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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Nontrivial critical behavior at magnetic transitions: A case study of Sm 7 ⁢Pd 3

We present a comprehensive analysis of the critical behavior of Sm 7 ⁢Pd 3 in the vicinity of its second-order magnetoelastic transition at 𝑇 c =173 K. The critical exponents (CEs) 𝛽 and 𝛾, determined using both the standard convergence procedure and the average normalized slope (ANS) method, diverge at 𝑇 c –-a characteristic typically associated with first-order transitions. Notably, none of the established universality classes satisfactorily describe the critical behavior of Sm 7 ⁢Pd 3 , and we discuss the possible origins of this deviation in the context of the strong spin-lattice coupling intrinsic to the sample. We emphasize the importance of accurately selecting the critical temperature and magnetic field ranges to ensure robust critical behavior analysis, and propose a quantitative approach to assess the reliability of the extracted CEs. Additionally, we demonstrate that in the ANS method, the critical exponents 𝛽 and 𝛾 should be calculated separately using data for 𝑇 ⩽ 𝑇 c and 𝑇 ⩾ 𝑇 c , respectively. In conclusion, our findings underscore the need for a revised theoretical framework to accurately describe second-order magnetoelastic transitions.

Ferrimagnets↗
DOE OSTI2020

Inverting infrasound data for the seismoacoustic source time functions and surface spall at the Source Physics Experiments Phase II: Dry Alluvium Geology

This report presents the infrasound data recorded as part of the Source Physics Experiment - Phase 2, Dry Alluvium Geology. This experiment, also known colloquially as DAG, consisted of four underground chemical explosions at the Nevada National Security Site. We focus our analysis on only the fourth explosion (DAG-4) as we determined that this was the only event that produced clear source-generated infrasound energy as recorded by the DAG sensors. We analyze the data using two inversion methods. The first method is designed to estimate the point-source seismoacoustic source time functions, and the second inversion method is designed to estimate the first-order characteristics (e.g. horizontal dimensions and maximum amplitude) of the actual spall surface. For both analysis methods, we are able to fit the data reasonably well, with various assumptions of the source model. The estimated seismoacoustic source appears to be a combination of a buried, isotropic explosion with a maximum amplitude of ~2 x 10 9 Nm and a vertically oriented force, applied to the Earth's surface with a maximum amplitude of 4 x 10 7 N. We use the vertically oriented force to simulate surface spall. The estimated spall surface has an approximate radius of ~40 m with a maximum acceleration magnitude in the range of 0.8 to 1.5 m/s/s. These estimates are approximately similar to the measured surface acceleration at the site.

58 GEOSCIENCES↗
DOE OSTI2022

Exploring the sensitivity of charge-exchange ( p , n ) reactions to the neutron density distribution

The determination of the nuclear neutron properties suffers from uncontrolled uncertainties, which have attracted considerable attention recently, such as in the context of the PREX experiment. Our aim is to analyze the sensitivity of charge-exchange (p,n) reactions to the neutron density distribution ρ n and constrain the neutron characteristics in the nuclear structure models. Method: By combing the folding and the mean-field models, the nucleon-nucleus (N,A) potential can be obtained from the nuclear density distribution. Further, the (p,p) and (p,n) cross sections for 48 Ca and 208 Pb are calculated following the distorted-wave Born approximation method. As a result, compared with the (p,p) cross section, the effects of ρ n variation on the (p,n) cross section are significant, which is due to the impact of isovector properties. Based on the global folding model analyses of data, it is found that 48 Ca and 208 Pb have relatively large neutron skin thickness ΔR np . Results illustrate that the charge-exchange (p,n) reaction is a sensitive probe of ρ n . The results in this paper can offer useful guides for future experiments of neutron characteristics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Synchrotron experiment and simulation studies of magnesium-steel interface manufactured by impact welding

The effective weight reduction in the automotive industry by the wide adoption of lightweight magnesium (Mg) alloys demands high-quality joint between magnesium alloys and massively-used steels in order to wring the excess weight with strength and safety assurance. However, Mg-steel joint is difficult to achieve because there is no mutual solubility between magnesium and steel and huge disparity in physical properties. An impact-based welding method recently showed successful Mg-steel joining. In this work, the characteristics of Mg-steel interface joined by the impact welding method were investigated. Synchrotron high-energy X-ray computed tomography and diffraction were applied to characterize the microstructure across Mg-steel interface. Results revealed a deposit layer formed at the joint interface where Fe-rich particles spread deep into the Mg matrix. High-resolution 3D morphology of Mg-steel interface demonstrated the trapped pores and cracks inside the deposit layer. Finally, the formation of the deposit layer and the void/cracking evolution were analyzed by using finite element models. These findings provide insights into the immiscible Mg-steel joining process.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis

