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DOE OSTI2026

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction

In many university and healthcare projects, models are built for very different data types such as tables, institutional time series, and medical images, but they are deployed as separate applications. In this work, that separation made testing and maintenance difficult because each module had its own pipeline and runtime requirements. This paper presents an integrated AI lakehouse-style implementation that runs three model pipelines inside one containerized backend. For medical imaging, we used MRI datasets from IEEE DataPort: a four-class classification set with 7012 images (5708 train/1304 test) and a segmentation set with 3063 image–mask pairs. The classification model (ResNet50 transfer learning) is evaluated using a proper train–validation–test protocol across multiple splits (80/10/10, 70/10/20, 60/10/30, and 10/30/60), achieving a test accuracy of 99.00% under the standard 80/10/10 split. Additionally, a patient-level evaluation is conducted using an external glioma dataset to provide a more realistic assessment without data leakage. The segmentation model (DeepLabV3-ResNet50) achieved 83.09% validation mIoU and 88.79% Dice score. For university KPI forecasting, we used annual IPEDS and NSF HERD data from 2010 to 2023 for three universities (BSU, EOU, and UAB). To examine the effect of preprocessing on forecasting performance, two case studies are conducted. In the first case, linear interpolation is applied to generate semester-level data. In the second case, the original annual data is used directly without interpolation. Random Forest regression and ARIMA models are evaluated using MAE, RMSE, MAPE, and R 2 . The results showed that interpolation improved apparent forecasting performance due to smoothing, while evaluation on the original annual data provided a more realistic assessment of model behavior. To further validate the framework on a larger dataset, an additional case study is conducted using a student dropout dataset. For water potability, we trained and compared multiple tabular classifiers on a large dataset (1,048,575 samples). A Random Forest model (100 trees, max depth 10) achieved 85.86% test accuracy and high recall for unsafe samples (0.8447). All modules are served via FastAPI and deployed together using Docker, with workflow automation routing requests to the correct endpoint. System-level benchmarking indicates that the backend maintains stable throughput and latency under concurrent requests.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Dynamic Power Convertor Development for Radioisotope Power Systems at NASA GRC

The Thermal Energy Conversion Branch at NASA Glenn Research Center (GRC) is supporting the development of high-efficiency power convertors in support of Radioisotope Power System (RPS) development. Significant progress was made towards such a system that utilized Stirling conversion machines during the 2001 to 2015 timeframe. Flight development of the Advanced Stirling Radioisotope Generator (ASRG) was cancelled in 2013 by the Department of Energy (DOE) and NASA Headquarters primarily due to budget constraints, and the Advanced Stirling Convertor (ASC) technology contract was subsequently concluded in 2015. A new chapter of technology development has recently been initiated by the NASA RPS Program. This new effort is considering all dynamic power convertor options, such as Stirling and Brayton cycles. Four convertor development contracts supporting this effort were awarded in 2017. The awarded contracts include two free-piston Stirling, one thermoacoustic Stirling, and one turbo-Brayton designs. The technology development contracts each consist of up to three phases: Design, Fabricate, and Test. As of May 2018, all contracts have completed the Design Phase, and each underwent a design review with an independent review board. Two of the contracts have been awarded the Phase 2 option for fabrication. Convertors manifesting from these development efforts will then undergo independent validation and verification at NASA facilities, which will consist of convertor performance and RPS viability demonstrations. Example tests include launch vibration simulation, performance mapping over the environmental temperature range, and static acceleration exposure. In parallel with this renewed development effort, NASA GRC is still demonstrating free-piston Stirling convertor technology using assets from previous projects. The Stirling Research Laboratory (SRL) is still operating 10 convertors from previous development projects. Four of which are flexure-bearing based, and the other six are gas-bearing based. One of the flexure-bearing convertors has accumulated over 110,000 hours of operation, and holds the current maintenance-free heat-engine run-time record. Another flexure-bearing convertor was recently manually shutdown after 105,620 hours of operation, then disassembled and inspected. This inspection produced a wealth of information about the effects of this amount of runtime on the technology's components. One of the engineering unit flexure-bearing convertors recently underwent launch simulation vibration test, a static acceleration exposure up to 20 g, and was then placed on extended operation. Amongst the gas-bearing convertors, the longest running unit has accumulated over 70,000 hours of operation. Four high-fidelity gas-bearing convertors from the ASRG project are still operating continuously, for which the longest runtime has reached 26,000 hours.

