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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tetrahedral Trees: A Family of Hierarchical Spatial Indexes for Tetrahedral Meshes

In this work, we address the problem of performing efficient spatial and topological queries on large tetrahedral meshes with arbitrary topology and complex boundaries. Such meshes arise in several application domains, such as 3D Geographic Information Systems (GISs), scientific visualization, and finite element analysis. To this aim, we propose Tetrahedral trees, a family of spatial indexes based on a nested space subdivision (an octree or a kD-tree) and defined by several different subdivision criteria. We provide efficient algorithms for spatial and topological queries on Tetrahedral trees and compare to state-of-the-art approaches. Our results indicate that Tetrahedral trees are an improvement over R*-trees for querying tetrahedral meshes; they are more compact, faster in many queries, and stable at variations of construction thresholds. They also support spatial queries on more general domains than topological data structures, which explicitly encode adjacency information for efficient navigation but have difficulties with domains with a non-trivial geometric or topological shape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Covariate Dependent Sparse Functional Data Analysis

This study proposes a method to incorporate covariate information into sparse functional data analysis. The method aims at cases where each subject has a limited number of longitudinal measurements and is associated with static covariates. This research is motivated by several use cases in practice. One representative example is void swelling, a nuclear-specific material degradation mechanism. Void swelling is affected by many covariates, including alloy composition and irradiation type. How to accurately model the complicated joint effects of such covariates on the swelling process is the key to mitigating the effect of swelling and ensuring safe operation. Unlike most of the existing methods, the proposed method can handle high-dimensional covariates with the informative covariate identification procedure and sparse and irregularly spaced measurements, that is, does not require complete or dense observations. The main innovation of the proposed method is that we model the variation coming from covariates and the variation left conditioned on covariates, such that the functional principal component analysis and Gaussian process can be conducted in a unified manner. Further, we also propose a systematic approach to identify important covariates in the hypothesis testing context. The methodology is demonstrated on applications in nuclear engineering and healthcare and simulation studies.

42 ENGINEERING↗
DOE OSTI2024

Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Codebase release r1.4 for CoVVVR

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named COVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

Shyamsundar, Prasanth↗
DOE OSTI2025

Towards a Robust Adaptive Digital Twin for Fusion Applications

The development of a digital twin system for fusion applications is essential for enhancing the prediction, analysis, and optimization of complex plasma processes. Machine learning (ML), particularly deep learning has demonstrated strong capabilities in modeling such highly nonlinear and intricate systems. However, two critical challenges limit the deployment of deep learning-based digital twins: Uncertainty Quantification (UQ) and data drift. UQ is vital for ensuring trustworthy predictions, especially in decision-support scenarios. Additionally, data-driven models are often sensitive to changes in the underlying data distribution, such as shot-to-shot variations in fusion experiments, which can lead to performance degradation over time. To address these challenges, we are developing an uncertainty-aware, adaptive digital twin framework. Our approach incorporates deep learning models enhanced with Gaussian Process approximations for predictive uncertainty estimation, coupled with an online learning mechanism that enables continuous model adaptation to new experimental data. This adaptive capability allows the data driven models to respond effectively to evolving plasma behaviors and equipment conditions. Specifically, to mitigate the effects of shot-to-shot drift, our system updates itself incrementally as new data becomes available, improving both robustness and fidelity. Our vision is to evolve this data driven model into a self-sustaining digital twin system that leverages UQ based feedback to continuously refine itself and potentially support real-time decision making. This presentation will cover a brief background on uncertainty quantification for ML, our ongoing effort on development of UQ capabilities for ML, our data science pipeline from data collection to model development and analysis and online learning framework for modeling coil deflection at DIII-D. I will also briefly touch upon opportunities and challenges in development of digital twin framework.

