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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A method of surface marker location optimization for tumor motion estimation in lung stereotactic body radiation therapy

Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparse optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Statistical learning framework for safety and failure analysis of a DNN-based autonomous aircraft system

Deep Neural Networks (DNNs) and Machine Learning technology is increasingly used for safety-critical applications in the Aerospace domain. To ensure safe operations, the DNN and the system must undergo rigorous verification and validation, including advanced statistical analyses. Performance and safety of the DNN and system behavior must not only be analyzed for the nominal case, but under numerous off-nominal and failure cases. In this paper we will describe how our statistical learning framework SYSAI can efficiently perform such analyses using the tool’s unique combination of advanced learning modeling and statistical analysis techniques. SYSAI can effectively explore the high-dimensional state and failure space of the system under test; geometrical shape detection of safety regions and boundaries support explainability of the results to the designer. In this paper, we report experiments and results obtained with a vision-based DNN control system (ACT) that is capable of autonomously steering an aircraft down a runway.

Yuning He↗
DOE OSTI2023

Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Abstract We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochastic emulators are particularly relevant because of the statistical nature of climate model ensembles, as discussed in previous work of the authors (Castruccio et al. in J Clim 32:8511–8522, 2019; Hu and Castruccio in J Clim 34:8409–8418, 2021). We thank the authors for sharing of some of their data with us in order to illustrate this discussion. In the following, in Sect. 1 we discuss alternative techniques currently used and studied, namely lossy compression and ideas emerging from the climate modeling community, that could feed the discussion on ensemble and storage. In that section, we also present numerical results of compression performed on the data shared by the authors. In Sect. 2, we discuss the current statistical model proposed by the authors and its context. We discuss other potential uses of stochastic emulators in climate and Earth modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Evaluating Clouds, Aerosols, and their Interactions in Three Global Climate Models using COSP and Satellite Observations

Accurately representing aerosol-cloud interactions in global climate models is challenging. As parameterizations evolve, it is important to evaluate their performance with appropriate use of observations. In this work we compare aerosols, clouds, and their interactions in three climate models (AM3, CAM5, ModelE) to MODIS satellite observations. Modeled cloud properties were diagnosed using the CFMIP Observations Simulator Package (COSP). Cloud droplet number concentrations (N) were derived using the same algorithm for both satellite-simulated model values and observations. We find that aerosol optical depth tau simulated by models is similar to observations. For N, AM3 and CAM5 capture the observed spatial pattern of higher values in near-coast versus remote ocean regions, though modeled values in general are higher than observed. In contrast, ModelE simulates lower N in most near-coast versus remote regions. Aerosol- cloud interactions were computed as the sensitivity of N to tau for marine liquid clouds off the coasts of South Africa and Eastern Asia where aerosol pollution varies in time. AM3 and CAM5 are in most cases more sensitive than observations, while the sensitivity for ModelE is statistically insignificant. This widely used sensitivity could be subject to misinterpretation due to the confounding influence of meteorology on both aerosols and clouds. A simple framework for assessing the N – tau sensitivity at constant meteorology illustrates that observed sensitivity can change from positive to statistically insignificant when including the confounding influence of relative humidity. Satellite simulated values of N were compared to standard model output and found to be higher with a bias of 83 cm-3.

aerosols↗
DOE OSTI2013

REACTIONS FORMING C{sub n=2,10}{sup (0,+)}, C{sub n=2,4}H{sup (0,+)}, AND C{sub 3}H{sub 2}{sup (0,+)} IN THE GAS PHASE: SEMIEMPIRICAL BRANCHING RATIOS

