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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 OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2026

Scientific Data Compression for Large Scale Computational Fluid Dynamics (CFD) Simulations

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and General Electric (GE) investigated methods for reducing the size of large computational fluid dynamics (CFD) simulation datasets using scientific data compression techniques. The work focused on adapting the MultiGrid Adaptive Reduction of Data (MGARD) compression framework and integrating it with high-performance I/O and visualization tools used in CFD workflows. MGARD uses hierarchical multilevel decomposition to enable error-controlled compression of floating-point scientific data while preserving quantities of interest. During the project, MGARD compression was integrated with the ADIOS I/O framework and visualization tools such as ParaView to enable efficient storage, transfer, and analysis of simulation data. The collaboration also explored approaches for improving compression performance for CFD data defined on unstructured meshes. Results demonstrate that scientific data compression can significantly reduce storage requirements and improve data management for large-scale CFD simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

High fidelity actuator line data from 9 turbine wind farm simulations using ExaWind

This data was generated with the ExaWind code suite (https://github.com/Exawind) to investigate the performance of different Active Wake Mixing turbine control in a wind farm situated in a stable atmospheric boundary layer. All cases correspond to a 3x3 wind farm in a 10km x 10km domain using a total mesh size that varied between 1.6 X 10^9 to 1.85 X 10^9 grid cells. The simulations were run across 1800-2000 GPUs on Frontier. The case description and data generation process is fully documented in Yalla, G. R., Brown, K., Cheung, L., Houck, D., deVelder, N., and Balaji, J. (2025). "Estimating annual energy production of wake mixing control strategies including comparisons to wake steering." Wind Energy Sciences (https://doi.org/10.5194/wes-2025-250).

17 WIND ENERGY↗
DOE OSTI2022

Genetic programming for interpretable, data-driven continuum damage models.

The damage mechanisms that lead to failure in engineering alloys have been studied extensively, but converting this knowledge into constitutive models that are suitable for engineering-scale analysis remains a challenge. Evolution laws for continuum damage have been developed in the past and have proven effective but suffer from many non-physical assumptions that inhibit the overall accuracy of the model. Further, the assumptions inherent in these existing models prevent them from being applicable to a broad class of materials. At the same time, computational models of fine-scale damage mechanisms continue to advance making it tractable to generate large training data sets through computer simulation. Data-driven machine learning approaches can leverage these data sets to avoid making limiting assumptions, and instead produce models directly from the results of microstructural simulations and/or experiments. Many of these machine learning approaches are rapid and accurate, but they offer little to no insight into the underlying relationships among state variables being discovered. Conversely, genetic programming symbolic regression (GPSR) is a machine learning method that produces analytic expressions relating the state variables, allowing maximal insight and interpretability. To that end, we propose using GPSR as a data-driven method of obtaining microstructurally informed continuum damage models. Data is generated using microstructural simulations of damage evolution, parameterized over microstructural statistics (i.e., pore shape) and nominally applied deformations. Analytic expressions for damage evolution are obtained from the data using GPSR, and these expressions are then utilized within a continuum constitutive model. Overall, this approach is a promising method of automatically obtaining analytic relations describing constitutive phenomena in a material.

Buche, Michael Robert↗
DOE OSTI2014

Constrained simulation of the Bullet Cluster

In this work, we report on a detailed simulation of the Bullet Cluster (1E0657-56) merger, including magnetohydrodynamics, plasma cooling, and adaptive mesh refinement. We constrain the simulation with data from gravitational lensing reconstructions and the 0.5-2 keV Chandra X-ray flux map, then compare the resulting model to higher energy X-ray fluxes, the extracted plasma temperature map, Sunyaev-Zel'dovich effect measurements, and cluster halo radio emission. We constrain the initial conditions by minimizing the chi-squared figure of merit between the full two-dimensional (2D) observational data sets and the simulation, rather than comparing only a few features such as the location of subcluster centroids, as in previous studies. A simple initial configuration of two triaxial clusters with Navarro-Frenk-White dark matter profiles and physically reasonable plasma profiles gives a good fit to the current observational morphology and X-ray emissions of the merging clusters. There is no need for unconventional physics or extreme infall velocities. The study gives insight into the astrophysical processes at play during a galaxy cluster merger, and constrains the strength and coherence length of the magnetic fields. The techniques developed here to create realistic, stable, triaxial clusters, and to utilize the totality of the 2D image data, will be applicable to future simulation studies of other merging clusters. This approach of constrained simulation, when applied to well-measured systems, should be a powerful complement to present tools for understanding X-ray clusters and their magnetic fields, and the processes governing their formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

