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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Cholangiographic Tumor Classification for Simple Patient Selection Prior to Hepatopancreatoduodenectomy for Cholangiocarcinoma

Background: Hepatopancreatoduodenectomy (HPD) is employed for patients with laterally advanced cholangiocarcinoma. However, the survival benefit of this extended approach remains controversial. The aim of this study is to identify a tumor feature benefiting from HPD from the standpoint of long-term survival. Patients and Methods: Patients with cholangiocarcinoma who underwent HPD with curative intent between 2001 and 2017 were retrospectively analyzed. Tumors were radiologically classified by preoperative cholangiogram. Diffuse type was defined as significant tumor/stricture located from the hilar to intrapancreatic duct; localized type was defined as tumor otherwise. Univariable and multivariable analyses were performed to identify prognostic indicators. Results: Of 100 study patients, 28 (28%) patients had diffuse tumor type, while the remaining 72 (72%) patients had localized tumors. The former group showed significantly longer lateral length (43 versus 22 mm, P < 0.001) and more frequent pancreatic invasion (50% versus 32%, P = 0.110), advanced T classification (64% versus 49%, P = 0.185), and nodal metastasis (57% versus 47%, P = 0.504), compared with the latter group. The survival for patients with diffuse tumor type was significantly worse than that for patients with localized tumor type, with 5-year survival rates of 59.0% versus 26.3%, respectively (P = 0.003). Multivariable analysis identified four independent factors deteriorating long-term survival: cholangiographic diffuse tumor (P = 0.021), higher age (P = 0.020), percutaneous biliary drainage (P = 0.007), and portal vein resection (P = 0.007). Conclusions: Presurgical cholangiographic classification, diffuse or localized type, is a tumor-related factor closely associated with survival probability; therefore, it may be a useful feature for patient selection prior to HPD for cholangiocarcinoma.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Knowledge-Informed Uncertainty-Aware Machine Learning for Time Series Forecasting of Dynamical Engineered Systems

The high complexity and multiscale nature of many engineered systems—such as those in nuclear power plants—make representing and forecasting their dynamic behavior challenging. Physics-based models can be overly complex and computationally intractable, whereas machine learning (ML) tools are often data-hungry and prone to unphysical solutions. This study proposes a knowledge-informed ML-aided hybrid residual modeling approach that offers accurate and efficient time series forecasting for the operation of dynamical engineered systems. Hybrid residual modeling entails a baseline solution from domain knowledge and known physics expressions about the system dynamics integrated with an ML model to capture undiscovered information from the mismatch (i.e., residuals) between true states from measurements and baseline-predicted outputs. This study further quantifies the ML model uncertainty to provide trustworthy solutions. Real-time operational data from thermal-hydraulic flow loops of the cryogenic moderator system in Oak Ridge National Laboratory’s Spallation Neutron Source facility were used to demonstrate the potential of knowledge-informed uncertainty-aware ML in real-world applications. The state variables of the cryogenic helium loop were modeled with (1) first principles–based system identification (sysID), (2) long short-term memory (LSTM) neural network, and (3) hybrid sysID (baseline) + LSTM (residual). The superior predictive capability of the sysID+LSTM model versus stand-alone sysID and LSTM is confirmed by average performance metrics and individual data points across different prediction horizons. By creating a robust representation of the underlying physical system, the widely applicable hybrid residual modeling approach will enable the future development of digital twins for performance prediction, prognostics, and operation control.

Zhao, Xingang↗
DOE OSTI2024

Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing

To license new and advanced reactor designs, regulators must be convinced that their unique safety cases—relative to existing large scale reactors—have been adequately addressed by the designed reactor protection systems. In water cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Proteomic Analysis of Saliva in HIV-positive Heroin Addicts Reveals Proteins Correlated with Cognition

The prevalence of HIV-associated neurocognitive disorders (HAND) remains high despite effective antiretroviral therapies. Multiple etiologies have been proposed over the last few years to account for this phenomenon, including the neurotoxic effects of antiretrovirals and co-morbid substance abuse. However, no underlying molecular mechanism has been identified. Emerging evidence in several fields has linked the gut to brain diseases, but the effect of the gut on the brain during HIV infection has not been explored. Saliva is the most accessible gut biofluid, and is therefore of great scientific interest for diagnostic and prognostic purposes. This study presents a longitudinal, liquid chromatography-mass spectrometry-based quantitative proteomics study investigating saliva samples taken from 8 HIV-positive (HIV+) and 11 -negative (HIV-) heroin addicts. In the HIV+ group, 58 proteins were identified that show significant correlations with cognitive scores and that implicate disruption of protein quality control pathways by HIV. Notably, no proteins from the HIV- heroin addict cohort showed significant correlations with cognitive scores. In addition, the majority of correlated proteins have been shown to be associated with exosomes, allowing us to propose that the salivary glands and/or oral epithelium may modulate brain function during HIV infection through the release of discrete packets of proteins in the form of exosomes.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2014

