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NASA NTRS1988

Three degree-of-freedom force feedback control for robotic mating of umbilical lines

The use of robotic manipulators for the mating and demating of umbilical fuel lines to the Space Shuttle Vehicle prior to launch is investigated. Force feedback control is necessary to minimize the contact forces which develop during mating. The objective is to develop and demonstrate a working robotic force control system. Initial experimental force control tests with an ASEA IRB-90 industrial robot using the system's Adaptive Control capabilities indicated that control stability would by a primary problem. An investigation of the ASEA system showed a 0.280 second software delay between force input commands and the output of command voltages to the servo system. This computational delay was identified as the primary cause of the instability. Tests on a second path into the ASEA's control computer using the MicroVax II supervisory computer show that time delay would be comparable, offering no stability improvement. An alternative approach was developed where the digital control system of the robot was disconnected and an analog electronic force controller was used to control the robot's servosystem directly, allowing the robot to use force feedback control while in rigid contact with a moving three-degree-of-freedom target. An alternative approach was developed where the digital control system of the robot was disconnected and an analog electronic force controller was used to control the robot's servo system directly. This method allowed the robot to use force feedback control while in rigid contact with moving three degree-of-freedom target. Tests on this approach indicated adequate force feedback control even under worst case conditions. A strategy to digitally-controlled vision system was developed. This requires switching between the digital controller when using vision control and the analog controller when using force control, depending on whether or not the mating plates are in contact.

Fullmer, R. Rees↗

DOE OSTI2026

V1G Frequency Regulation: Algorithm Development, Validation & Analysis at Scale

Researchers at Argonne National Laboratory developed and validated a high-fidelity digital twin of a smart charging (V1G) ecosystem to model the participation of up to 1,000 unique electric vehicles (EVs) in the PJM frequency regulation market. Utilizing a discrete-event framework, the simulation models complex interactions, from dynamic grid signals (updated every 2 seconds) to individual EV charging dynamics. The simulation incorporates multiple EV models created from real-world lab test data. Researchers tested multiple control algorithms to balance the dual objectives of maximizing aggregator’s revenue and driver charging needs. Results demonstrate that aggregated EVs function as a controllable, highly effective grid resource, achieving high PJM Performance Scores (80–90%). Additionally, an optimized, market-aware bidding strategy was identified as key to profitability. The platform was shown to provide drivers with an average charging discount of nearly 50%. The algorithm was further validated in the lab using production EVs and charging stations to compare simulation results with real-world performance.

Manne, Nithin↗

NASA NTRSDate not supplied

Performance and Accuracy Assessment of Line Marching Algorithm Computations Utilizing GPUs Within a Predictive GNSS Quality Service

This paper presents a detailed analysis of the accuracy and performance of line marching algorithms executing on a GPU. In the context of an accurate Global Navigation Satellite System(GNSS) quality of service simulation, horizon sky-plots are a useful tool to determine satellite visibility in the presence of obstructions from objects, such as buildings or dense foliage. In order to accurately model satellite visibility at a point of interest on a map, a horizon plot can identify the viewing angles at which objects are blocking the sky. This computation requires traversing a line starting at the point of interest on a 2D altitude map, moving outward for every azimuth angle. To explore the performance of this computation, we propose a new dynamic stopping condition for the traversal of the line, benefiting from objects close to the point of interest. We compare the accuracy of common line marching algorithms, and consider their parallel performance when developed in CUDA. We find that our proposed stopping condition for line marching provides a significant improvement in performance in urban canyon sky-plots, as compared to previous work. Additionally, these results show that simpler algorithms, such as the digital differential analyzer line algorithm, are better suited for GPUs than more sophisticated schemes such as Bresenham’s algorithm, specifically in the context of sky-plothorizon computations. The trade-off between accuracy and performance is analyzed and providing guidance that depends on the targeted goal of the GNSS application.

