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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Software-Defined Network for End-to-end Networked Science at the Exascale

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work we presen here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Communication-efficient certificate revocation management for Advanced Metering Infrastructure and IoT Integration

Advanced Metering Infrastructure forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication between smart meters could be secured utilizing public-key cryptography, however, public-key cryptography still has certain challenges in terms of certificate revocation and management particularly related distribution and storage overhead of revoked certificates. To address this challenge, in this work, we propose a novel revocation management approach by utilizing cryptographic accumulators which reduces the space requirements for revocation information significantly and thus enables efficient distribution of such information to all smart meters. We implemented the proposed approach on both ns-3 network simulator and a testbed. We demonstrated its superior performance with respect to traditional methods for revocation management.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗
DOE OSTI2020

Characterization of flow and transport in a fracture network at the EGS Collab field experiment through stochastic modeling of tracer recovery

Energy extraction from subsurface reservoirs is important for addressing the increasing energy demand and environmental concerns such as global warming. However, the characterization of subsurface reservoirs, particularly reservoirs dominated by fracture networks remains a challenge due to the lack of means to directly observe subsurface processes. This study explores the feasibility and efficacy of characterizing fracture flow and transport processes in an enhanced geothermal system (EGS) testbed through stochastic tracer modeling. There are two enabling factors that allow application of stochastic modeling to characterize a subsurface reservoir. First, an abundance of geological and geophysical measurements enables the development of a high-fidelity and well-constrained fracture network model. Second, high-performance computing (HPC) allows running massive realizations efficiently. Six conservative tracer tests were stochastically modeled and produced satisfactory realizations that successfully reproduce field tracer recovery data from each tracer test. The evolution of flow and transport processes in the fracture network was then analyzed from these satisfactory realizations. This work demonstrates that stochastic tracer modeling on a high-fidelity fracture network model is feasible and can provide important insights regarding flow and transport characteristics in subsurface fractured reservoirs.

58 GEOSCIENCES↗
DOE OSTI2025

Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins

Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.

Additive Manufacturing↗
DOE OSTI2023

Galactic cosmic ray simulation at the NASA space radiation laboratory – Progress, challenges and recommendations on mixed-field effects

For missions beyond low Earth orbit to the moon or Mars, space explorers will encounter a complex radiation field composed of various ion species with a broad range of energies. Such missions pose significant radiation protection challenges that need to be solved in order to minimize exposures and associated health risks. An innovative galactic cosmic ray simulator (GCRsim) was recently developed at the NASA Space Radiation Laboratory (NSRL) at Brookhaven National Laboratory (BNL). The GCRsim technology is intended to represent major components of the space radiation environment in a ground analog laboratory setting where it can be used to improve understanding of biological risks and serve as a testbed for countermeasure development and validation. The current GCRsim consists of 33 energetic ion beams that collectively simulate the primary and secondary GCR field encountered by humans in space over the broad range of particle types, energies, and linear energy transfer (LET) of interest to health effects. A virtual workshop was held in December 2020 to assess the status of the NASA baseline GCRsim. Workshop attendees examined various aspects of simulator design, with a particular emphasis on beam selection strategies. Experimental results, modeling approaches, areas of consensus, and questions of concern were also discussed in detail. This report includes a summary of the GCRsim workshop and a description of the current status of the GCRsim. This information is important for future advancements and applications in space radiobiology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A reduced-order model of concentration polarization in reverse osmosis systems with feed spacers

Feed spacers in reverse osmosis systems generate complex fluid flows that limit computational fluid dynamics (CFD) simulations to small length and time scales. That limits our ability to simulate mineral scaling and other membrane fouling phenomena, which occur over longer length and time scales. Thus motivated, we develop a reduced model that replaces the CFD simulation of the velocity field with an analytical model that mimics spacers. This focuses the remaining numerical effort on simulating the advection–diffusion equation governing solute transport. We motivate and validate the model with CFD simulations and bench-scale experiments of spacer filaments in three different arrangements, including cases of unsteady vortex shedding. We show that the model produces a roughly 10,000-fold speedup compared to CFD, and accurately reproduces CFD predictions of not only the average and maximum concentrations, but also the local concentration distribution along the membrane. Here, we also demonstrate the model for simulating a feed channel with a length-to-height ratio of 200. The model provides a simple testbed for exploratory studies of multispecies transport, precipitation, and membrane fouling phenomena for which simulating spacers is often prohibitive.