Systems Level Analysis and Identification of Pathways and Networks Associated with Liver Fibrosis

Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Towards continual machine learning for particle accelerators

This talk covers our work on errant beam prognostics at the Spallation Neutron Source (SNS), focusing on the end-to-end process from data collection to the development and deployment of predictive models in specific. A short overview of AIML work done for accelerators and current trends will be presented. We will walk through key steps involved in creating robust Machine Learning (ML) models, including model training, validation, and deployment in an operational setting. In addition to presenting our technical approach, we will share valuable lessons learned, emphasizing the importance of infrastructure to support the continuous adaptation of models to evolving data and system behaviors. This talk will provide insights into the challenges and solutions involved in applying ML to real-world operational environments, with a particular focus on managing data drift and changes in accelerator setup while ensuring model resilience over time.

Accelerator Physics↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗
DOE OSTI2013

FAM83D promotes cell proliferation and motility by downregulating tumor suppressor gene FBXW7

Amplification of chromosome 20q is frequently found in various types of human cancers, including breast cancer. The list of candidate oncogenes in 20q has expanded over the past decade. Here, we investigate whether FAM83D (family with sequence similarity 83, member D) on chromosome 20q plays any role in breast cancer development. The expression level of FAM83D is significantly elevated in breast cancer cell lines and primary human breast cancers. High expression levels of FAM83D are significantly associated with poor clinical outcome and distant metastasis in breast cancer patients. We show that ectopic expression of FAM83D in human mammary epithelial cells promotes cell proliferation, migration and invasion along with epithelial-mesenchymal transition (EMT). Ablation of FAM83D in breast cancer cells induces apoptosis and consequently inhibits cell proliferation and colony formation. Mechanistic studies reveal that overexpression of FAM83D downregulates FBXW7 expression levels through a physical interaction, which results in elevated protein levels of oncogenic substrates downstream to FBXW7, such as mTOR, whose inhibition by rapamycin can suppress FAM83D-induced cell migration and invasion. The results demonstrate that FAM83D has prognostic value for breast cancer patients and is a novel oncogene in breast cancer development that at least in part acts through mTOR hyper-activation by inhibiting FBXW7.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Establishment of a Hub for the Light Water Reactor Sustainability Online Monitoring Community

Implementation of online monitoring and prognostics in existing U.S. nuclear power plants will involve coordinating the efforts of national laboratories, utilities, universities, and private companies. Internet-based collaborative work environments provide necessary communication tools to facilitate interaction between geographically diverse participants. Available technologies were considered, and a collaborative workspace was established at INL as a hub for the light water reactor sustainability online monitoring community.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Final Technical Report for "Ice nuclei relation to aerosol properties: Data analysis and model parameterization for IN in mixed-phase clouds" (DOE/SC00002354)

Clouds play an important role in weather and climate. In addition to their key role in the hydrologic cycle, clouds scatter incoming solar radiation and trap infrared radiation from the surface and lower atmosphere. Despite their importance, feedbacks involving clouds remain as one of the largest sources of uncertainty in climate models. To better simulate cloud processes requires better characterization of cloud microphysical processes, which can affect the spatial extent, optical depth and lifetime of clouds. To this end, we developed a new parameterization to be used in numerical models that describes the variation of ice nuclei (IN) number concentrations active to form ice crystals in mixed-phase (water droplets and ice crystals co-existing) cloud conditions as these depend on existing aerosol properties and temperature. The parameterization is based on data collected using the Colorado State University continuous flow diffusion chamber in aircraft and ground-based campaigns over a 14-year period, including data from the DOE-supported Mixed-Phase Arctic Cloud Experiment. The resulting relationship is shown to more accurately represent the variability of ice nuclei distributions in the atmosphere compared to currently used parameterizations based on temperature alone. When implemented in one global climate model, the new parameterization predicted more realistic annually averaged cloud water and ice distributions, and cloud radiative properties, especially for sensitive higher latitude mixed-phase cloud regions. As a test of the new global IN scheme, it was compared to independent data collected during the 2008 DOE-sponsored Indirect and Semi-Direct Aerosol Campaign (ISDAC). Good agreement with this new data set suggests the broad applicability of the new scheme for describing general (non-chemically specific) aerosol influences on IN number concentrations feeding mixed-phase Arctic stratus clouds. Finally, the parameterization was implemented into a regional cloud-resolving model to compare predictions of ice crystal concentrations and other cloud properties to those observed in two intensive case studies of Arctic stratus during ISDAC. Our implementation included development of a prognostic scheme of ice activation using the IN parameterization so that the most realistic treatment of ice nuclei, including their budget (gains and losses), was achieved. Many cloud microphysical properties and cloud persistence were faithfully reproduced, despite a tendency to under-predict (by a few to several times) ice crystal number concentrations and cloud ice mass, in agreement with some other studies. This work serves generally as the basis for improving predictive schemes for cloud ice crystal activation in cloud and climate models, and more specifically as the basis for such a scheme to be used in a Multi-scale Modeling Format (MMF) that utilizes a connected system of cloud-resolving models on a global grid in an effort to better resolve cloud processes and their influence on climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Light Water Reactor Sustainability Program

