the measurements, and of the limits of the theoretical modelling of the observables. One of the main challenge of galaxy photometric surveys like DES is to correctly estimate the distance to the galaxies. Photometric surveys observe the galaxies in a limited number of broad optical bands, and the estimate of their \textit{redshifts} is somewhat uncertain, which constitutes one of the main limiting factor of their cosmological analyses. Part II and its Chapter 4 are devoted to a new auxiliary technique recently developed that allows to infer (or calibrate) redshift distributions exploiting cross-correlation with small reference samples with secure redshifts. The so called ``clustering-redshift'' method is discussed in depth, as well as its role in the overall DES Y3 redshift calibration strategy. Part III and Chapter 5 are devoted to another key aspect of photometric surveys, that enables the stu dy of weak gravitational lensing: the shape catalogue. The official DES Y3 shape catalogue is presented, together with all the systematic tests performed to validate it such that it can be considered science-ready. Last, part IV focuses on how to use weak gravitational lensing and the DES Y3 shape catalogue to create weak lensing mass maps, i.e., maps of the mass distribution of the Universe. Chapter 6 discusses different map making techniques, whereas Chapter 7 presents a simulated cosmological analysis using the high order moments of the mass maps.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and Shell International Exploration & Production, Inc

Introducing CO₂ into geothermal systems as a working fluid in reservoirs can enhance geothermal conductivity, production, and pressure maintenance. A cross-disciplinary interaction of geothermal reservoir stimulation and CO₂ utilization satisfies renewable energy demands and operations that support sustainable energy infrastructure. Challenges to implementing CO₂-stimulated geothermal enhancement (CS-GE) include (1) accurately characterizing and imaging CO₂-stimulated geothermal reservoirs; (2) quantitatively inferring CS-GE evolution under current and future engineered conditions for cost effective operations; and (3) monitoring resources by improving observational methods to advance the understanding of complex geothermal systems for sweep efficiency and, ultimately, cost effectiveness. NETL and Shell will collaborate under this CRADA to develop software to image key features, including CO₂-stimulated fracture imaging, CO₂ fluid sweep imaging and heat transfer and exchange imaging by leveraging available datasets and applying advanced Artificial Intelligence/Machine Learning (AI/ML), multi-level data analytics and data/information fusion to better understand the comprehensive mechanisms of CO₂-stimulated geothermal systems.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Correlations between the Neutron Star Mass–Radius Relation and the Equation of State of Dense Matter

We develop an analytic method of inverting the Tolman–Oppenheimer–Volkoff relations to high accuracy. In principle, a specified energy density–pressure relation gives a unique mass–radius (M–R) relation and vice versa. Our method is developed from the strong correlations that are shown to exist between the neutron star mass–radius curve and the equation of state (EOS) or pressure–energy density relation. Selecting points that have masses equal to fixed fractions of the maximum mass, we find a semi-universal power-law relation between the central energy densities, pressures, sound speeds, chemical potentials, and number densities of those stars, with the maximum mass and the radii of one or more fractional maximum mass points. Rms fitting accuracies, for EOSs without large first-order phase transitions, are typically 0.5% for all quantities at all mass points. The method also works well, although less accurately, in reconstructing the EOS of hybrid stars with first-order phase transitions. These results permit, in effect, an analytic method of inverting an arbitrary M–R curve to yield its underlying EOS. We discuss applications of this inversion technique to the inference of the dense matter EOS from measurements of neutron star masses and radii as a possible alternative to traditional Bayesian approaches.

Bayesian statistics↗

NASA NTRS1974

Grain orientation in lunar soil

Orientation of lunar soil particles in a vertical plane, as seen in the radiographs of core tubes was characterized by preparing orientation diagrams for the different stratigraphic units. Radiographs of double-core drive tubes 64001/64002, 60009/60010, and 60013/60014 were used. The orientation results reinforced the stratigraphic differences. Another source of fabric data was the laboratory-deposited sample 14163,148. The artificial deposition results showed that the grain arrangements were dependent upon the method of deposition. These results from lunar soil and other data from a crushed basalt simulant can be a basis for the inference that lunar soil grain orientation and properties are useful in interpreting lunar surface history.

Mahmood, A.↗

NASA NTRS1989

Constructing R-Curves From Residual-Strength Data

Old data exploited in new concept. Method devised for estimating crack-extension resistance curve (R-curve) from residual-strength data on precracked fracture specimens. Enables inference of additional information from simple test results, and information used to estimate failure loads of more complicated structures of same material and thickness.