Oriti, Salvatore↗

NASA NTRS2018

Dynamic Power Convertor Development for Radioisotope Power Systems at NASA Glenn Research Center

The Thermal Energy Conversion Branch at NASA Glenn Research Center (GRC) is supporting the development of high-efficiency power convertors for use in Radioisotope Power Systems (RPS). Significant progress was made towards such a system that utilized Stirling conversion during the 2001 to 2015 timeframe. Flight development of the Advanced Stirling Radioisotope Generator (ASRG) was cancelled in 2013 by the Department of Energy (DOE) and NASA Headquarters primarily due to budget constraints, and the Advanced Stirling Convertor (ASC) technology contract was subsequently concluded in 2015. A new chapter of technology development has recently been initiated by the NASA RPS Program. This effort is considering all dynamic power convertor options, such as Stirling and Brayton cycles. Four convertor development contracts supporting this effort were awarded in 2017. The awarded contracts include two free-piston Stirling, one thermoacoustic Stirling, and one turbo-Brayton designs. The technology development contracts each consist of up to three phases: Design, Fabricate, and Test. As of May 2018, all contracts have completed the Design Phase, and each underwent a design review with an independent review board. Three of the contracts are planned to execute the Phase 2 option for fabrication. Convertors manifesting from these development efforts will then undergo independent validation and verification at NASA facilities, which will consist of convertor performance and RPS viability demonstrations. Example tests include launch vibration simulation, performance mapping over the environmental temperature range, and static acceleration exposure. In parallel with this renewed development effort, NASA GRC is still demonstrating free-piston Stirling convertor technology using assets from previous projects. The Stirling Research Laboratory (SRL) is still operating several convertors from previous development projects which have similarities and relevance to current contract designs. Four of which are flexure-bearing based, and another six are gas-bearing based. One of the flexure-bearing convertors has accumulated over 110,000 hours of operation, and holds the current record for maintenance-free heat-engine run-time. Another flexure-bearing convertor was recently manually shutdown after 105,620 hours of operation, then disassembled and inspected. This inspection produced a wealth of information about the effects of this amount of runtime on the technology's components. One of the engineering unit flexure-bearing convertors recently underwent launch simulation vibration test, a static acceleration exposure up to 20 g, and was then placed on extended operation. Amongst the gas-bearing convertors, the longest running unit has accumulated over 70,000 hours of operation. Four high-fidelity gas-bearing convertors from the ASRG project are still operating continuously, for which the longest runtime has reached 28,000 hours.

Oriti, Salvatore M.↗

DOE OSTI2020

Calibration and Uncertainty Estimation Using the Ensemble Kalman Filter with a Large Subsurface Flow and Transport Model - 20321

