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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Tip- and Plasmon-Enhanced Infrared Nanoscopy for Ultrasensitive Molecular Characterizations

Herein, we propose a method for ultrasensitive infrared (IR) vibrational spectroscopy of molecules with nanoscale footprints by combining the tip enhancement of a scattering-type scanning near-field optical microscope (s-SNOM) and the plasmon enhancement of breathing-mode (BM) plasmon resonances of graphene nanodisks (GNDs). To demonstrate this, we develop a quantitative model that is capable of computing accurately the s-SNOM signals of nanoscale samples. With our modeling, we show that the s-SNOM tip can effectively excite gate-tunable BM plasmonic resonances in GNDs with strong field enhancement and sensitive dependence on the size of GND. Moreover, we demonstrate that the intense electric field of tip-excited plasmonic BMs can strongly enhance the IR vibrational modes of molecules. As a result, IR vibrational signatures of individual molecular particles with sizes down to 1–2 nm are readily observable by s-SNOM. Our study sheds light on future ultrasensitive IR biosensing that takes advantage of both the tip and plasmon enhancement.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Perturbative treatment of nonlocal chiral interactions in auxiliary-field diffusion Monte Carlo calculations

Nuclear many-body systems, ranging from nuclei to neutron stars, are some of the most interesting physical phenomena in our universe, and quantum Monte Carlo (QMC) approaches are among the most accurate many-body methods currently available to study them. In recent decades, interactions derived from chiral effective field theory (EFT) have been widely adopted in the study of nuclear many-body systems. One drawback of the QMC approach is the requirement that the nuclear interactions need to be local, whereas chiral EFT interactions usually contain nonlocalities. In this work, we leverage the capability of computing second-order perturbative corrections to the ground-state energy in order to develop a self-consistent approach to including nonlocal operators in QMC calculations. In conclusion, we investigate both the deuteron and the neutron-matter equation of state in order to show the robustness of our technique and pave the way for future QMC calculations at higher orders in the EFT, where nonlocal operators cannot be avoided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Demonstration of Algorithmic Quantum Speedup

Despite the development of increasingly capable quantum computers, an experimental demonstration of a provable algorithmic quantum speedup employing today’s non-fault-tolerant devices has remained elusive. Here, in this study, we unequivocally demonstrate such a speedup within the oracular model, quantified in terms of the scaling with the problem size of the time-to-solution metric. We implement the single-shot Bernstein-Vazirani algorithm, which solves the problem of identifying a hidden bitstring that changes after every oracle query, using two different 27-qubit IBM Quantum superconducting processors. The speedup is observed on only one of the two processors when the quantum computation is protected by dynamical decoupling but not without it. The quantum speedup reported here does not rely on any additional assumptions or complexity-theoretic conjectures and solves a bona fide computational problem in the setting of a game with an oracle and a verifier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Digital Twin of an ANPC inverter with integrated Design-For-Trust

Reducing costs and increasing manufacturing output while creating reliable products are three essential components needed to keep a business competitive. In the past, having an asset that could address these three challenges at the same time would be challenging. However, recent advances in computer processing capabilities coupled with increases to network speeds allowed such a system to become possible; and this is known as a Digital Twin (DT), which represents a virtual copy of a production system or a product. Here, this work exhibits a DT application of an Active Neutral Point Clamped (ANPC) inverter using a controller. The controller emulates the ANPC responses of a new Digital Signal Processing (DSP) firmware and validates this new firmware by using Design-For-Trust (DFTr) characteristics. If the new firmware is malicious, then it should be rejected, thus adding preemptive protection measures for inverter based distributed energy resources.

cybersecurity↗
DOE OSTI2014

MO-G-BRF-05: Determining Response to Anti-Angiogenic Therapies with Monte Carlo Tumor Modeling

