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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling efficient and equitable distribution of COVID-19 vaccines

Producing and distributing COVID-19 vaccine during the pandemic is a major logistical challenge requiring careful planning and efficient execution. This report presents information on logistical, policy and technical issues relevant to rapidly fielding a COVID-19 vaccination program. For this study we (a) conducted literature review and subject matter expert elicitation to understand current vaccine manufacturing and distribution capabilities and vaccine allocation strategies, (b) designed a baseline vaccine distribution strategy and modeling strategy to provide insight into the potential for targeted distribution of limited initial vaccine supplies, and (c) developed parametric interfaces to enable vaccine distribution scenarios to be analyzed in depth with Sandias Adaptive Recovery Model that will allow us evaluate the additional sub- populations and alternative distribution scenarios from a public health benefit and associated economic disruption Principal issues, challenges, and complexities that complicate COVID-19 vaccine delivery identified in our literature and subject matter expert investigation include these items: The United States has not mounted an urgent nationwide vaccination campaign in recent history. The existing global manufacturing and distribution infrastructure are not able to produce enough vaccine for the population immediately. Vaccines, once available will be scarce resources. Prioritization for vaccine allocation will be built on existing distribution networks. Vaccine distribution may not have a universal impact on disease transmission and morbidity because of scarcity, priority population demographics, and underlying disease transmission rates. Considerations for designing a vaccine distribution strategy are discussed. A baseline distribution strategy is designed and tested using the Adaptive Recovery Model, which couples a deterministic compartmental epidemiological model and a stochastic network model. We show the impact of this vaccine distribution strategy on hospitalizations, mortality, and contact tracing requirements. This model can be used to quantitatively evaluate alternative distribution scenarios, guiding policy decisions as vaccine candidates are narrowed down.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Distributionally Robust Building Load Control to Compensate Fluctuations in Solar Power Generation

This paper investigates the use of a collection of dispatchable heating, ventilation and air conditioning (HVAC) systems to absorb low-frequency fluctuations in renewable energy sources, especially in solar photo-voltaic (PV) generation. Given the uncertain and time-varying nature of solar PV generation, its probability distribution is difficult to be estimated perfectly, which poses a challenging problem of how to optimally schedule a fleet of HVAC loads to consume as much as local PV generation. We formulate a distributionally robust chance-constrained (DRCC) model to ensure that PV generation is consumed with a desired probability for a family of probability distributions, termed as an ambiguity set, built upon mean and covariance information. We benchmark the DRCC model with a deterministic optimization model and a stochastic programming model in a one-day simulation. We show that the DRCC model achieves constantly good performance to consume most PV generation even in the case with the presence of probability distribution ambiguity.

Zhang, Yiling↗
NASA NTRS1969

Stochastic crew motion modeling.

Representative data of actual forces and moments applicable to large spacecraft attitude control system for typical crew activities obtained through simulation programs

Hendricks, T. C.↗
NASA NTRS1999

Multi-Parent Clustering Algorithms from Stochastic Grammar Data Models

We introduce a statistical data model and an associated optimization-based clustering algorithm which allows data vectors to belong to zero, one or several "parent" clusters. For each data vector the algorithm makes a discrete decision among these alternatives. Thus, a recursive version of this algorithm would place data clusters in a Directed Acyclic Graph rather than a tree. We test the algorithm with synthetic data generated according to the statistical data model. We also illustrate the algorithm using real data from large-scale gene expression assays.

Mjoisness, Eric↗
NASA NTRS2010

GUI to Facilitate Research on Biological Damage from Radiation

A graphical-user-interface (GUI) computer program has been developed to facilitate research on the damage caused by highly energetic particles and photons impinging on living organisms. The program brings together, into one computational workspace, computer codes that have been developed over the years, plus codes that will be developed during the foreseeable future, to address diverse aspects of radiation damage. These include codes that implement radiation-track models, codes for biophysical models of breakage of deoxyribonucleic acid (DNA) by radiation, pattern-recognition programs for extracting quantitative information from biological assays, and image-processing programs that aid visualization of DNA breaks. The radiation-track models are based on transport models of interactions of radiation with matter and solution of the Boltzmann transport equation by use of both theoretical and numerical models. The biophysical models of breakage of DNA by radiation include biopolymer coarse-grained and atomistic models of DNA, stochastic- process models of deposition of energy, and Markov-based probabilistic models of placement of double-strand breaks in DNA. The program is designed for use in the NT, 95, 98, 2000, ME, and XP variants of the Windows operating system.