NRAP-Open-IAM: NRAP Open Source Integrated Assessment Model

Note: This is the last version (a2.6.1) of NRAP-Open-IAM released during NRAP Phase II in 2022. The latest version of NRAP-Open-IAM is available here: https://edx.netl.doe.gov/dataset/phase-iii-nrap-open-iam NRAP-Open-IAM is an open-source software product that enables quantification of containment effectiveness and leakage risk at storage sites in the context of system uncertainties and variability. NRAP-Open-IAM represents the next-generation in a line of systems-based computational models developed for quantitative geological carbon storage (GCS) risk assessment. The model comprises a set of reduced-order and analytical models of various components of the GCS system, potential leakage pathways, receptors of concern including impact to groundwater resources and the atmosphere, a framework to support stochastic simulation, time stepping, uncertainty quantification, other analytical functionality for scenario and risk-performance evaluation, and a basic graphical user interface to support scenario development, data input simulation definition, and basic post-processing and results display. As the NRAP Open-IAM functionality continues to evolve, we continue to add to its capability to develop quantitative, probabilistic, and time-dependent profiles of the evolution of risk at a GCS site and evaluate the influence of uncertain parameters on uncertainty in predicted risk. It can be used to quantify the dynamics of reservoir saturation plume and pressure-affected area, for evaluation of the area of potential groundwater impact (i.e., Area of Review) and monitoring requirements to support cost and regulatory analysis, and for consideration of different post-injection site care and closure scenarios. This submission contains the current version of NRAP-Open-IAM available for evaluation and testing. To use the NRAP-Open-IAM, download the source code (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/4c24a3da-3b40-4ffe-9892-c807ae9f8760) then open the NRAP-Open-IAM user's guide (https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669) to read more about the tool. Installation instructions for Windows, Mac, and Linux can be found in the "installers" folder of the extracted NRAP-Open-IAM folder and describe setup of environment (e.g., Python libraries) needed for proper work of the tool. Test of installation can be done by running "python openiam_setup_tests.py" in the "setup" folder. The installation test also runs a test suite to see if the NRAP-Open-IAM has been installed correctly. To run the test suite separately, run "python iam_test.py" in the "test" folder. User's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/8b27335a-343c-4836-b8a3-3ad0bdc9e669 Developer's guide: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/3bc6ee7d-609d-4eb6-80ba-fa6130ee0313 Reservoir simulation data used in some examples distributed with NRAP-Open-IAM: - Kimberlina: https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/eb62cece-61b2-4037-9b6d-32407dde2ab8 - Kimberlina (compartmentalized): https://edx.netl.doe.gov/dataset/08f396c8-bc5f-44ad-a028-6e98a6ea6d70/resource/366f9530-3b32-4b84-affe-ab2df1d9a8b5 - FutureGen 2.0: https://edx.netl.doe.gov/dataset/futuregen-2-0-1008-simulation-reservoir-lookup-table NRAP-Open-IAM GitLab repository: https://gitlab.com/NRAP/OpenIAM Related publications: - Bacon, D., Yonkofski, C., Brown, C., Demirkanli, D. and Whiting, J., 2019. Risk-based post injection site care and monitoring for commercial-scale carbon storage: Reevaluation of the FutureGen 2.0 site using NRAP-Open-IAM and DREAM. International Journal of Greenhouse Gas Control 90: 102784. - Bacon, D. Demirkanli, D., and White, S., 2020. Probabilistic risk-based Area of Review (AoR) determination for a deep-saline carbon storage site. International Journal of Greenhouse Gas Control 102: 103153. - Harp, D., Oldenburg, C., and Pawar, R., 2019. A metric for evaluating conformance robustness during geologic CO2 sequestration operations. International Journal of Greenhouse Gas Control 85: 100-108. - Lackey, G., Vasylkivska, V., Huerta, N., King, S., and Dilmore, R., 2019. Managing well leakage risks at a geologic carbon storage site with many wells, International Journal of Greenhouse Gas Control, 88 :182-194. - Vasylkivska, V., Dilmore, R., Lackey, G., Zhang, Y., King, S., Bacon, D., Chen, B., Mansoor, K., and Harp, D., 2021. NRAP-Open-IAM: A flexible open-source integrated assessment model for geologic carbon storage risk assessment and management, Environmental Modelling & Software, 143: 105114. Presentations: - Chen, B., Harp, D., and Pawar, R., A data assimilation approach (ES-MDA) coupling with NRAP-Open-IAM for quantifying uncertainty reduction in geological CO2 sequestration. AGUFM 2019: T44A-02. - Chen, B., and Harp, D., Improving risk analysis precision for geologic CO2 sequestration by quantifying the uncertainty reduction before and after acquiring monitoring data. 14th Greenhouse Gas Control Technologies Conference, Melbourne, Australia, 2018, pp. 21-26. - Harp, D., National Risk Assessment Partnership Task 2: Containment Assurance. No. LA-UR-19-28654, Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2019. - Vasylkivska, V., King, S., Bacon, D., Harp, D., Chen, B., Mansoor, K., Onishi, T., Yang, Y., Zhang, Y., and Keating, E., NRAP-Open-IAM: An open-source integrated assessment model, poster, Mastering the Subsurface Through Technology Innovation, Partnerships and Collaboration: Carbon Storage and Oil and Natural Gas Technologies Review Meeting, Pittsburgh, PA, August 13-16, 2018. - Vasylkivska, V., Lackey, G., King, S., Wentworth, A., Huerta, N., Creason, C., DiGiulio, J., Yang, Y., and Dilmore, R., Long-term risk analysis of a geologic CO2 storage project during the post-injection period, SIAM Conference on Computational Science and Engineering, Spokane, WA, February 25-March 1, 2019. - Vasylkivska, V., Overview of the NRAP-Open-IAM tool for carbon storage (beta release), 2019 Annual NRAP Tool Users Meeting, Pittsburgh, PA, August 27, 2019. - Vasylkivska, V., Bacon, D., Chen, B., Dilmore, R., Harp, D., King, S., Lackey, G., Lindner, E., Liu, G., Mansoor, K. and Zhang, Y., NRAP-Open-IAM: A new, open-source code for integrated assessment of geologic carbon storage containment effectiveness and leakage risk, poster, American Geophysical Union Fall Meeting 2020 (virtual meeting), December 2020. - Vasylkivska, V., NRAP open-source integrated assessment model and relevant application, oral presentation, NRAP workshop "NRAP Tools for Geologic Carbon Storage Risk-Based Decision Making" held in conjunction with Groundwater Protection Council (GWPC) 2021 Annual Forum (virtual meeting), Salt Lake City, UT, September 2021. - Vasylkivska, V., NRAP-Open-IAM: open-source integrated assessment model, digital poster/demonstration, software demonstration session, 2022 Carbon Management Project Review Meeting, August 16, 2022

