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DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗

NASA NTRS2018

Real-Time Parameter Estimation for Flexible Aircraft

A method for estimating aeroelastic stability and control derivatives for flexible aircraft is developed and demonstrated using flight test data for the X-56A subscale demonstrator. The method uses the equation-error approach with frequency-domain data, and can be applied post-flight or in real time during flight. The non-dimensional aeroelastic forces and moments and the explanatory variables (including generalized displacement, rate, and acceleration states for the vibration modes) are estimated using a finite element model and onboard sensor measurements in both a least squares and Kalman filtering framework. The data are then transformed into the frequency domain for parameter estimation using equation error. This method can result in a more efficient analysis than with other iterative methods, and can leverage existing statistical tools for model structure determination, data collinearity detection, combining multiple maneuvers or prior information, and others to improve model quality.

Grauer, Jared A.↗

DOE OSTI2021

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

Engineering↗

NASA NTRS1991

VERDEX: A virtual environment demonstrator for remote driving applications

One of the key areas of the National Advanced Robotics Centre's enabling technologies research program is that of the human system interface, phase 1 of which started in July 1989 and is currently addressing the potential of virtual environments to permit intuitive and natural interactions between a human operator and a remote robotic vehicle. The aim of the first 12 months of this program (to September, 1990) is to develop a virtual human-interface demonstrator for use later as a test bed for human factors experimentation. This presentation will describe the current state of development of the test bed, and will outline some human factors issues and problems for more general discussion. In brief, the virtual telepresence system for remote driving has been designed to take the following form. The human operator will be provided with a helmet-mounted stereo display assembly, facilities for speech recognition and synthesis (using the Marconi Macrospeak system), and a VPL DataGlove Model 2 unit. The vehicle to be used for the purposes of remote driving is a Cybermotion Navmaster K2A system, which will be equipped with a stereo camera and microphone pair, mounted on a motorized high-speed pan-and-tilt head incorporating a closed-loop laser ranging sensor for camera convergence control (currently under contractual development). It will be possible to relay information to and from the vehicle and sensory system via an umbilical or RF link. The aim is to develop an interactive audio-visual display system capable of presenting combined stereo TV pictures and virtual graphics windows, the latter featuring control representations appropriate for vehicle driving and interaction using a graphical 'hand,' slaved to the flex and tracking sensors of the DataGlove and an additional helmet-mounted Polhemus IsoTrack sensor. Developments planned for the virtual environment test bed include transfer of operator control between remote driving and remote manipulation, dexterous end effector integration, virtual force and tactile sensing (also the focus of a current ARRL contract, initially employing a 14-pneumatic bladder glove attachment), and sensor-driven world modeling for total virtual environment generation and operator-assistance in remote scene interrogation.

Stone, Robert J.↗

NASA NTRS2000

An Architecture for Real-Time Interpretation and Visualization of Structural Sensor Data in a Laboratory Environment

A visualization system is being developed out of the need to monitor, interpret, and make decisions based on the information from several thousand sensors during experimental testing to facilitate development and validation of structural health monitoring algorithms. As an added benefit the system will enable complete real-time sensor assessment of complex test specimens. Complex structural specimens are routinely tested that have hundreds or thousands of sensors. During a test, it is impossible for a single researcher to effectively monitor all the sensors and subsequently interesting phenomena occur that are not recognized until post-test analysis. The ability to detect and alert the researcher to these unexpected phenomena as the test progresses will significantly enhance the understanding and utilization of complex test articles. Utilization is increased by the ability to halt a test when the health monitoring algorithm response is not satisfactory or when an unexpected phenomenon occurs, enabling focused investigation potentially through the installation of additional sensors. Often if the test continues, structural changes make it impossible to reproduce the conditions that exhibited the phenomena. The prohibitive time and costs associated with fabrication, sensoring, and subsequent testing of additional test articles generally makes it impossible to further investigate the phenomena. A scalable architecture is described to address the complex computational demands of structural health monitoring algorithm development and laboratory experimental test monitoring. The researcher monitors the test using a photographic quality 3D graphical model with actual sensor locations identified. In addition, researchers can quickly activate plots displaying time or load versus selected sensor response along with the expected values and predefined limits. The architecture has several key features. First, distributed dissimilar computers may be seamlessly integrated into the information flow. Second, virtual sensors may be defined that are complex functions of existing sensors or other virtual sensors. Virtual sensors represent a calculated value not directly measured by particular physical instrument. They can be used, for example, to represent the maximum difference in a range of sensors or the calculated buckling load based on the current strains. Third, the architecture enables autonomous response to preconceived events, where by the system can be configured to suspend or abort a test if a failure is detected in the load introduction system. Fourth, the architecture is designed to allow cooperative monitoring and control of the test progression from multiple stations both remote and local to the test system. To illustrate the architecture, a preliminary implementation is described monitoring the Stitched Composite Wing recently tested at LaRC.

