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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Development of CFD models to support LEU Conversion of ORNL s High Flux Isotope Reactor

The US Department of Energy s National Nuclear Security Administration (NNSA) is participating in the Global Threat Reduction Initiative to reduce and protect vulnerable nuclear and radiological materials located at civilian sites worldwide. As an integral part of one of NNSA s subprograms, Reduced Enrichment for Research and Test Reactors, HFIR is being converted from the present HEU core to a low enriched uranium (LEU) core with less than 20% of U-235 by weight. Because of HFIR s importance for condensed matter research in the United States, its conversion to a high-density, U-Mo-based, LEU fuel should not significantly impact its existing performance. Furthermore, cost and availability considerations suggest making only minimal changes to the overall HFIR facility. Therefore, the goal of this conversion program is only to substitute LEU for the fuel type in the existing fuel plate design, retaining the same number of fuel plates, with the same physical dimensions, as in the current HFIR HEU core. Because LEU-specific testing and experiments will be limited, COMSOL Multiphysics was chosen to provide the needed simulation capability to validate against the HEU design data and previous calculations, and predict the performance of the proposed LEU fuel for design and safety analyses. To achieve it, advanced COMSOL-based multiphysics simulations, including computational fluid dynamics (CFD), are being developed to capture the turbulent flows and associated heat transfer in fine detail and to improve predictive accuracy [2].

COMSOL↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of machine learning (ML), specifically neural networks, has grown significantly and has spurred research in its applicability to digital instrumentation and control systems (DI&C). While ML models have shown promise in operational contexts, the trustworthiness of using such algorithms has not been adequately assessed. Failures of ML integrated systems are not well understood, and the lack of comprehensive risk modeling can degrade the trustworthiness in these systems. In recent reports by the National Institute for Standards and Technology (NIST) [1] and the Nuclear Regulatory Commission (NRC) [2], they indicate that trustworthiness in ML is a critical barrier and will play a vital role in the safe, accountable, and secure operation of intelligent systems. Thus, in this work, we demonstrate a dynamic model-agnostic method to quantify the relative reliability of AI/ML predictions by incorporating out-of-distribution (OOD) detection on the training dataset. It is well documented that most ML algorithms excel at interpolation (or near-interpolation) tasks but experience significant performance degradation at extrapolation. The method, referenced as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets which can used to the relative reliability of AI/ML predictions. LADDR is then demonstrated on a feedforward neural network based digital twin used for the prediction of safety significant factors during a loss-of-flow transient. LADDR is used to demonstrate how training data can be used as evidence to support the relative reliability of ML/AI predictions enhancing the overall trustworthiness of the system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Computational Approaches for Clean Energy Materials

Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.

density functional theory↗
DOE OSTI2014

A magnetically shielded room with ultra low residual field and gradient

A versatile and portable magnetically shielded room with a field of (700 ± 200) pT within a central volume of 1 m × 1 m × 1 m and a field gradient less than 300 pT/m, achieved without any external field stabilization or compensation, is described. This performance represents more than a hundredfold improvement of the state of the art for a two-layer magnetic shield and provides an environment suitable for a next generation of precision experiments in fundamental physics at low energies; in particular, searches for electric dipole moments of fundamental systems and tests of Lorentz-invariance based on spin-precession experiments. Studies of the residual fields and their sources enable improved design of future ultra-low gradient environments and experimental apparatus. This has implications for developments of magnetometry beyond the femto-Tesla scale in, for example, biomagnetism, geosciences, and security applications and in general low-field nuclear magnetic resonance (NMR) measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

EKAT v.1.0

E3SM Kokkos Application Toolkit (EKAT) is a collection of C++, Fortran, and CMake utilities for providing a single implementation of common kernels based on the Kokkos programming model. The library contains utilities for vectorization, tridiagonal linear system solvers, and linear interpolation as well as some general-purpose utilities such as testing utilities, parameter lists, representation of physical units, and additional interfaces. The goal is to provide a centralized implementation for high-performance computing structures and common utilities that reduce code duplication and streamline maintenance efforts. 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. SAND2022-1327 O

Bertagna, Luca↗
DOE OSTI2013

Carbon Nanotube Membranes for Energy Efficient Carbon Sequestration (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Porifera, Inc. to develop Carbon Nanotube Membranes for Energy-Efficient Carbon Sequestration. Porifera, Inc. was a recently founded start-up company whose sole mission is to bring the carbon nanotube-based technology for effective membrane separations to the marketplace. This CRADA project resulted from an ARPA-E award received by Porifera (ARPA-E Co-Operative Agreement number DE AR 0000025), in which LLNS was identified as a proposed subcontractor with valuable experience, skill and ability which made LLNS uniquely positioned to perform the research for this project.