Sammuli, Brian [General Atomics]↗
DOE OSTI2020

Benchmarking Adaptive Variational Quantum Eigensolvers

By design, the variational quantum eigensolver (VQE) strives to recover the lowest-energy eigenvalue of a given Hamiltonian by preparing quantum states guided by the variational principle. In practice, the prepared quantum state is indirectly assessed by the value of the associated energy. Novel adaptive derivative-assembled pseudo-trotter (ADAPT) ansatz approaches and recent formal advances now establish a clear connection between the theory of quantum chemistry and the quantum state ansatz used to solve the electronic structure problem. Here we benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves for a few selected diatomic molecules, namely H 2 , NaH, and KH. Using numerical simulation, we find both methods provide good estimates of the energy and ground state, but only ADAPT-VQE proves to be robust to particularities in optimization methods. Another relevant finding is that gradient-based optimization is overall more economical and delivers superior performance than analogous simulations carried out with gradient-free optimizers. The results also identify small errors in the prepared state fidelity which show an increasing trend with molecular size.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Analysis of Near-Field Magnetic Responses on ZrTe 5 through Cryogenic Magneto-THz Nano-Imaging

One manifestation of light-Weyl fermion interaction is the emergence of chiral magnetic effects under magnetic fields. Probing real space magnetic responses at terahertz (THz) scales is challenging but highly desired, as the local responses are less affected by the topologically trivial inhomogeneity that is ubiquitous in spatially averaged measurements. Here, we implement a cryogenic THz microscopy instrument under a magnetic field environment—a task only recently achieved. We explore the technical approach of this system and characterize the magnetic field’s influence on our AFM operation by statistical noise analysis. We find evidence for local near-field spatial variations in the topological semimetal ZrTe 5 up to a 5-Tesla magnetic field and obtain near-field THz spectra to discuss their implications for future studies on the chiral magnetic effect.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Techno-Economic Evaluation of Strategies to Approach Net-Zero Carbon Sustainable Aviation Fuel via Woody Biomass Gasification and Fischer-Tropsch Synthesis

Reducing the carbon dioxide (CO2) emissions from the transportation industry is a key target for achieving global net-zero carbon goals. Wide-spread electrification, efficient engine design, and alternative fuel implementations have been introduced for light-duty vehicles and are projected to significantly reduce light-duty emissions in the near future. Conversely, the aviation sector contributes considerably towards transportation-based carbon emissions, but current projections do not show substantial reductions in carbon emissions over time. Challenges for the aviation sector include low compatibility with electrification, relative inflexibility to variations in fuel properties, and requirement of high energy density fuels. Thus, identifying pathways to decarbonize the aviation sector via liquid low- or net-zero carbon biofuels that are compatible with current aviation infrastructure is crucial. This work investigates the economic feasibility of approaching net-zero carbon sustainable aviation fuel (SAF) from woody biomass via gasification and Fischer-Tropsch synthesis. In doing so, this assessment identifies the economic opportunities and trade-offs of several carbon mitigation strategies coupled with renewable resource interventions required to approach net-zero carbon fuels via biomass. Renewable natural gas and green hydrogen utilization strategies are assessed to examine the impact of fuel yield improvements versus renewable resource cost. Additionally, both carbon capture and sequestration (CCS) and carbon capture and utilization (CCU) are considered in this analysis to help mitigate carbon loss to the atmosphere. Each scenario was assessed on cost, carbon efficiency, energy efficiency, and overall technology-readiness level (TRL). The results of this analysis show that renewable natural gas, green hydrogen, and CCS can be viewed as a low-cost, near-term carbon abatement strategies. However, in the long-term, deployment of more expensive and less technologically mature CCU technologies can make use of point sources of CO2 to boost overall fuel production.

aviation fuel↗
DOE OSTI2023

Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach

The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D e ), clay porosities (ε), and adsorption distribution coefficients (K D ) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D e –ε–K D ) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D e –ε–K D modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers.

58 GEOSCIENCES↗
DOE OSTI2024

Dosage optimization for reducing tumor burden using a phenotype-structured population model with a drug-resistance continuum

Abstract Drug resistance is a significant obstacle to effective cancer treatment. To gain insights into how drug resistance develops, we adopted a concept called fitness landscape and employed a phenotype-structured population model by fitting to a set of experimental data on a drug used for ovarian cancer, olaparib. Our modeling approach allowed us to understand how a drug affects the fitness landscape and track the evolution of a population of cancer cells structured with a spectrum of drug resistance. We also incorporated pharmacokinetic (PK) modeling to identify the optimal dosages of the drug that could lead to long-term tumor reduction. We derived a formula that indicates that maximizing variation in plasma drug concentration over a dosing interval could be important in reducing drug resistance. Our findings suggest that it may be possible to achieve better treatment outcomes with a drug dose lower than the levels recommended by the drug label. Acknowledging the current limitations of our work, we believe that our approach, which combines modeling of both PK and drug resistance evolution, could contribute to a new direction for better designing drug treatment regimens to improve cancer treatment.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2020

