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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods

One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants’ activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Finally, outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Probing the limits of metal plasticity with molecular dynamics simulations

Ordinarily, the strength and plasticity properties of a metal are defined by dislocations—line defects in the crystal lattice whose motion results in material slippage along lattice planes. Dislocation dynamics models are usually used as mesoscale proxies for true atomistic dynamics, which are computationally expensive to perform routinely. However, atomistic simulations accurately capture every possible mechanism of material response, resolving every “jiggle and wiggle” of atomic motion, whereas dislocation dynamics models do not. We present fully dynamic atomistic simulations of bulk single-crystal plasticity in the body-centred-cubic metal tantalum. Our goal is to quantify the conditions under which the limits of dislocation-mediated plasticity are reached and to understand what happens to the metal beyond any such limit. In our simulations, the metal is compressed at ultrahigh strain rates along its [001] crystal axis under conditions of constant pressure, temperature and strain rate. To address the complexity of crystal plasticity processes on the length scales (85–340 nm) and timescales (1 ns–1μs) that we examine, we use recently developed methods of in situ computational microscopy to recast the enormous amount of transient trajectory data generated in our simulations into a form that can be analysed by a human. Our simulations predict that, on reaching certain limiting conditions of strain, dislocations alone can no longer relieve mechanical loads; instead, another mechanism, known as deformation twinning (the sudden re-orientation of the crystal lattice), takes over as the dominant mode of dynamic response. Below this limit, the metal assumes a strain-path-independent steady state of plastic flow in which the flow stress and the dislocation density remain constant as long as the conditions of straining thereafter remain unchanged. In this distinct state, tantalum flows like a viscous fluid while retaining its crystal lattice and remaining a strong and stiff metal.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Wound Ballistics Modeling for Blast Loading Blunt Force Impact and Projectile Penetration.

Light body armor development for the warfighter is based on trial-and-error testing of prototype designs against ballistic projectiles. Torso armor testing against blast is nonexistent but necessary to protect the heart and lungs. In tests against ballistic projectiles, protective apparel is placed over ballistic clay and the projectiles are fired into the armor/clay target. The clay represents the human torso and its behind-armor, permanent deflection is the principal metric used to assess armor protection. Although this approach provides relative merit assessment of protection, it does not examine the behind-armor blunt trauma to crucial torso organs. We propose a modeling and simulation (M&S) capability for wound injury scenarios to the head, neck, and torso of the warfighter. We will use this toolset to investigate the consequences of, and mitigation against, blast exposure, blunt force impact, and ballistic projectile penetration leading to damage of critical organs comprising the central nervous, cardiovascular, and respiratory systems. We will leverage Sandia codes and our M&S expertise on traumatic brain injury to develop virtual anatomical models of the head, neck, and torso and the simulation methodology to capture the physics of wound mechanics. Specifically, we will investigate virtual wound injuries to the head, neck, and torso without and with protective armor to demonstrate the advantages of performing injury simulations for the development of body armor. The proposed toolset constitutes a significant advance over current methods by providing a virtual simulation capability to investigate wound injury and optimize armor design without the need for extensive field testing.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Chronic Cardiotoxicity Assays Using Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes (hiPSC-CMs)

Cardiomyocytes (CMs) differentiated from human induced pluripotent stem cells (hiPSCs) are increasingly used in cardiac safety assessment, disease modeling and regenerative medicine. A vast majority of cardiotoxicity studies in the past have tested acute effects of compounds and drugs; however, these studies lack information on the morphological or physiological responses that may occur after prolonged exposure to a cardiotoxic compound. In this review, we focus on recent advances in chronic cardiotoxicity assays using hiPSC-CMs. We summarize recently published literature on hiPSC-CMs assays applied to chronic cardiotoxicity induced by anticancer agents, as well as non-cancer classes of drugs, including antibiotics, anti-hepatitis C virus (HCV) and antidiabetic drugs. We then review publications on the implementation of hiPSC-CMs-based assays to investigate the effects of non-pharmaceutical cardiotoxicants, such as environmental chemicals or chronic alcohol consumption. We also highlight studies demonstrating the chronic effects of smoking and implementation of hiPSC-CMs to perform genomic screens and metabolomics-based biomarker assay development. The acceptance and wide implementation of hiPSC-CMs-based assays for chronic cardiotoxicity assessment will require multi-site standardization of assay protocols, chronic cardiac maturity marker reproducibility, time points optimization, minimal cellular variation (commercial vs. lab reprogrammed), stringent and matched controls and close clinical setting resemblance. A comprehensive investigation of long-term repeated exposure-induced effects on both the structure and function of cardiomyocytes can provide mechanistic insights and recapitulate drug and environmental cardiotoxicity.