The online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) implemented fault signatures for generator step-up transformers (GSUs) and emergency diesel generators (EDGs). INL, in collaboration with Electric Power Research Institute (EPRI), demonstrated the OLM capabilities for GSUs and EDGs using the EPRI Fleet-Wide Prognostic and Health Management (FW PHM) Suite software.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

National Center of Excellence for Energy Storage Technology 168.10 (Final Report for Period 10/1/2010-12/31/2011)

This report documents the performance of the Ohio State University (OSU) and Edison Welding Institute (EWI) in the period from 10/1/2010 to 12/31/2012. The objective of the project is to establish a Center of Excellence that leverages the strengths of the partners to establish a unique capability to develop and transfer energy storage industries to establish a unique capability in the development and transfer of energy storage system technology through a fundamental understanding of battery electrical and thermal performance, damage and aging mechanisms, and through the development of reliable, high-speed processes for joining substrates in battery cell, module and pack assemblies with low manufacturing variability. During this period, the OSU activity focused on procuring the equipment, materials and supplies necessary to conduct the experiments planned in the statement of project objectives. In detail, multiple laboratory setups were developed to enable for characterizing the open-circuit potential of cathode and anode materials for Li-ion batteries, perform experiments on calorimetry, and finally built multiple cell and module battery cyclers to be able to perform aging campaign on a wide variety of automotive grade battery cells and small modules. This suite of equipment feeds directly into the development, calibration of battery models ranging from first principle electrochemical models to electro-thermal equivalent circuit models suitable for use in control and xEV vehicle simulations. In addition, it allows to develop and calibrate ‘aging’ models for Li-ion batteries that enable the development of diagnostics and prognostics tools to characterize and predict battery degradation from automotive usage under a wide array of environmental and usage scenarios. The objective of the EWI work scope is to develop improved processes for making metal-tometal joints in advanced battery cells and packs. It will focus on developing generic techniques for making functional (electrically conductive and mechanically robust) metal-to-metal joints between thin substrates. Joints with multiple layers and bimetallic constituents will be investigated. During the current period of performance, EWI has defined the test matrix to evaluate the application of different welding technologies (laser welding, ultrasonic welding, resistance welding) to specific components of battery cells and modules, such as foils-to-tabs, tabs-to-tabs, and tabs-to-bus bars. The test matrix also includes a range of substrates (aluminum 1145 and 1100, copper 110 and nickel 200 as substrates). Furthermore, a set of procedures was defined to perform mechanical and electrical testing of the samples, including metallography, and non-destructive evaluations. Both on the OSU and EWI, this project enabled to leverage very significant industrial collaborations with a wide array of companies ranging from battery manufacturers and pack integrator all the ways to Tier 1 automotive suppliers and OEMs during the period of exercise of the project, and in the future for years to come.

25 ENERGY STORAGE↗
DOE OSTI2014

Developing Model Constraints on Northern Extra-Tropical Carbon Cycling Based on measurements of the Abundance and Isotopic Composition of Atmospheric CO 2

The objective of this project was to perform CO 2 data syntheses and modeling activities to address two central questions: 1) how much has the seasonal cycle in atmospheric CO 2 at northern high latitudes changed since the 1960s, and 2) how well do prognostic biospheric models represent these changes. This project also supported the continuation of the Scripps time series of CO 2 isotopes and concentration at ten baseline stations distributed globally.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

MURMoT. Design and Application of Microbial Uranium Reduction Monitoring Tools

Uranium (U) contamination in the subsurface is a major remediation challenge at many DOE sites. Traditional site remedies present enormous costs to DOE; hence, enhanced bioremediation technologies (i.e., biostimulation and bioaugmentation) combined with monitoring efforts are being considered as cost-effective corrective actions to address subsurface contamination. This research effort improved understanding of the microbial U reduction process and developed new tools for monitoring microbial activities. Application of these tools will promote science-based site management decisions that achieve contaminant detoxification, plume control, and long-term stewardship in the most efficient manner. The overarching hypothesis was that the design, validation and application of a suite of new molecular and biogeochemical tools advance process understanding, and improve environmental monitoring regimes to assess and predict in situ U immobilization. Accomplishments: This project (i) advanced nucleic acid-based approaches to elucidate the presence, abundance, dynamics, spatial distribution, and activity of metal- and radionuclide-detoxifying bacteria; (ii) developed proteomics workflows for detection of metal reduction biomarker proteins in laboratory cultures and contaminated site groundwater; (iii) developed and demonstrated the utility of U isotopic fractionation using high precision mass spectrometry to quantify U(VI) reduction for a range of reduction mechanisms and environmental conditions; and (iv) validated the new tools using field samples from U-contaminated IFRC sites, and demonstrated their prognostic and diagnostic capabilities in guiding decision making for environmental remediation and long-term site stewardship.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Advanced Small Modular Reactor Economics Status Report