Technical Gaps in Hanford High-Level Waste Solids Settling Behavior and Settling Time Evaluation for Direct Feed High-Level Waste (DFHLW) Operations

Settling of high-level waste (HLW) solids in process vessels is a key conceptual process step in providing HLW feed directly to the Hanford Waste Treatment and Immobilization Plant (WTP) HLW Vitrification Facility. Direct Feed High-Level Waste (DFHLW) is a potential approach to initiating HLW vitrification prior to completing of the WTP Pretreatment Facility. Settling would be used with subsequent supernatant decant to concentrate HLW feed. To support planning for DFHLW, Washington River Protection Solutions (WRPS) requested support from the Pacific Northwest National Laboratory to evaluate the current data set available to predict the time needed for HLW solids to settle, to identify gaps in the understanding and predictive capability of HLW solids waste settling times, and to provide scoping estimates of the potential settling time. Eight technical gaps were identified for predicting settling times and characteristics of the formed sediment layers including: Gap 1: In-Tank Settling Rates Faster than Settling of Laboratory Samples, Gap 2: Effect of Sludge Leaching/Washing on Predicted Settling Times, Gap 3: Predicting Waste Settling from Waste Chemistry (Waste Type), Gap 4: Predicting Waste Settling from Particle Size and Density Distributions (PSDDs), Gap 5: Insufficient Laboratory and In-Tank Settling Data to Represent Hanford Waste, Gap 6: Methods for Real-Time, In-Tank Tracking of Settling, Gap 7: Prediction of Sediment Erosion Resistance as a Function of Settling Time, and Gap 8: Prediction of Sediment Solids Content as a Function of Settling Time. In addition to the data gaps, an overarching observation of the settling rate and settled layer data is the significant variation in behavior. At similar solids concentrations, settling rates can vary by as much as 3 orders of magnitude depending on the source waste tank, and significantly different settling rates are noted between laboratory and in situ tests for the same waste tank. The range of average solids concentration in existing HLW sediment, which may have been quiescent for decades, can vary from less than 7 wt% to greater than 74 wt% solids. The shear strengths (or yield stresses) measured on laboratory samples range from less than 27 Pa to greater than 6400 Pa. These variations can challenge process planning for the application of a settle/decant process for DFHLW. This report describes the significance of the gaps to the settle/decant process and presents uncertainties by way of examples. Potential technical approaches for resolving these gaps are described and the estimated difficulty in resolving these gaps is evaluated. Based on the significance of the gap and the difficulty of resolution, recommendations are made to address specific gaps. Scoping estimates of the potential settling times for DFHLW solids have been made based on the existing data set with its associated gaps. Depending on the process vessel depth and final sediment concentration, substantial fractions of the scoping estimate results for settling times for characterized HLW exceed the 2-week period that has been previously assumed for process planning. There is also significant disparity, potentially greater than a factor of 5000 difference, in the estimated settling times depending on process vessel depth and final sediment solid concentration. This variation in results underscores the significance of the identified gaps and uncertainties with respect to process planning for utilizing settle/decant operations for DFHLW.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Designing Dynamic Wireless Power Transfer Corridors for Heavy Duty Battery Electric Commercial Freight Vehicles

The use of wireless power transfer systems, consisting of inductive electrical coils on the vehicle and the power source may be designed for dynamic operations where the vehicle will absorb energy at highway speeds from transmitting coils in the road. This has the potential to reduce the onboard energy storage requirements for vehicles while enabling significantly longer missions. This paper presents an approach to architecting a dynamic wireless power transfer corridor for heavy duty battery electric commercial freight vehicles. By considering the interplay of roadway power capacity, roadway and vehicle coil coverage, seasonal road traffic loading, freight vehicle class and weight, vehicle mobility energy requirements, on-board battery chemistry, non-electrified roadway vehicle range requirements, grid capacity, substation locations, and variations in electricity costs, we minimize the vehicle TCO by architecting the electrified roadway and the vehicle battery simultaneously. The idea optimizes battery size and chemistry so that the depth of discharge between recharge events and expected life are balanced, thus fully utilizing the energy available throughout the course of the battery system's life. The approach is illustrated by applying it to the I-75 freight corridor, where the framework developed may be expanded and applied to a larger interstate system, expanded regional corridor, or other transportation network.

