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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Application of multiple-isotope and groundwater-age data to identify factors affecting the extent of denitrification in a shallow aquifer near a river in South Korea

The extent of denitrification in a small agricultural area near a river in Yangpyeong, South Korea, was determined using multiple isotopes, groundwater age, and physicochemical data for groundwater. The shallow groundwater at one monitoring site had high concentrations of NO{sub 3}-N (74–83 mg L{sup −1}). The δ{sup 15}N-NO{sub 3} values for groundwater in the study area ranged between +9.1 and +24.6‰ in June 2014 and +12.2 to +21.6‰ in October 2014. High δ{sup 15}N-NO{sub 3} values (+10.7 to +12.5‰) in both sampling periods indicated that the high concentrations of nitrate in the groundwater originated from application of organic fertilizers and manure. In the northern part of the study area, some groundwater samples showed elevated δ{sup 15}N-NO{sub 3} and δ{sup 18}O-NO{sub 3} values, which suggest that nitrate was removed from the groundwater via denitrification, with N isotope enrichment factors ranging between −4.8 and −7.9‰ and O isotope enrichment factors varying between −3.8 and −4.9‰. Similar δD and δ{sup 18}O values of the surface water and groundwater in the south appear to indicate that groundwater in that area was affected by surface-water infiltration. The mean residence times (MRTs) of groundwater showed younger ages in the south (10–20 years) than in the north (20–30 years). Hence, it was concluded that denitrification processes under anaerobic conditions with longer groundwater MRT in the northern part of the study area removed considerable amounts of nitrate. This study demonstrates that multi-isotope data combined with physicochemical data and age-dating information can be effectively applied to characterize nitrate contaminant sources and attenuation processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Adaptive anomaly detection for identifying attacks in cyber-physical systems: A systematic literature review

Modern cyberattacks in cyber-physical systems (CPS) rapidly evolve and cannot be deterred effectively with most current methods, which focus on characterizing past threats. Adaptive anomaly detection (AAD) is among the most promising techniques to detect evolving cyberattacks, with an emphasis on fast data processing and model adaptation. AAD has been researched extensively; however, to the best of our knowledge, our work is the first systematic literature review (SLR) on current research in this field. We present a comprehensive SLR, gathering 397 relevant papers and systematically analyzing 65 of them (47 research and 18 survey papers) on AAD in CPS from 2013 to November 2023. We introduce a novel taxonomy considering attack types, CPS application, learning paradigm, data management, and algorithms. Our findings show that most studies addressed either model adaptation or data processing, but rarely both simultaneously. This indicates a research gap in fully adaptive solutions. We also categorize algorithms, datasets, and attack characteristics, and summarize strengths and weaknesses across the literature. Our review provides a structured and accessible reference for researchers and practitioners, offering insights into key trends and highlighting limitations in current approaches. Finally, we outline several future research directions, including the need for integrated real-time processing and adaptive learning, explainability, and uncertainty quantification in AAD for CPS.

Adaptation↗
DOE OSTI2019

Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways

Discrete fracture networks (DFN) are often used to model flow and transport in fractured porous media. The accurate resolution of flow and transport behavior on a large DFN involving thousands of fractures is computationally expensive. This makes uncertainty quantification studies of quantities of interest such as travel time through the network computationally intractable, since hundreds to thousands of runs of the DFN model are required to get bounds on the uncertainty of the predictions. Prior works on the subject demonstrated that the complexity of a DFN could be reduced by considering a sub-network of it (often termed a “backbone” sub-network), one whose flow and transport properties were then shown to be similar to that of the full network. The technique is tantamount to partitioning the complete set of fractures of a network into two disjoint sets, one of which is the backbone sub-network while the other its complement. It is in this context that we present a system-reduction technique for DFNs using supervised machine learning via a Random Forest Classifier that selects a backbone sub-network from the full set of fractures. The in-sample errors (in terms of precision and recall scores) of the trained classifier are found to be very accurate indicators of the out-of-sample errors, thus exhibiting that the classifier generalizes well to test data. Moreover, this system-reduction technique yields sub-networks as small as 12% of the full DFN that still recover transport characteristics of the full network such as the peak dosage and tailing behavior for late times. Most importantly, the sub-networks remain connected, and their size can be controlled by a single dimensionless parameter. Furthermore, measures of KL-divergence and KS-statistic for the breakthrough curves of the sub-networks with respect to the full network show physically realistic trends in that the measures decrease monotonically as the size of the sub-networks increase. In conclusion, the computational efficiency gained by this technique depends on the size of the sub-network, but large reductions in computational time can be expected for small sub-networks, yielding as much as 90% computational savings for sub-networks that are as small as 10-12% of the full network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Identifying crack tip position and stress intensity factors from displacement data

