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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DRDMannTurb: A Python package for scalable, data-driven synthetic turbulence

Synthetic turbulence models (STMs) are used in wind engineering to generate realistic flow fields and are employed as inputs to industrial wind simulations. Examples include prescribing inlet conditions in large eddy simulations that model loads on wind turbines and tall buildings. We are interested in STMs capable of generating fluctuations based on prescribed second-moment statistics since such models can simulate environmental conditions that closely resemble on-site observations. To this end, the widely used Mann model (see Mann, 1994, 1998) is the inspiration for DRDMannTurb. The Mann model is described by three physical parameters: a magnitude parameter influencing the global variance of the wind field and corresponding to the Kolmogorov constant multiplied by the rate of viscous dissipation of the turbulent kinetic energy to the two-thirds, αϵ 2/3 , a turbulence length scale parameter L, and a nondimensional parameter Γ related to the lifetime of the eddies. A number of studies, as well as international standards (e.g., those by the International Electrotechnical Commission (IEC)), include recommended values for these three parameters with the goal of standardizing wind simulations according to observed energy spectra. Yet, having only three parameters, the Mann model faces limitations in accurately representing the diversity of observable spectra. This Python package enables users to extend the Mann model and more accurately fit field measurements through flexible neural network models of the eddy lifetime function. Following Keith et al. (2021), we refer to this class of models as Deep Rapid Distortion (DRD) models. DRDMannTurb also includes a general module implementing an efficient method for synthetic turbulence generation based on a domain decomposition technique. This technique is also described in Keith et al. (2021).

17 WIND ENERGY↗
DOE OSTI2024

Equilipy: a python package for calculating phase equilibria

The CALPHAD (CALculation of PHAse Diagram) approach (Nigel Saunders & Miodownik, 1998) provides predictions for thermodynamically stable phases in multicomponent-multiphase materials across a wide range of temperatures. Consequently, the CALPHAD calculations became an essential tool in materials and process design (Luo, 2015). Such design tasks frequently require navigating a high-dimensional space due to multiple components involved in the system. This increasing complexity demands high-throughput CALPHAD calculations, especially in the rapidly evolving field of alloy design. In response to the need, we developed Equilipy an open-source Python package designed for calculating phase equilibria of multicomponent-multiphase systems. Equilipy is specifically tailored for high-throughput CALPHAD calculations, offering parallel computations across multiple processors and nodes with the given NPT input conditions namely elemental compositions (N), pressure (P), and temperature (T). Equilipy utilizes the program structure and Gibbs energy functions from the Fortran-based program, Thermochimica (Piro et al., 2013), with incorporating a new Gibbs energy minimization algorithm. This algorithm, originally developed by Capitani and Brown in 1987 (Capitani & Brown, 1987), has been revised and implemented to enhance the stability and performance of calculations. The Fortran codes are precompiled and interfaced with Python via F2PY, ensuring high computation speed. Benchmark tests shown in Figure 1 demonstrate that Equilipy’s computation speed is comparable to those of established commercial software, TC-Python and PanPython. This result highlights its efficiency and potential applications in various scientific and industrial fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

gaia: An R package to estimate crop yield responses to temperature and precipitation

gaia is an open-source R package designed to estimate crop yield shocks in response to annual weather variations and CO 2 concentrations at the country scale for 17 major crops. This innovative tool streamlines the workflow from raw climate data processing to projections of annual shocks to crop yields at the country level, using the response surfaces from an empirical econometric model developed and documented in Waldhoff et al. (2020), which leverages historical weather, CO 2 , and crop yield data for robust empirical fitting for 17 crops. gaia uses these response surfaces with monthly temperature and precipitation projections (e.g., from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O’Neill et al., 2016) climate data bias-adjusted and statistically downscaled by the ISIMIP3BASD approach (Lange, 2019) in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014)) to project yield shocks that can be applied to agricultural productivity changes at the country level for use in multisectoral economic models. The historical and future projections use gridded, country-and-crop specific monthly growing season precipitation and temperature data, aggregated to the national level, and weighted by cropland area derived from the global Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset (Portmann et al., 2010). These annual, country, and crop-specific yield shocks can be aggregated to different definitions of regions, crop commodities, and time periods, as needed by specific multisectoral economic models. gaia serves as a lightweight, powerful tool that can aid exploration of crop yield responses under a broad range of future climate projections, enhancing human-Earth system analysis capabilities.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