GNSS↗

DOE OSTI2023

The ARENA Test Bed – A Versatile Resource for I&C Development and Validation

The Accelerated and Real-time Experimental Nodal Assessment (ARENA) Test Bed at the Pacific Northwest National Laboratory (PNNL) is a versatile resource for development and validation of instrumentation and control (I&C) technologies. This capability was created to facilitate in-situ testing of nuclear electrical cables in various simulated operational environments. Using cable trays, a control box, and selected test components, low voltage cables can be staged to experience local adverse environments such as elevated temperature and water immersion. Cable condition can be continuously monitored over time to track the effect of local stresses using nondestructive assessment tools. A heads-up display (ARENA TV) plots key data in real-time for users. The ARENA Test Bed has recently been used to evaluate the potential for spread spectrum time domain reflectometry (SSTDR) to monitor thermal aging of a portion of live cable powering a three-phase motor. The arrangement provided the opportunity to directly compare the performance of the novel online SSTDR method with offline results from the more standard frequency domain reflectometry (FDR) method. The ability of SSTDR and FDR to identify the presence of water in immersed shielded and unshielded cables and to detect ground faults was also assessed. A digital twin is being developed to track and predict FDR signals from a thermally aging conceptual cable region to compare with measured signals from the ARENA physical counterpart. The test bed concept addresses an important need in nuclear I&C monitoring tool development. New tools and techniques can be developed in the test bed and validated versus known methods and physical measurements. Digital twins and machine learning engines can be populated with measured data in a controlled environment that would not be readily available in the actual nuclear power plant. Proposed monitoring strategies can be confirmed for effectiveness through objective evaluation. It is anticipated that the PNNL ARENA Test Bed will be a valuable resource in advancing nuclear plant instrumentation.

nuclear electrical cables, ARENA Test Bed, conditi↗

DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Lawrence Livermore National Laboratory FY 2016 Laboratory Directed Research and Development Annual Report

The LDRD annual report for FY2016 consists of two parts: The Overview. This section contains a broad description of the LDRD Program, highlights of recent accomplishments and awards, Program statistics, and the LDRD portfolio-management processes. Project Reports. Project reports are submitted by all principal investigators at the end of the fiscal year. The length and depth of the report depends on the project’s lifecycle. For projects that will be continuing the following year, the principal investigator submits a continuing project report, which is a brief update containing descriptions of the goals, scope, motivation, relevance (to DOE/NNSA and Livermore mission areas), and technical progress achieved in FY16, as well as a list of selected publications and presentations that resulted from the research. For projects that concluded in FY16, a more detailed final report is provided that is technical in nature and includes the background, objectives, scientific approach, accomplishments, and impacts on the Laboratory missions, as well as a list of publications and presentations that resulted from the research. Project reports are listed under their research topics and organized by year and type, such as exploratory research (ER), feasibility study (FS), laboratory-wide competition (LW), and strategic initiative (SI). Each project is assigned a unique tracking code, an identifier that consists of three elements. The first is the fiscal year in which the project began, the second represents the project type, and the third identifies the serial number of the project for that fiscal year. For example, 16-ERD-100 means the project is an exploratory research project that began in FY16. The three-digit number (100) represents the serial number for the project.

36 MATERIALS SCIENCE↗

NASA NTRS2004

An Experimental Investigation of Unsteady Thrust Augmentation Using a Speaker-Driven Jet

An experimental investigation is described in which a simple speaker-driven jet was used as a pulsed thrust source (driver) for an ejector configuration. The objectives of the investigation were twofold: first, to add to the experimental body of evidence showing that an unsteady thrust source, combined with a properly sized ejector generally yields higher thrust augmentation values than a similarly sized, steady driver of equivalent thrust. Second, to identify characteristics of the unsteady driver that may be useful for sizing ejectors, and predicting what thrust augmentation values may be achieved. The speaker-driven jet provided a convenient source for the investigation because it is entirely unsteady (having no mean component) and because relevant parameters such as frequency, time-averaged thrust, and diameter are easily variable. The experimental setup will be described, as will the various measurements made. These include both thrust and Digital Particle Imaging Velocimetry of the driver. It will be shown that thrust augmentation values as high as 1.8 were obtained, that the diameter of the best ejector scaled with the dimensions of the emitted vortex, and that the so-called Formation Number serves as a useful dimensionless number by which to characterize the jet and predict performance.