Fracture prognosis and characterization efforts require knowledge of crack tip position and the Stress Intensity Factors (SIFs) acting in the vicinity of the crack. Here, in this work, we present an efficient numerical approach to infer both of these characteristics under a consistent theoretical framework from noisy, unstructured displacement data. The novel approach utilizes the separability of the asymptotic linear elastic fracture mechanics fields to expedite the search for crack tip position and is particularly useful for noisy displacement data. The manuscript begins with an assessment of the importance of accurately locating crack tip position when quantifying the SIFs from displacement data. Next, the proposed separability approach for quickly inferring crack tip position is introduced. Comparing to the widely used displacement correlation approach, the performance of the separability approach is assessed. Cases involving both noisy data and systematic deviation from the asymptotic linear elastic fracture mechanics model are considered, e.g. inelastic material behavior and finite geometries. An open source python implementation of the proposed approach is available for use by those doing field and laboratory work involving digital image correlation and simulations, e.g. finite element, discrete element, molecular dynamics and peridynamics, where the crack tip position is not explicitly defined.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Optical Methods for Identifying Hard Clay Core Samples During Petrophysical Studies

X-ray phase analysis of the general mineralogical composition of core samples from one of the West Siberian fields was performed. Electronic absorption spectra of the clay core samples with an added indicator were studied. The speed and availability of applying the two methods in petrophysical laboratories during sample preparation for standard and special studies were estimated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Using In Situ Neutron Diffraction to Isolate Specific Features of Additively Manufactured Microstructures in 304L Stainless Steel and Identify Their Effects on Macroscopic Strength

Additive manufacturing of metal components results in unique microstructures with, necessarily, mechanical properties that are distinct from conventionally produced components. In this paper, four distinct microstructural features associated with directed energy deposition of 304L stainless steels, their stability, and their influences on flow strength were examined. These were (1) high dislocation density comparable with deformed materials, (2) increased ferrite content, (3) local chemical heterogeneity, and (4) tortuous grain morphology. In situ neutron diffraction measurements were used to monitor the evolution of the as-built microstructure during post-build heat treatment and relate the specific microstructural features to the strength behavior of the material following the heat treatment. The increased flow strength of the additively manufactured material relative to wrought counterparts is found to be due primarily to an increased dislocation density in the as-built material. However, the increased dislocation density does not completely account for the increased strength and it is hypothesized that some of the additional strength is related to the unique AM grain structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fundamental Issues Identified for Thermodynamic Description of Molten Salt Systems

Thermodynamic modeling of the molten salt systems is critical in the design of advanced nuclear power plants, materials recycling, and many other important clean energy applications. As one of the most capable computational thermodynamic approaches, the CALPHAD (Calculation of Phase Diagrams) method has been widely used for multicomponent thermodynamic database development and further support more physics-based modeling. Although considerable CALPHAD modeling efforts for molten salt systems have been made, we found that no evaluation has been performed regarding the unary thermodynamic models for the molten salt systems despite the fact that more than one pure substance database is available for the CALPHAD modeling. Therefore, the thermodynamic descriptions of the pure salts should be critically evaluated to ensure a high-fidelity molten salt thermodynamic database. In this work, we comprehensively analyzed some selected molten salts using different available thermodynamic descriptions from two databases as an example. One is the SSUB database released by the SGTE (Scientific Group Thermodata Europe), and the other is the FactPS database from the FactSage. Finally, the significant discrepancies observed in the thermodynamic description between the existing CALPHAD pure substance databases and experiments call for an urgent effort to improve.

42 ENGINEERING↗
DOE OSTI2018

High-throughput screening identifies small molecules that bind to the RAS:SOS:RAS complex and perturb RAS signaling

K-RAS is mutated in approximately 30% of human cancers, resulting in increased RAS signaling and tumor growth. Thus, RAS is a highly validated therapeutic target, especially in tumors of the pancreas, lung and colon. Although directly targeting RAS has proven to be challenging, it may be possible to target other proteins involved in RAS signaling, such as the guanine nucleotide exchange factor Son of Sevenless (SOS). We have previously reported on the discovery of small molecules that bind to SOS1, activate SOS-mediated nucleotide exchange on RAS, and paradoxically inhibit ERK phosphorylation. In this paper, we describe the discovery of additional, structurally diverse small molecules that also bind to SOS1 in the same pocket and elicit similar biological effects. We tested>160,000 compounds in a fluorescence-based assay to assess their effects on SOS-mediated nucleotide exchange. X-Ray structures revealed that these small molecules bind to the CDC25 domain of SOS1. Compounds that elicited high levels of nucleotide exchange activity in vitro increased RAS-GTP levels in cells, and inhibited phospho ERK levels at higher treatment concentrations. The identification of structurally diverse SOS1 binding ligands may assist in the discovery of new molecules designed to target RASdriven tumors.

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

High-throughput surface characterization to identify porosity defects in additively manufactured 316L stainless steel

In the present study, a high throughput (HT) methodology is applied to rapidly assess the surface characteristics of 83 additively manufactured 316L stainless steel. The variation in surface roughness (S a ) showed a good correlation with a dimensionless number (π) and provided direct linkages to the internal porosity defects. Lack-of-fusion regime relates to high surface roughness (S a > 5 µm), low dimensionless number (π < 61), and the presence of cavities in between the melt pool tracks. Balling regime correlates to high surface roughness (S a > 5 µm), intermediate dimensionless number (61 < π < 146), and non-uniform melt pool track width. Keyhole regime shows low surface roughness (S a < 5 µm), high dimensionless number (π > 146), and a curved melt pool track. This approach accelerates the process parameter discovery and minimizes the porosity defects for the LPBF process. The impact of the defects on the as-processed tensile mechanical properties demonstrates that samples with porosity exhibit up to 10% less tensile strength and 30% less ductility than optimal samples.