Computational fluid dynamics↗
DOE OSTI2022

Using artificial intelligence to detect human errors in nuclear power plants: A case in operation and maintenance

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Advancement of LANSCE accelerator facility as a 1-MW Fusion Prototypic Neutron Source

The Fusion Prototypic Neutron Source (FPNS) is considered to be a testbed for scientific understanding of material degradation in future nuclear fusion reactors (Zinkle and Moeslang, 2013; Summary Report on the FPNS Workshop, 2018; Pitcher et al., 2019). The primary mission of FPNS is to provide a damage rate in iron samples of 8-11 dpa/calendar year with He/dpa ratio of 10 appm in irradiation volume of 50 cm 3 or larger with irradiation temperature 300–1000 °C and flux gradient less than 20%/cm in the plane of the sample. The Los Alamos Neutron Science Center (LANSCE) is an attractive candidate for the FPNS project. The Accelerator Facility was designed and operated for an extended period as a 0.8-MW Meson Factory. The existing setup of the LANSCE accelerator complex can nearly fulfill requirements of the fusion neutron source station. The primary function of the upgraded accelerator systems is the safe and reliable delivery of a 1.25-mA continuous proton beam current at 800-MeV beam energy from the switchyard to the target assembly to create 1 MW power of proton beam interacting with a solid tungsten target. The present study describes existing accelerator setup and further development required to meet the needs of FPNS project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Particle track classification using quantum associative memory

Pattern recognition algorithms are commonly employed to simplify the challenging and necessary step of track reconstruction in sub-atomic physics experiments. Aiding in the discrimination of relevant interactions, pattern recognition seeks to accelerate track reconstruction by isolating signals of interest. In high collision rate experiments, such algorithms can be particularly crucial for determining whether to retain or discard information from a given interaction even before the data is transferred to tape. As data rates, detector resolution, noise, and inefficiencies increase, pattern recognition becomes more computationally challenging, motivating the development of higher efficiency algorithms and techniques. Quantum associative memory is an approach that seeks to exploits quantum mechanical phenomena to gain advantage in learning capacity, or the number of patterns that can be stored and accurately recalled. Here, we study quantum associative memory based on quantum annealing and apply it to the particle track classification. We focus on discrimination models based on Ising formulations of quantum associative memory model (QAMM) recall and quantum content-addressable memory (QCAM) recall. We characterize classification performance of these approaches as a function detector resolution, pattern library size, and detector inefficiencies, using the D-Wave 2000Q processor as a testbed. Discrimination criteria is set using both solution-state energy and classification labels embedded in solution states. We find that energy-based QAMM classification performs well in regimes of small pattern density and low detector inefficiency. In contrast, state-based QCAM achieves reasonably high accuracy recall for large pattern density and the greatest recall accuracy robustness to a variety of detector noise sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Leveraging the High Flux Isotope Reactor for nuclear fuel development: a review of experiments, facilities, and capabilities

Materials testing reactors (MTRs) have been used to develop in-core nuclear fuels and materials since the outset of the nuclear power industry. However, the closure of prominent MTRs worldwide and protracted construction timelines for new facilities have increased reliance on existing infrastructure for near-term irradiation testing needs. One facility that can support these needs is the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. HFIR boasts the highest steady-state neutron flux in the Western Hemisphere and, among other roles, has been used to rapidly administer high fluences on fuels and materials for fission and fusion reactor applications. This paper reviews HFIR facilities and infrastructure, fuel-bearing irradiation experiments conducted in HFIR, and select nonfueled experiments that demonstrate advanced techniques transferable to fuels experiments. Collectively, these examples underscore HFIR's potential role as a nuclear fuels testbed supporting both the existing reactor fleet and advanced reactor fuel development.

Fuel qualification↗
DOE OSTI2020

Ultracentral Collisions of Small and Deformed Systems at RHIC

We study a range of collision systems involving deformed ions and compare the elliptic and triangular flow harmonics produced in a hydrodynamics scenario versus a color glass condensate (CGC) scenario. For the hydrodynamics scenario, we generate initial conditions using TRENTO and work within a linear response approximation to obtain the final flow harmonics. For the CGC scenario, we use the explicit calculation of two-gluon correlations taken in the high-p T “(semi)dilute-(semi)dilute” regime to express the flow harmonics in terms of the density profile of the collision. Here, we consider ultracentral collisions of deformed ions as a testbed for these comparisons because the difference between tip-on-tip and side-on-side collisions modifies the multiplicity dependence in both scenarios, even at zero impact parameter. We find significant qualitative differences in the multiplicity dependence obtained in the initial conditions+hydrodynamics scenario and the CGC scenario, allowing these collisions of deformed ions to be used as a powerful discriminator between models. We also find that sub-nucleonic fluctuations have a systematic effect on the elliptic and triangular flow harmonics which are most discriminating in 0–1% ultracentral symmetric collisions of small deformed ions and in 0–10% d197Au collisions. The collision systems we consider are 238 U 238 U, d 197 Au, 9 Be 197 Au, 9 Be 9 Be, 3 He 3 He, and 3 He 197 Au.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Dramatic effect of electrode type on tunnel junction based molecular spintronic devices