Orange, Thomas W.↗

NASA NTRS1989

Shade images of forested areas obtained from LANDSAT MSS data

The pixel size in the present day Remote Sensing systems is large enough to include different types of land cover. Depending upon the target area, several components may be present within the pixel. In forested areas, generally, three main components are present: tree canopy, soil (understory), and shadow. The objective is to generate a shade (shadow) image of forested areas from multispectral measurements of LANDSAT MSS (Multispectral Scanner) data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure, i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The Constrained Least Squares (CLS) method is used to generate shade images for forest of eucalyptus and vegetation of cerrado using LANDSAT MSS imagery over Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on three crown cover for vegetation of cerrado.

Shimabukuro, Yosio Edemir↗

NASA NTRS1989

Shade images of forested areas obtained from Landsat MSS data

The objective of this report is to generate a shade (shadow) image of forested areas from Landsat MSS data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure; i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The constrained least-squares method is used to generate shade images for forest of eucalyptus and vegetation of 'cerrado' over the Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on tree crown cover for vegetation of cerrado.

Shimabukuro, Yosio Edemir↗

NASA NTRS1992

The statistical analysis of circadian phase and amplitude in constant-routine core-temperature data

Accurate estimation of the phases and amplitude of the endogenous circadian pacemaker from constant-routine core-temperature series is crucial for making inferences about the properties of the human biological clock from data collected under this protocol. This paper presents a set of statistical methods based on a harmonic-regression-plus-correlated-noise model for estimating the phases and the amplitude of the endogenous circadian pacemaker from constant-routine core-temperature data. The methods include a Bayesian Monte Carlo procedure for computing the uncertainty in these circadian functions. We illustrate the techniques with a detailed study of a single subject's core-temperature series and describe their relationship to other statistical methods for circadian data analysis. In our laboratory, these methods have been successfully used to analyze more than 300 constant routines and provide a highly reliable means of extracting phase and amplitude information from core-temperature data.

NASA Discipline Regulatory Physiology↗

NASA NTRS2012

On the Ice Nucleation Spectrum

This work presents a novel formulation of the ice nucleation spectrum, i.e. the function relating the ice crystal concentration to cloud formation conditions and aerosol properties. The new formulation is physically-based and explicitly accounts for the dependency of the ice crystal concentration on temperature, supersaturation, cooling rate, and particle size, surface area and composition. This is achieved by introducing the concepts of ice nucleation coefficient (the number of ice germs present in a particle) and nucleation probability dispersion function (the distribution of ice nucleation coefficients within the aerosol population). The new formulation is used to generate ice nucleation parameterizations for the homogeneous freezing of cloud droplets and the heterogeneous deposition ice nucleation on dust and soot ice nuclei. For homogeneous freezing, it was found that by increasing the dispersion in the droplet volume distribution the fraction of supercooled droplets in the population increases. For heterogeneous ice nucleation the new formulation consistently describes singular and stochastic behavior within a single framework. Using a fundamentally stochastic approach, both cooling rate independence and constancy of the ice nucleation fraction over time, features typically associated with singular behavior, were reproduced. Analysis of the temporal dependency of the ice nucleation spectrum suggested that experimental methods that measure the ice nucleation fraction over few seconds would tend to underestimate the ice nuclei concentration. It is shown that inferring the aerosol heterogeneous ice nucleation properties from measurements of the onset supersaturation and temperature may carry significant error as the variability in ice nucleation properties within the aerosol population is not accounted for. This work provides a simple and rigorous ice nucleation framework where theoretical predictions, laboratory measurements and field campaign data can be reconciled, and that is suitable for application in atmospheric modeling studies.

