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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗
DOE OSTI2015

Systematically improvable multiscale solver for correlated electron systems

The development of numerical methods capable of simulating realistic materials with strongly correlated electrons, with controllable errors, is a central challenge in quantum many-body physics. Here we describe a framework for a general multiscale method based on embedding a self-energy of a strongly correlated subsystem into a self-energy generated by a method able to treat large weakly correlated systems approximately. As an example, we present the embedding of an exact diagonalization self-energy into a self-energy generated from self-consistent second-order perturbation theory. Using a quantum impurity model, generated from a cluster dynamical mean field approximation to the two-dimensional Hubbard model, as a benchmark, we illustrate that our method allows us to obtain accurate results at a fraction of the cost of typical Monte Carlo calculations. We test the method in multiple regimes of interaction strengths and dopings of the model. Here, the general embedding framework we present avoids difficulties such as double counting corrections, frequency-dependent interactions, or vertex functions. As it is solely formulated at the level of the single-particle Green's function, it provides a promising route for the simulation of realistic materials that are currently difficult to study with other methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

MO-F-CAMPUS-T-04: Development and Evaluation of a Knowledge-Based Model for Treatment Planning of Lung Cancer Patients Using Stereotactic Body Radiotherapy (SBRT)

Purpose: To describe the development of a knowledge-based treatment planning model for lung cancer patients treated with SBRT, and to evaluate the model performance and applicability to different planning techniques and tumor locations. Methods: 105 lung SBRT plans previously treated at our institution were included in the development of the model using Varian’s RapidPlan DVH estimation algorithm. The model was trained with a combination of IMRT, VMAT, and 3D–CRT techniques. Tumor locations encompassed lesions located centrally vs peripherally (43:62), upper vs lower (62:43), and anterior vs posterior lobes (60:45). The model performance was validated with 25 cases independent of the training set, for both IMRT and VMAT. Model generated plans were created with only one optimization and no planner intervention. The original, general model was also divided into four separate models according to tumor location. The model was also applied using different beam templates to further improve workflow. Dose differences to targets and organs-at-risk were evaluated. Results: IMRT and VMAT RapidPlan generated plans were comparable to clinical plans with respect to target coverage and several OARs. Spinal cord dose was lowered in the model-based plans by 1Gy compared to the clinical plans, p=0.008. Splitting the model according to tumor location resulted in insignificant differences in DVH estimation. The peripheral model decreased esophagus dose to the central lesions by 0.5Gy compared to the original model, p=0.025, and the posterior model increased dose to the spinal cord by 1Gy compared to the anterior model, p=0.001. All template beam plans met OAR criteria, with 1Gy increases noted in maximum heart dose for the 9-field plans, p=0.04. Conclusion: A RapidPlan knowledge-based model for lung SBRT produces comparable results to clinical plans, with increased consistency and greater efficiency. The model encompasses both IMRT and VMAT techniques, differing tumor locations, and beam arrangements. Research supported in part by a grant from Varian Medical Systems, Palo Alto CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

MODEL AND ALGORITHM EVALUATION FOR THE HYBRID UF6 CONTAINER INSPECTION SYSTEM

ABSTRACT Pacific Northwest National Laboratory (PNNL) is developing an automated UF6 cylinder verification station concept based on the combined collection of traditional enrichment-meter (186 keV photons from U-235) data and non-traditional, neutron-induced, high-energy gamma-signatures (3-8.5 MeV) with an array of collimated, medium-resolution scintillators. Previous (2010) work at PNNL demonstrated proof-of-principle that this hybrid method yields accurate, full-volume assay of the cylinder enrichment, reduces systematic errors when compared to several other enrichment assay methods, and provides simplified instrumentation and algorithms suitable for long-term unattended operations. We used Monte Carlo modeling with MCNP5 to support system design (e.g., number and configuration of detector arrays, and design of iron/poly collimators for enhanced (n,γ) conversion) and enrichment algorithm development. We developed a first-generation modeling framework in 2010. These tools have since been expanded, refined and benchmarked against field measurements with a prototype system of a 30B cylinder population (0.2 to 4.95 weight % U-235). The MCNP5 model decomposes the radiation transport problem into a linear superposition of “basis spectra” representing contributions from the different uranium isotopes and gamma-ray generation mechanisms (e.g. neutron capture). This scheme accommodates fast generation of “virtual assay signatures” for arbitrary enrichment, material age, and fill variations. Ongoing (FY-2011) refinements to the physics model include accounting for generation of bremsstrahlung photons, arising primarily from the beta decay of Pa-234m, a U-238 daughter. We are using the refined model to optimize collimator design for the hybrid method. The traditional assay method benefits from a high degree of collimation (to isolate each detector’s field-of-view) and relatively small detector area, while the non-traditional method benefits from a wide field-of-view, i.e. less collimation and larger detectors. We implement the enrichment-meter method by applying a square-wave digital filter to a raw spectrum and extracting the 186-keV peak area directly from the convolute spectrum. Ongoing enhancements to this approach include mitigating a systematic peak-area measurement deficit arising from curvature in the spectrum continuum shape. An optimized system prototype based on model results is utilized in a new set of 2011 field measurements, and model and measurement enrichment assay uncertainties are compared.