A new class of molecular spintronic devices can be fabricated by chemically bonding magnetic molecular channels to the electrodes of a prefabricated tunnel junction with exposed side edges. Prior experimental studies showed that the cyanide-bridged octametallic molecular cluster, [(pzTp)FeIII(CN)3]4[NiII(L)]4¬[O3SCF3]4 [(pzTp) = tetra(pyrazol-1-yl)borate; L = 1-S(acetyl)tris(pyrazolyl)decane] molecule impact depended on the type of metallic electrodes used in the tunnel junction testbed. Experimental magnetization and transport studies showed a dramatic difference in molecule response on tunnel junctions with different combinations of metallic electrodes. Transport via paramagnetic molecular channels on a tunnel junction involving paramagnetic and ferromagnetic metal electrodes was dramatically different than the suppressed current state observed on tunnel junctions involving two ferromagnetic electrodes. We conducted theoretical studies to understand the experimental data and explore a wide range of electrode materials on tunnel junction-based molecular spintronics devices (TJMSD). Here, we report a Monte Carlo simulation study that focuses on understanding the effect of electrodes on the magnetic and physical properties of TJMSD. A 3D Heisenberg model of cross-junction-shaped TJMSD was used for the simulation study. We studied the effects of ferromagnetic, paramagnetic, and antiferromagnetic electrode materials. This study provides insights for designing and understanding futuristic molecular spintronics devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

History and status of spent fuel treatment at the INL Fuel Conditioning Facility

Experimental Breeder Reactor II (EBR-II) had an illustrious operational history from 1964 to 1994 as one of the first sodium-cooled fast reactors to use sodium-bonded metallic driver fuels and blankets. EBR-II was a testbed for fuel development and reactor design principles. Following shutdown, it was decided to use electrometallurgical methods to treat the spent fuel and blanket materials for final disposition. Here, this paper provides a brief history of U.S. liquid metal cooled reactor development with emphasis on EBR-II, and a technical description of the electrometallurgical methods.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Cyber threat assessment of machine learning driven autonomous control systems of nuclear power plants

We report advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)-based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber–physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Comparison of volatiles evolving from selected highland and mare lunar regolith simulants during vacuum sintering

Volatiles evolving from JSC-1A, NU-LHT-4M and CSM-LHT-1G lunar regolith simulants during in vacuo thermal processing were analyzed using mass spectrometry as a function of temperature. Two high-fidelity simulants, JSC-1A (mare) and NU-LHT-4M (highland), were compared to a newly developed CSM-LHT-1G highland simulant, modified to closely match lunar geochemistry. Large autogenous gas loads were observed for all investigated materials. Mineralogical knowledge was used to identify and attribute individual volatile species to reacting, transforming, or decomposing constituents (hydrates, carbonates, sulfates, sulfides, clays, etc.) of the respective regolith simulant in the self-generated gas environment. Cumulative mass losses for individual simulant components as a function of temperature were quantified using mass spectrometry in conjunction with thermogravimetric analysis. Investigation of the four components of CSM-LHT-1G – anorthosite, basalt, augite, and glass – aided the attribution of volatile species to specific compounds and their respective sources. The results showed significant decomposition of non-lunar phases present in the man-made regolith simulants below the typical glass crystallization temperatures, which paves the way to devising methods for enhancing the fidelity of the simulants. Finally, high gas loads and corrosive gases (HF and HCl) were recognized as potential hazards, pertaining to the development of large testbed facilities.

42 ENGINEERING↗
DOE OSTI2021

Optical spectroscopy and modeling of uranium gas-phase oxidation: Progress and perspectives

We report that studies related to U gas-phase oxidation through plasma- and thermo-chemistry are important for many fields, including environmental monitoring, forensic analysis, debris analysis in a weapon detonation event, and nucleation physics. Recently, significant efforts have been made to understand the chemical pathways involved in the progression from U atoms to diatoms (UO) and polyatomic molecules (UxOy), employing optical spectroscopy tools and computational modeling. In many studies, laser ablation of U or a U-containing flow reactor are used as a highly resource-efficient, repeatable, tunable, and lab-scale testbed for studying gas-phase oxidation in U plasmas. The spectroscopic analysis of high-temperature gas-phase oxidation of U is challenging due to the congested U spectra, resolution limitations of instrumentation, and the numerous chemical reaction pathways possible. This article focuses on the current understanding and challenges related to studying U plasma chemistry, specifically U gas-phase oxidation and molecular formation, via optical spectroscopy of plasmas and associated computational and spectral modeling. The physical and chemical processes involved in the evolution from U atoms to U oxide molecules to nanoparticles and agglomerates (i.e., debris) are discussed in the context of optical spectroscopic studies. The article concludes by highlighting opportunities for future research efforts based on existing knowledge published in the literature.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