Barahona, D.↗

NASA NTRS2015

Solar Flare Track Exposure Ages in Regolith Particles: A Calibration for Transmission Electron Microscope Measurements

Mineral grains in lunar and asteroidal regolith samples provide a unique record of their interaction with the space environment. Space weathering effects result from multiple processes including: exposure to the solar wind, which results in ion damage and implantation effects that are preserved in the rims of grains (typically the outermost 100 nm); cosmic ray and solar flare activity, which result in track formation; and impact processes that result in the accumulation of vapor-deposited elements, impact melts and adhering grains on particle surfaces. Determining the rate at which these effects accumulate in the grains during their space exposure is critical to studies of the surface evolution of airless bodies. Solar flare energetic particles (mainly Fe-group nuclei) have a penetration depth of a few millimeters and leave a trail of ionization damage in insulating materials that is readily observable by transmission electron microscope (TEM) imaging. The density of solar flare particle tracks is used to infer the length of time an object was at or near the regolith surface (i.e., its exposure age). Track measurements by TEM methods are routine, yet track production rate calibrations have only been determined using chemical etching techniques [e.g., 1, and references therein]. We used focused ion beam-scanning electron microscope (FIB-SEM) sample preparation techniques combined with TEM imaging to determine the track density/exposure age relations for lunar rock 64455. The 64455 sample was used earlier by [2] to determine a track production rate by chemical etching of tracks in anorthite. Here, we show that combined FIB/TEM techniques provide a more accurate determination of a track production rate and also allow us to extend the calibration to solar flare tracks in olivine.

Berger, Eve L.↗

NASA NTRS2017

Simulating Matrix Crack and Delamination Interaction in a Clamped Tapered Beam

Blind predictions were conducted to validate a discrete crack methodology based on the Floating Node Method to simulate matrix-crack/delamination interaction. The main novel aspects of the approach are: (1) the implementation of the floating node method via an 'extended interface element' to represent delaminations, matrix-cracks and their interaction, (2) application of directional cohesive elements to infer overall delamination direction, and (3) use of delamination direction and stress state at the delamination front to determine migration onset. Overall, good agreement was obtained between simulations and experiments. However, the validation exercise revealed the strong dependence of the simulation of matrix-crack/delamination interaction on the strength data (in this case transverse interlaminar strength, YT) used within the cohesive zone approach applied in this work. This strength value, YT, is itself dependent on the test geometry from which the strength measurement is taken. Thus, choosing an appropriate strength value becomes an ad-hoc step. As a consequence, further work is needed to adequately characterize and assess the accuracy and adequacy of cohesive zone approaches to model small crack growth and crack onset. Additionally, often when simulating damage progression with cohesive zone elements, the strength is lowered while keeping the fracture toughness constant to enable the use of coarser meshes. Results from the present study suggest that this approach is not recommended for any problem involving crack initiation, small crack growth or multiple crack interaction.

De Carvalho, N. V.↗

DOE OSTI2022

Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks

Measuring the structural parameters (size, total brightness, light concentration, etc.) of galaxies is a significant first step towards a quantitative description of different galaxy populations. In this work, we demonstrate that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty quantification, of such morphological parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values. Our method is also significantly faster, which is very important with the advent of the era of large galaxy surveys and big data in astrophysics.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

I Know I'm Right, But Does My Phone?

Transportation is the largest source of green-house gas emissions in the United States. Reducing transportation emissions depends on human travel behavior, which relies on local land use and planning. Travel diaries, consisting of sequences of trips between places for a particular individual, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of GPS tracking surveys. Mode inference algorithms in particular have been well represented in literature. However, these algorithms have typically been validated using prompted recall of pre-segmented trips, which doesn't account for segmentation error, thus disregarding the continuity of mode inference. Furthermore, phone operating systems and applications have adopted battery-conserving techniques, but we are not aware of prior work that has characterized the resulting data collection errors or evaluated procedures to mitigate them. We introduce a framework to evaluate accuracy of trip length computations and mode inference. We develop a temporal alignment procedure in analyzing continuous mode-segmented trajectories for groups of trips. We then apply our framework to evaluate an example set of travel diary algorithms from the open-source OpenPATH travel diary platform against MobilityNet, a public dataset containing information from three artificial timelines that cover 15 different travel modes. Our results show that inference based on an integration with map features results in weighted F_1 scores of 0.60 (iOS) and 0.74 (android). We also show that OpenPATH tends to under count trip length, with mean of signed relative error of -0.0438 on android and -0.0704 on iOS. We hope that other travel diary algorithms will be evaluated using this standardized process, and that the results used to understand and improve the state-of-the-art in this field.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

DOE OSTI2013

Cast Stone Oxidation Front Evaluation: Preliminary Results For Samples Exposed To Moist Air