Paxson, Daniel E.↗

DOE OSTI2024

Digital risk analysis in nuclear engineering projects: Designing for safety, performance, reliability, and security

Cyber-informed engineering and security-by-design frameworks are important in promoting the need to identify cybersecurity concerns early in the systems engineering lifecycle so risks from adversarial cyber-attacks can be eliminated or reduced through engineering design practices. In addition to adversarial risk, risk in operational technology systems also includes non-adversarial and unintentional risk from other factors such as human performance errors, environmental conditions, design flaws, and device degradation or failure. This paper introduces a new concept for characterizing digital risk, both adversarial and non-adversarial, and provides the basis for initial research into a novel digital risk analysis approach focused on incorporating attack difficulty into a multi-attribute analysis technique using robust decision-making. This digital risk characterization is also used to frame a discussion on the challenges of competing objectives and competing stakeholder requirements in an integrated energy system project that incorporates a small modular reactor and industrial facility.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Massive Quenched Galaxies at z ∼ 0.7 Retain Large Molecular Gas Reservoirs

The physical mechanisms that quench star formation, turning blue star-forming galaxies into red quiescent galaxies, remain unclear. In this Letter, we investigate the role of gas supply in suppressing star formation by studying the molecular gas content of post-starburst galaxies. Leveraging the wide area of the Sloan Digital Sky Survey, we identify a sample of massive intermediate-redshift galaxies that have just ended their primary epoch of star formation. We present Atacama Large Millimeter/submillimeter Array CO(2-1) observations of two of these post-starburst galaxies at z ∼ 0.7 with M{sub ∗}∼2×10{sup 11} M{sub ⊙}. Their molecular gas reservoirs of (6.4±0.8) ×10{sup 9} M{sub ⊙} and (34.0±1.6)×10{sup 9} M{sub ⊙} are an order of magnitude larger than comparable-mass galaxies in the local universe. Our observations suggest that quenching does not require the total removal or depletion of molecular gas, as many quenching models suggest. However, further observations are required both to determine if these apparently quiescent objects host highly obscured star formation and to investigate the intrinsic variation in the molecular gas properties of post-starburst galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

A Privacy-Preserving Strategy for the Trust Layer of the Energy Grid of Things Distributed Energy Resource Management System

Emergent from the shadows of the traditional grid flaws, the Smart Grid (SG) idea was born and led by government mandates toward cleaner energy production. The SG represents the next generation of electricity distribution systems that subsume recent technological innovations. It uses digital communication between its components and entities to attain more automation, self-sufficiency, and reliability. Unfortunately, this relatively new concept is not flawless; the intrinsic reliance on increased digital communication spreads open attack paths for adversaries. Therefore, finding solutions that address information exchange vulnerabilities has become imperative. The Energy Grid of Things (EGoT) is Portland State University’s (PSU’s) implementation of a Distributed Energy Resource Management System (DERMS). The EGoT DERMS requires access to customers’ information to achieve operational objectives. The system’s access to customers’ information needs to be restricted such that it does not violate customers’ privacy. Applying privacy protection models such as K-anonymity to EGoT DERMS sub-components safeguards that privacy. This thesis work proposes a strategy to ensure communication in the EGoT DERMS is privacy-preserving and secure. Specifically, it provides an approach to applying the Mondrian Algorithm to ensure data within the system excludes Personally Identifiable Information (PII) and provides means for securing the communication according to industry standards (IEEE 2030.5). Results suggest that the generalization hierarchy derived for the EGoT DERMS exhibits an Identical Generalization Hierarchy structure. Guarantees of sameness manifested in the test feeder topology would not hold in real-world scenarios. This thesis work proposes a strategy to ensure communication in the EGoT DERMS is privacy-preserving and secure. Specifically, it provides an approach to applying the Mondrian Algorithm to ensure data within the system excludes Personally Identifiable Information (PII) and provides means for securing the communication according to industry standards (IEEE 2030.5). Results suggest that the generalization hierarchy derived for the EGoT DERMS exhibits an Identical Generalization Hierarchy structure. Guarantees of sameness manifested in the test feeder topology would not hold in real-world scenarios.

