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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 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-time Intrusion Detection for High-bandwidth Research Networks using Unsupervised Deep Learning

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks across distributed collaboration sites for processing, storage, and analysis. Like general purpose networks, research networks experience attacks. However, monitoring traffic in such high-bandwidth network environments is challenging with conventional deep packet inspection (DPI)- based network intrusion detection systems (NIDS). Recently, machine learning (ML) algorithms have shown potential in detecting anomalous patterns with better generalizability and faster speed. Compared to the general purpose networks, traffic movement in research network is less volatile and more likely to benefit from the ML-based anomaly analysis. We present ThunderSecure, an efficient AI-powered system for detecting anomalies in 100G research networks. ThunderSecure implements a high-throughput packet processing infrastructure using multi-cores and GPUs. It takes time-series statistics from a variety of network measurements and uses 2D Convolutional Neural Network (CNN) to explore both spatial and temporal dependencies in network data stream. Patterns of science data traffic are learned with a one-class neural network, which blends an Adversarial Autoencoder (AAE) with Gaussian mixture density estimation. Testing traffic flows exhibiting significant deviations from the learned baseline of normality are marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets recorded from a 100G research network at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. To the best of our knowledge, this is the first ML work in the area of network anomaly detection that has been validated on such extreme scale datasets. Results show that ThunderSecure can recognize science data traffic that are captured long after the training and make nearly certain detection on those with anomalous flows injected, even in the case when the anomaly-to-normal mixing ratio is 0.1%.

Gong, Qian↗
DOE OSTI2022

Experiments and numerical modeling of surrogate droplet combustion for a 93 octane gasoline calibration fuel

Gasoline is the world’s most prolific liquid transportation fuel for ground transportation vehicles. Yet, a fundamental understanding of its burning characteristics is still lacking. Complicating this matter is the highly multicomponent nature of gasoline (comprised of hundreds of chemical species) and the seasonable variability of its composition due to variations in the crude oil feedstocks used in refining. The need to bring order to this complex problem has led to development of calibration fuels (Tier II in presented study) which on average will provide repeatable metrics that replicate performance of refined gasoline for various engine designs. This study presents results of experiments using the configuration of an isolated droplet to study the combustion dynamics of calibration fuel and three different surrogates (S4, S5, S6) for the calibration fuel. Droplet burning is under microgravity conditions, where gas transport is determined entirely by fuel evaporation in order to promote spherical symmetry in the burning process. The initial droplet diameters ranged between 0.55 mm and 0.60 mm. The droplets were spark-ignited and their burning histories were recorded by digital video cameras to obtain quantitative data. For each fuel, the evolutions of droplet, flame, and soot-shell diameters were measured to calculate the burn rates, flame-droplet diameter ratios (FSR) and soot-droplet diameter ratios. Additionally, a detailed numerical modeling is presented. This simulation includes multicomponent phase equilibrium, unsteady gas and liquid transport, radiative heat transfer, internal liquid diffusion and detailed combustion chemistry of different fuels during their burning process. Experiment results show that the calibration fuel and surrogates have a close burn rates and FSR, and S5 presents a relatively similar results with calibration fuel. Predictions from the numerical simulation using the surrogate are compared with experiment measurements, which showed a good agreement with the experiment results, including the droplet burning rate and FSR.

Guo, Songtao↗
DOE OSTI2022

Micro Baselines for Operational Technology Environments

Critical infrastructure stakeholders need to baseline their networks to understand expected communications. Top-down approaches to baselining rely on observables that are generally available but lack properties upon which traditional statistical tools depend. We propose to construct micro-baselines: signatures within operational networks based on observables associated with specific events. Such observables are informed by precursor analysis reports of historical cyber attacks on operational environments developed by Cybersecurity for Operational Technology Environments (CyOTE). Baseline measurements depend upon context beyond the cyber domain. An energy plant's baseline running in the summer may statistically differ from a similar facility in a colder region. Domain knowledge must be integrated to apply general micro-baselining algorithms to a facility-specific context. Therefore, we propose to explore the feasibility of transferring micro baselining algorithms across different facilities. Facilities that implement the same processes in different geographic locations will be compared relative to observable measurements used in micro-baselining for comparable events. One evaluation approach would condition or augment dynamic observables measured within a facility network testbed with additional observables derived from geographic context or infrastructure dependencies such as those provided by the All-Hazards Analysis tool.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Preconceptual Designs of Coupled Power Delivery between a 4-Loop PWR and 100-500 MWe HTSE Plants