Purpose: Patient response to anti-angiogenic therapies with vascular endothelial growth factor receptor - tyrosine kinase inhibitors (VEGFR TKIs) is heterogeneous. This study investigates key biological characteristics that drive differences in patient response via Monte Carlo computational modeling capable of simulating tumor response to therapy with VEGFR TKI. Methods: VEGFR TKIs potently block receptors, responsible for promoting angiogenesis in tumors. The model incorporates drug pharmacokinetic and pharmacodynamic properties, as well as patientspecific data of cellular proliferation derived from [18F]FLT-PET data. Sensitivity of tumor response was assessed for multiple parameters, including initial partial oxygen tension (pO{sub 2}), cell cycle time, daily vascular growth fraction, and daily vascular regression fraction. Results were benchmarked to clinical data (patient 2 weeks on VEGFR TKI, followed by 1-week drug holiday). The tumor pO{sub 2} was assumed to be uniform. Results: Among the investigated parameters, the simulated proliferation was most sensitive to the initial tumor pO{sub 2}. Initial change of 5 mmHg can already Result in significantly different levels of proliferation. The model reveals that hypoxic tumors (pO{sub 2} ≥ 20 mmHg) show the highest decrease of proliferation, experiencing mean FLT standardized uptake value (SUVmean) decrease for at least 50% at the end of the clinical trial (day 21). Oxygenated tumors (pO{sub 2} 20 mmHg) show a transient SUV decrease (30–50%) at the end of the treatment with VEGFR TKI (day 14) but experience a rapid SUV rebound close to the pre-treatment SUV levels (70–110%) at the time of a drug holiday (day 14–21) - the phenomenon known as a proliferative flare. Conclusion: Model's high sensitivity to initial pO{sub 2} clearly emphasizes the need for experimental assessment of the pretreatment tumor hypoxia status, as it might be predictive of response to antiangiogenic therapies and the occurrence of proliferative flare. Experimental assessment of other model parameters would further improve understanding of patient response.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC into IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical usages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Self-consistent effective Hamiltonian theory for fermionic many-body systems

Using a separable many-body variational wavefunction, we formulate a self-consistent effective Hamiltonian theory for fermionic many-body system. The theory is applied to the two-dimensional (2D) Hubbard model as an example to demonstrate its capability and computational effectiveness. Most remarkably for the Hubbard model in 2D, a highly unconventional quadruple-fermion non-Cooper pair order parameter is discovered.

Physics↗
DOE OSTI2023

Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors

This software offers methods and functions for training calibrated out-of-distribution detectors for medical image classification tasks. It includes functionalities for training, synthesizing data augmentations, calibration, and out-of-distribution detection. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, VivekSivaraman↗
DOE OSTI2024

Constitutive Models via Automatic Differentiation v.1.0.0

SAND2024-00905O Constitutive Models via Automatic Differentiation (CMAD) provides a software framework for solving constitutive or material model calibration problems. It relies on JAX's automatic differentiation capabilities to compute the derivatives needed for both forward and adjoint sensitivity analyses. The software can be used to implement constitutive models and calibrate the parameters from experimental data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Seidl, Daniel↗
DOE OSTI2021

PyMTensor 1.0.0

SAND2021-1654 O The Python Material Tensor code (PyMTensor) applies crystal symmetries to arbitrary material tensors. It uses symbolic algebra to exactly determine the zero tensor components and the relationships between the nonzero components for all crystallographic point groups. It is capable of computing components of higher-order material tensors needed when treating nonlinear effects. It has a simple and flexible interface for inputting tensor rank information and any known symmetries between tensor indices.Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Jensen, DanielS↗
DOE OSTI2024

DECIDER

This software offers methods and functions for building failure detectors for deep image classification models with the aid of vision-language models and LLMs. It includes functionalities for training baseline image classifiers, debiasing classifiers using vision-language models and LLMs, evaluating failure between models along with baselines. Developed using PyTorch, this software is compatible with standard neural network architectures used for imaging data. Additionally, it provides capabilities to compute evaluation metrics for assessing the performance and quality of the detectors.

