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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep learning-enabled natural language processing to identify directional pharmacokinetic drug–drug interactions

Background. During drug development, it is essential to gather information about the change of clinical exposure of a drug (object) due to the pharmacokinetic (PK) drug-drug interactions (DDIs) with another drug (precipitant). While many natural language processing (NLP) methods for DDI have been published, most were designed to evaluate if (and what kind of) DDI relationships exist in the text, without identifying the direction of DDI (object vs. precipitant drug). Here we present a method for the automatic identification of the directionality of a PK DDI from literature or drug labels. Methods. We reannotated the Text Analysis Conference (TAC) DDI track 2019 corpus for identifying the direction of a PK DDI and evaluated the performance of a fine-tuned BioBERT model on this task by following the training and validation steps prespecified by TAC. Results. This initial attempt showed the model achieved an F-score of 0.82 in identifying sentences as containing PK DDI and an F-score of 0.97 in identifying object versus precipitant drugs in those sentences. Discussion and conclusion. Despite a growing list of NLP methods for DDI extraction, most of them use a common set of corpora to perform general purpose tasks (e.g., classifying a sentence into one of several fixed DDI categories). There is a lack of coordination between the drug development and biomedical informatics method development community to develop corpora and methods to perform specific tasks (e.g., extract clinical exposure changes due to PK DDI). We hope that our effort can encourage such a coordination so that more “fit for purpose” NLP methods could be developed and used to facilitate the drug development process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

ZTF SN Ia DR2: Improved SN Ia colors through expanded dimensionality with SALT3+

Context. Type Ia supernovae (SNe Ia) are a key probe in modern cosmology, as they can be used to measure luminosity distances at gigaparsec scales. Models of their light curves are used to project heterogeneous observed data onto a common basis for analysis. Aims. The SALT model currently used for SN Ia cosmology describes SNe as having two sources of variability, accounted for by a color parameter c , and a “stretch” parameter x 1 . We extend the model to include an additional parameter we label x 2 , to investigate the cosmological impact of currently unaddressed light-curve variability. Methods. We constructed a new SALT model, that we dub “SALT3+”. This model was trained by an improved version of the SALTshaker code, using training data combining a selection of the second data release of cosmological SNe Ia from the Zwicky Transient Facility and the existing SALT3 training compilation. Results. We find additional, coherent variability in supernova light curves beyond SALT3. Most of this variation can be described as phase-dependent variation in g − r and r − i color curves, correlated with a boost in the height of the secondary maximum in i -band. These behaviors correlate with spectral differences, particularly in line velocity. We find that fits with the existing SALT3 model tend to address this excess variation with the color parameter, leading to less informative measurements of supernova color. We find that neglecting the new parameter in light-curve fits leads to a trend in Hubble residuals with x 2 of 0.039 ± 0.005 mag, representing a potential systematic uncertainty. However, we find no evidence of a bias in current cosmological measurements. Conclusions. We conclude that extended SN Ia light-curve models promise mild improvement in the accuracy of color measurements, and corresponding cosmological precision. However, models with more parameters are unlikely to substantially affect current cosmological results.

Kenworthy, W. D. (ORCID:0000000251535983)↗
DOE OSTI2024

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and don’t always predict therapeutic response. Here we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measured the proteome and phosphoproteome of 210 patients and combined them with genomics and transcriptomic measurements to identify four proteogenomic subtypes that complemented existing genetic subtypes. We then built a predictor to classify samples into subtypes based on 147 molecular features and mapped them to a ‘landscape’. Each region of this landscape corresponded to specific drug response patterns. We then built a drug response prediction model to identify drugs that target distinct subtypes. We can ultimately use these models to predict drug treatment response and prioritize treatments. Finally, we extended our models and mapped a series of cell lines representing various stages of quizartinib resistance into our subtype landscape, predicting and experimentally validating a switch in sensitivity to venetoclax to panobinostat, two drugs with very different mechanisms than quizartinib. Our results show how multi-omics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2024

Event-Based Energy Impact Tracking and Forecasting with Limited Measurements for Rooftop Units