MODEL AND ALGORITHM EVALUATION FOR THE HYBRID UF6 ↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Stochastic representation and conditioning of process-based geological model by deep generative and recognition networks

Accurate and realistic geological modeling is the core of oil and gas development and production. In recent years, process-based methods are developed to produce highly realistic geological models by simulating the physical processes that reproduce the sedimentary events and develop the geometry. However, the complex dynamic processes are extremely expensive to simulate, making process-based models difficult to be conditioned to field data. In this work, we propose a comprehensive generative adversarial network framework as a machine-learning-assisted approach for mimicking the outputs of process-based geological models with fast generation. The main objective of our work is to obtain a continuous parametrization of the highly realistic process-based geological models which enables us to calibrate the models and condition the models to data. Numerical results are presented to illustrate the capability of our proposed methodology.

58 GEOSCIENCES↗
DOE OSTI2013

THE COLLISIONAL EVOLUTION OF DEBRIS DISKS

We explore the collisional decay of disk mass and infrared emission in debris disks. With models, we show that the rate of the decay varies throughout the evolution of the disks, increasing its rate up to a certain point, which is followed by a leveling off to a slower value. The total disk mass falls off {proportional_to}t {sup -0.35} at its fastest point (where t is time) for our reference model, while the dust mass and its proxy-the infrared excess emission-fades significantly faster ({proportional_to}t {sup -0.8}). These later level off to a decay rate of M{sub tot}(t){proportional_to}t {sup -0.08} and M{sub dust}(t) or L{sub ir}(t){proportional_to}t {sup -0.6}. This is slower than the {proportional_to}t {sup -1} decay given for all three system parameters by traditional analytic models. We also compile an extensive catalog of Spitzer and Herschel 24, 70, and 100 {mu}m observations. Assuming a log-normal distribution of initial disk masses, we generate model population decay curves for the fraction of stars harboring debris disks detected at 24 {mu}m. We also model the distribution of measured excesses at the far-IR wavelengths (70-100 {mu}m) at certain age regimes. We show general agreement at 24 {mu}m between the decay of our numerical collisional population synthesis model and observations up to a Gyr. We associate offsets above a Gyr to stochastic events in a few select systems. We cannot fit the decay in the far-infrared convincingly with grain strength properties appropriate for silicates, but those of water ice give fits more consistent with the observations (other relatively weak grain materials would presumably also be successful). The oldest disks have a higher incidence of large excesses than predicted by the model; again, a plausible explanation is very late phases of high dynamical activity around a small number of stars. Finally, we constrain the variables of our numerical model by comparing the evolutionary trends generated from the exploration of the full parameter space to observations. Amongst other results, we show that erosive collisions are dominant in setting the timescale of the evolution and that planetesimals on the order of 100 km in diameter are necessary in the cascades for our population synthesis models to reproduce the observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Gearbox and Drivetrain Models to Study Dynamic Effects of Modern Wind Turbines: Preprint

Wind turbine drivetrains consist of components that directly convert kinetic energy from the wind to electrical energy. Guaranteeing robust and reliable drivetrain designs is therefore important to minimize turbine downtime. Current drivetrain models often lack the ability to model both the impacts of electrical transients as well as wind turbulence and shear in one package. In this work, thecapability of the FAST wind turbine computer-aided engineering tool, developed by the National Renewable Energy Laboratory, is enhanced through integration of a dynamic model of the drivetrain. The dynamic drivetrain model is built using Simscape in the MATLAB/Simulink environment and incorporates detailed electrical generator models. This model can be used in the future to test advanced controlschemes to extend life of the gearbox.

17 WIND ENERGY↗
DOE OSTI2018

Model of a SiGe 130-nm 10- to 30-GHz Chaotic Self-Oscillating System

The problem of generation of utlrawideband chaotic oscillations in the frequency range of 10–30 GHz is considered. This problem is relevant to fifth-generation communication systems. A generator model designed for the silicon–germanium 130-nm process technology is proposed and studied.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

dGen (Distributed Generation Market Demand) Model Data: Alpha Release

Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

14 SOLAR ENERGY↗
DOE OSTI2010

Physical model for the generation of ideal resources in multipartite quantum networking