Narayanaswamy, Vivek Sivaraman↗
DOE OSTI2012

Production of Energy Efficient Preform Structures (PEEPS)

Due to its low density, good structural characteristics, excellent fabrication properties, and attractive appearance, aluminum metal and its alloys continue to be widely utilized. The transportation industry continues to be the largest consumer of aluminum products, with aerospace as the principal driver for this use. Boeing has long been the largest single company consumer of heat-treated aluminum in the U.S. The extensive use of aluminum to build aircraft and launch vehicles has been sustained, despite the growing reliance on more structurally efficient carbon fiber reinforced composite materials. The trend in the aerospace industry over the past several decades has been to rely extensively on large, complex, thin-walled, monolithic machined structural components, which are fabricated from heavy billets and thick plate using high speed machining. The use of these high buy-to-fly ratio starting product forms, while currently cost effective, is energy inefficient, with a high environmental impact. The widespread implementation of Solid State Joining (SSJ) technologies, to produce lower buy-to-fly ratio starting forms, tailored to each specific application, offers the potential for a more sustainable manufacturing strategy, which would consume less energy, require less material, and reduce material and manufacturing costs. One objective of this project was to project the energy benefits of using SSJ techniques to produce high-performance aluminum structures if implemented in the production of the world fleet of commercial aircraft. A further objective was to produce an energy consumption prediction model, capable of calculating the total energy consumption, solid waste burden, acidification potential, and CO2 burden in producing a starting product form - whether by conventional or SSJ processes - and machining that to a final part configuration. The model needed to be capable of computing and comparing, on an individual part/geometry basis, multiple possible manufacturing pathways, to identify the best balance of energy consumption and environmental impact. This model has been created and populated with energy consumption data for individual SSJ processes and process platforms. Technology feasibility cases studies were executed, to validate the model, and confirm the ability to create lower buy-to-fly ratio performs and machine these to final configuration aircraft components. This model can now be used as a tool to select manufacturing pathways that offer significant energy savings and, when coupled with a cost model, drive implementation of the SSJ processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Assessment of MARMOT Grain Growth Model

This report assesses the MARMOT grain growth model by comparing modeling predictions with experimental results from thermal annealing. The purpose here is threefold: (1) to demonstrate the validation approach of using thermal annealing experiments with non-destructive characterization, (2) to test the reconstruction capability and computation efficiency in MOOSE, and (3) to validate the grain growth model and the associated parameters that are implemented in MARMOT for UO 2 . To assure a rigorous comparison, the 2D and 3D initial experimental microstructures of UO 2 samples were characterized using non-destructive Synchrotron x-ray. The same samples were then annealed at 2273K for grain growth, and their initial microstructures were used as initial conditions for simulated annealing at the same temperature using MARMOT. After annealing, the final experimental microstructures were characterized again to compare with the results from simulations. So far, comparison between modeling and experiments has been done for 2D microstructures, and 3D comparison is underway. The preliminary results demonstrated the usefulness of the non-destructive characterization method for MARMOT grain growth model validation. A detailed analysis of the 3D microstructures is in progress to fully validate the current model in MARMOT.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Atrial Model Development and Prototype Simulations CRADA No. TC02224.0

This project was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Amgen, Inc. ("Amgen"), to develop a capability to computationally evaluate the importance and roles of different ion channels in atrial fibrillation. The first phase of this collaboration was to implement the atrial model, run prototype simulations, and analyze the results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

BISON Investigation of the Effect of the Fuel- Cladding Contact Irregularities on the Peak Cladding Temperature and FCCI Observed in AFC-3A Rodlet 4

