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At least 1,315 records · Page 73

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗

Identifying Information Needs and Tools to Support Interactions between Upper Class E Traffic Management (ETM) Operations and the Air Traffic System (ATS)

With the introduction of high-altitude long endurance (HALE) vehicles and balloons designed to operate above 60,000 feet, the frequency and duration of operations in Upper Class E airspace are expected to increase. In response to the need for scalable traffic management for these diverse operations at higher altitudes, the FAA introduced the Upper Class E Traffic Management (ETM) concept. Like the successful demonstration of Uncrewed Aircraft System (UAS) Traffic Management (UTM), the ETM concept is also designed as a community-based, industry-driven cooperative approach to traffic management. As these vehicles and balloons ascend to/descend from ETM Cooperative Areas in Upper Class E, they will transit through Class A controlled airspace where they will interact with various entities of the conventional Air Traffic System (ATS) (e.g., Air Traffic Control (ATC)). This work explores tools that will help support ETM-ATS interactions for users throughout the ATS, as well as ETM Operators. An information needs analysis using ETM-ATS interaction use cases, revealed that the needed functionalities generally grouped themselves into two main themes, the visualization of flights and airspace designations, and digital communication capabilities across various human users. In this paper, we describe two envisioned tools, 1) an Integrated Visualization Tool to display flight information and airspace designations, and 2) an Integrated Digital Communication Tool to facilitate two-way information exchange between users about vehicle position information, the coordination of airspace approvals, and notifications. The tools we describe create an integrated visual representation of vehicles and airspace designations with a set of communication capabilities to consolidate information into a single display interface. These tools may be used to guide the development of prototype tools for demonstrations at the National Aeronautics and Space Administration (NASA) Ames Research Center to further explore ETM-ATS interactions within the ETM concept.

Upper Class E Traffic Management (ETM)↗

Identifying Information Needs and Tools to Support Interactions between Upper Class E Traffic Management (ETM) Operations and the Air Traffic System (ATS)

With the introduction of high-altitude long endurance (HALE) vehicles and balloons designed to operate above 60,000 feet, the frequency and duration of operations in Upper Class E airspace are expected to increase. In response to the need for scalable traffic management for these diverse operations at higher altitudes, the FAA introduced the Upper Class E Traffic Management (ETM) concept. Like the successful demonstration of Uncrewed Aircraft System (UAS) Traffic Management (UTM), the ETM concept is also designed as a community-based, industry-driven cooperative approach to traffic management. As these vehicles and balloons ascend to/descend from ETM Cooperative Areas in Upper Class E, they will transit through Class A controlled airspace where they will interact with various entities of the conventional Air Traffic System (ATS) (e.g., Air Traffic Control (ATC)). This work explores tools that will help support ETM-ATS interactions for users throughout the ATS, as well as ETM Operators. An information needs analysis using ETM-ATS interaction use cases, revealed that the needed functionalities generally grouped themselves into two main themes, the visualization of flights and airspace designations, and digital communication capabilities across various human users. In this paper, we describe two envisioned tools, 1) an Integrated Visualization Tool to display flight information and airspace designations, and 2) an Integrated Digital Communication Tool to facilitate two-way information exchange between users about vehicle position information, the coordination of airspace approvals, and notifications. The tools we describe create an integrated visual representation of vehicles and airspace designations with a set of communication capabilities to consolidate information into a single display interface. These tools may be used to guide the development of prototype tools for demonstrations at the National Aeronautics and Space Administration (NASA) Ames Research Center to further explore ETM-ATS interactions within the ETM concept.

Upper Class E Traffic Management (ETM)↗

Uncrewed Aerial System Traffic Management Beyond Visual Line of Sight Multi Operator Technology Assessment Simulation

UAS Traffic Management (UTM) is a rapidly evolving space with increasing demand for regulation surrounding more complex operations. Beyond visual line of sight (BVLOS) operations are among these complex operations. The NASA UTM BVLOS sub-project is focusing on enabling more routine BVLOS operations through data collection for support of standards formulation. The Multi Operator Technology Assessment (MOTA) simulation is one of these activities that collects data on BVLOS operations within the same geographical area while sharing operational intents through a USS. Data collected includes workload, situation awareness, and usability on nominal and off-nominal operations. Results of this study are intended to provide recommendations for standards as well as provide insight on improvements to the systems under test. NASA intends to incorporate those improvements into their operations to support and updated BVLOS waiver from the FAA’s Near-Term Approval Process (NTAP).