Cucinotta, Frances A.↗
NASA NTRS2011

The Analysis of the Patterns of Radiation-Induced DNA Damage Foci by a Stochastic Monte Carlo Model of DNA Double Strand Breaks Induction by Heavy Ions and Image Segmentation Software

To create a generalized mechanistic model of DNA damage in human cells that will generate analytical and image data corresponding to experimentally observed DNA damage foci and will help to improve the experimental foci yields by simulating spatial foci patterns and resolving problems with quantitative image analysis. Material and Methods: The analysis of patterns of RIFs (radiation-induced foci) produced by low- and high-LET (linear energy transfer) radiation was conducted by using a Monte Carlo model that combines the heavy ion track structure with characteristics of the human genome on the level of chromosomes. The foci patterns were also simulated in the maximum projection plane for flat nuclei. Some data analysis was done with the help of image segmentation software that identifies individual classes of RIFs and colocolized RIFs, which is of importance to some experimental assays that assign DNA damage a dual phosphorescent signal. Results: The model predicts the spatial and genomic distributions of DNA DSBs (double strand breaks) and associated RIFs in a human cell nucleus for a particular dose of either low- or high-LET radiation. We used the model to do analyses for different irradiation scenarios. In the beam-parallel-to-the-disk-of-a-flattened-nucleus scenario we found that the foci appeared to be merged due to their high density, while, in the perpendicular-beam scenario, the foci appeared as one bright spot per hit. The statistics and spatial distribution of regions of densely arranged foci, termed DNA foci chains, were predicted numerically using this model. Another analysis was done to evaluate the number of ion hits per nucleus, which were visible from streaks of closely located foci. In another analysis, our image segmentaiton software determined foci yields directly from images with single-class or colocolized foci. Conclusions: We showed that DSB clustering needs to be taken into account to determine the true DNA damage foci yield, which helps to determine the DSB yield. Using the model analysis, a researcher can refine the DSB yield per nucleus per particle. We showed that purely geometric artifacts, present in the experimental images, can be analytically resolved with the model, and that the quantization of track hits and DSB yields can be provided to the experimentalists who use enumeration of radiation-induced foci in immunofluorescence experiments using proteins that detect DNA damage. An automated image segmentaiton software can prove useful in a faster and more precise object counting for colocolized foci images.

Ponomarev, Artem↗
DOE OSTI2020

Stochastic Virtual Battery Modeling of Uncertain Electrical Loads using Variational Autoencoder

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Recently, there have been efforts to quantify the predictive flexibility of thermostatic loads (e.g. residential air-conditioners, electric water-heaters) using the notion of virtual battery (VB), whose state evolution is governed by a first order dynamics including self-dissipation rate, and power and energy capacities. Identifying the VB model parameters for a collection of thermostatic loads, however, is challenging primarily due to uncertainties and lack of information regarding the end-user behavior, underlying device models and parameters. In this paper, we propose a \textit{variational autoencoder}-based deep learning algorithm to identify the parameters of the VB model. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.

virtual battery, deep learning algorithms↗
DOE OSTI2024

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗
DOE OSTI2025

Stochastic parametric skeletal dosimetry model for humans: Anatomical-morphological basis and parameter evaluation

