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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 sources NASA NTRS DOE OSTI

DOE OSTI2026

Coupling Noah-Multiparameterization land-surface Model with Energy Research and Forecasting Model

The Energy Research and Forecasting (ERF) model is a high-performance atmospheric model built on the AMReX adaptive mesh refinement (AMR) framework, enabling efficient simulations on heterogeneous computing platforms that combine multicore processors with hardware accelerators. To support land–atmosphere interactions within ERF’s AMR-based environment, a land-surface model must be capable of operating directly on hierarchically refined meshes. In this work, we present a methodology for coupling the Fortran-based Noah-Multiparameterization (Noah-MP) land-surface model with ERF’s C++ codebase. Rather than rewriting Noah-MP, we construct a Fortran–C interoperability layer using CodeScribe, a tool that leverages large language models (LLMs) to automate the generation of interface code. CodeScribe applies structured prompting techniques to generate bindings that support efficient data exchange and function calls between ERF and Noah-MP. The coupling framework also incorporates AMR-aware data handling strategies, allowing NoahMP to operate seamlessly within ERF’s hierarchical mesh structure. This work provides a structured approach for integrating legacy Fortran models into modern C++-based modeling systems using LLM-assisted code generation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

MFANS 2024 - Dimensional Analysis Made Easy

There is a significant impact of dimensional errors in systems. Dimensional analysis is difficult due to the large size of systems. SA4U and Scalpel are practical programs which can have an impact beyond traditional software as they are able to perform precise dimensional analysis and repair C++ source code.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

CodeScribe Agent

SF-26-086 CodeScribe introduces a structured, multi-stage pipeline that combines deterministic program analysis with LLM-powered translation to enable incremental, testable Fortran-to-C++ migration. First, `code-scribe index` traverses the project directory tree and produces `scribe.yaml` metadata files recording all modules, subroutines, and functions at each level, giving the LLM accurate structural context instead of a hallucinated codebase model. Second, `code-scribe draft` performs the deterministic portion of translation — converting Fortran types to C++ equivalents, replacing `use` statements with `#include` and `using namespace` directives, and detecting constructs requiring special handling — while embedding`scribe-prompt` annotations that guide the LLM through non-trivial cases such as statement-function-to-lambda conversions and `extern "C"` wrapper generation. Third, `code-scribe translate` applies project-specific TOML-based few-shot prompt templates and submits the composed prompt to a pluggable LLM backend (OpenAI, Anthropic, Argonne ARGO, any OpenAI-compatible endpoint, or local Hugging Face checkpoints), producing a C++ source file, a header, and a Fortran-C++ interface file for each translated routine so the codebase compiles and runs correctly throughout the migration. Beyond translation, CodeScribe includes a tool-using coding agent (`code-scribe agent`) with read, bash, edit, and write capabilities, and a bounded loop mode (`code-scribe loop`) that runs repeated stateless agent sessions over a task file with restricted tool access — enabling sustained, auditable software development workflows for broader scientific computing tasks.

Dhruv, Akash [Argonne National Laboratory (ANL), A↗

DOE OSTI2025

Remote Instrumentation and Data Acquisition

This poster outlines the development and implementation of a remote data acquisition system for waveform analysis using a Rohde & Schwarz oscilloscope. The project involved capturing waveform data, and transferring it to a local machine for visualization and analysis. The core logic was developed in C++ with a focus on object oriented programming and the use of polymorphism so the main application can interact with any instrument without knowing its exact type, simplifying the overall logic and making it easier to add or swap out components without changing the rest of the codebase.. The system issues Standard Commands for Programmable Instruments (SCPI) via a socket connection and parses the oscilloscope s ASCII waveform data. The C++ application was containerized using Docker for ease of portability, and reproducibility. Emphasis was placed on secure networking practices, error handling, and effective data capture. The report describes the technical steps taken, challenges encountered, and future work, providing insight into the practical integration of hardware interfacing with remote computational environments.

Parikh, Jaymil [Illinois U., Urbana]↗

DOE OSTI2025

Remote Instrumentation and Data Acquisition: An Internship Research Report

This report outlines the development and implementation of a remote data acquisition system for waveform analysis using a Rohde & Schwarz oscilloscope. The project involved capturing waveform data, and transferring it to a local machine for visualization and analysis. The core logic was developed in C++ with a focus on object oriented programming and the use of polymorphism so the main application can interact with any instrument without knowing its exact type, simplifying the overall logic and making it easier to add or swap out components without changing the rest of the codebase.. The system issues Standard Commands for Programmable Instruments (SCPI) via a socket connection and parses the oscilloscope’s ASCII waveform data. The C++ application was containerized using Docker for ease of portability, and reproducibility. Emphasis was placed on secure networking practices, error handling, and effective data capture. The report describes the technical steps taken, challenges encountered, and lessons learned, providing insight into the practical integration of hardware interfacing with remote computational environments.