AoR↗
DOE OSTI2014

Causes and Predictability of the 2012 Great Plains Drought

Central Great Plains precipitation deficits during May-August 2012 were the most severe since at least 1895, eclipsing the Dust Bowl summers of 1934 and 1936. Drought developed suddenly in May, following near-normal precipitation during winter and early spring. Its proximate causes were reduction in atmospheric moisture transport into the Great Plains from the Gulf of Mexico. Processes that would provide air mass lift and condensation were mostly absent, including a lack of frontal cyclones in late spring followed by suppressed deep convection in mid-late summer owing to large-scale subsidence and atmospheric stabilization. In this sense, the drought resulted mostly from natural variations in weather. Seasonal forecasts did not predict the summer 2012 central Great Plains drought development, which therefore arrived without early warning. Climate simulations and empirical analysis suggest that neither ocean surface temperatures nor changes in greenhouse gases induced a substantial reduction in summertime rainfall over the central Great Plains during 2012. Diagnosis of historical data, climate simulation data, and seasonal forecasts paint a picture of an extreme drought that may not have had extreme forcing as its cause and that had limited long lead predictability. Yet, diagnosis of the retrospective climate simulations also reveals a regime shift toward warmer and drier summertime Great Plains conditions during the recent decade, most probably due to natural decadal variability. As a consequence, the probability for a severe summer Great Plains drought may have increased 5-fold in the last decade compared to the 1980s and 1990s, and the models show that the so-called tail-risk for severe drought was heightened in summer 2012.