Bryan J Petty↗

Developing a Cybersecurity Architecture for Extensible Traffic Management (xTM)

This paper explores the development of a cybersecurity architecture tailored for Extensible Traffic Management (xTM) to address emerging challenges in managing diverse aerial vehicles within the National Airspace System (NAS). Driven by technological advances and the rise of uncrewed aerial systems (UAS), urban air mobility (UAM), and high-altitude traffic (ETM), the NAS is undergoing a paradigm shift. Traditional air traffic management, reliant on traditional Federal Aviation Administration (FAA) control, will give way to decentralized coordination among autonomous and semi-autonomous systems. The proposed xTM Security Architecture, designed as a high-level framework, focuses on ensuring the confidentiality, integrity, and availability of data and operations in this evolving ecosystem. Utilizing threat modeling, the research identifies potential risks across key flight phases, operations and use cases to offer security control recommendations. Key objectives include analyzing interactions between novel airspace entrants and existing NAS traffic, cataloging vulnerabilities, and developing mitigative strategies to ensure safety, operational stability, and secure data exchanges. This research lays the groundwork for regulatory and industry adaptation, providing critical insights into managing cybersecurity risks in this complex, multi-domain environment.

UAM↗

Confidence-Based Buffer for Strategic Deconfliction with Probabilistic Operational Intent

This paper presents a methodology to expand the 95% confidence level of the elliptical geometry given by Unmanned Aircraft System (UAS) operators planning to fly Beyond Visual Line of Sight (BVLOS) to any confidence level before being fed to the strategic deconfliction (SD) module, effectively increasing the separation buffer between Operational Intents (OIs). To assess the performance of this approach, it is integrated within an adaptation of the Rolling Horizon with K-Position Search volume-based strategic deconfliction approach, previously developed at NASA Ames, preventing the 4D overlapping of OIs shaped by ellipses instead of traditional blocks. Safety and efficiency metrics are evaluated through the deconfliction of four simulated package delivery route network structures across the San Francisco Metropolitan Area with increasing numbers of crossing waypoints (network complexity). Safety assessment entails the in-house creation of a metric to quantify collision occurrences per flight hour based on the frequency at which the probabilistic operational volume segments are sampled, whereas efficiency is measured using ground delay. Results indicate that the largest buffer growth occurs when increasing the confidence level beyond 99.9% and demonstrate the negative impact of network complexity on both metrics, regardless of the OI geometry. Further, the ellipse-based SD adaptation more accurately estimates temporal separation at crossings, allowing deconflicted vehicles to be closer together. It is concluded that the proposed methodology enables the desired confidence level to serve as an effective controller of buffer size.

strategic deconfliction↗

Confidence-Based Buffer for Strategic Deconfliction with Probabilistic Operational Intent

This paper presents a methodology to expand the 95% confidence level of the elliptical geometry given by Unmanned Aircraft System (UAS) operators planning to fly Beyond Visual Line of Sight (BVLOS) to any confidence level before being fed to the strategic deconfliction (SD) module, effectively increasing the separation buffer between Operational Intents (OIs). To assess the performance of this approach, it is integrated within an adaptation of the Rolling Horizon with K-Position Search volume-based strategic deconfliction approach, previously developed at NASA Ames, preventing the 4D overlapping of OIs shaped by ellipses instead of traditional blocks. Safety and efficiency metrics are evaluated through the deconfliction of four simulated package delivery route network structures across the San Francisco Metropolitan Area with increasing numbers of crossing waypoints (network complexity). Safety assessment entails the in-house creation of a metric to quantify collision occurrences per flight hour based on the frequency at which the probabilistic operational volume segments are sampled, whereas efficiency is measured using ground delay. Results indicate that the largest buffer growth occurs when increasing the confidence level beyond 99.9% and demonstrate the negative impact of network complexity on both metrics, regardless of the OI geometry. Further, the ellipse-based SD adaptation more accurately estimates temporal separation at crossings, allowing deconflicted vehicles to be closer together. It is concluded that the proposed methodology enables the desired confidence level to serve as an effective controller of buffer size.

safety↗

Uncrewed Aerial System Traffic Management Beyond Visual Line of Sight Multi Operator Technology Assessment Simulation

UAS Traffic Management (UTM) is a rapidly evolving space with increasing demand for regulation surrounding more complex operations. Beyond visual line of sight (BVLOS) operations are among these complex operations. The NASA UTM BVLOS sub-project is focusing on enabling more routine BVLOS operations through data collection for support of standards formulation. The Multi Operator Technology Assessment (MOTA) simulation is one of these activities that collects data on BVLOS operations within the same geographical area while sharing operational intents through a USS. Data collected includes workload, situation awareness, and usability on nominal and off-nominal operations. Results of this study are intended to provide recommendations for standards as well as provide insight on improvements to the systems under test. NASA intends to incorporate those improvements into their operations to support and updated BVLOS waiver from the FAA’s Near-Term Approval Process (NTAP).