At routinely monitored groundwater contamination sites, periodically measured environmental conditions such as groundwater levels and contaminant concentrations are used to inform and confirm a conceptual site model (CSM) and guide the development and calibration of a numerical groundwater flow and transport model. The calibration of groundwater flow and transport models after each measurement (sampling) event can illuminate deficiencies in a CSM, identify areas where additional monitoring is warranted, and predict the behavior of the system to guide decision making. However, manual and automated (e.g. PEST) model calibration tools can be time-consuming and computationally expensive to implement after each sampling event. Perhaps as a result, such calibration tools generally utilize all available monitoring data simultaneously rather than sequentially assimilating monitoring data one sampling event at a time as the results from sampling become available. A more real-time data assimilation approach may reduce parameter uncertainty, quantify the value of additional monitoring data, and produce a usable model more quickly and with less effort. To mitigate the potential time-consuming aspects of manual and widely applied automated calibration techniques, a data assimilation algorithm called the ensemble Kalman filter (EnKF) was evaluated as a relatively efficient method of model calibration and uncertainty assessment via the sequential integration of monitoring data into a model. The EnKF was able to successfully and efficiently assimilate monitoring and modeling data to calibrate a complex flow and transport model at a real-world site with significant subsurface heterogeneity, uncertainty, and 12 years of monitoring data (over 4,000 individual measurements of groundwater levels and over 2,500 measurements of contaminant concentrations). Starting with an uncalibrated model data from annual sampling events were sequentially assimilated, and the resultant predication errors and estimated parameter uncertainties were tracked. After all monitoring data were assimilated, both flow and transport residuals at the end of the EnKF process were comparable to those produced via a concurrent PEST calibration effort but required fewer model simulations. Both uncertainty and prediction errors decreased over time. In a real-time application, the adequacy of the model could be assessed after each sampling event. The benefits of such a real-time approach to utilizing monitoring data include reduced costs (in the form of model updates or site characterization efforts), early flagging of possible errors in the CSM, and a reduced risk of overfitting and corresponding increased confidence in model predictions. This tool may be particularly useful compared to other calibration techniques (e.g. manual, PEST) when model runtimes are long, calibration parameters are many, or parameter uncertainty is large. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Godiva IV Simulated Radiation Field Characterization and Variance Reduction

Godiva IV is a system comprised of highly enriched uranium alloyed with molybdenum in the form of fuel plate rings. The reactor, along with its predecessors, was designed with the unique ability to satisfy interests in the super-prompt-critical reactor operation space. Originally, the reactor was part of the Los Alamos Critical Experiments Facility (LACEF) at Technical Area-18 (TA-18). The radiation field around Godiva at this facility was well characterized and understood. As a fast neutron system, the neutron spectrum in and around Godiva was close to a Watt Fission spectrum. The Kiva where Godiva IV was located at LACEF was made of thin, sheet metal walls which did not contribute significantly to the neutron spectrum. Following the transition of LACEF to the National Critical Experiments and Research Center (NCERC) in Nevada, Godiva-IV was moved from TA-18 to the Device Assembly Facility (DAF) at the Nevada National Security Site (NNSS). Part of this move brought renewed interest in radiation field characterization. The new facility introduced significant changes to the environment surrounding Godiva, and preliminary foil irradiation results suggested that the room contribution to the neutron spectrum was significant. Unlike at TA-18, a large thermal neutron signature was added to the fast spectrum from Godiva due to significant room return. A primary goal due to the additional complexity that the room return adds to the Godiva IV radiation emission spectrum was the development of an efficient Monte Carlo N-Particle (MCNP) calculation capable of characterizing the neutron spectrum anywhere in the room around Godiva. A campaign of activation foil irradiations and analysis were completed to support the validation of the MCNP model. The modeling of these foils in MCNP can be easily done with a standard volumetric neutron flux tally. However, given the multitude of locations and reaction rates to be modeled, further steps must be taken to increase the efficiency of these calculations in MCNP. During this study, a benchmark model currently under development for Godiva IV was used. A qualitative assessment of the thermal neutron contributors was performed using spatial neutron distribution plots. Additional detail was added to the model based on the qualitative results showing the thermal spectrum’s large sensitivity to hydrogenous material. Neutron energy spectra was evaluated at discrete locations in the room around Godiva to quantify the relative contribution of various components. It was discovered that the concrete walls are the largest contributor to the thermal signature, with minor contributions from plastic components surrounding Godiva. Following these results, two different variance reduction techniques were implemented to improve the problem efficiency in these calculations. In the first approach, an F5 point detector tally was implemented in the standard Godiva IV criticality problem. The second approach involved a weight-window generator implementation with an F5 point detector tally in a fixed source problem. The weight window implementation reduced the runtime from 42739.55 minutes to 1803.34 minutes (computer time), compared to the F5 KCODE implementation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