Radiation exposure of the hematopoietic system that results in a radiation dose to bone marrow of more than 100 mGy leads to an increase in the risk of leukemia in humans. Excess relative risk of leukemia was observed in cohorts whose members lived in the territories of the Southern Urals that were radioactively contaminated in the 1950s. As part of the dosimetric support of epidemiological studies of these cohorts, an original methodology for stochastic bone dosimetric modeling was developed, termed the Stochastic Parametric Skeletal Dosimetry (SPSD) model. The purpose of this work was to present the anatomical and morphological bases of the SPSD model, which includes an assessment of the parameters of the microstructure of the trabecular bone in the hematopoietic areas of the human skeleton, as well as a description of the macrostructural division (segmentation) of hematopoietic areas into simple bone segments. As a result, an anatomical-morphological basis of the SPSD model was created based on published data. Data collection work included the analysis of original articles, atlases, manuals, monographs and the formation of primary data files. Data on the duration of hematopoiesis in various parts of the skeleton; and data on age-related changes in the microstructure and linear dimensions of human bones and their segments were analyzed. The paper describes the full set of parameters to be used for the dosimetric model for newborns, children aged 1, 5 and 10 years, as well as for adolescents aged 15 years and adults; for the latter, sex differences in bone size were considered. In total, the SPSD model includes 289 unique basic (bone) phantom segments, each of which is described by 7 or more parameters describing the microstructure, thickness of the cortical layer and linear dimensions. Population variability was estimated for each parameter. The approach to SPSD modeling, i.e., the use of simple geometric shapes, was successfully verified using independent datasets on bone masses and volumes.

Science & Technology - Other Topics↗
DOE OSTI2024

Bounds on galaxy stochasticity from halo occupation distribution modeling

The joint probability distribution of matter overdensity and galaxy counts in cells is a powerful probe of cosmology, and the extent to which variance in galaxy counts at fixed matter density deviates from Poisson shot noise is not fully understood. The lack of informed bounds on this stochasticity is currently the limiting factor in constraining cosmology with the galaxy–matter probability distribution function (PDF). We investigate stochasticity in the conditional distribution of galaxy counts along lines of sight with fixed matter density, and we present a halo occupation distribution (HOD)-based approach for obtaining plausible ranges for stochasticity parameters. To probe the high-dimensional space of possible galaxy–matter connections, we derive a set of HODs that conserve the galaxies’ linear bias and number density to produce RED M A G I C-like galaxy catalogs within the A BACUS S UMMIT suite of N -body simulations. We study the impact of individual HOD parameters and cosmology on stochasticity and perform a Monte Carlo search in HOD parameter space subject to the constraints on bias and density. In mock catalogs generated by the selected HODs, shot noise in galaxy counts spans both sub-Poisson and super-Poisson values, ranging from 80% to 133% of Poisson variance for cells with mean matter density. Nearly all of the derived HODs show a positive relationship between local matter density and stochasticity. For galaxy catalogs with higher stochasticity, modeling galaxy bias to second order is required for an accurate description of the conditional PDF of galaxy counts at fixed matter density. The presence of galaxy assembly bias also substantially extends the range of stochasticity in the super-Poisson direction. This HOD-based approach leverages degrees of freedom in the galaxy–halo connection to obtain informed bounds on nuisance model parameters and can be adapted to study other parametrizations of shot noise in galaxy counts, in particular to motivate prior ranges on stochasticity for cosmological analyses.

Britt, Dylan (ORCID:000000019905601X)↗
NASA NTRS2014

A Multiscale Progressive Failure Modeling Methodology for Composites that Includes Fiber Strength Stochastics

A multiscale modeling methodology was developed for continuous fiber composites that incorporates a statistical distribution of fiber strengths into coupled multiscale micromechanics/finite element (FE) analyses. A modified two-parameter Weibull cumulative distribution function, which accounts for the effect of fiber length on the probability of failure, was used to characterize the statistical distribution of fiber strengths. A parametric study using the NASA Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) was performed to assess the effect of variable fiber strengths on local composite failure within a repeating unit cell (RUC) and subsequent global failure. The NASA code FEAMAC and the ABAQUS finite element solver were used to analyze the progressive failure of a unidirectional SCS-6/TIMETAL 21S metal matrix composite tensile dogbone specimen at 650 degC. Multiscale progressive failure analyses were performed to quantify the effect of spatially varying fiber strengths on the RUC-averaged and global stress-strain responses and failure. The ultimate composite strengths and distribution of failure locations (predominately within the gage section) reasonably matched the experimentally observed failure behavior. The predicted composite failure behavior suggests that use of macroscale models that exploit global geometric symmetries are inappropriate for cases where the actual distribution of local fiber strengths displays no such symmetries. This issue has not received much attention in the literature. Moreover, the model discretization at a specific length scale can have a profound effect on the computational costs associated with multiscale simulations.models that yield accurate yet tractable results.

Generalized Method of Cells↗