Packaged air conditioning units and heat pumps, also known as rooftop units (RTUs), are responsible for almost 133 billion kWh of electricity usage annually on site for space cooling U.S. commercial buildings. In addition, the use of heat pumps is a trend we expect to accelerate as buildings transition from fossil fuel-based heating to electricity as a key step for decarbonizing the U.S. commercial buildings sector. However, the operation conditions and energy use of RTUs and heat pumps are usually not well monitored as they are not commonly integrated with building automation systems and lack exposed sensing and control points. To fill this gap, this paper proposes a framework for tracking and forecasting energy impacts resulting from degradation of performance and improved performance for unit servicing using limited data. The proposed framework makes use of a constrained dataset, specifically measurements of the outdoor air temperature and the power demand of individual RTUs, to track and forecast changes in energy use associated with changes in performance over various temporal horizons ranging from days to weeks. Following the detection of an RTU fault, performance degradation, or performance improvement, the framework employs a prediction model to assess the cumulative energy impact. We demonstrate the effectiveness of the method with field-collected data for servicing and degradation examples and compare the predicting accuracy of Gradient Boosting Decision Tree (GBDT) Regression models to Support Vector Regression and Linear Regression models. The results show that GBDT achieved the best accuracy for time-series validation datasets for the servicing and degradation cases, and the prediction model was able to track the cumulative energy impacts of events. The proposed framework can inform building owners of the cumulative change in energy usage of RTUs associated with performance degradation, performance improvement, or a fault.

packaged air conditioners, packaged heat pumps, ro↗
DOE OSTI2020

Mind the Gap: Building Simulation in the Architectural Design Studio

Building modelling and simulation approaches are increasingly being utilized in architectural design studios to guide and inform the design process and offer evidence-based feedback on proposed building performance. The development of intuitive and simplified simulation interfaces has greatly contributed to achieving this integration. One aspect that is often overlooked is the workflow that governs and regulates integrated design, which can have significant impacts on final design outcomes. Currently there are numerous software packages available for building performance simulations. This makes it challenging to select an appropriate tool that provides accurate results yet allows a designer to make informed architectural decisions with a designer-friendly interface. Furthermore, workflows to incorporate simulations into the design process proved to highly impact student’s project design integration. Yet, it is not clear what type of workflows are successful to achieve this goal, under what conditions, and/or for which building and site typologies. This paper addresses these issues by first reviewing three different workflows for integrating building performance simulation processes and highlighting their strengths and weaknesses. Second, a comparative case study approach was employed to test three of the most common workflows in three different integrated design architectural studios at the senior and vertical studio levels as well as in two courses that run parallel and complementary to the design studios. The workflows, processes, and the resultant student projects were further analyzed based on criteria for better integrated design and architectural excellence as outlined in the American Institute of Architects Committee on the Environment (AIA COTE) Top 10 Student’s Competition. Third, to situate the pedagogical case studies’ results within a larger context, a survey of the AIA COTE Top 10 student competition award recipients over the last five years was conducted. The results are summarized in a pedagogical framework that outlines best strategies of the type of workflows, software, design process used, methods to achieve desired interaction between design process and analytical feedback, and metrics for educators to evaluate the success of this integration and their learning outcomes in the design studio. The goal is to help bridge the gap between the building design and simulation within the design studio’s creative process for more integrated design outcomes.

Elzeyadi, Ihab↗
DOE OSTI2022

Dark Energy Survey Year 3 results: marginalization over redshift distribution uncertainties using ranking of discrete realizations

ABSTRACT Cosmological information from weak lensing surveys is maximized by sorting source galaxies into tomographic redshift subsamples. Any uncertainties on these redshift distributions must be correctly propagated into the cosmological results. We present hyperrank, a new method for marginalizing over redshift distribution uncertainties, using discrete samples from the space of all possible redshift distributions, improving over simple parametrized models. In hyperrank, the set of proposed redshift distributions is ranked according to a small (between one and four) number of summary values, which are then sampled, along with other nuisance parameters and cosmological parameters in the Monte Carlo chain used for inference. This approach can be regarded as a general method for marginalizing over discrete realizations of data vector variation with nuisance parameters, which can consequently be sampled separately from the main parameters of interest, allowing for increased computational efficiency. We focus on the case of weak lensing cosmic shear analyses and demonstrate our method using simulations made for the Dark Energy Survey (DES). We show that the method can correctly and efficiently marginalize over a wide range of models for the redshift distribution uncertainty. Finally, we compare hyperrank to the common mean-shifting method of marginalizing over redshift uncertainty, validating that this simpler model is sufficient for use in the DES Year 3 cosmology results presented in companion papers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Deep Learning and Likelihood Approaches for Viral Phylogeography Converge on the Same Answers Whether the Inference Model Is Right or Wrong