We propose a physical model for generating multipartite entangled states of spin-s particles that have important applications in distributed quantum information processing. Our protocol is based on a process where mobile spins induce the interaction among remote scattering centers. As such, a major advantage lies in the management of stationary and well-separated spins. Among the generable states, there is a class of N-qubit singlets allowing for optimal quantum telecloning in a scalable and controllable way. We also show how to prepare Aharonov, W, and Greenberger-Horne-Zeilinger states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

The impact of coupled air–sea interaction on extreme East Asian summer monsoon simulation in CMIP5 models

Abstract In this study, the relationship between the ability to simulate air–sea interactions over the western North Pacific (WNP), and to reproduce the extreme East Asian summer monsoon (EASM), were investigated by comparing the performances of several global climate models (GCMs). High ranked in air–sea interaction simulation (HRA) and low ranked in air–sea interaction simulation (LRA) models were selected, according to their performance in simulating relations between sea surface temperature (SST) and precipitation over the WNP, from the ensemble of models that participated in the third and fifth phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5). Compared with CMIP3 models, CMIP5 models exhibited improved simulations of the distinctive air–sea interaction over the WNP, namely, the strong atmospheric forcing on the ocean. Among CMIP5 models, HRA models, which reproduced intrinsic negative correlations between precipitation and SST over the WNP, could simulate the extreme EASM better than LRA models. In particular, HRA models generated a more realistic spatial distribution of the extreme EASM compared with LRA models. The defects of the LRA models resulted from distorted synoptic fields, including underestimated geopotential height and overestimated low‐level wind over the WNP, inducing unrealistic moisture supply and convection due to the exaggerated SST forcing. In contrast, reasonable air–sea interactions represented in HRA models lead to realistic synoptic fields over the WNP, and proper simulation of the extreme EASM.

Kim, Taehyung↗
DOE OSTI2024

Feasibility of Formulating Ecosystem Biogeochemical Models From Established Physical Rules

Abstract To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive‐transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process‐level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules‐based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Structural phase transitions in a MoWSe 2 monolayer: Molecular dynamics simulations and variational autoencoder analysis

Electrical and optoelectronic properties of two-dimensional (2D) transition metal dichalcogenides (TMDCs) can be tuned by exploiting their structural phase transitions. Here semiconducting (2H) to metallic (1T) phase transition is investigated in a strained MoWSe 2 monolayer using molecular dynamics (MD) simulations. Novel intermediate structures called α and β are found between the 2H and 1T phases. These intermediate structures are similar to those observed in a 2D MoS 2 by scanning transmission electron microscopy. Here, a deep generative model, namely the variational autoencoder (VAE) trained by MD data, is used to generate novel heterostructures with α and β interfaces. Quantum simulations based on density functional theory show that these heterostructures are stable and suitable for novel nanoelectronics applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Closed-Form Formulas for Estimation of Kinetic Parameters in One- and Two-Compartment Models

.Nowadays the most reliable approach to estimate the kinetic parameters is to minimize an objective function which is essentially the distance between the measured data and the model generated pseudo data. Due to the highly non-linear nature of the model, common optimization algorithms usually fail to find the true minimum. A brute-force search method sometimes must be used to find the true minimum. This paper attempts to use the Laplace transform and Z-transform methods to derive closed-form formulas for kinetic parameter estimation problems, which assume one- or two-compartment models. The proposed method is computationally efficient and its solution is unique. When data sampling interval is small, the proposed method is able to accurately estimate the kinetic parameter; when the interval is larger, the proposed method fails to give a meaningful estimate. The one-compartment method is more robust than the two-compartment method.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Modeling the temporal dynamics of nonstructural carbohydrate pools in forest trees

Trees store carbohydrates, in the form of sugars and starch, as reserves to be used to power both future growth as well as to support day-to-day metabolic functions. These reserves are particularly important in the context of how trees cope with disturbance and stress—for example, as related to pest outbreaks, wind or ice damage, and extreme climate events. In this project, we measured the size of carbon reserves in forest trees, and determined how quickly these reserves are used and replaced—i.e., their “turnover time”. Our work was conducted at Harvard Forest, a temperate deciduous forest in central Massachusetts. Through field sampling, laboratory-based chemical analyses, and allometric modeling, we scaled these measurements up to whole-tree NSC budgets. We used these data to test and improve computer simulation models of carbon flow through forest ecosystems. Our modeling focused on the mathematical representation of these stored carbon reserves, and we examined the sensitivity of model performance to different model structures. This project contributes to DOE’s goal to improve next-generation models of the earth system, and to understand the impacts of climate change on terrestrial ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Apollo

Modern performance portability frameworks provide application developers with a flexible way to determine how to run application kernels, however, they provide no guidance as to the best configuration for a given kernel. Apollo provides a model-generation framework that, when integrated with the RAJA library, uses lightweight decision tree models to select the fastest execution configuration on a per-kernel basis

Beckingsal, David↗