Bryan J Petty↗

Design Considerations of an Ascent Abort Monitor Algorithm for Use During Service Module Aborts

In support of human rating the Artemis missions, NASA's Orion program requires continuous abort coverage from liftoff through mission destination. During a portion of the ascent trajectory, the currently achievable abort mode is determined by an Orion algorithm using the onboard navigated vehicle state. This ascent abort monitor determines achievability for Orion's Mode 2 abort, Untargeted Abort Splashdown (UAS), by propagating the current vehicle state through ascent abort events to determine sufficient timing to perform the abort and to a ballistic touchdown point to approximate landing location relative to prescribed keep out boundaries. The algorithm was updated for Artemis 2 to allow the capability to limit loads for the majority of ascent. Performance of the algorithm has been demonstrated and verified through dispersed trajectory analysis with emulated flight software, unit testing, and hardware in the loop testing.

Esteban Guzman↗

Deriving Simulation Parameters for Storage-Type Water Heaters Using Ratings Data Produced from the Uniform Energy Factor Test Procedure: Preprint

Building energy modeling (BEM) is commonly used to estimate the energy usage of residential buildings. Uses for BEM include calculating home energy ratings, demonstrating compliance with performance-based energy codes, and establishing whether designs meet voluntary program requirements, such as ENERGY STAR® qualified homes. BEM requires simulating subsystems within the building, including storage-type water heaters. To properly simulate the in-situ performance of residential storage water heaters, it is necessary to determine key water heater parameters, including the overall heat loss coefficient (UA) and conversion efficiency (?c) of the water heater, based on the rated efficiency of the water heater. The testing procedure and rating standard for residential water heaters have recently changed: the new rating standard provides a Uniform Energy Factor (UEF) rather than the Energy Factor (EF) used previously. This paper discusses how to derive the necessary model parameters from the ratings data produced from the latest test procedure.

30 DIRECT ENERGY CONVERSION↗

Counter Unmanned Aircraft Systems, 2021

FY 2017 National Defense Authorization Act gave NNSA authorities to protect facilities from unauthorized unmanned aircraft systems (UAS) that may pose threats to the safety or security of assets and personnel

Source record↗

A Non-Intrusive Optical (NIO) Method to Measure Optical Errors of in-situ Heliostats in Utility-Scale Power Tower Plants: Detecting Uncertainties in Heliostat Geometry

Heliostat optical errors can account for significant losses in efficiency of power tower concentrating solar power (CSP) plants. Accurately measuring heliostat optical errors can help to improve plant performance. A Non-Intrusive Optical (NIO) method has been developed to efficiently measure heliostat optical errors from UAS collected images of the mirror surface reflection [1]–[3]. In some cases, plant data of heliostat geometry can be incomplete or contain inaccuracies, in which case field collected data can be used to detect and correct uncertainties, which is valuable information for plant operators.