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

Artificial Intelligence

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PowerAnalytics.jl: User-Centric Power Systems Analysis in Julia

The National Laboratory of the Rockies recently released version 1 of PowerAnalytics.jl, an analysis module for the outputs of its popular open-source electrical power systems modeling platform Sienna. It features an extensible framework - based on the flexible selecting of components, the execution of arbitrary metrics on them, and a familiar DataFrames-based output interface with embedded metadata - to process results in the Sienna style while keeping the interface as simple as possible for non-Julia experts. Here, I describe the package and where it fits into the Sienna ecosystem, how I harnessed user-centered design and Julia features to achieve beginner friendliness without sacrificing performance and expressibility, and what lessons might be drawn from the package's design and implementation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H’s (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPH ErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

HP-FLEX: Field demonstration of the semantics-driven configuration of a Model Predictive Control system to make heat pumps flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2025

HP-FLEX: Field Demonstration of the Semantics-Driven Configuration of a Model Predictive Control System to Make Heat Pumps Flexible

Model Predictive Control (MPC) has demonstrated significant potential for optimizing building operations and enabling demand flexibility. However, the widespread adoption of MPC is hindered by complex manual configuration and commissioning processes that must be conducted by control experts working alongside building operators. These challenges drive up costs and reduce scalability, particularly when technical human resources and building automation systems are limited, such as in small and medium commercial buildings (SMCBs). This paper demonstrates how semantic standards, specifically ASHRAE 223P, can accelerate the adoption of MPC applications for load flexibility in SMCBs. The authors present a replicable control framework, titled “HP-FLEX” that leverages a building’s semantic model to bootstrap the required data configuration for an MPC controller developed for optimizing heat pump systems as flexible grid resources. The semantic model helps streamline the deployment workflow, particularly for site setup, data/control commissioning, and model setup. This integration enhances portability, transferability, and scalability of the HP-FLEX MPC, which has been developed to support MPC-based supervisory HVAC controllers in SMCBs. Additionally, the paper details the required building and thermostat metadata information to enable the HP-FLEX MPC based on field demonstrations. The new workflow was tested in a small commercial building located in California, U.S., and demonstrated a load shifting performance of 9% based on a dynamic pricing signal that varies by the hour. This work provides a practical pathway for transitioning sophisticated building applications from custom to standardized semantic representations, supporting the broader adoption of advanced control strategies like MPC. The framework also establishes a foundation for evolving metadata requirements as applications mature while maintaining compatibility with industry standards.

Paul, Lazlo↗
DOE OSTI2015

Detection of microcalcifications by characteristic magnetic susceptibility effects using MR phase image cross-correlation analysis