A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented here. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale features of fibrils (<100 nm) and lamellae (>100 nm) to resolve in 3D. Phase contrast nano X-ray computed tomographic data are used to reconstruct the lamellae phase, while 2D scanning electron microscopy data are used to characterize sub-100 nm microstructure features such as the thin fibrils that are beyond the effective resolution of X-ray computed tomography. Fibril geometries are reconstructed stochastically based on the 2D higher resolution data, and integrated with the lamellae geometries in 3D space. Transport property analyses are performed to investigate the bias of microstructure models without considering the fibrils. A sensitivity study is also conducted to facilitate understanding of the relationship between microstructure characteristics and transport properties.

25 ENERGY STORAGE↗
DOE OSTI2023

Advanced Modeling and Simulation Methods for Evaluation of Thermal Neutron Scattering Materials

With the rise of interest in thermal neutron scattering data for advanced reactor, criticality safety, and shielding applications, new experimental data are required for evaluation of new materials or for re-evaluation (or validations) of previously evaluated materials. New experimental data are evaluated in a three-step process: (1) computing the phonon characteristics, (2) computing the dynamic structure factor (DSF) from the data, and (3) using the experimental setup to simulate the experimental data. All three steps have challenges, ranging from the need for a sufficiently general material simulation code—a processing code that can compute the corresponding DSF—to having a detailed layout of the instrument/beamline/facility where the data were measured. Whereas phonon characteristics of materials can be computed using various methods (molecular dynamics, density functional theory, etc.), a high-fidelity computation of the DSF and the simulation of the experiment based on the DSF is vital to the accuracy of the evaluation. The latter two steps can be achieved by using the two corresponding code systems developed by instrument scientists at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory: (1) OCLIMAX, a program that calculates the dynamic structure factor from DFT and MD simulation results, and (2) MCViNE, a Monte Carlo neutron ray-tracing program designed to simulate neutron scattering experiments. Recently, polyethylene and yttrium hydride were measured at the Wide Angular-Range Chopper (ARCS) and SEQUOIA instrument stations of the SNS. These experiments are simulated using the density functional theory code, the Cambridge Serial Total Energy Package (CASTEP), to compute its phonon characteristics (eigenvalues/vectors and PDOS), which is then processed using OCLIMAX to yield the DSF, and finally the data at each instrument station are simulated by the MCViNE for comparison to the measured data for evaluation. For comparison to conventional evaluation methods, the scattering data processed from OCLIMAX are compared against those processed from the LEAPR module of NJOY, and the results from MCViNE simulations are compared against previously used simplified beamline models implemented in the Monte Carlo N-Particle (MCNP) code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2020

Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The study shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Data associated with “Different methods of estimating riverbed sediment grain size diverge at the basin scale ” (v2)

This data package is associated with the publication “Different methods of estimating riverbed sediment grain size diverge at the basin scale” published in Frontiers in Earth Science (Regier et al., 2025). The distribution of sediment grain size in streams and rivers is often quantified by the median grain size (d50), a key metric for understanding and predicting hydrologic and biogeochemical function of streams and rivers. Manual methods to measure d50 are time-consuming and ignore larger grains, while model-based methods to estimate d50 often over-generalize basin characteristics, and therefore cannot accurately represent site-scale heterogeneity. Here, we apply a machine learning-enabled photogrammetry methodology (You Only Look Once, or YOLO) for estimating d50 for grains > 2 mm based on images collected from streams and rivers throughout the Yakima River Basin (YRB). To understand how such methods may help bridge the gaps in resolution and accuracy between manual and catchment characteristics model-based d50 estimates, we compared YOLO d50 values to manual and model-based estimates across the YRB. We found distinct differences among methods for d50 averages and variability, and relationships between d50 estimates and basin characteristics. Source images can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052. This data package was originally published in May 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. In addition to the readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) and subfolders containing data, figures, and scripts. The data folder contains datasets used for the analyses in the manuscript in image, text-delimited or geospatially-referenced formats. The figures folder contains the figures from the manuscript in different formats. The scripts folder contains all of the scripts used to complete the analyses in the manuscript. All files are .csv, .rds, .dbf, .prj, .shp, .shx, .jpg, .png, .R, .Rproj, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Laser powder bed fusion parameter estimation with k-NN