Alsiad, Mohammed↗

DOE OSTI2018

Improving Spectral Image Classification through a Custom Data Sparsing and Pixel Clustering Algorithm

The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of Digital Globe’s World View-3 (WV3) to detect and map UNE signatures. WV3 has proven capabilities in the detection of large-scale, anomalous, man-made features; however, it has relatively limited spectral and spatial resolution as compared to airborne hyperspectral imaging. These limitations impede standard image classifiers from identifying UNE signatures that are spatially small and lack distinct spectral features. In order to improve image classification results, we developed a custom algorithm that increases the true positive rate, while decreasing the false positive rate. Our algorithm’s flexible front-end allows the user to choose the method used to sparse the data. These methods include band-ratio optimization, correlation coefficient threshold, or Adaptive Coherence Estimator (ACE) threshold. The resulting sparsed pixels are clustered into like-objects. A novel weighted-sum of ACE and correlation coefficient scores is used to classify each object based on the average spectrum of its constituent pixels. WV3 and AVIRIS data of Cuprite, Nevada were used as a verification data set. This algorithm improves overall pixel classification accuracy over standard classifiers by up to 14%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

U.S. Building Stock Characterization Study: A National Typology for Decarbonizing U.S. Buildings

To support the U.S. Department of Energy's (DOE's) Advanced Building Construction (ABC) Collaborative, the National Renewable Energy Laboratory (NREL) has been tasked with characterizing the U.S. building stock and developing a national typology of buildings. The potential use cases of such a typology are flexible and evolving, but in this initial phase, the primary intention is to help identify technology requirements and engineering solutions for moving the U.S. building stock toward a zero-carbon future by mid-century. This typology will also support the development of appropriate ABC research goals for existing buildings, such as cost targets for new technology development, and in a later phase, the typology can be used to support the implementation of ABC solutions by informing market aggregation and business model development. The ABC Initiative invests in new technologies that enable high building performance, can be deployed quickly with minimal onsite construction time, and are affordable and appealing to building owners, investors, and occupants. Funding awardees use many innovations, including new building materials, 3D printing, offsite manufacturing, robotics, and digital art-to-part. Although the goals of ABC cover a broad range of objectives around energy, comfort, and health, the primary ABC-related application of this national building characterization study is the development of retrofit packages that can be applied to reduce thermal loads in buildings. Retrofit packages will be determined collaboratively by the DOE and the ABC Collaborative. We anticipate a range of upgrade measures covering envelope-, HVAC-, and water-heating-related loads.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2016

Digital Image Correlation for Performance Monitoring

Evaluating the health of a mechanism requires more than just a binary evaluation of whether an operation was completed. It requires analyzing more comprehensive, full-field data. Health monitoring is a process of non-destructively identifying characteristics that indicate the fitness of an engineered component. In order to monitor unit health in a production setting, an automated test system must be created to capture the motion of mechanism parts in a real-time and non-intrusive manner. One way to accomplish this is by using high-speed video and Digital Image Correlation (DIC). In this approach, individual frames of the video are analyzed to track the motion of mechanism components. The derived performance metrics allow for state-of-health monitoring and improved fidelity of mechanism modeling. The results are in-situ state-of-health identification and performance prediction. This paper introduces basic concepts of this test method, and discusses two main themes: the use of laser marking to add fiducial patterns to mechanism components, and new software developed to track objects with complex shapes, even as they move behind obstructions. Finally, the implementation of these tests into an automated tester is discussed.

