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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Captan+X Data Converter Integration

Fermi National Accelerator Laboratory's CAPTAN (Compact And Programmable daTa Acquisition Node) series provides a flexible hardware platform for data acquisition across a range of experiments and facilities. The latest iteration, CAPTAN+X, is built around a Kintex-7 FPGA supporting four FPGA Mezzanine Card (FMC) connections. As part of a broader laboratory effort to bring facility systems under a Model-Based Systems Engineering (MBSE) framework, CAPTAN+X is one of several systems slated to be incorporated into this modeling environment in the near term. A necessary step toward that goal is incorporating the platform's core functionality, which centers on integration with the LXD31K4 FMC, a data converter module combining dual AD9652 analog-to-digital converters and dual AD9142A digital-to-analog converters. Achieving compatibility required resolving pin-mapping conflicts between the LXD31K4's High Pin Count connector and the CAPTAN+X's available pin types, adapting a Board Support Project originally written for an UltraScale-class evaluation board to the Kintex-7 architecture, replacing incompatible primitives, restructuring clock distribution, and manually configuring chip initialization in place of an unsupported soft-processor-based approach. Functional verification of the ADC and DAC channels, followed by closed-loop testing combining both converters with real-time filtering, confirmed correct operation of the integrated system. These results establish a working hardware and firmware baseline for the CAPTAN+X platform, positioning it for future inclusion in the laboratory's growing MBSE modeling effort.

Espinoza, David [Illinois U., Urbana (main)]↗
DOE OSTI2026

CAPTAN+X Data Converter Integration

Fermi National Accelerator Laboratory's CAPTAN (Compact And Programmable daTa Acquisition Node) series provides a flexible hardware platform for data acquisition across a range of experiments and facilities. The latest iteration, CAPTAN+X, is built around a Kintex-7 FPGA supporting four FPGA Mezzanine Card (FMC) connections. As part of a broader laboratory effort to bring facility systems under a Model-Based Systems Engineering (MBSE) framework, CAPTAN+X is one of several systems slated to be incorporated into this modeling environment in the near term. A necessary step toward that goal is incorporating the platform's core functionality, which centers on integration with the LXD31K4 FMC, a data converter module combining dual AD9652 analog-to-digital converters and dual AD9142A digital-to-analog converters. Achieving compatibility required resolving pin-mapping conflicts between the LXD31K4's High Pin Count connector and the CAPTAN+X's available pin types, adapting a Board Support Project originally written for an UltraScale-class evaluation board to the Kintex-7 architecture, replacing incompatible primitives, restructuring clock distribution, and manually configuring chip initialization in place of an unsupported soft-processor-based approach. Functional verification of the ADC and DAC channels, followed by closed-loop testing combining both converters with real-time filtering, confirmed correct operation of the integrated system. These results establish a working hardware and firmware baseline for the CAPTAN+X platform, positioning it for future inclusion in the laboratory's growing MBSE modeling effort.

Espinoza, David [Illinois U., Urbana (main)]↗
DOE OSTI2024

DIRECT RF SAMPLING BASED LLRF CONTROL SYSTEM FOR C-BAND LINEAR ACCELERATOR

Low Level RF (LLRF) control systems of linear accel- erators (LINACs) are typically implemented with hetero- dyne based architectures, which have complex analog RF mixers for up and down conversion. The Gen 3 Radio Fre- quency System-on-Chip (RFSoC) device from AMD Xilinx integrates data converters with maximum RF frequency of 6 GHz. This enables direct RF sampling of C-band LLRF signal typically operated at 5.712 GHz without any analogue mixers, which can significantly simplify the system architec- ture. The data converters sample RF signals in higher order Nyquist zones and then up or down convert digitally by the integrated data path in RFSoC. The closed-loop feedback control firmware implemented in FPGA integrated in RF- SoC can process the base-band signal from the ADC data path and calculate the updated phase and amplitude to be up- mixed by the DAC data path. We have developed a C-band LLRF control RFSoC platform with direct RF sampling, which targets Cool Copper Collider (𝐶3) and other C or S band LINAC research and development projects. In this paper, the architecture of the platform will be described. We have optimized the configuration of the data converter and characterized performance of them with RF pulses. The test results for some of the key performance parameters for the LLRF platform with our custom solid-state amplifier, such as phase and amplitude stability, will be discussed in this paper.