Doggett, William↗

DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Indoor Occupancy Sensing via Networked Nodes (2012–2022): A Review

In the past decade, different sensing mechanisms and algorithms have been developed to detect or estimate indoor occupancy. One of the most recent advancements is using networked sensor nodes to create a more comprehensive occupancy detection system where multiple sensors can identify human presence within more expansive areas while delivering enhanced accuracy compared to a system that relies on stand-alone sensor nodes. The present work reviews the studies from 2012 to 2022 that use networked sensor nodes to detect indoor occupancy, focusing on PIR-based sensors. Methods are compared based on pivotal ADPs that play a significant role in selecting an occupancy detection system for applications such as Health and Safety or occupant comfort. These parameters include accuracy, information requirement, maximum sensor failure and minimum observation rate, and feasible detection area. We briefly describe the overview of occupancy detection criteria used by each study and introduce a metric called “sensor node deployment density” through our analysis. This metric captures the strength of network-level data filtering and fusion algorithms found in the literature. It is hinged on the fact that a robust occupancy estimation algorithm requires a minimal number of nodes to estimate occupancy. This review only focuses on the occupancy estimation models for networked sensor nodes. It thus provides a standardized insight into networked nodes’ occupancy sensing pipelines, which employ data fusion strategies, network-level machine learning algorithms, and occupancy estimation algorithms. This review thus helps determine the suitability of the reviewed methods to a standard set of application areas by analyzing their gaps.

Emad-Ud-Din, Muhammad (ORCID:0000000279515538)↗

NASA NTRS1994

Directional and polarimetric effects in the optical domain

Papers focused on land surface, atmospheric, and ocean properties are reported. Specific comments pertaining to polarization, models and inversion, and measurements, are given. Recommendations are: continued research into the application potential of the BRDF (Bidirectional Reflectance Distribution Function) and polarization properties of ground surface and atmospheric targets; three dimensional models, which account for the statistical behavior of remotely sensed data, should be extended and inverted in order to support analysis of data potentially covering rolling terrain such that pixels represent heterogeneous mixtures of surface cover types and project ground footprints with sizes between 10 to 6 km, the ground pixel sizes of planned future sensors; available reflectance models should be further validated by means of multi dimensional (directional, spectral, temporal) field data and existing models should be intercompared in more depth to evaluate their performance and limitations; existing methods for model inversion should be validated in more depth in order to quantify the practical limitations and the expected accuracy of the parameters retrieved and new approaches should be developed based upon apriori knowledge of plant canopy development and spectral BRDF properties; there is a need to establish a protocol of validation and intercomparison of the indices and compositing techniques which have been proposed during these last years.

Vanderbilt, V. C.↗

NASA NTRS2004

High-speed Imaging of Global Surface Temperature Distributions on Hypersonic Ballistic-Range Projectiles

The NASA-Ames ballistic range provides a unique capability for aerothermodynamic testing of configurations in hypersonic, real-gas, free-flight environments. The facility can closely simulate conditions at any point along practically any trajectory of interest experienced by a spacecraft entering an atmosphere. Sub-scale models of blunt atmospheric entry vehicles are accelerated by a two-stage light-gas gun to speeds as high as 20 times the speed of sound to fly ballistic trajectories through an 24 m long vacuum-rated test section. The test-section pressure (effective altitude), the launch velocity of the model (flight Mach number), and the test-section working gas (planetary atmosphere) are independently variable. The model travels at hypersonic speeds through a quiescent test gas, creating a strong bow-shock wave and real-gas effects that closely match conditions achieved during actual atmospheric entry. The challenge with ballistic range experiments is to obtain quantitative surface measurements from a model traveling at hypersonic speeds. The models are relatively small (less than 3.8 cm in diameter), which limits the spatial resolution possible with surface mounted sensors. Furthermore, since the model is in flight, surface-mounted sensors require some form of on-board telemetry, which must survive the massive acceleration loads experienced during launch (up to 500,000 gravities). Finally, the model and any on-board instrumentation will be destroyed at the terminal wall of the range. For these reasons, optical measurement techniques are the most practical means of acquiring data. High-speed thermal imaging has been employed in the Ames ballistic range to measure global surface temperature distributions and to visualize the onset of transition to turbulent-flow on the forward regions of hypersonic blunt bodies. Both visible wavelength and infrared high-speed cameras are in use. The visible wavelength cameras are intensified CCD imagers capable of integration times as short as 2 ns. The infrared camera uses an Indium Antimonide (InSb) sensor in the 3 to 5 micron band and is capable of integration times as short as 500 ns. The projectiles are imaged nearly head-on using expendable mirrors offset slightly from the flight path. The proposed paper will discuss the application of high-speed digital imaging systems in the NASA-Ames hypersonic ballistic range, and the challenges encountered when applying these systems. Example images of the thermal radiation from the blunt nose of projectiles flying at nearly 14 times the speed of sound will be given.

