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DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1982

Gas turbine rotor/case structural response to rotating stall: Experimental documentation and analytical approach

The forcing functions and structural responses characterizing gas turbine rotor/case system vibration due to rotating stall in an axial flow compressor are described. Two data sets with fundamentally different response characteristics are presented; one is supersynchronous and the other subsynchronous. Conventional beam element rotor dynamics analysis is shown to be severely limited in its ability to predict these responses. A new analytical approach, which significantly increases structural response predictive capability for these phenomena, is briefly discussed.

Haley, P. J.↗

NASA NTRSDate not supplied

Thermal Effects of Pressure Surge and Adiabatic Compression in the Priming of Propulsion and Propellant Transfer Systems

Surge pressures and adiabatic compression during priming of evacuated lines in propulsion and propellant transfer systems has been considered. Test data were obtained for desaturated and saturated water for surge pressures and adiabatic compression for various supply pressures and temperatures. Scaling of surge pressures a t different temperatures has been carried out with the aid of Joukowsky model. The thermal effects associated with surge pressure and the attendant adiabatic compression have been investigated with the a id of a simplified analytical model, which represents an extension of the model developed by Lecourt and Steelant. It has been shown that the predictions from the model compare satisfactorily with test data for a water/helium system, and existing test data for different liquid/gas combinations.

Pressure Surge↗

DOE OSTI2022

Correlations for the specific heat capacity of ( U x Pu 1 - x ) 1 - y Gd y O 2 - z derived from molecular dynamics

We report UO 2 is the primary conventional fuel used in most nuclear reactors with Gd 2 O 3 commonly added as a burnable absorber to produce a more level power distribution in the reactor core at the beginning of operation. It can also be mixed with other actinide oxides to produce mixed oxide (MOx) fuel. In this study, molecular dynamics simulations were used to predict the specific heat capacity of Gd-doped PuO 2 , UO 2 and (U, Pu)O 2 MOx accommodating Gd 3+ substituted at cation sites via two charge compensation mechanisms - oxygen vacancy formation and the oxidation of U 4+ to U 5+ . The specific heat capacity values for PuO 2 and UO 2 are in good agreement with other studies showing a distinct peak at high temperatures - above 1800 K. As Gd 3+ is added, the peak height reduces for each composition considered. An analytical fit was applied to the data where Gd 3+ was fully charge compensated by either oxygen vacancies or U 5+ . The expression was then validated by predicting the specific heat capacity for three compositions of (Ux Pu 1-x ) 1-y Gd y O 2-z containing both oxygen vacancies and U 5+ , and compared to molecular dynamics data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2024

Crew Health and Performance Integrated Data Architecture Project Updates

Future Human Exploration missions will face new constraints as crews move further from terrestrial communication, resupply, and the real-time support enjoyed by Low Earth Orbit missions today. Exploration crews will need to be more self-reliant and able to respond to emergencies without immediate support from ground-based personnel. A new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to assist the crew’s work, help maintain their health, and inform the decisions they make on these future Exploration missions. The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project is laying a foundation for these future technologies by integrating sources of data generated by and around the crew and then providing them though common data models and Application Programming Interfaces to external systems. The combined data model makes comprehensive Crew Health and Performance data accessible and more meaningful to the decision-making process. This presentation will describe the currently ongoing effort to develop and evaluate a path-to-flight concept of the CHP-IDA software, current integrations, updates to the architecture, and examples of exploration scenarios in which CHP-IDA would be used.

B Schmitt↗

NASA NTRS2024

Crew Health and Performance Integrated Data Architecture Project Update

Future Human Exploration missions will face new constraints as crews move further from terrestrial communication, resupply, and the real-time support enjoyed by Low Earth Orbit missions today. Exploration crews will need to be more self-reliant and able to respond to emergencies without immediate support from ground-based personnel. A new generation of technologies, employing advanced analytical and predictive modeling techniques, is needed to assist the crew’s work, help maintain their health, and inform the decisions they make on these future Exploration missions. The Crew Health and Performance Integrated Data Architecture (CHP-IDA) project is laying a foundation for these future technologies by integrating sources of data generated by and around the crew then providing them though common data models and Application Programming Interfaces to external systems. The combined data model makes comprehensive Crew Health and Performance data accessible and more meaningful to the decision-making process. This presentation will describe the currently ongoing effort to develop a path-to-flight concept of the CHP-IDA software, current integrations that have been developed, updates to the architecture, and examples of the corresponding exploration scenarios in which CHP-IDA would be used.