predictability, 2012, great, plains, drought↗
DOE OSTI2016

Sandia Hyperspectral Upper-Bound Spectrum Version 1.0

The Sandia hyperspectral upper-bound spectrum algorithm (hyper-UBS) is a cosmic ray despiking algorithm for hyperspectral data sets. When naturally-occurring, high-energy (gigaelectronvolt) cosmic rays impact the earth’s atmosphere, they create an avalanche of secondary particles which will register as a large, positive spike on any spectroscopic detector they hit. Cosmic ray spikes are therefore an unavoidable spectroscopic contaminant which can interfere with subsequent analysis. A variety of cosmic ray despiking algorithms already exist and can potentially be applied to hyperspectral data matrices, most notably the upper-bound spectrum data matrices (UBS-DM) algorithm by Dongmao Zhang and Dor Ben-Amotz which served as the basis for the hyper-UBS algorithm. However, the existing algorithms either cannot be applied to hyperspectral data, require information that is not always available, introduce undesired spectral bias, or have otherwise limited effectiveness for some experimentally relevant conditions. Hyper-UBS is more effective at removing a wider variety of cosmic ray spikes from hyperspectral data without introducing undesired spectral bias. In addition to the core algorithm the Sandia hyper-UBS software package includes additional source code useful in evaluating the effectiveness of the hyper-UBS algorithm. The accompanying source code includes code to generate simulated hyperspectral data contaminated by cosmic ray spikes, several existing despiking algorithms, and code to evaluate the performance of the despiking algorithms on simulated data.

Anthony, Stephen↗
DOE OSTI2019

Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Parameterization retrieval of trace gas volume mixing ratios from Airborne MAX-DOAS

We present a parameterization retrieval of volume mixing ratios (VMRs) from differential slant column density (dSCD) measurements by Airborne Multi-AXis Differential Optical Absorption Spectroscopy (AMAX-DOAS). The method makes use of the fact that horizontally recorded limb spectra (elevation angle 0°) are strongly sensitive to the atmospheric layer at instrument altitude. These limb spectra are analyzed using reference spectra that largely cancel out column contributions from above and below the instrument, so that the resulting limb dSCDs, i.e., the column integrated concentration with respect to a reference spectrum, are almost exclusively sensitive to the atmospheric layers around instrument altitude. The conversion of limb dSCDs into VMRs is then realized by calculating box air mass factors (Box-AMFs) for a Rayleigh atmosphere and applying a scaling factor constrained by O 4 dSCDs to account for aerosol extinction. An iterative VMR retrieval scheme corrects for trace gas profile shape effects. Benefits of this method are (1) a fast conversion that only requires the computation of Box-AMFs in a Rayleigh atmosphere; (2) neither local aerosol extinction nor the slant column density in the DOAS reference (SCD ref ) needs to be known; and (3) VMRs can be retrieved for every measurement point along a flight track, thus increasing statistics and adding flexibility to capture concentration gradients. Sensitivity studies are performed for bromine monoxide (BrO), iodine monoxide (IO) and nitrogen dioxide (NO 2 ), using (1) simulated dSCD data for different trace gas and aerosol profiles and (2) field measurements from the Tropical Ocean tRoposphere Exchange of Reactive halogen species and Oxygenated VOC (TORERO) field experiment. For simulated data in a Rayleigh atmosphere, the agreement between the VMR from the parameterization method (VMR para ) and the true VMR (VMR true ) is excellent for all trace gases. Offsets, slopes and R 2 values for the linear fit of VMR para over VMR true are, respectively (0.008 ± 0.001) pptv, 0.988 ± 0.001, 0.987 for BrO; (-0.0066 ± 0.0001) pptv, 1.0021 ± 0.0003, 0.9979 for IO; (-0.17 ± 0.03) pptv, 1.0036 ± 0.0001, 0.9997 for NO 2 . The agreement for atmospheres with aerosol shows comparable R 2 values to the Rayleigh case, but slopes deviate a bit more from one: (0.093 ± 0.002) pptv, 0.933 ± 0.002, 0.907 for BrO; (0.0021 ± 0.0004) pptv, 0.887 ± 0.001, 0.973 for IO; (8.5 ± 0.1) pptv, 0.8302 ± 0.0006, 0.9923 for NO 2 . VMR para from field data are further compared with optimal estimation retrievals (VMR OE ). Least orthogonal distance fit of the data give the following equations: BrO para = (0.1 ± 0.2) pptv + (0.95 ± 0.14) × BrO OE ; IO para = (0.01 ± 0.02) pptv + (1.00 ± 0.12) × IO OE ; NO 2para = (3.9 ± 2.5) pptv + (0.87 ± 0.15) × NO 2OE . Overall, we conclude that the parameterization retrieval is accurate with an uncertainty of 20 % for IO, 30 % for BrO and NO 2 , but not better than 0.05 pptv IO, 0.5 pptv BrO and 10 pptv NO 2 . Finally, the retrieval is applicable over a wide range of atmospheric conditions and measurement geometries and not limited to the interpretation of vertical profile measurements in the remote troposphere.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Λ Baryon Production in ν¯µ Interactions in the MicroBooNE Detector