Mitchell, Rebecca↗

GPS-Based Gamma Survey for Characterizing and Decommissioning NORM Sites - 20389

Gamma survey techniques are an especially powerful decommissioning tool at naturally occurring radioactive material (NORM) sites due to both the low cost to obtain data over a large spatial scale and the abundance of gamma emitters in the uranium and thorium decay series. Gamma surveys are executed by coupling a detector - most often a sodium iodide crystal - to a global positioning system (GPS), then reporting a location and gross gamma reading coincidentally to a data logger. Systems may be carried by workers or mounted to a car, all-terrain vehicle, or unmanned aerial system (UAS). The resulting data set provides a high-resolution but low precision map of the gamma radiation field over the area surveyed. Frequently this map is also correlated to soil concentrations of NORM radionuclides (most often, Ra-226) and/or exposure rate. Gamma survey parameters such as movement speed, transect spacing, and data logging frequency define the spatial resolution of the resulting surface, and can be optimized depending on the desired survey sensitivity. This paper examines gamma survey as a tool for decommissioning NORM sites and provides an overview of current gamma survey technology designed to improve the efficiency and effectiveness of the decommissioning process. Topics to be discussed in the paper include: - An overview of gamma survey systems, and the utility of different delivery vehicles depending on desired cost, desired spatial resolution, and site topography. - The influence of physical detector characteristics on detection sensitivity and survey planning. - The tradeoff between high-resolution and large spatial extent, but inherently uncertain data, and low-resolution, low spatial extent, but highly certain data, as well as the specific utility of each of these types of data during NORM facility decommissioning. - Confounding variables that may limit the utility of gamma survey at some sites (e.g., radon gas and spatial heterogeneity / hot spots), and methods to plan for and control these conditions. Results show that the confounding variables, such as radon and data output can greatly influence the overall data quality associated with the decommissioning process. In addition, the use of real-time and aerial survey platforms provides a method for ensuring proper spatial extent of the data. When applied thoughtfully, gamma survey is a powerful tool for detecting NORM radionuclides in the environment and a cost-effective technique for identifying areas requiring remediation. However, entities performing or using gamma survey as a decommissioning tool must be aware of both its advantages and its limitations before basing remediation or regulatory action on gamma survey results. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Fermilab PIP-II CDS and CM Cryogenic Controls System

Details on Final design for Cryogenic Electrical & Controls System for Fermilab' s next-gen particle accelerator PIP-II. Electrical Controls System includes instrumentation and controls of Cryogenics Distribution System and Cryomodules. Design includes Siemens PCS7 Controls System with 26 Remote IO Rittal Cabinets and 48 Relay Racks for Temperature Readouts, Valve Positioners, Level, Heater Controls etc. Electrical Drawings and Design have been completed with focus now on fabrication of the 26 Rittal Cabinets and 48 Relay Racks. All materials have been procured. EPICS will be used as a SCADA system communicating to S7 Controllers via OPC UA.

Patel, Pratik [Fermilab]↗

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗

A Machine Learning based Approach of Estimating Equivalent Circuit Model Parameters at Different SoCs of Li-ion Batteries from Voltage Relaxation

Abstract: In this study, an approach of estimating the equivalent circuit model (ECM) parameters for Li-ion batteries (LIBs) is proposed based on the voltage value at different intervals while relaxing the LIB after discharge. The typical approach for estimating ECM parameters of a LIB is to conduct electrochemical impedance spectroscopy (EIS) measurements at different frequencies and fit them to a predefined circuit model, which requires additional measuring arrangements and specialized devices. The proposed methodology utilizes four different voltages at 0s, 60s, 360s, and 1800s alongside the specific state of charge (SoC) value for a specific constant discharge current value of ~1C until the relaxation stage to train and evaluate three regression-based machine learning models— Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gaussian Process Regression (GPR)—for estimating the ECM parameters of the selected model. Bayesian optimization is employed for hyperparameter tuning to achieve optimal performance for all the regressor models, among which, the GPR provided the best performance with the root-mean-squared error (RMSE) of less than 4x10-4 on average for the resistive components and less than 0.27 for capacitive components with excellent R2 scores. The simplicity of the approach enables it to eliminate the need for sophisticated measuring equipment and computation power.

Sagar, Md. Samiul [The University of Alabama (UA)]↗

Comparative Evaluation of the Single-Phase Shift and Extended-Phase Shift Control for Isolated DC-DC Converter

Abstract: This paper presents a comparative evaluation of the Single-Phase Shift (SPS) and Extended-Phase Shift (EPS) controls for the isolated DC-DC Converter. The analysis is based on the power regulation range, flexibility of operation, current stress on power devices, system efficiency while focusing on the issue of backflow power, control complexity, design and implementation overhead, and stability of the entire system. It is also examined how the backflow influences power circulation and increases current stress. Here, the Dual Active Bridge (DAB) Converter is employed to assess the performance of the control methods. Compared to SPS control, EPS offers a wider power regulation range, greater flexibility, lower current stress, and better system efficiency. However, EPS has increased control complexity, design and implementation overhead, and often requires sophisticated feedback control loops. The comparison is made through mathematical modeling of power transfer, backflow, and current stress. Simulation results validate the comparative analysis presented.

Amir, Aamir [The University of Alabama (UA)]↗

Building envelope anomaly characterization and simulation using drone time-lapse thermography

Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