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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

SPIDARman: System-Level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors

In nuclear power plants (NPPs), anomalies arising from sensors or human errors (HEs) can undermine the performance and reliability of plant operations. Anomaly detection models can be employed to detect sensor errors and HEs. Additionally, physics-informed machine learning models can utilize the known physics of the system, as described by mathematical equations, to ensure that sensor values are consistent with physical laws. Hence, we propose SPIDARman: System-level Physics-Informed Detection of Anomalies in Reactor Collected Data Considering Human Errors, a holistic physics-informed anomaly detection approach based on generative adversarial networks (GANs) to detect anomalies in both automatically collected sensor data and manually collected surveillance data. Here we test our approach on data collected from a flow loop testbed, showcasing its potential to detect anomalies. Results demonstrate that the proposed model performs better than the baseline GAN-based models in detecting sensor and surveillance anomalies, suggesting the potential of physics-informed anomaly detection GAN models in NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Physics-constrained fault diagnosis framework for monitoring a standalone component of a thermal hydraulic system

A method for diagnosing faults includes receiving a description of a component of a thermal hydraulic system, where the description also indicates one or more sensors of the component. The method also includes constructing, based on a physical conservation law and using the description, a physics-based model describing operation of the component, the physics-based model including one or more unknown parameters. The method further includes calibrating the physics-based model by calculating the one or more unknown parameters using historical measurements to produce a calibrated model. Further, the method includes receiving sensor measurements captured by the one or more sensors, and calculating residuals corresponding to differences between measurements predicted by the calibrated model and the sensor measurements. The method also includes determining, based on the calculated residuals, a fault of the component or of a sensor of the one or more sensors, and generating an alert indicating the fault.

Nguyen, Tat Nghia↗
NASA NTRS1987

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 2: Analysis of the ERBE integrating sphere ground calibration

An explicit solution of the spectral radiance leaving an arbitrary point on the wall of a spherical cavity with diffuse reflectivity is obtained. The solution is applicable to spheres with an arbitrary number of openings of any size and shape, an arbitrary number of light sources with possible non-diffuse characteristics, a non-uniform sphere wall temperature distribution, non-uniform and non-diffuse sphere wall emissivity and non-uniform but diffuse sphere wall spectral reflectivity. A general measurement equation describing the output of a sensor with a given field of view, angular and spectral response measuring the sphere output is obtained. The results are applied to the Earth Radiation Budget Experiment (ERBE) integrating sphere. The sphere wall radiance uniformity, loading effects and non-uniform wall temperature effects are investigated. It is shown that using appropriate interpretation and processing, a high-accuracy short-wave calibration of the ERBE sensors can be achieved.

Halyo, Nesim↗
NASA NTRS2006

Performance of Dispersed Fringe Sensor in the Presence of Segmented Mirror Aberrations - Modeling and Simulations

Wavefront aberrations will lower the DFS fringe visibility and DFS signal SNR. The specific effect of wavefront aberration on DFS depends on the aberration type. Due to the "pixel spatial filter" effect the extracted DFS signal can tolerate moderate amount of wavefront aberrations..By averaging piston detection results from multiple traces extracted from fringe images DFS can further increase its robustness against wavefront aberration. Line-of-sight jitter can cause significant loss of fringe visibility will affect more on the aberrated system..Modeling results have shown that gravity sag on the JWST segment mirror during I&T will lower the DHS fringe visibility by factor of 2-3X and lower the DHS signal intensity by factor of approx.5X. Under gravity sag the RMS DHS detection error approx. 100 nm.

James Webb Space Telescope (JWST)↗
DOE OSTI2023

Physics-constrained fault diagnosis framework for monitoring a multi-component thermal hydraulic system

A method for diagnosing faults includes receiving a system description of a thermal hydraulic system, the system description indicating a plurality of components and sensors. The method also includes constructing, based on physical conservations laws and using the system description, a plurality of physics-based models for the plurality of components, each of the plurality of physics-based models including unknown parameters. The method further includes receiving historical measurements and calibrating the physics-based models by calculating the unknown parameters of each of the physics-based models using the historical measurements to produce calibrated models. The method also includes receiving sensor measurements of the sensors, and calculating residuals corresponding to differences between measurements predicted by the plurality of calibrated models and the sensor measurements. The method also includes determining, based on the calculated residuals, a fault of a component or a sensor, and generating an alert indicating the fault.