This report describes the data collection work performed for an advanced small modular reactor (AdvSMR) economics analysis activity at the Oak Ridge National Laboratory. The methodology development and analytical results are described in separate, stand-alone documents as listed in the references. The economics analysis effort for the AdvSMR program combines the technical and fuel cycle aspects of advanced (non-light water reactor [LWR]) reactors with the market and production aspects of SMRs. This requires the collection, analysis, and synthesis of multiple unrelated and potentially high-uncertainty data sets from a wide range of data sources. Further, the nature of both economic and nuclear technology analysis requires at least a minor attempt at prediction and prognostication, and the far-term horizon for deployment of advanced nuclear systems introduces more uncertainty. Energy market uncertainty, especially the electricity market, is the result of the integration of commodity prices, demand fluctuation, and generation competition, as easily seen in deregulated markets. Depending on current or projected values for any of these factors, the economic attractiveness of any power plant construction project can change yearly or quarterly. For long-lead construction projects such as nuclear power plants, this uncertainty generates an implied and inherent risk for potential nuclear power plant owners and operators. The uncertainty in nuclear reactor and fuel cycle costs is in some respects better understood and quantified than the energy market uncertainty. The LWR-based fuel cycle has a long commercial history to use as its basis for cost estimation, and the current activities in LWR construction provide a reliable baseline for estimates for similar efforts. However, for advanced systems, the estimates and their associated uncertainties are based on forward-looking assumptions for performance after the system has been built and has achieved commercial operation. Advanced fuel materials and fabrication costs have large uncertainties based on complexities of operation, such as contact-handled fuel fabrication versus remote handling, or commodity availability. Thus, this analytical work makes a good faith effort to quantify uncertainties and provide qualifiers, caveats, and explanations for the sources of these uncertainties. The overall result is that this work assembles the necessary information and establishes the foundation for future analyses using more precise data as nuclear technology advances.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Final Report to the U.S. Department of Energy for studies of Evaluation of Turbulence Parameterizations for Cloud-Resolving Models

The intermediately-prognostic higher-order turbulence closure (IPHOC) introduces a joint double-Gaussian distribution of liquid water potential temperature (θ l ), total water mixing ratio (q t ), and vertical velocity (w ) to represent any skewed turbulence circulations .The distribution is inferred from the first-, second-, and third-order moments of the variables given above, and is used to diagnose cloud fraction and grid-mean liquid water mixing ratio, as well as the buoyancy and fourth-order terms in the equations describing the evolution of the second- and third-order moments. Only three third-order moments (those of θ l , q t , and w ) are predicted in the IPHOC.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

RILEM TC ISR Summer 2015 Activity Report

With aging infrastructures, instances of Alkali Silica Reaction (ASR) and Delayed Ettringite Formation (DEF), broadly covered under the term Internal Swelling Reaction (ISR), are increasingly being detected. They have been observed in bridges, dams, and most recently in nuclear power plants. Concrete swelling may result in bridge partial failure, dams with structural cracks and misaligned turbine shafts, and locked slice gates. For nuclear reactors micro-cracks may cause increased gas permeability which will jeopardize the containment integrity and may decrease the residual structural resistance under accidental loading. This TC, which limits its activity to structures with known expansive concrete, seeks to address two complementary but fundamental questions: a) What is the kinetics of the reaction and b) How would it affect the integrity of the structure (serviceability and strength) and thus establish a science based prognostic to the structure owner.

42 ENGINEERING↗
DOE OSTI2015

Improving Boundary-layer Turbulence and Cloud Processes in CAM with a Higher-order Turbulence Closure Scheme and ASR Measurement

The intermediately-prognostic higher-order turbulence closure (IPHOC) introduces a joint double-Gaussian distribution of liquid water potential temperature (θ l ), total water mixing ratio (q t ), and vertical velocity (w) to represent any skewed turbulence circulation. The distribution is inferred from the first-, second-, and third-order moments of the variables given above, and is used to diagnose cloud fraction and gridmean liquid water mixing ratio, as well as the buoyancy term and fourth-order terms in the equations describing the evolution of the second- and third-order moments. Only three third-order moments, i.e., the triple moments of θ l , q t , and w, are predicted in IPHOC.

58 GEOSCIENCES↗