Parikh, Jaymil [Fermilab]↗

DOE OSTI2021

A survey of software implementations used by application codes in the Exascale Computing Project

The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Keras2c: A library for converting Keras neural networks to real-time compatible C

With the growth of machine learning models and neural networks in measurement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time consuming work to integrate into existing code bases, or only support a very limited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural network models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent layers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ~ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

EMPIRE-PIC: A Performance Portable Unstructured Particle-in-Cell Code

In this study we introduce EMPIRE-PIC, a finite element method particle-in-cell (FEM-PIC) application developed at Sandia National Laboratories. The code has been developed in C++ using the Trilinos library and the Kokkos Performance Portability Framework to enable running on multiple modern compute architectures while only requiring maintenance of a single codebase. EMPIRE-PIC is capable of solving both electrostatic and electromagnetic problems in two- and three-dimensions to second-order accuracy in space and time. In this paper we validate the code against three benchmark problems — a simple electron orbit, an electrostatic Langmuir wave, and a transverse electromagnetic wave propagating through a plasma. We demonstrate the performance of EMPIRE-PIC on four different architectures: Intel Haswell CPUs, Intel's Xeon Phi Knights Landing, ARM Thunder-X2 CPUs, and NVIDIA Tesla V100 GPUs attached to IBM POWER9 processors. This analysis demonstrates scalability of the code up to more than two thousand GPUs, and greater than one hundred thousand CPUs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Validation of Modern Nuclear Data Processing in SCALE

The nuclear data (ND) community is continuously developing more accurate, diversified, and comprehensive data for radiation transport modeling to support the nuclear science community. As these community efforts progress, it is crucial that ND processing tools like AMPX (used for SCALE [1] ND) also be developed in parallel to incorporate these new data into transport codes and actually deliver those data to end users. AMPX is a mature, well-tested code that was developed by many people at Oak Ridge National Laboratory (ORNL) over the course of the past few decades. A large portion of the AMPX codebase, however, was outdated, difficult to maintain, and incompatible with modern code development tools. Some of the most important parts of the AMPX code have now been replaced with modern C++ code that can be maintained more cost-effectively and can be tested more rigorously.

AMPX↗

DOE OSTI2021

ArborX

ArborX library tackles a problem of efficiently finding geometric objects that are close in space. Variations of this problem, such as finding the nearest neighbors of a point, or finding all objects within a certain distance, are inherent components of applications in many fields. The data may be large so that solving the problem efficiently may require significant computational resources, such as multiple processors or accelerators such as general purpose GPUs. ArborX' main advantage in its ability to solve large problems efficiently utilizing a combination of distributed and on-node parallelism. ArborX can be run efficiently on a wide variety of hardware, including GPUs from different vendors, which distinguishes it from other available libraries which typically choose only few of these. The other advantage is that it supports both types of user problems: spatial problems (useful for intersections and finding objects within certain distance), and nearest neighbor problems. ArborX also supports flexible interface in its interaction with a user. Particularly, it allows a user to call user's own function on a positive match, a functionality not rarely available in other libraries. ArborX implements construction and traversal algorithms using efficient tree structures, such as bounding volume hierarchy (BVH). At its core, it uses linear BVH for its low construction cost and sufficient quality. ArborX is written using C++, and is parallelized using the message passing interface (MPI) for the distributed communication, and the Kokkos library for on-node parallelism. This approach allows ArborX to be run on a wide variety of hardware, from common laptops and desktops to supercomputers while using the same codebase. ArborX also implements several advanced algorithms using geometric search, such as density-based clustering algorithm DBSCAN.

ECP↗

DOE OSTI2025

ArborX 2.0

ArborX library tackles a problem of efficiently finding geometric objects that are close in space. Variations of this problem, such as finding the nearest neighbors of a point, or finding all objects within a certain distance, are inherent components of applications in many fields. The data may be large so that solving the problem efficiently may require significant computational resources, such as multiple processors or accelerators such as general purpose GPUs. ArborX' main advantage in its ability to solve large problems efficiently utilizing a combination of distributed and on-node parallelism. ArborX can be run efficiently on a wide variety of hardware, including GPUs from different vendors, which distinguishes it from other available libraries which typically choose only few of these. The other advantage is that it supports both types of user problems: spatial problems (useful for intersections and finding objects within certain distance), and nearest neighbor problems. ArborX also supports flexible interface in its interaction with a user. Particularly, it allows a user to call user's own function on a positive match, a functionality not rarely available in other libraries. ArborX implements construction and traversal algorithms using efficient tree structures, such as bounding volume hierarchy (BVH). At its core, ArborX uses linear BVH for its low construction cost and sufficient quality. ArborX implements both spatial and nearest-neighbor traversal algorithms. ArborX also provides several clustering algorithms (minimum spanning tree, DBSCAN, HDBSCAN*), interpolation using minimum least squares and ray tracing. ArborX is written using C++, and is parallelized using the message passing interface (MPI) for the distributed communication, and the Kokkos library for on-node parallelism. This approach allows ArborX to be run on a wide variety of hardware, from common laptops and desktops to supercomputers while using the same codebase.