popclass: A Python Package for Classifying Microlensing Events

popclass is a Python package that provides a flexible, probabilistic framework for classifying the lens of a gravitational microlensing event. Gravitational microlensing occurs when a massive foreground object (e.g., a star, white dwarf or black hole) passes in front of and lenses the light from a distant background source. This causes an apparent brightening, and shift in position, of the background source. In most cases, characteristics of the microlensing signal do not contain enough information to definitively identify the lens type. Different lens types lie in different but overlapping regions of the characteristics of the microlensing signal. For example, black holes tend to be more massive than stars and therefore cause microlensing signals that are longer. Current Galactic simulations enable us to predict where different lens types lie in the observational space and can therefore be used to classify events (e.g., Lam et al., 2020). popclass allows the user to match the characteristics of a microlensing signal with a simulation of the Galaxy to calculate lens type probabilities for the event (see Figure 1). Constraints on any microlensing signal properties and any Galactic model can be used. popclass comes with an interface to ArviZ (Kumar et al., 2019) and PyMultiNest (Buchner et al., 2014) for microlensing signal constraints, as well as pre-loaded Galactic models, plotting functionality, and methods to quantify the classification uncertainty. The probabilistic framework for popclass was developed in Perkins et al. (2024), used in Fardeen et al. (2024) and has been applied to classifying events in Kaczmarek et al. (2025).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AutoUncertainties: A Python Package for Uncertainty Propagation

Propagation of uncertainties is of great utility in the experimental sciences. While the rules of (linear) uncertainty propagation are straightforward, managing many variables with uncertainty information can quickly become complicated in large scientific software stacks. Often, this requires programmers to keep track of many variables and implement custom error propagation rules for each mathematical operator and function. The Python package AutoUncertainties, described here, provides a solution to this problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sunny.jl: A Julia Package for Spin Dynamics

Sunny is a Julia package designed to serve the needs of the quantum magnetism community. It supports the specification of a very broad class of spin models and a diverse suite of numerical solvers. These include powerful methods for simulating spin dynamics both in and out of equilibrium. Uniquely, it features a broad generalization of classical and semiclassical approaches to SU(N) coherent states, which is useful for studying systems exhibiting strong spin-orbit coupling or local entanglement effects. Sunny also offers a well-developed framework for calculating the dynamical spin structure factor, enabling direct comparison with scattering experiments. Ease of use is a priority, with tools for symmetry-guided modeling and interactive visualization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

SDA: a symbolic differential algebra package in C++

Truncated Power Series Algebra (TPSA), or Differential Algebra (DA), is a well-established tool in accelerator physics, commonly used for generating high-order maps of dynamic systems, as well as in symplectic tracking, normal form analysis, verified integration, optimization, and fast multipole methods. This package is the first to perform symbolic DA computations, enabling traceability of initial condition contributions and runtime reduction for repeated DA calculations, potentially expanding DA’s applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Field Aging of Photovoltaic Module Packaging Materials: DuraMAT Field Module Library

To understand and develop models for silicon photovoltaic module degradation, accelerated testing is often used, however, outdoor field testing is necessary for validation. Outdoor field testing publications are often limited by the lack of a pristine, control module to compare the fielded module to. In this work, commercially available modules were purchased from seven different manufacturers for outdoor fielding then destructive characterization to investigate packaging material degradation on

14 SOLAR ENERGY↗
DOE OSTI2024

Benchmark Calculation for Turkey Point Unit 3 Cycles 1-3 Using the SCALE 6.3/Polaris–PARCS v3.4.2 Code Package