Liu, C↗
DOE OSTI2024

Grid Parameters and Voltage Estimation Approach Integrating Data-Driven Converter Model

With Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems. Grid parameters vary through time during the faults and especially in the converter interfaced resources (CIRs) rich power grid, and thus accurate estimation is critical to avoid the mis-operation of the P&C systems. In this paper, a moving horizon estimation (MHE) as an observer is devised and applied to estimate the grid line parameters and grid voltages for protection enhancement. Due to the proprietary and confidentiality of CIRs, the proposed approach uses the black-box model to represent their dynamics. Leveraging the easily accessible measurements of output current from the black-box model of CIR and voltage at the point of common coupling, the proposed method estimates the grid impedance and grid voltage during normal and faulty operating conditions. The performance shows that the optimization-based observer was able to closely observe the accurate states and parameters, which can be utilized by the P&C systems.

Subedi, Sunil↗
DOE OSTI2024

Data-Driven Protection Software to classify fault locations by protective zone in distribution systems with high PV penetration

The software contains (a) the source codes to generate Point-on-Wave (PoW) transient data for any feeder model in Alternative Transient Program (ATP) format. Codes provide options to change different steady state settings, including the loading condition and PV capacity and transient state setting like faults type, location and initiation time (b) data post-processing source code to converted data from native format to COMTRADE, csv, HDF5 (c) Docker container to train CNN to classify fault locations by protective zone. The container takes dataset and other training parameters (sampling rate, training epochs, batch size etc) as input to train CNN. The container writes back the trained CNN model, training and testing metrics and plots to the local workstation

Ramesh, Meghana↗
DOE OSTI2023

Visualization Quality Assessment

Understanding how inaccuracies in visualizations affect users’ perception and understanding of scientific data is hard. Inaccuracies in visualizations are quite common and could arise from a range of sources such as errors in the original dataset arising from compression artifacts, errors in the capturing device, noise during transmission of the data, effects due to the algorithm being used to convert data to visualization images, images generated from neural networks, and sources we have yet to discover. Many image quality assessment metrics have been developed to quantify image errors. However, these are usually focused on “natural images” rather than visualizations of scientific data. Common image quality assessment metrics (IQAs) include MSE, PSNR, perceptual metrics such SSIM, FSIM as well as perceptual metrics using deep learning approaches. However, a critical part of understanding how errors are perceived by humans, and subsequently developing more accurate quality assessment metrics, is through user evaluation studies. The goal of this software is to develop a visualization quality assessment (VQA) process that will enable the generation of VQAs that can be used to quantify errors in scientific data visualizations. The VQA development process will include software to support user evaluation experimental design, analysis of visualization differences against standard quality metrics, and the ability to develop additional VQA metrics specific to scientific visualization images.

Grosset, Andre↗
DOE OSTI2023

Perspectives for self-driving labs in synthetic biology

Self-driving labs (SDLs) combine fully automated experiments and data collection with artificial intelligence (AI) and control algorithms that decide not only the set of parameters for the next experiment, but also potentially which scientific hypotheses to test. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. Whereas there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single, easily accessible, target for affecting the incredibly wide repertoire of biological cell behavior. Since they can provide large amounts of high-quality data, SDLs can be a platform for AI to develop approaches to systematically convert data into scientific knowledge systems. These knowledge systems can be used both to understand the biological world and to design bioengineered systems to fit a desired specification (inverse design). However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Performance and Commissioning of the BigBite Timing Hodoscope for Nucleon Form Factor Measurements at Jefferson Lab