Prokopenko, Andrey [Oak Ridge National Laboratory ↗

DOE OSTI2024

Development and validation of fully open-source R2S shutdown dose rate capabilities in OpenMC *

We present the first fully open-source capabilities for shutdown dose rate (SDR) calculations of fusion energy facilities based on the Rigorous 2-Step (R2S) methodology. These capabilities have been implemented in the OpenMC Monte Carlo particle transport code, building on its existing capabilities while also leveraging new features that have been added to the code to support SDR calculations, such as decay photon source generation. Each of the individual physics components in the R2S workflow—neutron transport, activation, decay photon source generation, and photon transport—have been verified through code-to-code comparisons with MCNP6.2 and FISPACT-II 4.0. These comparisons generally demonstrate excellent agreement between codes for each of the physics components. The full cell-based R2S workflow was validated by performing a simulation of the first experimental campaign from the Frascati Neutron Generator (FNG) ITER dose rate benchmark problem from the Shielding INtegral Benchmark Archive and Database (SINBAD). For short cooling times, the dose calculated by OpenMC agrees with the experimental measurements within the stated experimental uncertainties. For longer cooling times, an overprediction of the shutdown dose was observed relative to experiment, which is consistent with previous studies in the literature. Altogether, these features constitute a combination of capabilities in a single, open-source codebase to provide the fusion community with a readily-accessible option for SDR calculations and a platform for rapidly analyzing the performance of fusion technology.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

AstraAI v1

AstraAI is an open-source, structure-aware AI coding agent designed for large scientific and DOE-HPC codebases such as AMReX-based applications. Unlike general-purpose coding assistants, AstraAI combines retrieval-augmented generation (RAG) with compiler-level Abstract Syntax Tree (AST) analysis to perform precise, scope-constrained code modifications. It identifies exact function spans, enforces locality of edits, and maintains cross-file invariants, enabling deterministic and build-safe transformations in complex C++/GPU environments. AstraAI is intended for developers working on large, evolving HPC frameworks where correctness, reproducibility, and structural integrity are critical. Typical use cases include modifying physics kernels, updating GPU device lambdas, and performing multi-file refactors without breaking compilation or runtime semantics. Compared to conventional LLM-based coding agents - even those with repository access - AstraAI provides structural guarantees rather than free-form text patches. It minimizes unintended diffs, prevents scope drift, preserves formatting and build stability, and reduces structural hallucinations. By integrating compiler tooling directly into the generation loop, AstraAI transforms AI-assisted coding from probabilistic text editing into deterministic, structure-preserving program transformation suitable for mission-critical scientific software.

Natarajan, Mahesh [Lawrence Berkeley National Labo↗

DOE OSTI2024

EAGLES Liquid Cloud Testbed Large Eddy Simulation Library (v2)

This library consists of large eddy simulation (LES) model output using the PINACLES codebase coupled to the Hebrew University Fast Spectral Bin Microphysics scheme representing shallow, liquid phase clouds from a range of global liquid cloud testbed regions as well as some well known LES model intercomparison cases. This data is particularly suitable for examining microphysical modeling assumptions in coarser-scale models, and was used for this purpose in the work "“Evaluation of Autoconversion Representation in E3SM.v2 using an Ensemble of Large-Eddy Simulations of Low-Level Warm Clouds” by M. Ovchinnikov, P.-L. Ma, C. M. Kaul, K. G. Pressel, M. Huang, J. Shpund, and S. Tang

Kaul, Colleen M↗

DOE OSTI2020

CAN-D: Controller Area Network Decoder Codebase

CAN-D codebase takes as input a set of collected CAN data logs from a vehicle, and produces a .dbc with partial or full signal definitions. Once this .dbc file is produced it can be used to translate subsequent messages from that vehicle in real time or post-drive processing. code also provides methods for many visualizations and operations on the translated data.

Verma, Kiren E↗

DOE OSTI2021

OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms

OpenOA is an open source framework for operational data analysis of wind energy plants, implemented in the Python programming language. OpenOA provides a common data model, high level analysis workflows, and low-level convenience functions that engineers, analysts, and researchers in the wind energy industry can use to facilitate analytics workflows on operational data sets. OpenOA contains documentation, worked out examples in Jupyter notebooks, and a corresponding example dataset from the Engie Renewable’s La Haute Borne Dataset.

17 WIND ENERGY↗

DOE OSTI2023

KiT-RT: An Extendable Framework for Radiative Transfer and Therapy

Here, in this article, we present Kinetic Transport Solver for Radiation Therapy (KiT-RT), an open-source C++-based framework for solving kinetic equations in therapy applications available at https://github.com/CSMMLab/KiT-RT . This software framework aims to provide a collection of classical deterministic solvers for unstructured meshes that allow for easy extendability. Therefore, KiT-RT is a convenient base to test new numerical methods in various applications and compare them against conventional solvers. The implementation includes spherical harmonics, minimal entropy, neural minimal entropy, and discrete ordinates methods. Solution characteristics and efficiency are presented through several test cases ranging from radiation transport to electron radiation therapy. Due to the variety of included numerical methods and easy extendability, the presented open-source code is attractive for both developers, who want a basis to build their numerical solvers, and users or application engineers, who want to gain experimental insights without directly interfering with the codebase.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