Abstract Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are often computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare, and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among 5 locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression that we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior density (HPD) and greatly overlap with HPDs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster after training. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

Evolutionary Biology↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Neutrino heating in 1D, 2D, and 3D core-collapse supernovae: characterizing the explosion of high-compactness stars

Massive stars can end their lives with a successful supernova explosion (leaving behind a neutron star or, more rarely, a black hole), or a failed explosion that leaves behind a black hole. The density structure of the pre-collapse progenitor star already encodes much of the information regarding the outcome and properties of the explosion. However, the complexity of the collapse and subsequent shock expansion phases prevents drawing a straightforward connection between the pre-collapse and post-explosion properties. In order to derive such a connection several explodability studies have been performed in recent years. However, different studies can predict different explosion outcomes. In this article, we show how compactness, which is related to the average density of the star’s core, has an important role in determining the efficiency of neutrino heating, and therefore the outcome of the explosion. Commonly, high-compactness progenitors are assumed to yield failed explosions, due to their large mass accretion rates, preventing the shock from expanding. We show by analysing ~150 2D flash and F ornax simulations and 20 3D F ornax simulations that this is not the case. Instead, due to the rapid increase of neutrino heating with compactness, high-compactness progenitors lead to successful shock revival. We also show that 1D+ simulations that include v-driven convection using a mixing-length theory approach correctly reproduce this trend. Finally, we compare 1D+ models, which we show can reproduce some aspects of multi-D simulations with reasonable accuracy, with other widely used 1D models in the literature.

neutrinos↗
DOE OSTI2023

Methodology and Tool for the Physical Security Analysis of Micro and Advanced Reactors

This work proposes a dynamic evaluation methodology to relax the conservatism in physical security evaluation, by leveraging an ongoing work in the Light Water Reactor Sustainability pathway. This methodology is implemented in a dynamic risk assessment tool named Event Modeling Risk Assessment using Linked Diagrams (EMRALD). The work extends EMRALD’s capability to support a sandbox feature where analysts can easily create attack scenarios and modify advanced/small modular reactor (A/SMR) security and safety features using templates. This approach saves time and cost since the analysis does not require creating detailed computer-aided design models, as is commonly required in commercial force-on-force software tools. EMRALD is completely free to use at https://emraldapp.inl.gov. We have developed basic templates including physical barriers, intrusion sensors, physical areas, and safety actions, that can be downloaded from EMRALD’s GitHub site: https://github.com/idaholab/EMRALD. These templates use generic data commonly used for training purposes, which do not reflect any actual operating nuclear reactor. Users may adjust the data in the templates with their own dataset and/or create new templates in EMRALD. The proposed methodology combines security and safety by assessing sabotage effects up to the radiological consequence to the public instead of merely the core damage state. This practice follows the industry standard for advanced non-light-water reactors currently proposed for endorsement by the Nuclear Regulatory Commission. The combination of security and safety is expressed in an achievability-consequence chart. EMRALD can be used to generate data for this chart. A hypothetical case study using a representative sodium-cooled fast reactor (SFR) facility is presented in this report to demonstrate this methodology. This case study does not contain any actual nuclear plant information. This work will benefit A/SMR vendors and utilities to implement security by design during the reactor design iteration phase, such that they do not have to perform upgrades and retrofits to the reactor after it is installed to improve its physical protection system. The tool may also be used to analyze domestic or foreign reactor designs to support the International Nuclear Security Techniques for Advanced Reactors (INSTAR) bilateral missions. Future works are planned to implement the methodology on a reference SFR reactor and a reference high-temperature gas-cooled reactor to obtain insights and lessons-learned for the A/SMR community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗
DOE OSTI2021

Spatial–Temporal Patterns of Historical, Near-Term, and Projected Drought in the Conterminous United States