Fornasiero, Francesco↗
DOE OSTI2015

Structural color printing based on plasmonic metasurfaces of perfect light absorption

Subwavelength structural color filtering and printing technologies employing plasmonic nanostructures have recently been recognized as an important and beneficial complement to the traditional colorant-based pigmentation. However, the color saturation, brightness and incident angle tolerance of structural color printing need to be improved to meet the application requirement. Here we demonstrate a structural color printing method based on plasmonic metasurfaces of perfect light absorption to improve color performances such as saturation and brightness. Thin-layer perfect absorbers with periodic hole arrays are designed at visible frequencies and the absorption peaks are tuned by simply adjusting the hole size and periodicity. Near perfect light absorption with high quality factors are obtained to realize high-resolution, angle-insensitive plasmonic color printing with high color saturation and brightness. Moreover, the fabricated metasurfaces can be protected with a protective coating for ambient use without degrading performances. The demonstrated structural color printing platform offers great potential for applications ranging from security marking to information storage.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

THE GRISM LENS-AMPLIFIED SURVEY FROM SPACE (GLASS). IV. MASS RECONSTRUCTION OF THE LENSING CLUSTER ABELL 2744 FROM FRONTIER FIELD IMAGING AND GLASS SPECTROSCOPY

We present a strong and weak lensing reconstruction of the massive cluster Abell 2744, the first cluster for which deep Hubble Frontier Fields (HFF) images and spectroscopy from the Grism Lens-Amplified Survey from Space (GLASS) are available. By performing a targeted search for emission lines in multiply imaged sources using the GLASS spectra, we obtain five high-confidence spectroscopic redshifts and two tentative ones. We confirm one strongly lensed system by detecting the same emission lines in all three multiple images. We also search for additional line emitters blindly and use the full GLASS spectroscopic catalog to test reliability of photometric redshifts for faint line emitters. We see a reasonable agreement between our photometric and spectroscopic redshift measurements, when including nebular emission in photometric redshift estimations. We introduce a stringent procedure to identify only secure multiple image sets based on colors, morphology, and spectroscopy. By combining 7 multiple image systems with secure spectroscopic redshifts (at 5 distinct redshift planes) with 18 multiple image systems with secure photometric redshifts, we reconstruct the gravitational potential of the cluster pixellated on an adaptive grid, using a total of 72 images. The resulting mass map is compared with a stellar mass map obtained from the deep Spitzer Frontier Fields data to study the relative distribution of stars and dark matter in the cluster. We find that the stellar to total mass ratio varies substantially across the cluster field, suggesting that stars do not trace exactly the total mass in this interacting system. The maps of convergence, shear, and magnification are made available in the standard HFF format.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

PARAFAC_T1

This is a method of performing trilinear analysis on large data sets using a modification of the PARAFAC-ALS algorithm. It iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer?s main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC-ALS on the full data set. SAND2020-12649 M 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.

Van Benthem, Mark↗
DOE OSTI2026

Multi-Fidelity Bayesian Optimization with Gaussian Processes for Double Shell Inertial Confinement Fusion Target Design

Reliable, secure access to energy is a major focus for national security efforts. One potential route to such energy is through fusion reactions in inertial confinement fusion (ICF) experiments. Such experiments are carried out at facilities such as the National Ignition Facility (NIF) in Livermore, California, where high powered lasers are used to compress a DT fuel-containing target to the necessary high temperature, high pressure conditions. These experiments are limited in number, which creates a heavy dependence on high fidelity predictive physics simulations and analysis performed “pre shot,” or before the experiment occurs. Many of these simulations in higher dimensions (2D and 3D) are computationally expensive, so finding optimal simulation-based designs presents its own challenges. In this work, we present our multi-fidelity Bayesian optimization with Gaussian processes (GPs) for ICF double shell targets, where a 1D surrogate model is used to help find a 2D surrogate model, enabling us to find optimal targets in the higher fidelity (2D), while saving computational cost.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Melt Blending: A Tool to Simplify Plastic Scintillator Synthesis