Purpose: To develop and evaluate a new method for detecting calcium deposits using their characteristic magnetic susceptibility effects on magnetic resonance (MR) images at high fields and demonstrate its potential in practice for detecting breast microcalcifications. Methods: Characteristic dipole signatures of calcium deposits were detected in magnetic resonance phase images by computing the cross-correlation between the acquired data and a library of templates containing simulated phase patterns of spherical deposits. The influence of signal-to-noise ratio and various other MR parameters on the results were assessed using simulations and validated experimentally. The method was tested experimentally for detection of calcium fragments within gel phantoms and calcium-like inhomogeneities within chicken tissue at 7 T with optimized MR acquisition parameters. The method was also evaluated for detection of simulated microcalcifications, modeled from biopsy samples of malignant breast cancer, inserted in silico into breast magnetic resonance imaging (MRIs) of healthy subjects at 7 T. For both assessments of calcium fragments in phantoms and biopsy-based simulated microcalcifications in breast MRIs, receiver operator characteristic curve analyses were performed to determine the cross-correlation index cutoff, for achieving optimal sensitivity and specificity, and the area under the curve (AUC), for measuring the method’s performance. Results: The method detected calcium fragments with sizes of 0.14–0.79 mm, 1 mm calcium-like deposits, and simulated microcalcifications with sizes of 0.4–1.0 mm in images with voxel sizes between (0.2 mm){sup 3} and (0.6 mm){sup 3}. In images acquired at 7 T with voxel sizes of (0.2 mm){sup 3}–(0.4 mm){sup 3}, calcium fragments (size 0.3–0.4 mm) were detected with a sensitivity, specificity, and AUC of 78%–90%, 51%–68%, and 0.77%–0.88%, respectively. In images acquired with a human 7 T scanner, acquisition times below 12 min, and voxel sizes of (0.4 mm){sup 3}–(0.6 mm){sup 3}, simulated microcalcifications with sizes of 0.6–1.0 mm were detected with a sensitivity, specificity, and AUC of 75%–87%, 54%–87%, and 0.76%–0.90%, respectively. However, different microcalcification shapes were indistinguishable. Conclusions: The new method is promising for detecting relatively large microcalcifications (i.e., 0.6–0.9 mm) within the breast at 7 T in reasonable times. Detection of smaller deposits at high field may be possible with higher spatial resolution, but such images require relatively long scan times. Although mammography can detect and distinguish the shape of smaller microcalcifications with superior sensitivity and specificity, this alternative method does not expose tissue to ionizing radiation, is not affected by breast density, and can be combined with other MRI methods (e.g., dynamic contrast-enhanced MRI and diffusion weighted MRI), to potentially improve diagnostic performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Considerations of the Differences between Bedded and Domal Salt Pertaining to Disposal of Heat-Generating Nuclear Waste

Salt formations hold promise for eternal removal of nuclear waste from our biosphere. Germany and the United States have ample salt formations for this purpose, ranging from flat-bedded formations to geologically mature dome structures. As both nations revisit nuclear waste disposal options, the choice between bedded, domal, or intermediate pillow formations is once again a contemporary issue. For decades, favorable attributes of salt as a disposal medium have been extoled and evaluated, carefully and thoroughly. Yet, a sense of discovery continues as science and engineering interrogate naturally heterogeneous systems. Salt formations are impermeable to fluids. Excavation-induced fractures heal as seal systems are placed or natural closure progresses toward equilibrium. Engineering required for nuclear waste disposal gains from mining and storage industries, as humans have been mining salt for millennia. This great intellectual warehouse has been honed and distilled, but not perfected, for all nuances of nuclear waste disposal. Nonetheless, nations are able and have already produced suitable license applications for radioactive waste disposal in salt. A remaining conundrum is site location. Salt formations provide isolation and geotechnical barriers reestablish impermeability after waste is placed in the geology. Between excavation and closure, physical, mechanical, thermal, chemical, and hydrological processes ensue. Positive attributes for isolation in salt have many commonalities independent of the geologic setting. In some cases, specific details of the environment will affect the disposal concept and thereby define interaction of features, events and processes, while simultaneously influencing scenario development. Here we identify and discuss high-level differences and similarities of bedded and domal salt formations. Positive geologic and engineering attributes for disposal purposes are more common among salt formations than are significant differences. Developing models, testing material, characterizing processes, and analyzing performance all have overlapping application regardless of the salt formation of interest.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Considerations of the Differences between Bedded and Domal Salt Pertaining to Disposal of Heat-Generating Nuclear Waste

