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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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An example apparatus can comprise an emitter to emit radio frequency radiation, an absorber that changes temperature based on emissions from the emitter, and one or more sensors to measure a temperature difference between a sample and a reference coupled to the absorber.
Argonne National Laboratory outside Chicago is creating a research program that will empower investigators to apply the sensing qualities of X-rays to the challenge of seeing what goes on inside scramjets, rotating detonation engines and other hypersonic propulsion concepts. The program’s architects describe the technology’s heritage and its time- and cost-saving benefits.
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The Log-N/Period system utilizes a compensated ion chamber to measure neutron radiation in the reactor core. This analysis focuses on determining a more accurate value for the sensitivity of the installed compensated ion chambers for the Log-N/Period system at the ATR. Using calibration procedures a formula for indirectly calculating the sensitivity of the chambers was derived. Using historical data from approximately ten reactor cycles a new average sensitivity for both channels of the Log-N/Period system was determined.
Application of data-driven solutions across an industry is challenging, since the data are often stored locally, and increasing privacy and security concerns restrict access to the data. In addition, it is highly unlikely that all potential data patterns are captured in a single data source. Because it is highly unlikely that all potential data patterns are captured in a single data source, machine learning (ML) models developed from a single source cannot be robust enough. An alternative is to train the ML model at each source and develop a distributed knowledge discovery and aggregation approach to build global knowledge. In this paper, we develop and demonstrate a distributed ML model, federated transfer learning (FTL), using a multi-kernel-based adaptive support vector machine (MK-A-SVM). For federated learning (FL), the multi-kernel (MK) approach enables feature-specific model aggregation under data heterogeneity; whereas for transfer learning (TL) the adaptive model enables utilization of an aggregated model from a different task. The proposed approach is validated using nuclear power plant (NPP) vertical motor-driven pump data to predict the health condition of vertical motor-driven pumps as an anomaly detection. The efficiency of the proposed approach is also quantified and compared with neural network.
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This report describes the activities related to the construction of an in-cell thermal creep test facility for small-scale specimens in the irradiation-assisted stress-corrosion cracking (IASCC) hotcell located at the Fuels and Applied Science Building (FASB) at INL. The whole project includes three stages: (i) conceptual design; (ii) out-of-cell demonstration tests; and (iii) in-cell demonstration testing. During the current fiscal year (FY)-2022, materials and equipment required to construct the conceptual design outlined in a previous report were procured. Frames for conducting out-of-cell testing have been constructed, and testing plans have been developed to assess the operation of the newly constructed frames. Preliminary testing of flat subsized tensile specimens on ASTM creep frames has also been completed for two sample thicknesses: 0.75 mm and 1.0 mm.
Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.
Nuclear energy is a low-carbon technology that generates bulk baseload electricity and supports long-lasting eco-friendly sustainability goals. However, it still faces challenges, such as the securing and managing of long-lived nuclear actinides and fission products—most notably the used fuel, which is considered high-level waste (HLW). Direct geological waste disposal would be the most economical option for HLW but requires highly qualified sites whose geophysical behavior will remain stable for 10,000 years. Partitioning and transmutation (P&T) of used fuel reduces radioactivity and decay heat generation, enabling efficient geological waste disposal. P&T research and development, along with the prospects of P&T techniques such as wet/dry separation and accelerator-driven systems, were reviewed to identify critical needs that must be met to foster their successful implementation.
Material Test Reactors (MTRs) such as the Advanced Test Reactor (ATR) at the Idaho National Laboratory (INL) are used to irradiate nuclear fuels and materials to evaluate their performance after high levels of exposure to a reactor in-pile environment. The test environment within these reactors are extremely harsh and long-term exposure causes sensor decalibration or failure. Most tests only require data points at daily intervals meaning that the sensor does not necessarily need to be in place continuously for the full experiment. One proposed solution to the issue described above is to have a sensor which can be periodically inserted through a guide tube into the test region and retracted; this would act as a reference check on the sensors installed permanently. If a compact and robust enough design can be made, it has the potential to extend the life of the sensor and keep it within calibration. This LRS entry is for a poster summarizing a presentation that will be given at the ANIMMA 2023 conference. The poster will be on display in the Measurement Sciences Laboratory booth at ANIMMA. Most of the material on this poster came from LRS entry INL/CON-23-72797.
This study focuses on requirements and challenges pertaining to advanced transportable nuclear energy systems, which are currently in development and feature varying degrees of mobility. These reactors mostly use high-assay low-enriched uranium (HALEU) fuel, and only limited refueling is required during their lifecycles. Due to the smaller source terms, higher burnup fuel, and potentially higher thermal efficiency involved, the waste inventory will be less than that generated by conventional light-water reactors. However, the relevant regulatory requirements must be met when transporting HALEU fuel (whether fresh or used) inside the reactor vessel—mainly the fuel material transportation, siting, and physical protection requirements outlined in US Nuclear Regulatory Commission (NRC) regulations 10 CFR 71 and 73. Spent nuclear fuel (SNF), as well as irradiated fuel (used during any interval of reactor operation), contains highly radioactive materials that generate heat and necessitate special precautions during transportation. This study addresses the challenges that this incurs, and outlines potential solutions.
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