Plastic scintillators are widely used as radiation detection media in homeland security and nuclear physics applications. Their attributes include low cost, scalability to large detector volumes, and additive compounding to enable additional material and detection features, such as pulse shape discrimination (PSD), gamma-ray spectroscopy, aging resistance, and coincidence timing. However, traditional chemically cured plastic scintillators (CCS) require long reaction times, and hazardous wet chemical procedures performed by specially trained personnel, and can leave residual monomer, resulting in deleterious optical and material properties. Here, we synthesize melt blended scintillators (MBSs) in 2.5 days using easily accessible solid-state compounding of commercially-available poly(styrene) with 30–60 wt% fluorene-based compound “P2” to create monolithic detectors with < 100 ppm residual monomer, in several form factors. Further, the best scintillation performance was recorded for 60 wt% P2 in Styron 665, including gamma-ray light yield 139% of EJ- 200 commercial scintillator and PSD figure of merit (FOM) value of 2.65 at 478 keVee, approaching P2 organic glass scintillator (OGS). The capability of MBS to generate fog-resistant scintillators and poly(methyl methacrylate) (PMMA)-based scintillators for use in challenging environments is also demonstrated.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2017

Mixed Waste Landfill Annual Long-Term Monitoring & Maintenance Report April 2016-March 2017

Sampling activities for this reporting period included two semiannual monitoring events each for groundwater, soil-vapor, and radon. Annual soil-moisture monitoring was conducted in April 2016, annual tritium surface soil sampling was conducted in August 2016, and annual biota sampling (metals and radionuclides) was conducted in September 2016. All monitoring activities were conducted in accordance with LTMMP requirements and no monitoring results exceeded LTMMP trigger levels. All monitoring results were consistent with historic MWL monitoring data. Inspections of the MWL final cover system, storm-water diversion structures, compliance monitoring systems, and security fence were performed in accordance with LTMMP requirements. Required maintenance and repairs were minor and were generally performed during the inspections. The Evapotranspirative (ET) Cover continues to meet successful revegetation criteria and is in excellent condition with even coverage of mature, native perennial grasses. Maintenance was performed during the reporting period as a best management practice for ET Cover vegetation, and included a lower level of effort relative to previous years. The purpose of ongoing ET Cover maintenance efforts is to promote the growth and health of the desired native grass species by reducing competition with weedy species for limited moisture and nutrients. Based on previous inspections, additional best management practice activities were conducted during this reporting period to improve the site and reduce long-term ET Cover and site maintenance. These activities included improvements to site access and drainage (i.e., improvements to the access and perimeter road) and the installation of erosion and burrow control measures at the ten perimeter monitoring well locations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Material Control & Accountancy for Molten Salt Reactors (FY2021 Report)

There is significant domestic and international interest, investment, and research and development momentum to pursue advanced nuclear reactor technologies. Molten salt reactor (MSR) concepts display the largest variability in fuel type and design features among the current advanced concepts. MSRs have been proposed with various core designs, sizes (power), and fuel cycles. Salt-fueled molten salt systems represent the only advanced reactor type with fuel that is not in a solid form during operation. These “liquid-fueled” MSRs are unique from perspectives of fuel fabrication, spent irradiated fuel and waste components, licensing, and material control and accountability (MC&A) including the potential of fissile material holdup. The liquid fuel salt is the defining distinction in comparison to other advanced reactors that propose TRI-structural ISOtropic particle fuel pebbles, various coolant options (e.g., molten salts or metals, high temperature gas), or small modular alternatives using solid fuel variants including both light water reactors and non-light water reactors. MSRs are appealing to the nuclear energy industry because of the diverse reactor characteristics they can support including various neutron energy spectra, fueling requirements, fuel cycles, and/or fuel utilization. However, because of the significant deviation and diversity of a salt-fueled system compared to traditional solid fuel light water-cooled reactors (LWRs), the history, regulatory licensing framework, modeling capabilities, and supporting engineering technology are either lacking or, in some cases, nonexistent. Therefore, the research community is actively supporting advanced MSR development on many of these fronts in particular to assist MSR vendors with licensing requirements. ORNL is leading the research and development of respective MC&A approaches for salt-fueled MSRs. This report summarizes the research performed at Oak Ridge National Laboratory (ORNL) under the US Department of Energy, Office of Nuclear Energy, Advanced Reactor Safeguards (ARS) program to investigate safeguards and security by design concepts, licensing and regulatory considerations, and dynamic system-level modeling to understand radioisotope concentrations for salt-fueled MSRs. The report builds upon the previous research and literature, identifies the MC&A challenges inherent to a salt-fueled MSR, reviews current regulatory frameworks for LWRs and their applicability towards salt-fueled MSRs, summarizes the status and progress of an MSR dynamic modeling tool, and discusses a prospective MC&A approach based on the Molten Salt Demonstration Reactor (MSDR) model.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Multi-functional Sorbent Development for Separation of Critical Minerals and Pollutants