Nguyen, Tat Nghia↗
DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Roadmap for a Lightning Modeling Grand Challenge

This document is a roadmap for building an interconnected model of the physical processes that produce a lightning discharge, and its observable optical and radio signals. We call this a Lightning Modeling Grand Challenge, recognizing that significant effort and coordination of human and financial resources is required to realize the capability. The roadmap serves to outline the coordination of resources necessary to enable stitching together existing knowledge and model components to make a lightning prediction, and to test these predictions with observations. Such a capability does not currently exist. The roadmap is motivated not only by a spirit of scientific inquiry, but by practical challenges faced by US Federal and societal stakeholders. Advancements in lightning observations have outpaced our tests of integrated understanding, leaving many stakeholders unsure how to design their missions to properly detect and discriminate lightning, and unsure how to apply the sometimes-disagreeing lightning signals from diverse instruments. The time is right to connect existing theories and models to support stakeholders in understanding the signals they observe, for needs as diverse as climate monitoring, national security, weather forecasting, public safety, and protection of natural and built environments. The roadmap’s two main technical sections describe the components of a linked physical model, followed by a description of models of lightning signals and sensors that are driven by outputs from the physical model. The goal is to predict the time-varying physical properties of lightning that are self-consistent with the thunderstorm’s structure and dynamics. These lightning signals then propagate through the storm, with realistic dispersion and attenuation, to receivers on the ground or in space. At a high level, the model begins with weather (cloud) model output, including explicit prediction of the electrification of cloud particles. The cloud’s electrical structure drives a model of lightning physics, from initiation, through channel development, and discharges along those channels. Key lightning parameters, such as the temperature and currents in the channel, and their space and time distribution, are then used to produce optical and electromagnetic signal sources that propagate to modeled receivers. This architecture therefore generates a dataset suitable for comparison to existing and envisioned observing systems. The need for additional measurements and field campaigns to support model development is described. In each model sub-component, inputs, outputs, uncertainties, evaluation methods, and next steps are summarized, interleaved with references to the scientific literature. Identifying boundaries between the model sub-components aids in segmenting an integrated, complex model into practical work packages and system sub-components, allowing a diverse team to contribute and maintain the system. We estimate that at least five years of effort and a $\$$10M initial investment is necessary to make a significant step forward. Mechanisms to facilitate community coordination, including annual workshops and open-source code repositories, are described.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2008

NASA C-17 Usage Overview

The usage and integrated vehicle health management of the NASA C-17. Propulsion health management flight objectives for the aircraft include mapping of the High Pressure Compressor in order to calibrate a Pratt and Whitney engine model and the fusion of data collected from existing sensors and signals to develop models, analysis methods and information fusion algorithms. An additional health manage flight objective is to demonstrate that the Commercial Modular Aero-Propulsion Systems Simulation engine model can successfully execute in real time onboard the C-17 T-1 aircraft using engine and aircraft flight data as inputs. Future work will address aircraft durability and aging, airframe health management, and propulsion health management research in the areas of gas path and engine vibration.

Miller, Christopher R.↗
NASA NTRSDate not supplied

Overview of the Integrated Adaptive Wing Technology Maturation Wind-Tunnel Test Objectives

The Integrated Adaptive Wing Technology Maturation wind-tunnel test is the culmination of a cooperative effort between NASA and Boeing to develop control techniques that can improve the performance and safety of higher aspect ratio flexible transport aircraft. The wind-tunnel test article, to be tested at the NASA Langley Research Center Transonic Dynamics Tunnel, is a semispan model of a modern transport configuration based on the NASA Common Research Model with an aspect ratio 13.5 wing. Multiobjective control laws have been developed targeting drag optimization, maneuver load alleviation, gust load alleviation, and active flutter suppression. In this paper, the integrated model design, active control system, and sensor suite will be presented, and pretest modeling and characterization activities will be described. A summary of the planned wind-tunnel test entries will provide an overview of the experimental approach and expected outcomes.

wind-tunnel↗
NASA NTRSDate not supplied

Overview of the Integrated Adaptive Wing Technology Maturation Wind-Tunnel Test Objectives

The Integrated Adaptive Wing Technology Maturation wind-tunnel test is the culmination of a cooperative effort between NASA and Boeing to develop control techniques that can improve the performance and safety of higher aspect ratio flexible transport aircraft. The wind-tunnel test article, to be tested at the NASA Langley Research Center Transonic Dynamics Tunnel, is a semispan model of a modern transport configuration with an aspect ratio 13.5 wing based on the NASA Common Research Model. Multiobjective control laws have been developed targeting drag optimization, maneuver load alleviation, gust load alleviation, and modal suppression. In this paper, the integrated model design, active control system, and sensor suite will be presented, and pretest modeling and characterization activities will be described. A summary of the planned wind-tunnel test entries will provide an overview of the experimental approach and expected outcomes.