The aim of this paper is to provide a new set of branching ratios (BRs) for interstellar and planetary chemical networks based on a semiempirical model. We applied, instead of zero-order theory (i.e., only the most exoergic decaying channel is considered), a statistical microcanonical model based on the construction of breakdown curves and using experimental high velocity collision BRs for their parameterization. We applied the model to ion-molecule, neutral-neutral, and ion-pair reactions implemented in the few popular databases for astrochemistry, such as KIDA, OSU, and UMIST. We studied the reactions of carbon and hydrocarbon species with electrons, He{sup +}, H{sup +}, CH{sup +}, CH, C, and C{sup +} leading to intermediate complexes of the type C{sub n=2,10}, C{sub n=2,4}H, C{sub 3}H{sub 2}, C{sub n=2,10}{sup +}, C{sub n=2,4}H{sup +}, or C{sub 3}H{sub 2}{sup +}. Comparison of predictions with measurements supports the validity of the model. Huge deviations with respect to database values are often obtained. Effects of the new BRs in time-dependent chemistry for dark clouds and for photodissociation region chemistry with conditions similar to those found in the Horsehead Nebula are discussed.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Foundational Research for Fusion Systems Safety Assessment Overview

Presentation detailing work package to develop comprehensive fusion device safety models over the course of the next three years, including references to a blanket, divertor and magnet design, and statistical reliability models

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Evolution of Precipitation Particle Size Distributions within MC3E Systems and its Impact on Aerosol-Cloud-Precipitation Interactions: Final Report

This is a multi-institutional, collaborative project using observations and modeling to study the evolution (e.g. formation and growth) of hydrometeors in continental convective clouds. Our contribution was in data analysis for the generation of high-value cloud and precipitation products and derive cloud statistics for model validation. There are two areas in data analysis that we contributed: i) the development of novel, state-of-the-art dual-wavelength radar algorithms for the retrieval of cloud microphysical properties and ii) the evaluation of large domain, high-resolution models using comprehensive multi-sensor observations. Our research group developed statistical summaries from numerous sensors and developed retrievals of vertical air motion in deep convection.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Computational and Experimental Mechanistic Insights into the Ethanol-to-Butanol Upgrading Reaction over MgO

The mechanism of ethanol upgrading to higher products is still under debate, especially regarding intermediate species and hydrogenation and dehydrogenation steps. In this work, we conducted a combined theoretical and experimental approach to contribute to this discussion. For such, detailed electronic structure density functional theory calculations (aiming at probing density of states, infrared spectra, geometric parameters, charge densities, and reaction energetics) and diffuse reflectance infrared Fourier transform spectroscopy experiments were carried out revealing the relevance of an appropriate combination of reactive surface sites to support the formation of several intermediates that are formed in the C-C coupling over MgO. The roles of Mg and O sites were also studied under an electronic perspective and different geometrical arrangements. We found that a kink configuration was the most adequate for ethanol to 1-butanol upgrading. Our calculations also gave us arguments to propose distinct reaction routes, whose mutual predominance would depend upon reaction temperature. At temperatures up to 573 K, the so-called β-route, which goes through scission of a Cβ-H bond and formation of an oxametallacycle-like intermediate, would dominate the coupling, whereas at higher temperatures, up to 673 K, a more usual Guerbet mechanism, via an aldol coupling step and then consecutive hydrogenations, would be expected. Here, the theoretical conclusions were followed by a careful experimental strategy using sequential experimental planning techniques in order to estimate accurate parameters with the lowest possible experimental load. Information from these different sources were coupled to develop a mathematical model for the rate of the ethanol upgrading reaction, using a Langmuir-Hinshelwood-Hougen-Watson approach. The developed and statistically validated model adequately described the experimental data at 673 K and 1.1 bar total pressure for ethanol partial pressures in the range from 0 to 20 kPa.

09 BIOMASS FUELS↗
DOE OSTI2019

Debiasing cosmic gravitational wave sirens

ABSTRACT Accurate estimation of the Hubble constant, and other cosmological parameters, from distances measured by cosmic gravitational wave sirens requires sufficient allowance for the dark energy evolution. We demonstrate how model-independent statistical methods, specifically Gaussian process regression, can remove bias in the reconstruction of H(z), and can be combined to model independently with supernova distances. This allows stringent tests of both H0 and Λ cold dark matter, and can detect unrecognized systematics. We also quantify the redshift systematic control necessary for the use of dark sirens, showing that it must approach spectroscopic precision to avoid significant bias.