Sujan, Vivek↗
DOE OSTI2025

Preserving nonlinear constraints in variational flow filtering data assimilation

Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by a computational model. The states of many dynamical systems of interest obey nonlinear physical constraints, and the corresponding dynamics is confined to a certain sub-manifold of the state space. Standard data assimilation techniques applied to such systems yield posterior states lying outside the manifold, violating the physical constraints. This work focuses on particle flow filters which use stochastic differential equations to evolve state samples from a prior distribution to samples from an observation-informed posterior distribution. The variational Fokker-Planck (VFP)—a generic particle flow filtering framework—is extended to incorporate non-linear, equality state constraints in the analysis. To this end, two algorithmic approaches that modify the VFP stochastic differential equation are discussed: (i) VFPSTAB, to inexactly preserve constraints with the addition of a stabilizing drift term, and (ii) VFPDAE, to exactly preserve constraints by treating the VFP dynamics as a stochastic differential-algebraic equation (SDAE). Additionally, an implicit-explicit time integrator is developed to evolve the VFPDAE dynamics. The strength of the proposed approach for constraint preservation in data assimilation is demonstrated on three test problems: the double pendulum, Korteweg-de-Vries, and the incompressible Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest

Quantifying the spatially and temporally explicit life cycle environmental impacts of crop production is critical for designing sustainable supply chains for biofuel and animal sectors. This study assessed life cycle environmental impacts of soybean production in around 1000 Midwest counties over 9 years. A combination of Environmental Policy Integrated Climate model and process-based life cycle assessment model was used to estimate the spatially and temporally explicit life cycle global warming (GW), eutrophication (EU) and acidification (AD) impacts. Sequentially, a machine learning approach was applied to identify the top influential factors among soil, climate and farming practices for spatially and temporally explicit life cycle environmental impacts. The results indicated that significant variations existed in life cycle environmental impacts among counties and across years. Life cycle GW impacts ranged from -11.4 to 22.0 kg CO2-eq. kg soybean-1, whereas life cycle EU and AD impacts varied by a factor of 302 and 44, respectively. Nitrogen application rates, temperature in March and soil texture were the top influencing factors for the spatial-temporal variations in life cycle GW impacts. In contrast, soil organic content and nitrogen application rate were the top influencing factors for the spatial-temporal variations in life cycle EU and AD impacts.

Soybean, life cycle analysis, sustainability, spat↗
DOE OSTI2024

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantifying and Visualizing Uncertainty for Source Localisation in Electrocardiographic Imaging

Electrocardiographic imaging (ECGI) presents a clinical opportunity to noninvasively understand the sources of arrhythmias for individual patients. To help increase the effectiveness of ECGI, we provide new ways to visualise associated measurement and modelling errors. Here, we study source localisation uncertainty in two steps: First, we perform Monte Carlo simulations of a simple inverse ECGI source localisation model with error sampling to understand the variations in ECGI solutions. Second, we present multiple visualisation techniques, including confidence maps, level-sets, and topology-based visualisations, to better understand uncertainty in source localization. Our approach offers a new way to study uncertainty in the ECGI pipeline.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

DeepClone: Lightweight State Replication of Deep Learning Models for Data Parallel Training

Training modern deep neural network (DNN) models involves complex workflows triggered by model exploration, sensitivity analysis, explainability, etc. A key primitive in this context is the ability to clone a model training instance, i.e. “fork” the training process in a potentially different direction, which enables comparisons of different evolution paths using variations of training data and model parameters. However, in a quest improve the training throughput, a mix of data parallel, model parallel, pipeline parallel and layer-wise parallel approaches are making the problem of cloning highly complex. In this paper, we explore the problem of efficient cloning under such circumstances. To this end, we leverage several properties of data-parallel training and layer-wise parallelism to design DeepClone, a cloning approach based on augmenting the execution graph to gain direct access to tensors, which are then sharded and reconstructed asynchronously in order to minimize runtime overhead, standby duration, readiness duration. Compared with state-of-art approaches, DeepClone shows orders of magnitude improvement for several classes of DNN models.

data-parallel training↗