The Cabibbo suppressed production of $\Lambda$ baryons in anti-neutrino interactions with nuclei is a rare process that is yet to be measured with a modern neutrino detector with automated reconstruction. The cross section for this process is sensitive to a number of unique nuclear effects, most notably the secondary interactions of the produced hyperon while attempting to escape from the nucleus. Other interactions within the nuclear remnant can impact the estimation of neutrino energy in oscillation measurements, and thus an accurate description of the nuclear environment is required. The strangeness violating hyperon production process is only available to anti-neutrinos. The model of this interaction is implemented into the NuWro neutrino interaction Monte Carlo simulation, and some predictions are presented, focusing on the role of nuclear effects. This model introduces a hyperon-nucleus potential, which calculations from hypernuclear theory permit to be strongly repulsive in th e case of $\Sigma$ baryons. The presence of this potential is found to sculpt the shape of the differential cross section in some variables. The MicroBooNE detector will be described, followed by a description of a measurement of the flux averaged, restricted phase space cross section of Cabibbo suppressed $\Lambda$ baryon production. A sophisticated event selection is employed, as a very large quantity of background neutrino interactions must be removed to perform the measurement with any sensitivity. This selection introduces some novel techniques such as the island finding method, and achieves a background reduction of $\sim 10^6$, with an efficiency of around 7\%. The calculation of the systematic uncertainties will be explained, including two procedures explored to handle sources of background with extremely poor simulation statistics: an in-situ constraint using data from sidebands, and a visual inspection of the data and simulation to remove the troublesome background events. The sensitivity to the $\Lambda$ baryon production cross section is calculated in the form of Bayesian posterior probability distributions, combining the systematic uncertainties with data and simulation statistical uncertainties. As a rare process, the statistical uncertainties are highly non-Gaussian, and the Bayesian approach is applied to include the full shapes of these uncertainties. Data corresponding to $2.2 \times 10^{20}$ protons on target of neutrino mode running and $4.9 \times 10^{20}$ protons on target of anti-neutrino running is analysed. When the data was unblinded, five $\Lambda$ production candidates were selected from the data, consistent with the MC simulation prediction of $5.3 \pm 1.1$ events. The final estimated cross section is $1.8^{+2.0}_{-1.6} \times 10^{-40}$cm$^2/$Ar when employing the sideband constraint procedure. A similar result of $2.0^{+2.2}_{-1.8} \times 10^{-40}$cm$^2/$Ar is obtained when performing the visual scan instead. The methods used in t his analysis are intended to be easily exported to other LArTPC detectors such as the Short Baseline Near Detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Pressure solution creep of random packs of spheres