316L stainless steel↗
DOE OSTI2019

ICAT: A numerical scheme to minimize numerical diffusion in advection-dispersion modeling and its application in identifying flow channeling

The advection-dispersion equation for scalar transport is essential for the numerical modeling of many fluid dynamics problems. However, solutions from numerical schemes always suffer from numerical diffusion and/or oscillation. In this study, we develop an Intra-Cell Advection Tracking (ICAT) scheme to minimize numerical diffusion and preserve monotonicity for advection-dispersion modeling. The key idea is to introduce “queues” in each discretized cell, and using a sequential transport rule and a flow distribution mechanism to track the scalar transport in these queues temporally and spatially. The capability and limitations of ICAT are first investigated through three test cases. Compared with the results obtained from other numerical schemes, the results from ICAT show substantially reduced numerical diffusion and agree better with analytical solutions. We also employ ICAT to simulate the transport process of a conservative tracer in a fracture with a highly heterogeneous aperture distribution. We report discrete flow channels in the fracture are better discerned by ICAT than by other numerical schemes, indicating the suitability of ICAT for modeling tracer transport in channelized flow fields.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Numerical investigations to identify environmental factors for field-scale reactive transport of pathogens at riverbank filtration sites

While induced bank filtration is a proven method for facilitating sustainable drinking water production, it is at risk from surface water contaminations (e.g., pathogens). Induced bank filtration and pathogen transport in groundwater have been studied extensively. However, long-term studies that consider real-world conditions are missing. These conditions include seasonal changes to environmental conditions and waterworks operations. Therefore, to analyze the effect of seasonal changes on the transport of human pathogenic viruses and their indicators in induced bank filtration, concentrations of adenoviruses and pathogen indicators were monitored over 16 months at an active bank filtration plant at the Rhine River, in Düsseldorf (Germany). Based on this data, a 2D groundwater model was created in PFLOTRAN that simulated flow, heat transport, conservative transport of chloride and the resulting electrical conductivity, reactive transport of oxygen and nitrate, and colloid-based transport of coliforms, somatic coliphages, and adenoviruses. The results show that reduced travel time was the key factor determining periods with a low removal of coliforms and somatic coliphages in the aquifer. Travel time was controlled by river level variations during rainy seasons, and the waterworks extraction rates during dry seasons. Further, for adenovirus transport, travel distance in the subsurface appeared to be the key factor, while travel time had no significant impact. Coliform removal increased when the colmation layer permeability decreased, while coliphage and adenovirus removal was unaffected by the colmation layer permeability. Seasonal changes in temperature and oxygen content did not significantly impact the removal of coliphages and adenoviruses in groundwater. Denitrifying conditions correlated with a lowered coliform removal, but the modelling could not establish a connection between denitrifying conditions and coliform removal. Our study showed that removal of pathogens and pathogen indicators at induced bank filtration plants varies greatly in time and space (e.g., for coliforms from 1 to 4 log-levels at 20m travel distance), and that adenovirus transport differs considerably from transport of coliforms and somatic coliphages.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Sequence Analysis of 20,453 Severe Acute Respiratory Syndrome Coronavirus 2 Genomes from the Houston Metropolitan Area Identifies the Emergence and Widespread Distribution of Multiple Isolates of All Major Variants of Concern

Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there has been international concern about the emergence of virus variants with mutations that increase transmissibility, enhance escape from the human immune response, or otherwise alter biologically important phenotypes. In late 2020, several variants of concern emerged globally, including the UK variant (B.1.1.7), the South Africa variant (B.1.351), Brazil variants (P.1 and P.2), and two related California variants of interest (B.1.429 and B.1.427). These variants are believed to have enhanced transmissibility. For the South Africa and Brazil variants, there is evidence that mutations in spike protein permit it to escape from some vaccines and therapeutic monoclonal antibodies. On the basis of our extensive genome sequencing program involving 20,453 coronavirus disease 2019 patient samples collected from March 2020 to February 2021, we report identification of all six of these SARS-CoV-2 variants among Houston Methodist Hospital (Houston, TX) patients residing in the greater metropolitan area. Although these variants are currently at relatively low frequency (aggregate of 1.1%) in the population, they are geographically widespread. Houston is the first city in the United States in which active circulation of all six current variants of concern has been documented by genome sequencing. Finally, as vaccine deployment accelerates, increased genomic surveillance of SARS-CoV-2 is essential to understanding the presence, frequency, and medical impact of consequential variants and their patterns and trajectory of dissemination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Hazard analysis for identifying common cause failures of digital safety systems using a redundancy-guided systems-theoretic approach

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection, digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. Here, this paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the causal factors of potential single points of failure in the design of digital safety systems in advanced plant designs.

42 ENGINEERING↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