wind-tunnel↗
DOE OSTI2025

Federated Learning for Efficient Condition Monitoring and Anomaly Detection in Industrial Cyber-Physical Systems

Detecting and localizing anomalies in cyber-physical systems (CPS) has become increasingly challenging as systems grow in complexity, particularly due to varying sensor reliability and node failures in distributed environments. While federated learning (FL) offers a foundation for distributed model training, existing approaches lack mechanisms to handle these CPS-specific challenges. This paper presents an enhanced FL framework that introduces three key innovations: adaptive model aggregation based on sensor reliability, dynamic node selection for resource optimization, and Weibull-based checkpointing for fault tolerance. Our framework enables reliable condition monitoring while addressing the computational and reliability challenges of industrial CPS deployments. Experiments on NASA Bearing and Hydraulic System Datasets demonstrate superior performance over state-of-the-art FL methods, achieving 99.5% AUC-ROC in anomaly detection and maintaining accuracy under node failures. Statistical validation using Mann-Whitney (U) test confirms significant improvements (p < 0.05) in both detection accuracy and computational efficiency across diverse operational scenarios.1

Marfo, William [University of Texas at El Paso,Dep↗
NASA NTRS1987

Diagnosis and sensor validation through knowledge of structure and function

The liquid oxygen expert system 'LES' is proposed as the first capable of diagnostic reasoning from sensor data, using model-based knowledge of structure and function to find the expected state of all system objects, including sensors. The approach is generally algorithmic rather than heuristic, and represents uncertainties as sets of possibilities. Functional relationships are inverted to determine hypothetical values for potentially faulty objects, and may include conditional functions not normally considered to have inverses.

Scarl, Ethan A.↗
NASA NTRS1992

The radiobrightness thermal inertia measure of soil moisture

Radiobrightness thermal inertia (RTI) is proposed as a method for using day-night differences in satellite-sensed radiobrightness to monitor the moisture of Great Plains soils. Diurnal thermal and radiobrightness models are used to examine the sensitivity of the RTI method. Model predictions favor use of the 37.0 and 85.5 GHz, H-polarized channels of the Special Sensor Microwave/Imager (SSM/I). The model further predicts that overflight times near 2:00 AM/PM would be nearly optimal for RTI, that midnight/noon and 4:00 AM/PM are nearly as good, but that the 6:00 AM/PM overflight times of the current SSM/I are particularly poor. Data from the 37.0 GHz channel of the Scanning Multichannel Microwave Radiometer (SMMR) are used to demonstrate that the method is plausible.

England, Anthony W.↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1994

Using generic tool kits to build intelligent systems

The Intelligent Systems and Robots Center at Sandia National Laboratories is developing technologies for the automation of processes associated with environmental remediation and information-driven manufacturing. These technologies, which focus on automated planning and programming and sensor-based and model-based control, are used to build intelligent systems which are able to generate plans of action, program the necessary devices, and use sensors to react to changes in the environment. By automating tasks through the use of programmable devices tied to computer models which are augmented by sensing, requirements for faster, safer, and cheaper systems are being satisfied. However, because of the need for rapid cost-effect prototyping and multi-laboratory teaming, it is also necessary to define a consistent approach to the construction of controllers for such systems. As a result, the Generic Intelligent System Controller (GISC) concept has been developed. This concept promotes the philosophy of producing generic tool kits which can be used and reused to build intelligent control systems.

Miller, David J.↗
NASA NTRS1991

The process of control design for the NASA Langley Minimast structure

The design process used to select sensors for feedback and performance weights on the Minimast facility is discussed. Initially, a series of controllers are synthesized using H2 optimal control techniques for the given structural model, a variety of sensor locations and performance criteria to determine the best displacement sensor and/or accelerometers to be used for feedback. Upon selection of the sensors, controllers are formulated to determine the affect of using a reduced-order model of the Minimast structure instead of the higher-order structural analysis model for control design and the relationship between the actuator torque level and the closed-loop performance. Based on this information, controllers are designed using micro-synthesis techniques and implemented on the Minimast structure. Results of the implementation of these controllers on the Minimast experimental facility are presented.

Balas, Gary J.↗
NASA NTRS2016

Assessing the Transferability of Statistical Predictive Models for Leaf Area Index Between Two Airborne Discrete Return LiDAR Sensor Designs Within Multiple Intensely Managed Loblolly Pine Forest Locations in the South-Eastern USA

Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 (LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.

Sumnall, Matthew↗