Palaviccini, Miguel↗

DOE OSTI2019

Support for Reactor Operators in Case of Cyber-Security Threats (NEUP Final Report)

Increasing use of digital technology in nuclear power plants (NPPs) poses cyber-security as a crucial threat to public safety and to continuous energy production. Cyber-security risks are comprised of complex known and unknown interactions between various entities, system vulnerabilities, network protocols, human users and malicious attacks. There is little understanding or research geared towards plant operators’ response under cyber-security threats and operational procedures to cope with such threats. This is particularly critical when a cyber-security event masquerades as a safety incident or an evolving accident. Instead of leading operators to the remediation of the accident, the masked cyber event may lead them to circumvent the safety mechanisms of the plant. Given these challenges, the objective of this project is to develop a tool to characterize abnormal NPP events as “cyber” or “safety” incidents, and to further develop this tool as a real-time operator aid to assist in response to the incident. This NEUP Final Report summarizes the research efforts towards the above goals and the results in the following three aspects. First, a methodology for classifying abnormal NPP events as cyber-attacks or safety incidents was developed. This provides the basis for responding to potential cyber-attacks. In this methodology, the causal relationships between various variables (i.e., physical variables, network variables, and control variables) for a nuclear system under consideration are modeled using dynamic Bayesian networks. Based on observations of certain variables (i.e., the evidence), inference is performed to identify the state of hidden variables, which helps indicate the occurrence of cyber-attacks or safety incidents. The proposed methodology was applied to the full scope NPP simulator system at The Ohio State University as a case study. The result shows that the proposed methodology is capable of distinguishing between cyber-attacks and safety incidents. Second, a game-theoretic approach for responding to cyber-attacks on nuclear power plants was developed. This is the second major goal of the project, and the method is used to recommend optimal responses to reactor operators in the case of cyber-attacks. We consider two cases in this research. In the first case, the interaction between the defender (i.e., the reactor operator) and the attacker is modeled as a two-player, nonzero-sum, Markov game with an infinite time horizon. In the second case, we consider a semi-Markov game between the defender and the attacker within a finite time horizon. The model in the second case enables us to consider the time sensitive response of the defender. In each case, the method for deriving the Nash equilibrium is developed. The Nash Equilibrium of the game provides the valid prediction of both players’ actions because no single player can benefit from unilaterally deviating from the equilibrium policy if the other player adheres to his/hers, hence it provides the best response of the defender to cyber-attacks. The proposed method in each case is demonstrated through a simplified benchmark digital feedwater control system. Third, a prototype display is developed to integrate the above information. The prototype display depicts the communication information to the operator and incorporates the cyber-security tool developed by the team into the interface. It consists of the regular display in current control rooms, as well as a panel used to indicate the occurrence of cyber-attacks or safety events and a panel used to guide the operator’s response to detected cyber-attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2018

Lessons Learned from Performing a Human Factors Engineering Validation of an Upgraded Digital Control System in a Nuclear Power Plant Control Room

The United States' fleet of nuclear power plants (NPPs) have exceeded their original 40-year licenses and many are seeking license renewals to continue operations into the future. To support continued operation the Department of Energy's (DOE) Light Water Reactor Sustainability Program (LWRS) provides support for utilities to maintain and upgrade their power production capabilities. Control room modernization is one focus area, which aims to modernization the analog control rooms with digital controls and human-system interfaces (HSIs). The Turbine Control System (TCS) is a non-safety related system undergoing modernization efforts at multiple plants and utilities. Considerable human factors research efforts, comprised of expert reviews and operator in the loop studies were performed to develop human factors compliant and usable TCS. This report documents the final phase of the human factors engineering program used to design and validate the new digital TCS. According to the NUREG-0711 review guide, one of the last steps of human factors engineering program prior to implementation is human factors validation. This report details the method, results and conclusions from the human factors validation study of a collaborating utilities TCS slated to be implemented in one of their plants. One control room team consisting of two control room supervisors and two reactor operators participated in the validation. All team members had participated in the BNP TCS training program foregoing the TCS validation. Performance measures included task performance, situation awareness and workload, expert observation of any negative influences of human-system interface features on control room team performance, and interviews with the participating operators. Performance criteria were based on the performance scores and the topics identified by interviews and expert observations. The results showed that the crew using the new TCS clearly could achieve their overall objectives in the test scenarios with sufficient situation awareness. The new TCS compared to the old system provided clear improvements of the control room work regarding shell and chest warming and regarding valve and other system testing. However, a number of Human Engineering Discrepancies (HEDs) were identified. HEDs were prioritized (level 1, 2 or 3) according to potential safety, plant availability, operability or personnel performance consequences. Since no priority 1 HEDs were identified there is no HFE reason not to continue implementation of the TCS. The TCS upgrade is acceptable if the Priority 2 HEDs are resolved and dispositioned in a satisfactory way. Priority 3 HEDs should be noted and considered for future modifications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1998