This study develops a preconceptual design for the integration between a large-scale high-temperature electrolysis facility and a NPP. Two hydrogen facility sizes are considered: 100 MWnom and 500 MWnom. Both steam supply designs use cold reheat steam extraction as a heat source. A reboiler inside the protected area of the power plant transfers steam heat to the demineralized water supply for the hydrogen plant. After the heat transfer, the extracted steam condenses and returns to the condenser while the process steam routes out of the protected area to the electrolyzers. Electrical power is tapped off from the high voltage side of the GSU transformer, where it is then transported via a 345-kV transmission line to the hydrogen facility. Circuit breakers and disconnects are located at both ends of the transmission line. Step down transformers and miscellaneous switchgear/buses are located at the end of the transmission line inside the HTEF boundary. Control capabilities for the steam interfacing equipment and electrical dispatch are accessible from the Main Control Room, and protective relays for the transmission line are located inside the Relay Room. Computer modeling was performed for the thermal and electrical designs. PEPSE analysis provided the steady-state parameters for thermal extraction from the turbine cycle. These parameters were used to inform transients and size equipment in combination with Applied Flow Technology (AFT) Arrow and AFT Fathom modeling for steam and water piping, respectively. Electrical transients were analyzed using PSCAD. An ETAP model was used to evaluate power flow and short circuit, which enabled the sizing of transformers and protective equipment. A cost estimate was developed for both integration designs when considering plant separation distances of 250 m and 500 m. From these estimates, the modifications for thermal and electrical interfacing of a first-of-a-kind nuclear-integrated hydrogen facility are anticipated to cost between $60–250/kWnom., where the subscript “nom” refers to the nominal size of the hydrogen plant. On a thermal power basis, the thermal power has a cost of approximately $8/MWth for a 500 MWnom high temperature electrolysis plant located 500 m distant from the NPP. That value decreases to approximately $7.5/MWth for a 250 m separation distance between the hydrogen plant and the NPP. This value is lower than previous estimates of the cost of heat extracted from NPPs primarily because in this work the steam is extracted from cold reheat instead of the main steam line, which reduces the cost of the dispatched steam by approximately $3.5/MWth. Nuclear steam extraction can provide a profit avenue for many plants and is not restricted to hydrogen production. Ammonia production, oil refining, and paper production, among other industrial processes all require thermal energy, which can be provided by NPPs. Future work should look further at the details of thermal extraction for a variety of use cases. This can include increased levels of extraction and multiple simultaneous users. Additionally, site-specific studies should be performed to develop industry experience and improve cost accuracy.

08 HYDROGEN↗
DOE OSTI2023

Fluid power circuit having switch-mode power transformer and methods

A fluid power circuit with a switch-mode power transformer used to transfer power while keeping the pressure of power source and reverse osmosis processes relatively decoupled. The switch-mode power transformer uses the inertia of a hydraulic motor driven electric generator and switching of a hydraulic motor inlet between high and low-pressure ends to decrease the pressure at which power is being transmitted to a reverse osmosis process.

Simmons, II, Jeremy W.↗
DOE OSTI2020

CTF Theory Manual (V.4.2)

Coolant-Boiling in Rod Arrays—Two Fluids (COBRA-TF) is a thermal/hydraulic (T/H) simulation code designed for light water reactor (LWR) vessel analysis. It uses a two-fluid, three-field (i.e. fluid film, fluid drops, and vapor) modeling approach. Both sub-channel and 3D Cartesian forms of 9 conservation equations are available for LWR modeling. The code was originally developed by Pacific Northwest Laboratory in 1980 and had been used and modified by several institutions over the last few decades. COBRA-TF also found use at the Pennsylvania State University (PSU) by the Reactor Dynamics and Fuel Modeling Group (RDFMG) and has been improved, updated, and subsequently re-branded as CTF. As part of the improvement process, it was necessary to generate sufficient documentation for the open-source code which had lacked such material upon being adopted by RDFMG. This document serves mainly as a theory manual for CTF, detailing the many two-phase heat transfer, drag, and important accident scenario models contained in the code as well as the numerical solution process utilized. Coding of the models is also discussed, all with consideration for updates that have been made when transitioning from COBRA-TF to CTF. Further documentation outside of this manual is also available at RDFMG which focus on code input deck generation and source code global variable and module listings

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Beyond the Charge Transfer Mechanism for 2D Materials-Assisted Surface Enhanced Raman Scattering