Keeley, Ryan E.↗
DOE OSTI2026

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computation cost and device memory limits. Thus, simply increasing the batch-size leads to diminishing returns: training time and cost decrease initially but eventually plateaus, creating a knee-point in the time/cost vs. batch-size pareto curve. The optimal batch-size therefore depends on the underlying model, data and available compute resources. Large batches also suffer from worse model quality due to the well-known “generalization gap”. In this paper, we present Tula, an online service that automatically optimizes time, cost, and convergence quality for large-batch training of convolutional models. It combines parallel-systems modeling with statistical performance prediction to identify the optimal batchsize. Tula predicts training time and cost within 7.5−14% error across multiple models, and achieves up to 20× overall speedup and improves test accuracy by ≈9% on average over standard large-batch training on various vision tasks, thus successfully mitigating the generalization gap and accelerating training at the same time.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗
DOE OSTI2023

Quantifying model prediction sensitivity to model-form uncertainty

Computational and mathematical models are essential to understanding complex systems and phenomena. However, when developing such models, limited knowledge and/or resources necessitates the use of simplifying assumptions. It is therefore crucial to quantify the impact of such simplifying assumptions on the reliability and accuracy of resulting model predictions. This work develops a first-of-its-kind approach to quantify the impact of physics modeling assumptions on predictions. Here, we leverage the emerging field of model-form uncertainty (MFU) representations, which are parameterized modifications to modeling assumptions, in combination with grouped Sobol’ indices to quantitatively measure an assumption’s importance. Specifically, we compute the grouped Sobol’ index for the MFU representation’s parameters as a single importance measure of the assumption for which the MFU representation characterizes uncertainty. To ensure this approach is robust to the subjective choice of how to parameterize a MFU representation, we establish bounds for the difference between sensitivity results for two different MFU representations based on differences in model prediction statistics. The capabilities associated with this approach are demonstrated on three exemplar problems: an upscaled subsurface contaminant transport problem, ablation modeling for hypersonic flight, and nuclear waste repository modeling. We found that our grouped approach is able to assess the impact of modeling assumptions on predictions and offers computational advantages over classical Sobol’ index computation while providing more interpretable results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Nonhomogeneous Poisson Process for Fast Radio Burst Rates

This paper presents the non-homogeneous Poisson process (NHPP) for modeling the rate of fast radio bursts (FRBs) and other infrequently observed astronomical events. The NHPP, well-known in statistics, can model dependence of the rate on both astronomical features and the details of an observing campaign. This is particularly helpful for rare events like FRBs because the NHPP can combine information across surveys, making the most of all available information. The goal of the paper is two-fold. First, it is intended to be a tutorial on the use of the NHPP. Second, we build an NHPP model that incorporates beam patterns and a power law flux distribution for the rate of FRBs. Using information from 12 surveys including 15 detections, we find an all-sky FRB rate of 587 events per sky per day above a flux of 1 Jy (95% CI: 272, 924) and a flux power-law index of 0:91 (95% CI: 0.57, 1.25).

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1995

Abrasion-ablation model for neutron production in heavy ion reactions

In heavy ion reactions, neutron production at forward angles is observed to occur with a Gaussian shape that is centered near the beam energy and extends to energies well above that of the beam. This paper presents an abrasion-ablation model for making quantitative predictions of the neutron spectrum. To describe neutrons produced from the abrasion step of the reaction where the projectile and target overlap, the authors use the Glauber model and include effects of final-state interactions. They then use the prefragment mass distribution from abrasion with a statistical evaporation model to estimate the neutron spectrum resulting from ablation. Measurements of neutron production from Ne and Nb beams are compared with calculations, and good agreement is found.

