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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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The AMPX nuclear data processing code reads evaluated nuclear data files and prepares libraries in continuous energy (CE) and multigroup (MG) formats. These libraries are used by the transport and depletion codes in the SCALE code system.
This paper introduces an advanced input data processing layer for EMT simulations of large-scale bulk power systems. The paper proposes two versions of the RE-INTEGRATE EMT simulation tool, RE-INTEGRATE Gen-0 and RE-INTEGRATE Gen-1, which are developed to enhance simulation generalizability, scalability, and accuracy. The framework leverages a generic class design for components to incorporate linear equations, which are generated by discretizing the Differential-Algebraic Equations (DAEs) that represent the dynamics of the components. In addition, the framework employs a parsing algorithm that parses a power system’s raw and dyr files to generate a connectivity graph which is then traversed to form the overall system’s dynamics. The proposed input data processing layer is used to simulate the IEEE 39-bus test system. The obtained results demonstrate the framework’s capability to achieve simulation scalability and accuracy. Further, the results indicate that EMT simulations performed using the proposed automations can effectively handle complex grid configurations.
This dataset contains processed summary data from the ASSIST operated by NOAA Physical Science Laboratory on Nantucket Island for WFIP3.
This dataset contains processed, standardized data from the UND scanning Doppler lidar at WFIP3's BARG site, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains processed, standardized data from the ANL scanning Doppler lidar, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains processed, standardized data from the UND scanning Doppler lidar at WFIP3's BARG site, consisting of range- and time-resolved measurements of radial velocity, attenuated backscatter, intensity, and spectral width. We note that the beam azimuth angles are NOT referenced to true north.
This dataset contains processed raw data from the UND 94 GHz W-band Cloud Doppler Radar.
This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.
The NLR Data Processing Pipeline for MADIS software package is for downloading, processing, and performing QA/QC on MADIS data. Designed to handle the following steps: 1) Download all MADIS data as compressed netcdf files for a given time period. 2) Unpack netcdf files into timeseries csvs for each coordinate within the given bounding box. 3) Process the csvs to filter according to quality control checks and convert variables to correct units. 4) Write processed csvs to a single nc file.
This dataset contains processed and standardized data from DOE Buoy 140 deployed during WFIP3.
User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.
Phlex is a computing framework supporting the parallel, hierarchical, and layered execution of data-processing algorithms. It is based on the functional-programming paradigm, thus guaranteeing thread-safety when invoking user-defined pure functions. Phlex allows users to specify arbitrary graph-based hierarchies of data organization, enabling more flexible processing of data as required by the constraints of the program.
This dataset contains processed NREL thermodynamic profiler (Assist II-12) data from the WFIP3 BARG site.
This dataset contains processed NREL thermodynamic profiler (Assist II-12) data from the WFIP3 NOAA ship. The ship's movement is available in a separate channel.
This dataset contains processed NREL thermodynamic profiler (Assist II-12) data from the WFIP3 CACO site.
The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.
The purpose of the 5th International Atomic Energy Agency technical meeting on fusion data processing, validation and analysis (FDPVA) (Ghent University, Ghent, Belgium, 12–15 June 2023) was to provide a platform during which a set of topics relevant to FDPVA were discussed with the view of meeting the needs of next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge-based understanding of the physical processes governing the dynamics of these plasmas. This paper presents the recent progress and achievements in the domain of plasma diagnostics data analysis and synthetic diagnostics reported at the meeting, including concept description of new devices; fusion databases; integrated data analysis; inverse problems; uncertainty propagation, verification and validation; probabilistic methods and machine learning. The relevant results underline trends observed in the current major fusion confinement devices.
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.