Two-dimensional (2D) materials have been extensively implemented as surface-enhanced Raman scattering (SERS) substrates, enabling trace-molecule detection for broad applications. However, the accurate understanding of the mechanism remains elusive because most theoretical explanations are still phenomenological or qualitative based on simplified models and rough assumptions. To advance the development of 2D material-assisted SERS, it is vital to attain a comprehensive understanding of the enhancement mechanism and a quantitative assessment of the enhancement performance. Here, the microscopic chemical mechanism of 2D material-assisted SERS is quantitatively investigated. The frequency-dependent Raman scattering cross sections suggest that the 2D materials’ SERS performance is strongly dependent on the excitation wavelengths and the molecule types. By analysis of the microscopic Raman scattering processes, the comprehensive contributions of SERS can be revealed. Beyond the widely postulated charge transfer mechanisms, the quantitative results conclusively demonstrate that the resonant transitions within 2D materials alone are also capable of enhancing the molecular Raman scattering through the diffusive scattering of phonons. Furthermore, all of these scattering routines will interfere with each other and determine the final SERS performance. Our results not only provide a complete picture of the SERS mechanisms but also demonstrate a systematic and quantitative approach to theoretically understand, predict, and promote the 2D materials SERS toward analytical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Correlation of SWIR imaging with LPBF 304L stainless steel part properties

In the Laser Powder Bed Fusion (LPBF) process, the local thermal history can vary significantly over a part as the heat transfer characteristics and the laser scan path are geometry dependent. The variations introduce the potential for defects that lead to part failure, some of which are difficult to identify non-destructively with common ex-situ evaluation techniques. These defects include significant microstructural and mechanical property differences in the part interiors. In this paper, thermal features are extracted from in-situ Short-Wave Infrared (SWIR) imaging measurements to compile voxel based part representations and understand how the complexities in the thermal history affect part performance. The deviations in thermal features due to different laser processing parameters and complex scan pathing are explored. Empirical correlations are developed to map thermal features with the engineering properties (bulk yield strength, area percentage porosity, and local state) of 304L stainless steel parts manufactured by LPBF. Processing modes (insufficient melting and keyholing) are determined by mapping part property measurements with multiple thermal features. Generating the relationships between thermographic measurements and resulting SLM part properties lays the foundation for in-situ part qualification.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Shear Assisted Processing and Extrusion of Magnesium Alloy Machining Chips

The Pacific Northwest National Laboratory (PNNL) utilized Shear Assisted Processing and Extrusion (ShAPE) to convert magnesium alloy machining chips directly into wrought material. PNNL fabricated 0.25” and 0.50” round rod from cold compacted briquettes pressed from dry-machined chips by Loukus Technologies. The alloy composition is proprietary and protected by a Materials Transfer Agreement (MTA) between PNNL and Loukus. Extrusions were fabricated using the ShAPE 2.0 machine, tooling, and process. PNNL is evaluating tensile properties per ASTM E8 and microstructure for grain size, texture, and porosity using Scanning Electron Microscopy (SEM) and Electron Backscatter Diffraction (EBSD). Tensile properties and microstructure results were not available at the time of writing this document. Extruded rods have also been sent to Loukus for evaluation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression

Abstract The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra, which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep-learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot-Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parameterize the most general family of degeneracies among the input atmospheric parameters that affect the characterization of an exoplanet atmosphere through transit spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Perspectives on improving photosynthesis to increase crop yield

Abstract Improving photosynthesis, the fundamental process by which plants convert light energy into chemical energy, is a key area of research with great potential for enhancing sustainable agricultural productivity and addressing global food security challenges. This perspective delves into the latest advancements and approaches aimed at optimizing photosynthetic efficiency. Our discussion encompasses the entire process, beginning with light harvesting and its regulation and progressing through the bottleneck of electron transfer. We then delve into the carbon reactions of photosynthesis, focusing on strategies targeting the enzymes of the Calvin–Benson–Bassham (CBB) cycle. Additionally, we explore methods to increase carbon dioxide (CO2) concentration near the Rubisco, the enzyme responsible for the first step of CBB cycle, drawing inspiration from various photosynthetic organisms, and conclude this section by examining ways to enhance CO2 delivery into leaves. Moving beyond individual processes, we discuss two approaches to identifying key targets for photosynthesis improvement: systems modeling and the study of natural variation. Finally, we revisit some of the strategies mentioned above to provide a holistic view of the improvements, analyzing their impact on nitrogen use efficiency and on canopy photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Verification and Validation Approach for COMSOL Multiphysics to Support High Flux Isotope Reactor (HFIR)