Abstract We performed numerical calculations of compaction in aggregates of spherical grains, using Lehner and Leroy's (2004, hereinafter LL) constitutive model of pressure solution at grain contacts. That model is founded on a local definition of the thermodynamic driving force and leads to a fully coupled formulation of elastic deformation, dissolution, and diffusive transport along the grain boundaries. The initial geometry of the aggregate was generated by random packing of spheres with a small standard deviation of the diameters. During the simulations, isostatic loading was applied. The elastic displacements at the contacts were calculated according to Digby's (1981) nonlinear contact force model, and deformation by dissolution was evaluated using the LL formulation. The aggregate strain and porosity were tracked as a function of time for fixed temperature, applied effective pressure, and grain size. We also monitored values of the average and standard deviation of total load at each contact, the coordination number for packing, and the statistics of the contact dimensions. Because the simulations explicitly exclude processes such as fracturing, plastic flow, and transport owing to surface curvature, they can be used to test the influence of relative changes in the kinetics of dissolution and diffusion processes caused by contact growth and packing rearrangements. We found that the simulated strain data could be empirically fitted by two successive power laws of the form, ε x ∝ t ξ , where ξ was equal to 1 at very early times, but dropped to as low as 0.3 at longer times. The apparent sensitivity of strain rate to stress found in the simulations was much lower than predicted from constitutive laws that assume a single dominant process driven by average macroscopic loads. Likewise, the apparent activation enthalpy obtained from the simulated data was intermediate between that assumed for dissolution and diffusion, and, further, tended to decrease with time. These results are similar to the experimental observations of Visser et al.'s (2012), who used an aggregate geometry and physical conditions closely resembling the present numerical simulations.

Bernabé, Y.↗
DOE OSTI2017

Power spectra based Planck constraints on compensated isocurvature, and forecasts for LiteBIRD and CORE space missions

Compensated isocurvature perturbations (CIP), where the primordial baryon and cold dark matter density perturbations cancel, do not cause total matter isocurvature perturbation. Consequently, at the linear order in the baryon density contrast Δ, a mixture of CIP and the adiabatic mode leads to the same CMB spectra as the pure adiabatic mode. Only recently, Muñoz et al. showed that at the second order CIP leaves an imprint in the observable CMB by smoothing the power spectra in a similar manner as lensing. This causes a strong degeneracy between the CIP variance Δ{sub rms}{sup 2} ≡ (Δ{sup 2}) and the phenomenological lensing parameter A {sub L}. We study several combinations of the Planck 2015 data and show that the measured lensing potential power spectrum C {sub ℓ}{sup φφ} breaks the degeneracy. Nested sampling of the ΛCDM+Δ{sub rms}{sup 2}(+ A {sub L}) model using the Planck 2015 temperature, polarization, and lensing data gives Δ{sub rms}{sup 2} = (6.9{sup +3.0}{sub −3.1}) × 10{sup −3} at 68% CL. A non-zero value is favoured at 2.3σ (or without the polarization data at 2.8σ). CIP with Δ{sub rms}{sup 2} ≈ 7 × 10{sup −3} improves the bestfit χ{sup 2} by 3.6 compared to the adiabatic ΛCDM model. In contrast, although the temperature data favour A {sub L} ≅ 1.22, allowing A {sub L} ≠ 1 does not improve the joint fit at all, since the lensing data disfavour A {sub L} ≠ 1. Indeed, CIP provides a rare example of a simple model, which is capable of reducing the Planck lensing anomaly significantly and fitting well simultaneously the high (and low) multipole temperature and lensing data, as well as the polarization data. Finally, we derive forecasts for two future satellite missions (LiteBIRD proposal to JAXA/NASA and Exploring Cosmic Origins with CORE proposal to ESA's M5 call) and compare these to simulated Planck data. Due to its coarse angular resolution, LiteBIRD is not able to improve the constraints on Δ{sub rms}{sup 2} or A {sub L}, but CORE-M5 (almost) reaches the cosmic variance limit and improves the CIP constraint to Δ{sub rms}{sup 2} < 0.6 (1.4) × 10{sup −3} at 68 (95)% CL, which is nine times better than the current trispectrum based upper bound and six times better than obtained from the simulated Planck data. In addition, CORE-M5 will exquisitely distinguish between Δ{sub rms}{sup 2} and A {sub L}. No matter whether CIP is allowed for or not, the uncertainty of the lensing parameter will be σ( A {sub L}) ≈ 0.012, in the case where the simulated data are based on the adiabatic ΛCDM model with A {sub L} = 1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most state-of-the-art deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.

17 WIND ENERGY↗