Benchmark calculations were performed for Turkey Point Unit 3 cycles 1–3 to validate the SCALE 6.3/Polaris–PARCS v3.4.2 code with the ENDF/B–VII.1 56–group library by comparing the simulated results with the measured data. The benchmark results will be used in evaluating the SCALE/Polaris–PARCS code package’s uncertainties for pressurized water reactor physics analysis. That future analysis will include key nuclear parameters such as reactivity, control bank worth, temperature coefficients, and pin and assembly power peaking factors. The present document details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS, and PARCS. Additional details are provided with respect to the input and output files produced for the benchmark calculations. The benchmark results are summarized such that they can be used in evaluating uncertainties with other benchmark results for key nuclear parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Benchmark Calculation for Surry Unit 1 Cycles 1-3 Using the SCALE 6.3/Polaris–PARCS v3.4.2 Code Package

The benchmark calculations were performed for Surry Unit 1 cycles 1–3 to validate the SCALE 6.3/Polaris–Purdue Advanced Reactor Core Simulator (PARCS) v3.4.2 with the ENDF/B–VII.1 56–group library by comparing the simulated results with the measured data. The benchmark results will be used to evaluate uncertainties of the SCALE/Polaris–PARCS code package for pressurized water reactor physics analysis for key nuclear parameters such as reactivity, control bank worth, temperature coefficients, and pin and assembly power peaking factors. This report details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS, and PARCS. Additional details are provided for the input and output files produced for the benchmark calculations. The benchmark results were summarized such that they can be used in evaluating uncertainties with other benchmark results for key nuclear parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Benchmark Calculation for the Hatch Unit 1 Cycles 1-3 Using the SCALE 6.3/Polaris–PARCS v3.4.2 Code Package

This study was the performance of the benchmark calculation for the Hatch Unit 1 cycles 1–3, to validate the SCALE 6.3/Polaris–PARCS v3.4.2 with the ENDF/B-VII.1 AMPX 56-group library by comparing the simulated results with the measured data. The benchmark results will be used in evaluating uncertainties of the SCALE/Polaris–PARCS code package for boiling water reactor (BWR) physics analysis for key nuclear parameters such as reactivity and assembly power peaking factors. This report details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS and PARCS, and additionally, detailed information is provided for all the input and output files produced for the benchmark calculations. The benchmark results were summarized such that they can be used in evaluating uncertainties for key nuclear parameters with other BWR benchmark results.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Benchmark Calculation for the Peach Bottom Unit 2 Cycles 1-3 Using the SCALE 6.3/Polaris–PARCS v3.4.2 Code Package

In this study, benchmark calculations were performed for Peach Bottom Unit 2 cycles 1–3 to validate the SCALE 6.3/Polaris–PARCS v3.4.2 with the ENDF/B-VII.1 AMPX 56-group library by comparing the simulated results with the measured data. The benchmark results will be used to evaluate uncertainties of the SCALE/Polaris–PARCS code package for boiling water reactor physics analysis for key nuclear parameters such as reactivity and assembly power peaking factors. This report details plant and fuel design specifications and input data for SCALE/Polaris, GenPMAXS and PARCS. Additionally, detailed information is provided for all the input and output files produced for the benchmark calculations. The benchmark results were summarized such that they can be used to evaluate uncertainties with other benchmark results for key nuclear parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

ORNL Portable Diagnostic Package for Evaluating Mirror Performance

Oak Ridge National Laboratory (ORNL) and Realta Fusion successfully executed a one-year collaborative effort to deploy the Portable Diagnostic Package (PDP) on the Wisconsin HTS Axisymmetric Mirror (WHAM) and to assess future diagnostic needs. The PDP consists of an optical emission spectroscopy (OES) subsystem and a Thomson scattering (TS) subsystem. Within the first two months, the PDP OES subsystem was installed, integrated with WHAM timing, and handed over to trained Realta personnel. It immediately delivered multi-channel impurity surveys and the first spatially resolved plasma-rotation profiles inferred from C III Doppler shifts. The TS subsystem was commissioned within the first six months; transportation of the class IV laser system, approval of the safety plan, and the integration with WHAM timing with 100 ns resolution. The TS subsystem provided the first localized electron temperature measurements in WHAM commissioning shots. Leveraging these results, the team has discussed a diagnostic concept map for Realta’s next-step facility, Anvil, identifying line-of-sight constraints, required temporal resolution, and hardware upgrades.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