Critical Metals (CMs), including Rare Earth Elements (REEs), aluminum, manganese, cobalt, and others outlined by the U.S. Department of the Interior’s Geological Survey are essential to the national and economic security of the US, and whose supply chain is susceptible to disruption. Because of the negative environmental impacts inherent with the conventional mining and processing of solid ores for CMs, adsorption-based recovery of naturally dissolved species from coal wastewaters is appealing. Acid mine drainage (AMD) remains a relatively untapped source that can be rich in REEs, and especially in Al, Mn, and other CMs. NETL’s Multi-functional Sorbent Technology (MUST) has been developed originally from the functionalized silica sorbents for carbon capture. Ongoing work aims to find AMD sites enriched with either REEs or other CM, and to perform additional field tests at these sites. Overall, our work shows the viability of recovering CMs from AMD and other coal waste streams, using a fixed-bed adsorption system that can also employ a selective elution technique to achieve highly purified metal resources.

Shi, Fan↗
DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗
DOE OSTI2020

Detection and Diagnosis of Data Integrity Attacks in Solar Farms Based on Multilayer Long Short-Term Memory Network

Photovoltaic (PV) systems are becoming more vulnerable to cyber threats. In response to this emerging concern, developing cyber-secure power electronics converters has received increased attention from the IEEE Power Electronics Society that recently launched a cyber-physical-security initiative. Here this letter proposes a deep sequence learning based diagnosis solution for data integrity attacks on PV systems in smart grids, including dc–dc and dc–ac converters. The multilayer long short-term memory networks are used to leverage time-series electric waveform data from current and voltage sensors in PV systems. The proposed method has been evaluated in a PV smart grid benchmark model with extensive quantitative analysis. As a comparison, we have evaluated classic data-driven methods, including K-nearest neighbor, decision tree, support vector machine, artificial neural network, and convolutional neural network. Comparison results verify performances of the proposed method for detection and diagnosis of various data integrity attacks on PV systems.

42 ENGINEERING↗
DOE OSTI2017

Trinitromethyl Heterocyclic Oxidizers as a Solid Propellant Ingredient Final Report CRADA No TC02146.0

This was a collaborative effort between Lawrence Livermore National Security, LLC as manager and operator of Lawrence Livermore National Laboratory (LLNL) and Physical Sciences, Inc. (PSI), to develop a synthesis of two novel energetic heterocyclic oxidizers as possible replacements for ammonium perchlorate (AP) in rocket propellant formulations. This CRADA resulted from the award of the Phase I Small Business Technology Transfer (STTR) from DOD. The CRADA consisted of two phases. The goal for Phase 1 was to produce a new oxidizer called TNMDNP. Phase 2 is optional (based on the success of Phase 1) and the goal of Phase 2 (optional) was to produce a new oxidizer called TNMDNT. Phase 2 tasks would be performed based on the successful results of Phase 1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Role of Innovation in the Electric Utility Sector

Innovation is essential for future power systems to be safe and secure, clean and sustainable, affordable and equitable, and reliable and resilient, according to a recent National Academies report. But state regulatory reforms are needed to encourage adoption of new technologies to support evolution of the nation’s power systems.1 Berkeley Lab's report, The Role of Innovation in the Electric Utility Sector, provides consumer, labor, utility, third-party provider, and clean technology consultant perspectives on this theme. To achieve state targets for clean energy and greenhouse gas emissions, some state regulatory utility commissions are exploring new approaches to spur innovation: -For utilities, regulatory and marketing flexibility, increased funding for demonstration projects, and performance-based ratemaking including multi-year rate plans -For third parties, ways to provide utility customers with innovative products and services directly Among the questions the report addresses: 1. How are consumer advocate views evolving with respect to innovative regulatory and ratemaking approaches? 2. How can utility decarbonization and grid modernization initiatives provide opportunities for local communities and workers to receive tangible benefits and facilitate community support for siting electricity infrastructure? 3. How are electric utilities partnering with technology companies to provide innovative energy management services and sustainable energy solutions for utility customers? 4. What regulatory innovations are public utility commissions exploring to enable third-party providers to participate in the transition to a modern electric system? 5. What regulatory changes are needed to enable innovative solutions from utilities and third parties at the necessary speed and scale to meet state decarbonization goals? The report is the 13th in the Future Electric Utility Regulation series, which taps leading thinkers to tackle complex regulatory issues for electricity. 1 National Academies of Sciences, Engineering, and Medicine. 2021. The Future of Electric Power in the United States. The National Academies Press.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