Salt formations hold promise for eternal removal of nuclear waste from our biosphere. Germany and the United States have ample salt formations for this purpose, ranging from flat-bedded formations to geologically mature dome structures. As both nations revisit nuclear waste disposal options, the choice between bedded, domal, or intermediate pillow formations is once again a contemporary issue. For decades, favorable attributes of salt as a disposal medium have been extoled and evaluated, carefully and thoroughly. Yet, a sense of discovery continues as science and engineering interrogate naturally heterogeneous systems. Salt formations are impermeable to fluids. Excavation-induced fractures heal as seal systems are placed or natural closure progresses toward equilibrium. Engineering required for nuclear waste disposal gains from mining and storage industries, as humans have been mining salt for millennia. This great intellectual warehouse has been honed and distilled, but not perfected, for all nuances of nuclear waste disposal. Nonetheless, nations are able and have already produced suitable license applications for radioactive waste disposal in salt. A remaining conundrum is site location. Salt formations provide isolation, and geotechnical barriers reestablish impermeability after waste is placed in the geology. Between excavation and closure, physical, mechanical, thermal, chemical, and hydrological processes ensue. Positive attributes for isolation in salt have many commonalities independent of the geologic setting. In some cases, specific details of the environment will affect the disposal concept and thereby define interaction of features, events and processes, while simultaneously influencing scenario development. Here we identify and discuss high-level differences and similarities of bedded and domal salt formations. Positive geologic and engineering attributes for disposal purposes are more common among salt formations than are significant differences. Developing models, testing material, characterizing processes, and analyzing performance all have overlapping application regardless of the salt formation of interest.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Integrated program for department of energy environmental management office of soil and groundwater remediation - 15426

The subsurface problems that remain at the U.S. Department of Energy (DOE) Office of Environmental Management (EM) sites are technically complex and the costs to complete the cleanup mission are expected to range from $17 billion to $21 billion. There is growing recognition that conventional remedies will not meet regulatory goals at a number of sites and alternative approaches will be required. To address the challenge, the EM Office of Soil and Groundwater Remediation created an integrated program to develop innovative scientific and technical in conjunction with engagement of stakeholders, regulators, and the public. The integrated program provides a holistic approach to assess the risk associated with the remaining challenges facing EM and develop solutions that achieve remediation goals and protect human health and the environment. The integrated program uses a systems-based framework for achieving environmental remediation goals. The framework is based on developing and updating a site conceptual model and includes steps to 1) provide the technical basis for remedial actions, 2) assessments of potential remedies, and 3) implementation and management of remedies to achieve site restoration goals. The Office of Soil and Groundwater remediation defines research and development needed to successfully define and apply the framework. Research opportunities and targets are identified in characterization and conceptual model development, predictions of site conditions, remediation, monitoring, and remediation decision support. Characterization and quantification of key subsurface uncertainties is needed to support conceptual model development, identify contaminant source zones, quantify the flux of contaminants from source terms to points of compliance, and characterize controlling processes for remediation approaches including natural attenuation. Predictive analyses are needed to support development of remediation endpoints, select and design active and in situ or passive remedies, assess remedy performance, and predict long-term contaminant behavior for design of monitoring systems. Advancements are needed to support adaptive remediation approaches and to develop long-term contaminant management strategies for complex sites, such as effective in situ treatment methods for long-lived contaminants ({sup 99}Tc, {sup 129}I, U, Hg, etc.). Monitoring tools and approaches are needed to evaluate remedy performance and plume behavior and decision support methods are needed to quantitatively evaluate remediation alternatives. The Office of Soil and Groundwater Remediation leverages scientific understanding provided through partnerships with the DOE Office of Science, the Department of Defense, and Environmental Protection Agency. These partnerships are helping EM develop scientific understanding and technologies for implementing the assessment framework. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

A soil-inspired dynamically responsive chemical system for microbial modulation

Interactions between the microbiota and their colonized environments mediate critical pathways from biogeochemical cycles to homeostasis in human health. Here, in this study, we report a soil-inspired chemical system that consists of nanostructured minerals, starch granules and liquid metals. Fabricated via a bottom-up synthesis, the soil-inspired chemical system can enable chemical redistribution and modulation of microbial communities. We characterize the composite, confirming its structural similarity to the soil, with three-dimensional X-ray fluorescence and ptychographic tomography and electron microscopy imaging. We also demonstrate that post-synthetic modifications formed by laser irradiation led to chemical heterogeneities from the atomic to the macroscopic level. The soil-inspired material possesses chemical, optical and mechanical responsiveness to yield write-erase functions in electrical performance. The composite can also enhance microbial culture/biofilm growth and biofuel production in vitro. Finally, we show that the soil-inspired system enriches gut bacteria diversity, rectifies tetracycline-induced gut microbiome dysbiosis and ameliorates dextran sulfate sodium-induced rodent colitis symptoms within in vivo rodent models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Sensitivity Analysis of Successful Evacuee Proportion in Hypothetical Seismic Nuclear Emergency Situation