Cucinotta, Francis A.↗
NASA NTRS1996

The calculation of radial dose from heavy ions: predictions of biological action cross sections

The track structure model of heavy ion cross sections was developed by Katz and co-workers in the 1960s. In this model the action cross section is evaluated by mapping the dose-response of a detector to gamma rays (modeled from biological target theory) onto the radial dose distribution from delta rays about the path of the ion. This is taken to yield the radial distribution of probability for a "hit" (an interaction leading to an observable end-point). Radial integration of the probability yields the cross section. When different response from ions of different Z having the same stopping power is observed this model may be indicated. Since the 1960s there have been several developments in the computation of the radial dose distribution, in the measurement of these distributions, and in new radiobiological data against which to test the model. The earliest model, by Butts and Katz made use of simplified delta ray distribution functions, of simplified electron range-energy relations, and neglected angular distributions. Nevertheless it made possible the calculation of cross sections for the inactivation of enzymes and viruses, and allowed extension to tracks in nuclear emulsions and other detectors and to biological cells. It set the pattern for models of observable effects in the matter through which the ion passed. Here we outline subsequent calculations of radial dose which make use of improved knowledge of the electron emission spectrum, the electron range-energy relation, the angular distribution, and some considerations of molecular excitation, of particular interest both close to the path of the ion and the outer limits of electron penetration. These are applied to the modeling of action cross sections for the inactivation of several strains of E-coli and B. subtilis spores where extensive measurements in the "thin-down" region have been made with heavy ion beams. Such calculations serve to test the radial dose calculations at the outer limit of electron penetration. We lack data from which to test these calculations in regions close to the path of the ion aside from our earliest work on latent tracks in plastics, though it appears that the criterion then suggested for the threshold of track formation, of a minimal dose at a minimal distance (of about 20 angstroms, in plastics), remains valid.

NASA Discipline Radiation Health↗
NASA NTRS2015

The Safe Removal of Frozen Air from the Annulus of an LH2 Storage Tank

Large Liquid Hydrogen (LH2) storage tanks are vital infrastructure for NASA. Eventually, air may leak into the evacuated and perlite filled annular region of these tanks. Although the vacuum level is monitored in this region, the extremely cold temperature causes all but the helium and neon constituents of air to freeze. A small, often unnoticeable pressure rise is the result. As the leak persists, the quantity of frozen air increases, as does the thermal conductivity of the insulation system. Consequently, a notable increase in commodity boil-off is often the first indicator of an air leak. Severe damage can result from normal draining of the tank. The warming air will sublimate which will cause a pressure rise in the annulus. When the pressure increases above the triple point, the frozen air will begin to melt and migrate downward. Collection of liquid air on the carbon steel outer shell may chill it below its ductility range, resulting in fracture. In order to avoid a structural failure, as described above, a method for the safe removal of frozen air is needed. A thermal model of the storage tank has been created using SINDA/FLUINT modeling software. Experimental work is progressing in an attempt to characterize the thermal conductivity of a perlite/frozen nitrogen mixture. A statistical mechanics model is being developed in parallel for comparison to experimental work. The thermal model will be updated using the experimental/statistical mechanical data, and used to simulate potential removal scenarios. This paper will address methodologies and analysis techniques for evaluation of two proposed air removal methods.

cryogenic insulation↗
DOE OSTI2018

A physical-based gas–surface interaction model for rarefied gas flow simulation

Empirical gas–surface interaction models, such as the Maxwell model and the Cercignani–Lampis model, are widely used as the boundary condition in rarefied gas flow simulations. The accuracy of these models in the prediction of macroscopic behavior of rarefied gas flows is less satisfactory in some cases especially the highly non-equilibrium ones. Molecular dynamics simulation can accurately resolve the gas–surface interaction process at atomic scale, and hence can predict accurate macroscopic behavior. They are however too computationally expensive to be applied in real problems. In this work, a statistical physical-based gas–surface interaction model, which complies with the basic relations of boundary condition, is developed based on the framework of the washboard model. In virtue of its physical basis, this new model is capable of capturing some important relations/trends for which the classic empirical models fail to model correctly. As such, the new model is much more accurate than the classic models, and in the meantime is more efficient than MD simulations. Therefore, it can serve as a more accurate and efficient boundary condition for rarefied gas flow simulations. - Highlights: • The new physical-based gas–surface interaction model satisfies the basic relations of boundary condition so that it can be used in rarefied gas flow simulations. • The new physical model possesses several advantages comparing to the empirical gas–surface interaction models, hence it's a more accurate boundary condition for rarefied gas flow study. • The new physical model is a statistical model which can be efficiently implemented by Monte Carlo method. It's much more efficient comparing to the MD simulation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