Abstract Laser powder bed fusion (L-PBF) is a technique within additive manufacturing that uses a high power density laser to build parts from fused powdered metal alloy. This technology is well equipped to produce complex parts with otherwise impossible features, such as hidden voids or lattice structures. Alongside capability, reliability and quality are key characteristics considered when choosing a manufacturing method, and these are gaining attention as this method becomes more prevalent in industry. One main indicator of a stable L-PBF process is consistent melt pool geometry, and the properties of which are likely to determine the quality of the part produced. As computing power and sensing technologies become more advanced, this melt pool geometry could be studied in real time. This work addresses the challenge by leveraging a k-nearest neighbor (k-NN) model to identify key features within melt pool imagery and predict the energy density. The k-NN model was trained on data provided by the National Institute of Standards and Technology (NIST). Data preprocessing was performed on the images to extract features that were used in the k-NN model. This approach was used to accurately infer the energy density of unseen layers within the same part. The algorithm was subsequently tested with unique scan strategies and found to reasonably estimate the energy density of different parts. A fivefold cross validation found the algorithm to be consistently predicting the class of 91.4% of the in situ melt pool images.

Jung, Patrick (ORCID:0000000267890859)↗
DOE OSTI2022

It’s Not What You Take Up, It’s What You Keep: How Discoveries from Diverse Disciplines Directed the Development of the FDG PET/CT Scan

Although imaging glucose metabolism with positron emission tomography combined with X-ray CT (FDG-PET/CT) has become a standard diagnostic modality for the discovery and surveillance of malignant tumors and inflammatory processes, its origins extend back to more than a century of notable discoveries in the fields of inorganic and organic chemistry, nuclear physics, mathematics, biochemistry, solute transport physiology, metabolism, and imaging, accomplished by pioneering and driven investigators, of whom at least ten were recipients of the Nobel Prize. These tangled and diverse roots eventually coalesced into the FDG-PET/CT method, that through its many favorable characteristics inherent in the isotope used ( 18 F), the accurate imaging derived from coincidence detection of positron annihilation radiation combined with computed tomography, and the metabolic trapping of 2-deoxy-2-[ 18 F] fluoro-d-glucose (FDG) in tissues, provides safety, sensitivity, and specificity for tumor and inflammation detection. Here, the authors hope that this article will increase the appreciation among its readers of the insight, creativity, persistence, and drive of the many investigators who made this technique possible. This article is followed by a review of the many applications of FDG-PET/CT to the gastrointestinal tract and hepatobiliary system (Mandelkern in Dig Dis Sci 2022)

2-DG↗
DOE OSTI2021

Modeling the effect of habitat availability and quality on endangered winter-run Chinook salmon ( Oncorhynchus tshawytscha ) production in the Sacramento Valley

Reconciliation between water uses and sustaining aquatic species populations requires an effort to identify and quantify essential habitat characteristics for ecosystem health and valuation methods to predict ecosystem response to restoration actions. This process is particularly challenging for anadromous fish species such as California's Sacramento River winter-run Chinook salmon, due to their limited geographic range and diverse life history habitat requirements. Tools, such as life-cycle models, are needed to manage population dynamics and quantify the composite effects of processes across space and time. Nevertheless, complex institutions can hinder result interpretation and communication, and limit model use in decision-making. This paper focuses on the federally endangered and endemic Sacramento River winter-run Chinook (Oncorhynchus tshawytscha) by developing a Winter-Run Habitat-based Population Model (WRHAP). Additionally, WRHAP is a conceptual, freshwater rearing stage model that includes alternative rearing habitats reported in the literature (e.g., floodplains, off-channel and tributaries), defines rules of habitat use based on instream conditions, and incorporates a juvenile growth module that combines bioenergetics modeling with empirical growth rates. Model outputs reasonably follow observed out-migration patterns and provide a realistic smolt size distribution arriving at the San Francisco Bay. This effort demonstrates the importance of currently “non-critical” habitats (as defined by the Endangered Species Act) for juvenile development, with floodplain habitat contributing to a quarter of out-migrating biomass (despite < 18% availability and <10% of total rearing days), and off-channel growth being one of the most sensitive parameters (explaining ~13% of average juvenile weight variance). The model shows the utility of a simple population model to explore relationships between habitat quality/quantity and juvenile development, and to assist water/environmental management and decision-making processes focused on species recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Progress in development of characterization capabilities to evaluate candidate materials for direct air capture applications