A magnitude 5.8 earthquake, which is the strongest earthquake ever detected in Korea, occurred in Gyeongju on 12 September, 2016. People were worried about not only the impact to their safety and quality of life as directly affected from the earthquake, but also the secondary impact from such a nuclear power plant (NPP) accident incurred by the earthquake. Public awareness is changing such that the risk of earthquakes is no longer a story of other countries. Even if the current situation in Korea is not regarded, it is important to assess the seismic hazard to an NPP and prepare for preventing the effects related with the earthquake impact. Developing a seismic evacuation model is necessary in a part of a level 3 probabilistic safety assessment (PSA) because it is one of the most affecting factors of public radiation exposure and health effects in an emergency phase. In a previous study, a sensitivity analysis of the evacuation speed in a hypothetical NPP accident by an earthquake has been carried out, and population doses resulting from various seismic evacuation scenarios associated with evacuation speed have been compared. In addition to the previous study, the proportion of evacuees who evacuate successfully with adequate speed has been considered and a sensitivity analysis has been performed in this study.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Geomechanical Characterization and Modeling of the Newark Basin

To ensure long-term safety and containment during geologic carbon sequestration operations, understanding reservoir and cap rock behavior under increased pressure conditions is essential. In particular, the risk of induced seismicity has recently emerged as one of the largest concerns for geologic carbon sequestration and other injection operations. Little observational data are available about induced seismicity risks from carbon dioxide injection. However, an increasing number of earthquakes in the continental United States and worldwide are being attributed to human activities that involve other types of underground injection. Analysis of these events suggests that the net injected volume is one of the key factors, and a prolonged injection of large volumes of carbon dioxide is projected to have high potential for triggering similar seismic events. Increased pore pressure may activate an existing critically stressed discontinuity by reducing its frictional resistance to slip, and cause cap rock failure. Even when facture slip does not produce large seismic events felt by humans or cause visible damage, it can increase formation permeability and compromise cap rock integrity, thus increasing the risk of leakage. The Geomechanical Properties of Mesozoic Basins project performed unique research in the Newark Basin by combining detailed laboratory characterization and rock core testing with novel field operations performed on wireline to measure in-situ stress conditions.

58 GEOSCIENCES↗
DOE OSTI2019

Establishment and Characterization of Two Novel Cholangiocarcinoma Cell Lines

Background: Cholangiocarcinoma (CCA) is a rare, aggressive and highly lethal tumor. The disease is very difficult to diagnose and multi-modality treatments are ineffective. To improve our understanding of the biological characteristics of CCA, and facilitate the identification of valid treatments, an in-depth characterization of two novel Chinese patient-derived primary CCA cell lines was performed. Methods: Two CCA cell lines were developed and labelled ZJU-0826 and -1125. The two cell lines were characterized with respect to phenotypic, molecular, biomarker, functional and histological properties. Results: Two novel cell lines were cultured for 2 years, and maintained for more than 100 passages. They retained their typical biliary epithelial morphology and ultrastructure. The population doubling times of ZJU-0826, and -1125 were 63.84 h and 44.73 h, respectively. The cells exhibited near-triploid karyotypes with complex structural aberrations. ZJU-1125 cells had mutations in TP53 exons. Short tandem repeats genotyping confirmed the human origin and difference between lines. An immunophenotype analysis showed that ZJU-0826 is positive for CD44, CD29, Pdx1, CD236, FoxA1, FoxA2, and Nanog, and ZJU-1125 positive for CD44, CD29, CD133, Pdx1, FoxA1, FoxA2, and Nanog. ZJU-1125 had greater invasion ability in vitro and tumorigenicity than those of ZJU-0826. Conclusions: Our results confirm the validity of the ZJU-0826 and -1125 as representative models for the elucidation of the molecular pathogenesis of perihilar CCA, and intrahepatic CCA in both in vitro and in vivo studies, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