Wilder, Michael C.↗

NASA NTRS1995

High-precision buffer circuit for suppression of regenerative oscillation

Precision analog signal conditioning electronics have been developed for wind tunnel model attitude inertial sensors. This application requires low-noise, stable, microvolt-level DC performance and a high-precision buffered output. Capacitive loading of the operational amplifier output stages due to the wind tunnel analog signal distribution facilities caused regenerative oscillation and consequent rectification bias errors. Oscillation suppression techniques commonly used in audio applications were inadequate to maintain the performance requirements for the measurement of attitude for wind tunnel models. Feedback control theory is applied to develop a suppression technique based on a known compensation (snubber) circuit, which provides superior oscillation suppression with high output isolation and preserves the low-noise low-offset performance of the signal conditioning electronics. A practical design technique is developed to select the parameters for the compensation circuit to suppress regenerative oscillation occurring when typical shielded cable loads are driven.

Tripp, John S.↗

NASA NTRS2005

Biomimetic Models for An Ecological Approach to Massively-Deployed Sensor Networks

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications that will redefine the way we live and work. Due to small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors ubiquitous throughout our environment working in concert. Recent research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to parallel activities and does not scale to massive size networks. Execution of simple tasks in sparse networks will not lead to the sophisticated applications predicted. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in such a model, fully distributed data aggregation can be performed in a scalable fashion in massively deployed sensor networks, where motes operate on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. We show that such architectures may be used to facilitate communication and synchronization in a fault-tolerant manner, while balancing workload and required energy expenditure throughout the network.

Jones, Kennie H.↗

DOE OSTI2025

Fully‐Printed Ion Sensor Arrays for Measuring Agricultural Nitrogen and Potassium Concentrations Using Nernstian and AI Models

Abstract The chemical composition of growing media is a key factor for plant growth, impacting agricultural yield and sustainability. However, there is a lack of affordable chemical sensors for ubiquitous nutrient ion monitoring in agricultural applications. This work investigates using fully printed ion‐sensor arrays to measure the concentrations of nitrate, ammonium, and potassium in mixed‐electrolyte media. Ion sensor arrays composed of nitrate, ammonium, and potassium ion‐selective electrodes and a printed silver‐silver chloride (Ag/AgCl) reference electrode are fabricated and characterized in aqueous solutions in a range of concentrations that encompass what is typical for agricultural growing media (0.01 m m –1 m ). The sensors are also tested in mixed‐electrolyte solutions of NaNO 3 , NH 4 Cl, and KCl of varying concentrations, and the recorded potentials are input into Nernstian and artificial neural network models to compare the prediction accuracy of the models against ground truth. The artificial neural network models demonstrated higher accuracy over the Nernstian model, and the model using only ion‐sensor inputs is 7.5% more accurate than the Nernstian model under the same conditions. By enabling more precise and efficient fertilizer application, these sensor arrays coupled to computational models can help increase crop yields, optimize resource use, and reduce environmental impact.