Over the last several decades, many reactors have successfully been converted from high enriched uranium (HEU) to low enriched uranium (LEU) fuels in United States in support of its global non-proliferation objectives. Of the reactors slated for conversion, five high-performance research reactors (HPRRs) remain. The high-flux isotope reactor (HFIR) at Oak Ridge National Laboratory (ORNL) is one of the five. Conversion of HFIR requires the qualification of a new fuel. To aid in the conversion process as well as improve safety margins, COMSOL Multiphysics was chosen to support and eventually supplement the steady-state heat-transfer code (SSHTC). In order to use COMSOL as a supplementary tool, i.e. one used for safety basis calculations, the code needs to undergo a verification and validation process. Verification and validation are important processes for the use of any software; however, physics-based solvers have a unique challenge, in that the validation is much more rigorous and its domain is only applicable to the general scope of the problem. This document details the verification and validation of COMOSL for analysis of HFIR. This report is intended for two different users: (1) current and future users of COMSOL for HFIR and (2) for individuals interested in assessing the scope of the validation of COMSOL for HFIR. This report is divided into six sections. The first of which details the process of verification and validation. Section one provides an overview of key concepts from ASME V&V 10, 20, and 40 and how these concepts are used to provide a validation of any multiphysics software. Section two covers the approach needed in order to validate multiphysics software for a given HFIR analysis. Parts three through five split up the key concepts highlighted in section two. The final section provides concluding remarks which demonstrate the scope and limitations of the validation study and how these may be expanded and improved.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Lossy checkpoint compression in full waveform inversion: a case study with ZFPv0.5.5 and the overthrust model

This paper proposes a new method that combines checkpointing methods with error-controlled lossy compression for large-scale high-performance full-waveform inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution.

58 GEOSCIENCES↗
DOE OSTI2024

Multiple horizontal gene transfer events have shaped plant glycosyl hydrolase diversity and function

Plant glycosyl hydrolases (GHs) play a crucial role in selectively breaking down carbohydrates and glycoconjugates during various cellular processes, such as reserve mobilization, pathogen defense, and modification/disassembly of the cell wall. In this study, we examined the distribution of GH genes in the Archaeplastida supergroup, which encompasses red algae, glaucophytes, and green plants. We identified that the GH repertoire expanded from a few tens of genes in early archaeplastidians to over 400 genes in modern angiosperms, spanning 40 GH families in land plants. Our findings reveal that major evolutionary transitions were accompanied by significant changes in the GH repertoire. Specifically, we identified at least 23 GH families acquired by green plants through multiple horizontal gene transfer events, primarily from bacteria and fungi. We found a significant shift in the subcellular localization of GH activity during green plant evolution, with a marked increase in extracellular-targeted GH proteins associated with the diversification of plant cell wall polysaccharides and defense mechanisms against pathogens. In conclusion, our study sheds light on the macroevolutionary processes that have shaped the GH repertoire in plants, highlighting the acquisition of GH families through horizontal transfer and the role of GHs in plant adaptation and defense mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Novel Trichothecene Toxin Phenotype Associated with Horizontal Gene Transfer and a Change in Gene Function in Fusarium

Fusarium trichothecenes are among the mycotoxins of most concern to food and feed safety. Production of these mycotoxins and presence of the trichothecene biosynthetic gene (TRI) cluster have been confirmed in only two multispecies lineages of Fusarium: the Fusarium incarnatum-equiseti (Incarnatum) and F. sambucinum (Sambucinum) species complexes. Here, we identified and characterized a TRI cluster in a species that has not been formally described and is represented by Fusarium sp. NRRL 66739. This fungus is reported to be a member of a third Fusarium lineage: the F. buharicum species complex. Cultures of NRRL 66739 accumulated only two trichothecenes, 7-hydroxyisotrichodermin and 7-hydroxyisotrichodermol. Although these are not novel trichothecenes, the production profile of NRRL 66739 is novel, because in previous reports 7-hydroxyisotrichodermin and 7-hydroxyisotrichodermol were components of mixtures of 6–8 trichothecenes produced by several Fusarium species in Sambucinum. Heterologous expression analysis indicated that the TRI13 gene in NRRL 66739 confers trichothecene 7-hydroxylation. This contrasts the trichothecene 4-hydroxylation function of TRI13 in other Fusarium species. Phylogenetic analyses suggest that NRRL 66739 acquired the TRI cluster via horizontal gene transfer from a close relative of Incarnatum and Sambucinum. These findings provide insights into evolutionary processes that have shaped the distribution of trichothecene production among Fusarium species and the structural diversity of the toxins.

Food Science & Technology↗