The rate of oxidation is important to the long-term performance of reducing salt waste forms because the solubility of some contaminants, e.g., technetium, is a function of oxidation state. TcO 4 - in the salt solution is reduced to Tc(IV) and has been shown to react with ingredients in the waste form to precipitate low solubility sulfide and/or oxide phases. Upon exposure to oxygen, the compounds containing Tc(IV) oxidize to the pertechnetate ion, Tc(VII)O 4 - , which is very soluble. Consequently the rate of technetium oxidation front advancement into a monolith and the technetium leaching profile as a function of depth from an exposed surface are important to waste form performance and ground water concentration predictions. An approach for measuring contaminant oxidation rate (effective contaminant specific oxidation rate) based on leaching of select contaminants of concern is described in this report. In addition, the relationship between reduction capacity and contaminant oxidation is addressed. Chromate (Cr(VI) was used as a non-radioactive surrogate for pertechnetate, Tc(VII), in Cast Stone samples prepared with 5 M Simulant. Cast Stone spiked with pertechnetate was also prepared and tested. Depth discrete subsamples spiked with Cr were cut from Cast Stone exposed to Savannah River Site (SRS) outdoor ambient temperature fluctuations and moist air. Depth discrete subsamples spiked with Tc-99 were cut from Cast Stone exposed to laboratory ambient temperature fluctuations and moist air. Similar conditions are expected to be encountered in the Cast Stone curing container. The leachability of Cr and Tc-99 and the reduction capacities, measured by the Angus-Glasser method, were determined for each subsample as a function of depth from the exposed surface. The results obtained to date were focused on continued method development and are preliminary and apply to the sample composition and curing / exposure conditions described in this report. The Cr oxidation front (depth to which soluble Cr was detected) for the Cast Stone sample exposed for 68 days to ambient outdoor temperatures and humid air (total age of sample was 131 days) was determined to be about 35 mm below the top sample surface exposed. The Tc oxidation front, depth at which Tc was insoluble, was not determined. Interpretation of the results indicates that the oxidation front is at least 38 mm below the exposed surface. The sample used for this measurement was exposed to ambient laboratory conditions and humid air for 50 days. The total age of the sample was 98 days. Technetium appears to be more easily oxidized than Cr in the Cast Stone matrix. The oxidized forms of Tc and Cr are soluble and therefore leachable. Longer exposure times are required for both the Cr and Tc spiked samples to better interpret the rate of oxidation. Tc spiked subsamples need to be taken further from the exposed surface to better define and interpret the leachable Tc profile. Finally Tc(VII) reduction to Tc(IV) appears to occur relatively fast. Results demonstrated that about 95 percent of the Tc(VII) was reduced to Tc(IV) during the setting and very early stage setting for a Cast Stone sample cured 10 days. Additional testing at longer curing times is required to determine whether additional time is required to reduce 100 % of the Tc(VII) in Cast Stone or whether the Tc loading exceeded the ability of the waste form to reduce 100 % of the Tc(VII). Additional testing is required for samples cured for longer times. Depth discrete subsampling in a nitrogen glove box is also required to determine whether the 5 percent Tc extracted from the subsamples was the result of the sampling process which took place in air. Reduction capacity measurements (per the Angus-Glasser method) performed on depth discrete samples could not be correlated with the amount of chromium or technetium leached from the depth discrete subsamples or with the oxidation front inferred from soluble chromium and technetium (i.e., effective Cr and Tc oxidation fronts). Residual reduction capacity in the oxidized region of the test samples indicates that the remaining reduction capacity is not effective in re-reducing Cr(VI) or Tc(VII) in the presence of oxygen. Depth discrete sampling and leaching is a useful for evaluating Cast Stone and other chemically reducing waste forms containing ground granulated blast furnace slag (GGBFS) or other reduction / sequestration reagents to control redox sensitive contaminant chemistry and leachability in the near surface disposal environment. Based on results presented in this report, reduction capacity measured by the Angus-Glasser Ce(IV) method is not an appropriate or meaningful parameter for determining or predicting Tc and Cr oxidation / retentions, speciation, or solubilities in cementitious materials such as Cast Stone. A model for predicting Tc(IV) oxidation to soluble Tc(VII) should consider the waste form porosity (pathway for oxygen ingress), oxygen source, and the contaminant specific oxidation rates and oxidation fronts. Depth discrete sampling of materials exposed to realistic conditions in combination with short term leaching of crushed samples has potential for advancing the understanding of factors influencing performance. This information can be used to support conceptual model development.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