Major droughts in the United States have heavily impacted the hydrologic system, negatively effecting energy and food production. Improved understanding of historical drought is critical for accurate forecasts. Data from global climate models (GCMs), commonly used to assess drought, cannot effectively evaluate local patterns because of their low spatial scale. This research leverages downscaled (~4 km grid spacing) temperature and precipitation estimates from nine GCMs’ data under the business-as-usual scenario (Representative Concentration Pathway 8.5) to examine drought patterns. Drought severity is estimated using the Palmer Drought Severity Index (PDSI) with the Thornthwaite evapotranspiration method. The specific objectives were (1) To reproduce historical (1966–2005) drought and calculate near-term to future (2011–2050) drought patterns over the conterminous USA. (2) To uncover the local variability of spatial drought patterns in California between 2012 and 2018 using a network-based approach. Our estimates of land proportions affected by drought agree with the known historical drought events of the mid-1960s, late 1970s to early 1980s, early 2000s, and between 2012 and 2015. Network analysis showed heterogeneity in spatial drought patterns in California, indicating local variability of drought occurrence. The high spatial scale at which the analysis was performed allowed us to uncover significant local differences in drought patterns. This is critical for highlighting possible weak systems that could inform adaptation strategies such as in the energy and agricultural sectors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Leveraging Pre-Built Catalogs and Object-Level Scheduling to Eliminate I/O Bottlenecks in HPC Environments

Modern High-Performance Computing (HPC) environments face mounting challenges due to the shift from large to small file datasets, along with an increasing number of users and parallelized applications. As HPC systems rely on Parallel File Systems (PFS), such as Lustre for data processing, performance bottlenecks stemming from Object Storage Target (OST) contention have become a significant concern. Existing solutions, such as LADS with its object-level scheduling approach, fall short in large-scale HPC environments due to their inability to effectively address metadata I/O bottlenecks and the growing number of I/O processes. This study highlights the pressing need for a comprehensive solution that tackles both OST contention and metadata I/O challenges in diverse HPC workloads. To address these challenges, we propose SwiftLoad, an object-level I/O scheduling framework that leverages a metadata catalog to enhance the performance and efficiency of parallel HPC utilities. The adoption of the metadata catalog mitigates the metadata I/O bottlenecks that commonly occur in HPC utilities, a challenge that is particularly pronounced in object-level I/O scheduling. SwiftLoad addresses OST contention and the uneven distribution of I/O processes across different OSTs through mathematical modeling and incorporates a Loader Configuration Module to regulate the number of I/O processes. Evaluated with two representative utilities—data deduplication profiling and data augmentation—SwiftLoad achieved performance improvements of up to 5.63x and 11.0x, respectively, on a production supercomputer.

HPC↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI1995

Nuclear Safety [Vol. 36, No. 1, January-June 1995]

Nuclear Safety is a journal that covers significant issues in the field of nuclear safety. Its primary scope is safety in the design, construction, operation, and decommissioning of nuclear power reactors worldwide and the research and analysis activities that promote this goal, but it also encompasses the safety aspects of the entire nuclear fuel cycle, including fuel fabrication, spent-fuel processing and handling, and nuclear waste disposal, the handling of fissionable materials and radioisotopes, and the environmental effects of all these activities. Table of Contents for this issue follows. THE CHORNOBYL ACCIDENT: 1 The Chornobyl Accident Revisited, Part II: The State of the Nuclear Fuel Located Within the Chornobyl Sarcophagus, A A. Borovoi and A. R. Sich; GENERAL SAFETY CONSIDERATIONS: 33 Nuclear Power Safety in Central and Eastern Europe, R. Wilson; 46 Safety of Nuclear Power Reactors in the Former Eastern European Countries, S. Chakraborty; 53 Technical Note: On the Definition of Common-Cause Failures, H. Paula; ACCIDENT ANALYSIS: 58 Modeling and Analysis of Core-Debris Recriticality During Hypothetical Severe Accidents in the Advanced Neutron Source Reactor, S.-H. Kim, V. Georgevich, D. B. Simpson, C. O. Slater, and R. P. Taleyarkhan; 68 Ignitability of Hydrogen/Oxygen/Diluent Mixtures in the Presence of Hot Surfaces, R. K. Kumar and G. W. Koroll; 94 Coupled RELAP5 and CONTAIN Accident Analysis Using PVM, K. A. Smith, A. J. Baratta, and G. E. Robinson; CONTROL AND INSTRUMENTATION: 109 Application of Fuzzy Logic in Nuclear Reactor Control Part I: An Assessment of State-of-the-Art, A. S. Heger, N. K. Alang-Rashid, and M. Jamshidi; DESIGN FEATURES: 122 Twenty-Third DOE/NRC Nuclear Air-Cleaning and Treatment Conference, R. R. Bellamy, J. J. Hayes, and M. W. First; ENVIRONMENTAL EFFECTS: 135 Atmospheric Dispersion and the Radiological Consequences of Normal Airborne Effluents from a Nuclear Power Plant, D. Fang, C. Z. Sun, and L. Yang; 142 Calculation of Distribution Coefficients for Radionuclides in Soils and Sediments, I. Puigdomenech and U. Bergstrom: OPERATING EXPERIENCES: 155 Reactor Shutdown Experience, Compiled by J. W. Cletcher; U.S. NUCLEAR REGULATORY COMMISSION INFORMATION AND ANALYSES: 158 Operating Experience Feedback Report—Reliability of Safety-Related Steam Turbine-Driven Standby Pumps Used in U.S. Commercial Nuclear Power Plants, J. R. Boardman; 166 Turbine Building Hazards, H. L Ornstein; RECENT DEVELOPMENTS: 169 Reports, Standards, and Safety Guides, D. S. Queener; 175 Proposed Rule Changes as of Dec. 31,1994; ANNOUNCEMENTS: 32 Harvard School of Public Health In-Place Filter Testing Workshop; 134 International Conference on Advances in the Operational Safety of Nuclear Power Plants; 193 30th Tennessee Industries Week; 193 DOE Technical Standards Program 1995 Workshop; 194 Multiphase Flow Experiments and Instrumentation; 180 The Authors; 185 Indexes to Nuclear Safety, Volumes 34 and 35.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Economic analysis of CCUS: Accelerated development for CO 2 EOR and storage in residual oil zones under the context of 45Q tax credit