Interior Noise Reduction by Adaptive Feedback Vibration Control

The objective of this project is to investigate the possible use of adaptive digital filtering techniques in simultaneous, multiple-mode identification of the modal parameters of a vibrating structure in real-time. It is intended that the results obtained from this project will be used for state estimation needed in adaptive structural acoustics control. The work done in this project is basically an extension of the work on real-time single mode identification, which was performed successfully using a digital signal processor (DSP) at NASA, Langley. Initially, in this investigation the single mode identification work was duplicated on a different processor, namely the Texas Instruments TMS32OC40 DSP. The system identification results for the single mode case were very good. Then an algorithm for simultaneous two mode identification was developed and tested using analytical simulation. When it successfully performed the expected tasks, it was implemented in real-time on the DSP system to identify the first two modes of vibration of a cantilever aluminum beam. The results of the simultaneous two mode case were good but some problems were identified related to frequency warping and spurious mode identification. The frequency warping problem was found to be due to the bilinear transformation used in the algorithm to convert the system transfer function from the continuous-time domain to the discrete-time domain. An alternative approach was developed to rectify the problem. The spurious mode identification problem was found to be associated with high sampling rates. Noise in the signal is suspected to be the cause of this problem but further investigation will be needed to clarify the cause. For simultaneous identification of more than two modes, it was found that theoretically an adaptive digital filter can be designed to identify the required number of modes, but the algebra became very complex which made it impossible to implement in the DSP system used in this study. The on-line identification algorithm developed in this research will be useful in constructing a state estimator for feedback vibration control.

Lim, Tae W.↗

DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS2011

Musculoskeletal Modeling Component of the NASA Digital Astronaut Project

The NASA Digital Astronaut Project s (DAP) objective is to provide computational tools that support research of the physiological response to low gravity environments and analyses of how changes cause health and safety risks to the astronauts and to the success of the mission. The spaceflight risk associated with muscle atrophy is impaired performance due to reduced muscle mass, strength and endurance. Risks of early onset of osteoporosis and bone fracture are among the spaceflight risks associated with loss of bone mineral density. METHODS: Tools under development include a neuromuscular model, a biomechanical model and a bone remodeling model. The neuromuscular model will include models of neuromuscular drive, muscle atrophy, fiber morphology and metabolic processes as a function of time in space. Human movement will be modeled with the biomechanical model, using muscle and bone model parameters at various states. The bone remodeling model will allow analysis of bone turnover, loss and adaptation. A comprehensive trade study was completed to identify the current state of the art in musculoskeletal modeling. The DAP musculoskeletal models will be developed using a combination of existing commercial software and academic research codes identified in the study, which will be modified for use in human spaceflight research. These individual models are highly dependent upon each other and will be integrated together once they reach sufficient levels of maturity. ANALYSES: The analyses performed with these models will include comparison of different countermeasure exercises for optimizing effectiveness and comparison of task requirements and the state of strength and endurance of a crew member at a particular time in a mission. DISCUSSION: The DAP musculoskeletal model has the potential to complement research conducted on spaceflight induced changes to the musculoskeletal system. It can help with hypothesis formation, identification of causative mechanisms and supplementing small data samples.

Lewandowski, B. E.↗