Goodrich, Payton [University of California Berkele↗

NASA NTRS2010

Enhanced Modeling of First-Order Plant Equations of Motion for Aeroelastic and Aeroservoelastic Applications

A methodology is described for generating first-order plant equations of motion for aeroelastic and aeroservoelastic applications. The description begins with the process of generating data files representing specialized mode-shapes, such as rigid-body and control surface modes, using both PATRAN and NASTRAN analysis. NASTRAN executes the 146 solution sequence using numerous Direct Matrix Abstraction Program (DMAP) calls to import the mode-shape files and to perform the aeroelastic response analysis. The aeroelastic response analysis calculates and extracts structural frequencies, generalized masses, frequency-dependent generalized aerodynamic force (GAF) coefficients, sensor deflections and load coefficients data as text-formatted data files. The data files are then re-sequenced and re-formatted using a custom written FORTRAN program. The text-formatted data files are stored and coefficients for s-plane equations are fitted to the frequency-dependent GAF coefficients using two Interactions of Structures, Aerodynamics and Controls (ISAC) programs. With tabular files from stored data created by ISAC, MATLAB generates the first-order aeroservoelastic plant equations of motion. These equations include control-surface actuator, turbulence, sensor and load modeling. Altitude varying root-locus plot and PSD plot results for a model of the F-18 aircraft are presented to demonstrate the capability.

Pototzky, Anthony S.↗

NASA NTRS2021

Evaluation of Sensor Uncertainty Mitigation Methods for Detect-and-Avoid Systems

The impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates two such methods. One of them is the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. The second method is the Virtual Intruder State Aggregation (VISA), which averages multiple subsequent intruder states extrapolated to the current (most recent) time into a single ``aggregated`` intruder state. The VISA method can be used either individually as a sensor noise mitigation method in its own right, or in combination with DAIDALUS SUM. The performance of these methods is evaluated using three safety and operational suitability metrics and compared with a baseline configuration using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft, not equipped with a broadcasting transponder or ADS-B out system, are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the DAIDALUS SUM parameters for horizontal and vertical uncertainty improves the safety metric at the cost of increasing the number of actionable alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. VISA was found to be almost as effective as other noise mitigation methods even when it was used alone. Combining VISA with DAIDALUS SUM achieved the best performance among all investigated methods used with DAIDALUS. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.

Detect-and-Avoid Systems↗

NASA NTRSDate not supplied

Applying Sensor Uncertainty Mitigation Schemes to Detect-and-Avoid Systems

Impact of sensor noise on the performance of Detect-And-Avoid (DAA) systems can be reduced by implementing various mitigation schemes. This paper evaluates the Sensor Uncertainty Mitigation (SUM) method, implemented in the Detect and Avoid Alerting Logic for Unmanned Systems (DAIDALUS) algorithm, a reference implementation in the DAA minimum operational performance standards. DAIDALUS SUM performance is evaluated using a few safety and operational suitability metrics and compared with more traditional approaches using static safety buffers. A large number of encounters representative of low-speed unmanned aircraft against non-cooperative manned aircraft are simulated and evaluated. An air-to-air radar model produces representative sensor noise for the DAA system. Results show that increasing the tunable parameters for horizontal and vertical uncertainty in DAIDALUS SUM improves the safety metric at the cost of increasing the number of system alerts leading to increased workload. A range of SUM parameters is recommended as suitable values for the type of operations considered for this work. General trends and optimal SUM configurations were found to be nearly the same for two large and very different encounter data sets.

detect and avoid↗

DOE OSTI2022

Eddy current flow meter model validation with a moving solid rod*

We report that the eddy current flow meter (ECFM) has been used to measure velocities and temperatures of conductive flows, such as liquid metal flows in a nuclear fission reactor. The goal of this paper is to develop a finite element electromagnetic model that can characterize the ECFM sensor performance and validate this finite element model with detailed velocity measurements of a controlled, well-characterized moving conductive solid rod. Both measurements and modeling were performed for various parameters that are important for ECFM performance such as rod velocity, rod material, ECFM sensor coil length, number of sensor coils, applied alternating current (AC) current amplitude, and applied ECFM AC frequency. For all parametric scans, the measurement and modeling agree well in both magnitude and trend. The normalized root-mean-square error between measurement and modeling is less than 10% for all cases. These results suggest that electromagnetic modeling could eventually be used to cost-effectively design future ECFM sensors in arbitrary geometry for more challenging applications such as liquid metal nuclear fission reactors.

47 OTHER INSTRUMENTATION↗

NASA NTRS1975

Saltation on Mars and expected lifetime of Viking 75 wind sensors

With the use of the wind-tunnel measurements of Bagnold and Zingg, a model is developed for estimating the parameters that describe the flux of sand on Mars. Application of this model to the sensor-breakage problem indicates that the expected lifetime on Mars of the wind sensors of the Viking 75 Meteorology Instrument System is about 40 earth years. This expected lifetime is adequate for both the primary Viking 75 mission and for a proposed extended mission.

Henry, R. M.↗

DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