The primary objective of this report is to document results of BISON analyses supporting Fuel Cycle Research and Development (FCRD) activities. Specifically, the present report seeks to provide explanation for the microstructural features observed during post irradiation examination of the helium-bonded annular U-10Zr fuel irradiated during the AFC-3A experiment. Post irradiation examination of the AFC-3A rodlet revealed microstructural features indicative of the fuel-cladding chemical interaction (FCCI) at the fuel-cladding interface. Presence of large voids was also observed in the same locations. BISON analyses were performed to examine stress and temperature profiles and to investigate possible correlation between the voids and FCCI. It was found that presence of the large voids lead to a formation of circumferential temperature gradients in the fuel that may have redirected migrating lanthanides to the locations where fuel and cladding are in contact. Resulting localized increase of lanthanide concentration is expected to accelerate FCCI. The results of this work provide important guidance to the post irradiation examination studies. Specifically, the hypothesis of lanthanides being redirected from the voids to the locations where the fuel and the cladding are in contact should be verified by conducting quantitative electron microscopy or Electron Probe Micro-Analyzer (EPMA). The results also highlight the need for computer models capable of simulating lanthanide diffusion in metallic fuel and establish a basis for validation of such models.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Integrated Predictive Tools for Customizing Microstructure and Material Properties of Additively Manufactured Aerospace Components

Additive Manufacturing (AM) refers to a process by which digital three-dimensional (3-D) design data is converted to build up a component by depositing material layer-by-layer. United Technologies Corporation (UTC) is currently involved in fabrication and certification of several AM aerospace structural components made from aerospace materials. This is accomplished by using optimized process parameters determined through numerous design-of-experiments (DOE)-based studies. Certification of these components is broadly recognized as a significant challenge, with long lead times, very expensive new product development cycles and very high energy consumption. Because of these challenges, United Technologies Research Center (UTRC), together with UTC business units have been developing and validating an advanced physics-based process model. The specific goal is to develop a physics-based framework of an AM process and reliably predict fatigue properties of built-up structures as based on detailed solidification microstructures. Microstructures are predicted using process control parameters including energy source power, scan velocity, deposition pattern, and powder properties. The multi-scale multi-physics model requires solution and coupling of governing physics that will allow prediction of the thermal field and enable solution at the microstructural scale. The state-of-the-art approach to solve these problems requires a huge computational framework and this kind of resource is only available within academia and national laboratories. The project utilized the parallel phase-fields codes at Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL), along with the high-performance computing (HPC) capabilities existing at the two labs to demonstrate the simulation of multiple dendrite growth in threedimensions (3-D). The LLNL code AMPE was used to implement the UTRC phase field model that was previously developed for a model binary alloy, and the simulation results were compared against the UTRC simulation results, followed by extension of the UTRC model to simulate multiple dendrite growth in 3-D. The ORNL MEUMAPPS code was used to simulate dendritic growth in a model ternary alloy with the same equilibrium solidification range as the Ni-base alloy 718 using realistic model parameters, including thermodynamic integration with a Calphad based model for the ternary alloy. Implementation of the UTRC model in AMPE met with several numerical and parametric issues that were resolved and good comparison between the simulation results obtained by the two codes was demonstrated for two dimensional (2-D) dendrites. 3-D dendrite growth was then demonstrated with the AMPE code using nondimensional parameters obtained in 2-D simulations. Multiple dendrite growth in 2-D and 3-D were demonstrated using ORNL’s MEUMAPPS code using simple thermal boundary conditions. MEUMAPPS was then modified to incorporate the complex, time-dependent thermal boundary conditions obtained by UTRC’s thermal modeling of single track AM experiments to drive the phase field simulations. The results were in good agreement with UTRC’s experimental measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Open Collaboration and Research Capabilities on the LVOC/CREATE/High Performance Computing Innovation Center

While Lawrence Livermore National Laboratory (LLNL) and Sandia National Laboratories (SNL) have remained committed to their core nuclear weapons (NW) mission, the world has changed greatly since the opening of these national labs in the mid-20th century. During this era, U.S. universities were ranked as the top science centers in the world in essentially all fields, and the national laboratories were able to recruit the top graduates - who were predominantly U.S. citizens - from the nation's best academic institutions.

97 MATHEMATICS AND COMPUTING↗