The BigBite Timing Hodoscope detector is the primary subject of this thesis. The Super BigBite Spectrometer is a Jefferson Lab Hall A Collaboration project that has and will continue to measure nucleon electromagnetic form factors. This spectrometer includes the Timing Hodoscope which provides high resolution particle timing data for scattered electrons in the electron arm of BigBite. The Timing Hodoscope utilizes 90, 25 × 25 × 600 mm3 scintillator bars stacked on top of each other to form a single detector plane, and these bars are connected to 180 photo-multiplier tubes via light guides. Particles collide with the scintillating material creating a shower of optical photons and these particle events in the bars are collected to generate signals that are readout by the data acquisition (DAQ) electronics. NINO ASIC amplifier-discriminator cards output signals from the photo-multiplier tubes into analogue and logic signals, which are sent to analogue-to-digital (ADC) and time-to-digital (TDC) converter data acquisition readout modules. This data is then used for analysis of the detector. The focus of this thesis is the construction, commissioning, calibration, and performance of the BigBite Timing Hodoscope before and during the first of five nucleon electromagnetic form factor experiments at Jefferson Lab Hall A. Before the neutron magnetic form factor, G n M, experiment, cosmic ray data was collected during commissioning to confirm proper operation of the Timing Hodoscope electronics by observing the ADC and TDC data. Commissioning studies for charge normalization, gain matching, and other ADC and TDC detector data variables were performed before moving the detector into Hall A. Following installation in Hall A, several calibration studies were implemented to fine-tune the detector in preparation for use in the experiment. The calibration studies included analysis of timing cuts, TDC alignment, the time-walk effect, time difference offsets, and scintillator velocity corrections. Once the Timing Hodoscope was well-calibrated, data-taking during the experiment commenced and the beam-on-target data was used to characterize the Timing Hodoscope performance during the G n M experiment run-time. The performance analysis included studies observing energy deposit, cluster size, rates, accidentals, pile-up, tracking efficiency, position resolution, and time resolution. After application of physics cuts to ensure a data set comprised of particle tracks corresponding to elastic electrons, which is the main data of interest for measurement of G n M, the Timing Hodoscope is shown on average across all kinematic settings to have a >98% tracking efficiency, a position resolution of 4-6 cm in the non-dispersive plane and 1.5-2 cm in the dispersive plane, and a time resolution of 500-750 ps. These performance results are compared to a GEANT4 based performance simulation of the BigBite Timing Hodoscope for reference, showing to what degree the measured performance values match those taken from the simulation.

Marinaro, Ralph↗
DOE OSTI2023

Emulation automation and model checking

A method of automating emulations is provided. The method comprising collecting publicly available network data over a predefined time interval, wherein the collected network data might comprise structured and unstructured data. Any unstructured data is converted into structured data. The original and converted structured data is stored in a database and compared to known network vulnerabilities. An emulated network is created according to the collected network data and the comparison of the structured data with known vulnerabilities. Virtual machines are created to run on the emulated network. Director programs and guest actor programs are run on the virtual machines, wherein the actor programs imitate real user behavior on the emulated network. The director programs deliver task commands to the guest actor programs to imitate real user behavior. The imitated behavior is presented to a user via an interface.

Urias, Vincent↗
DOE OSTI2023

Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach

The reactive transport code CrunchClay was used to derive effective diffusion coefficients (D e ), clay porosities (ε), and adsorption distribution coefficients (K D ) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (D e –ε–K D ) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a D e –ε–K D modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers.

58 GEOSCIENCES↗
DOE OSTI2025

High-power test of a C-band linear accelerating structure with an RFSoC-based LLRF system

Normal conducting linear particle accelerators consist of multiple rf stations with accelerating structure cavities. Low-level rf (LLRF) systems are employed to set the phase and amplitude of the field in the accelerating structure and to compensate for the pulse-to-pulse fluctuation of the rf field in the accelerating structures with a feedback loop. The LLRF systems are typically implemented with analog rf mixers, heterodyne-based architectures, and discrete data converters. There are multiple rf signals from each of the rf stations, so the number of rf channels required increases rapidly with multiple rf stations. With a large number of rf channels, the footprint, component cost, and system complexity of the LLRF hardware will increase significantly. To meet the design goals of being compact and affordable for future accelerators, we have designed the next-generation LLRF (NG-LLRF) with a higher integration level based on RFSoC technology. The NG-LLRF system samples rf signals directly and performs rf mixing digitally. Further, the NG-LLRF has been characterized in loopback mode to evaluate the performance of the system and has also been tested with a standing-wave accelerating structure, a prototype for the Cool Copper Collider (C 3 ) with a peak rf power level up to 16.45 MW. The loopback test demonstrated amplitude fluctuation below 0.15% and phase fluctuation below 0.15°, which are considerably better than the requirements of C 3 . The rf signals from the different stages of the accelerating structure at different power levels are measured by the NG-LLRF, which will be critical references for the control algorithm designs. The NG-LLRF also offers flexibility in waveform modulation, so we have used rf pulses with various modulation schemes, which could be useful for controlling some of the rf stations in accelerators. In this paper, the high-power test results at different stages of the test setup will be summarized, analyzed, and discussed.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces

These data are from Bandopadhyay et al., "Soil microbial ecology and microbiome-metabolite linkages improve understanding of ecosystem states along terrestrial-aquatic interfaces". This study aims to understand the soil microbial ecology along terrestrial-aquatic interfaces of a freshwater and estuarine region and how it relates to organic matter. We analyzed soil microbial (16S rRNA gene) and organic matter (Fourier-transform ion cyclotron resonance mass spectrometry, FTICR-MS) composition from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie (freshwater) and Chesapeake Bay (estuarine) regions. This dataset includes 16S rRNA gene amplicon data (only processed file types included here) and organic matter composition from FTICR-MS data (raw and processed files included here) from upland (forested), transition (stressed forest), and wetland positions at three sites in each of the Lake Erie and Chesapeake Bay regions. These sites are part of the COMPASS-FME project (https://compass.pnnl.gov/FME/COMPASSFME). File formats and software needed to access files: 16S rRNA gene amplicon data: These files follow the format reported here https://ess-dive.gitbook.io/amplicon-sequencing-reporting-format#updates-in-v1.0.1. As per this format, there are four file types reported: 1. Taxon tables (also called sequence-by-sample or OTU (operational taxonomic unit)/ESV (exact sequence variant) tables) : available in a .txt file format and accessible using TextEdit or MS Excel. 2. Representative sequences (also called consensus sequences) : available in a .fasta format and accessible using TextEdit. 3. Sequencing metadata : available in a MS Excel workbook file format and CSV file format 4. Bioinformatic metadata : available in a MS Excel workbook file format and CSV file format FTICR-MS data: 1. Raw data converted to a processed file with intensities of the peaks in the given samples : available in a MS Excel CSV file format 2. Processed file used in analyses and visualizations (appended as icr_long_) : available in a MS Excel CSV file format 3. Metadata file for ICR features (appended as icr_meta) : available in a MS Excel CSV file format

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The Perils of AI-Assisted Time Conversion Critical Errors in J2000 Time to UNIX/UTC Conversions Using AI Assistants (Technical Memorandum)

This memo addresses a critical issue encountered when using AI assistants for time system conversions, specifically converting data tagged with J2000 seconds through UNIX time to UTC. Initial AI responses to this common conversion task were fundamentally incorrect, with errors exceeding 37 seconds - enough to cause significant issues in many applications. This document demonstrates the problem, quantifies the error, and provides best practices for using AI tools safely in technical work. Both Claude 4.5 Sonnet and ChatGPT GPT-5.5 Thinking gave the same answer.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Sensor and Instrumentation Systems within Advanced Condition Monitoring Technologies

Advanced condition monitoring (ACM) technologies, such as digital twins, are innovative strategies designed to provide real-time health insights, including the remaining useful life of components. The primary goal of ACM is to predict and alert operators to potential functional failures before they occur. ACM systems achieve this by integrating predictive models with various sensor instrumentation, analog-to-digital converters, data warehouses, and data pre-processors. These sensor and instrumentation systems (SIS) are essential for forming a comprehensive understanding of component conditions and ensuring the predictive success of ACM programs. Introducing new technologies like ACM involves varying degrees of risk that can impact plant reliability. Therefore, risk mitigation should be commensurate with the performance and reliability of the developed technology, following a risk-informed graded approach (RIGA). Establishing a RIGA process requires a clear understanding of the hazards and reliability of all subsystems, including their interdependencies and potential impacts on the overall system. Given the critical role of SIS in ACM, this work reviews hazard identification and reliability quantification methods for SIS. It also considers these methods' implications when developing a RIGA process for ACM.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Analysis on Evaluations of Monterey Bay Aquarium Research Institute’s Wave Energy Converter’s Field Data Using WEC-Sim and Gazebo: A Simulation Tool Comparison

Although many studies have validated wave energy converter (WEC) numerical models against scaled prototype experimental data, there remains a notable lack of validation using data from full-scale deployed WECs. This paper compares two numerical models of Monterey Bay Aquarium Research Institute’s Wave Energy Converter (MBARI-WEC), a two-body point absorber with an electro-hydraulic power take-off system (PTO). The models are implemented in WEC-Sim/Simscape and Gazebo Simulator. A statistical analysis of the models was performed, and field results were obtained to compare the models’ accuracy in predicting the RMS piston velocity, RMS motor speed, and mean electric power compared to field data for 56 observations across varying sea states. The Gazebo model demonstrated a closer agreement across all three parameters for a majority of the observations. When compared to the field data, the Gazebo and WEC-Sim models exhibited average mean electric power overestimations of 13% and 22%, respectively.

16 TIDAL AND WAVE POWER↗