Residual oil zones (ROZ) undergoing CO 2 Enhanced Oil Recovery (CO 2 -EOR) may benefit from specific strategies to maximize their value. We evaluated several strategies for producing from a Permian Basin, West Texas, USA field’s ROZ. This ROZ lies below the main pay zone (MPZ) of the field. Such brownfield ROZs occur in the Permian Basin and elsewhere. Since brownfield ROZs are hydraulically connected to the MPZs, development sequences and schemes influence oil production, CO 2 storage, and net present value (NPV). We conducted economic assessments of various CO 2 injection/production schemes in the stacked ROZ-MPZ reservoir based on flow simulations of a high-resolution geocellular model built from wireline logs and core data and calibrated through production history matching. Flow simulations of water alternating gas (WAG) injection, such as switching injection from the MPZ to the ROZ and commingled production, were studied. Simulation results showed that simultaneous CO 2 injection into the MPZ and ROZ lead to the largest oil production and, generally, the largest NPV. If instead, CO 2 was simultaneously injected into the MPZ and ROZ, then into the ROZ alone, this maximized CO 2 storage. CO 2 storage can be used as a tax credit under the Internal Revenue Code, Section 45Q. Storage performance depends on the development approach and WAG ratio. Developing the ROZ increased storage compared to only producing from the MPZ. The WAG ratio to maximize oil production did not always yield the largest NPV. These findings are potentially applied to other Brownfield ROZs, which are common below San Andres reservoirs in the Permian Basin and other basins. ROZ development can increase oilfields’ NPV and carbon storage potential. Our study can serve as an analog for similar reservoirs. Here this work provides valuable insights into the further optimization of brownfield ROZ development and information for operators to plan to develop stacked ROZ-MPZ reservoirs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Tool for the Risk-Informed Management of Critical Mission Resilience

We describe a methodology and tool for the risk-informed management and planning of mission resilience. By mapping concepts of resilience onto the elements of a streamlined risk model we are able to tap the substantial portfolio of established risk concepts to provide rapid insights in the evaluation and high-level screening of prospective resilience enhancement measures. This provides a risk-informed, levelized basis for the comparison of disparate resilience solutions and the means of establishing preferences. The methodology begins with identification of critical missions met by a site, and establishment of the supporting physical assets. Scenarios that would result in failure of these assets are systematically identified. Each scenario comprises three elements: realization of a hazard or threat resulting in loss of resources (the current focus being on power, natural gas, and water) to the asset, failure of measures in place to protect the asset against those losses, and realization of the consequent impacts. These scenarios are quantified in terms of their probabilities of occurrence and the magnitude of the resultant consequences (mission outage time), allowing risk-prioritization to focus resilience enhancement considerations. What-If? analyses are conducted through adjusting elements of the risk calculation to reflect the deployment of prospective resilience measures, by which means the efficacies of each of those measures can be compared using common risk metrics across diverse resilience strategies. This paper will also describe the insights from example applications.

resilience, risk, risk assesment, energy, water↗