As part of U.S. national efforts to combat the detrimental effect of global climate change, the National Institute of Standards and Technology (NIST) was recently tasked to support efforts in direct air capture (DAC) of carbon dioxide research and deployment. In order to develop test procedures, materials, and documentary standards, key characterization methods relevant to DAC materials have been investigated and used to identify desirable properties for a potential Standard Reference Material (SRM). Select amine-supported materials that previously showed potential for DAC applications have been characterized using commonly available laboratory methods. Further insights into the adsorption characteristics have been gained from developing and applying more specialized characterization tools ideal for probing low concentrations of carbon dioxide. A broad suite of capabilities that examine relevant properties under appropriate conditions gives the most profound insights into a material's specific performance. We advocate for even more specialized capabilities to be developed and standardized to quantitatively monitor the interactions of CO 2 with molecular species in complex and often disordered systems to advance DAC and support carbon dioxide reduction (CDR) in general.

carbon dioxide↗
DOE OSTI2020

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Imaging mechanism for hyperspectral scanning probe microscopy via Gaussian process modelling

We investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian Process (GP) methods. Even for weakly informative priors, GP methods allow unambiguous determination of the characteristic length scales of the imaging process both in spatial and frequency domains. We further show that BE data set tends to be oversampled in the spatial domains, with ~30% of original data set sufficient for high-quality reconstruction, potentially enabling faster BE imaging. At the same time, reliable reconstruction along the frequency domain requires the resonance peak to be within the measured band. This behavior suggests the optimal strategy for the BE imaging on unknown samples. Finally, we discuss how GP can be used for automated experimentation in SPM, by combining GP regression with non-rectangular scans.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High-accuracy waveforms for black hole-neutron star systems with spinning black holes

The availability of accurate numerical waveforms is an important requirement for the creation and calibration of reliable waveform models for gravitational wave astrophysics. For black hole-neutron star binaries, very few accurate waveforms are however publicly available. Most recent models are calibrated to a large number of older simulations with good parameter space coverage for low-spin non-precessing binaries but limited accuracy, and a much smaller number of longer, more recent simulations limited to non-spinning black holes. In this paper, we present long, accurate numerical waveforms for three new systems that include rapidly spinning black holes, and one precessing configuration. Here, we study in detail the accuracy of the simulations, and in particular perform for the first time in the context of BHNS binaries a detailed comparison of waveform extrapolation methods to the results of Cauchy Characteristic Extraction. The new waveforms have <0.1rad phase errors during inspiral, rising to ~(0.2-0.4)rad errors at merger, and ≲1% error in their amplitude. We compute the faithfulness of recent analytical models to these numerical results, and find that models specifically designed for BHNS binaries perform well (F>0.99) for binaries seen face-on. For edge-on observations, particularly for precessing systems, disagreements between models and simulations increase, and models that include precession and/or higher-order modes start to perform better than BHNS models that currently lack these features.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Observed Temperature and Moisture Advection Characteristics in the Southern Great Plains

Until recently, advection profiles in the planetary boundary layer have been difficult to quantify from observations. As a result, not much is known about its basic characteristics, like magnitude and diurnal cycles, or how these differ as a function of large-scale environments. Here, to provide insight into advective characteristics, a Green’s Theorem–based method for calculating profiles of advection in the lowest 3 km from an array of ground-based thermodynamic and kinematic profiling instruments has been applied to 2 years of observations from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observing site. Since advection largely depends on synoptic-scale forcing, a self-organizing map (SOM) was used to classify days based on mean sea level pressure analyses; from that, the diurnal evolution of vertical profiles of advection for different synoptic environments was quantified. The overall mean magnitude of the potential temperature advection is approximately ±3 K h −1 , while the moisture advection is approximately ±1.5 g kg −1 h −1 , with substantial variability in the sign and magnitude of the advective tendencies at different heights and throughout the diurnal cycle. Advection magnitude is strongly connected to the strength of synoptic-scale forcing, which varies depending on the time of year. Variability in advection is larger for potential temperature than it is for moisture, and strongly forced environments, like midlatitude cyclones, feature greater variability in the advection magnitude than weakly forced or quiescent environments.

advection↗