Exploration↗

DOE OSTI2022

HPC Analytics of Fused Thermal Plants Data to Optimize Operating Envelope

In this project, ORNL extensively reviewed the ORAP RAM data, and it guided us to develop machine learning models that can predict time to next failures and forecast failure trends, which will be useful for optimizing power plant operation strategies. More specifically, we trained multiple random forest models and evaluated the model accuracy to validate with 10+ years of historical data. In addition, we implemented a web-based graphical user interface system for the models to show how our models can be used in more intuitive ways. This proof of concept allowed exploration of model use with power plant operators in mind. Developed machine learning models will be helpful for managing risks, planning maintenance and operation, ultimately reducing the down time and increasing the service hours. For future work, there are several interesting research topics including but not limited to model enhancement, creating synergy with traditional failure modeling approaches, and data-driven actionable recommendation and suggestions.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS2019

Test and Analysis Correlation for Sandwich Composite Longitudinal Joint Specimens

The NASA Composite Technology for Exploration (CTE) project is tasked with evaluating methods to analyze and manufacture composite joints for potential use in block upgrades to the Space Launch System (SLS) launch-vehicle structures such as the Payload Attach Fitting (PAF). To perform this task, the CTE project has initiated test and analysis correlation studies for composite joints under various loading conditions. Herein, NASA-developed numerical models are correlated with the experimental results from a series of tension tests. Pretest strain results matched the far-field test data well, but did not capture the nonlinear response in the vicinity of the joint. A refined pretest analytical model was modified to represent progressive failure of the specimens at failure locations observed during the experimental tests. The nonlinear strain response from this progressive failure model predicted the delamination failure load within 15% of the test data, but underpredicted the nonlinearity of the strain response. Further study of composite material models that account for the nonlinear shear response of fabric composites is recommended for the composite joint structures considered in this paper.

Mason, Brian H.↗

NASA NTRS2019

Correlation of Full-Scale Isolated Proprotor Performance and Loads

A full-scale isolated proprotor test was recently conducted in the USAF National Full-Scale Aerodynamics Complex (NFAC) at NASA Ames Research Center. The test article was a 3-bladed research rotor derived from the right-hand rotor of the AW609. For this test, the NASA Tiltrotor Test Rig (TTR) and rotor were installed in the 40- by 80-foot test section. This paper presents correlations between data and predictions of rotor performance and blade moments using the newly acquired test data and the comprehensive analysis CAMRAD II. The operational conditions covered in this analytical study are: hover (actually, low speed vertical climb), cruise (airplane mode), conversion, and helicopter mode. Mean and 1/2 peak-to-peak quantities (hpp) are correlated; time-history correlation for the helicopter condition is also included. The correlation is reasonable to good. Also, the hover calculations turned out to be useful in providing reality checks on the test hardware such as: a) the functioning of the blade strain gages and b) calibration of the measurement of the collective pitch hardware. The time-history correlation shows that, compared to the rolled-up wake model, the multiple-trailer wake model improves the correlation slightly; the longitudinal cyclic correlation is reasonable but the lateral cyclic correlation is not good, and the collective is predicted well by the rolled-up wake model; the flap moment correlation is reasonable; the pitch link load and lag moment are underpredicted; and the torsion moment correlation is poor and needs further study.

Kottapalli, Sesi↗

DOE OSTI2022

Worldwide Physics-Based Lifetime Prediction of c-Si Modules Due to Solder-Bond Failure

Lifetime prediction of the fielded c-Si solar modules due to location-specific weather conditions has been an important topic of photovoltaic research and the economic viability of solar energy. Data analytic techniques such as the performance ratio method, Statistical clear sky model, and Suns-Vmp methods quantify the degradation from measured data of a solar farm, however, the nonlinear time-dependence and correlated degradations make it difficult to use the empirical degradation rates for ultimate lifetime projection. In this article, we propose a complementary physics-based model to predict the solder bond failure caused by mechanical stress associated with the variations of the temperature. Integrating the worldwide weather information from NASA/NSRDB databases, the model predicts the location-specific output-power degradation and the lifetime of a module due to solder bond failure. The model parameters are calibrated against qualification tests involving thermal cycling of specific batches of modules from a specific technology/manufacturer. The results may be summarized as: 1) Modules installed at higher latitudes show a longer lifetime due to reduced damage accumulation. 2) The reduction of temperature fluctuation close to large bodies of water, such as seashores, increases solder bond lifetime significantly. 3) Relatively speaking, modules installed close to the Tropic of Cancer/Capricorn (23.5 degrees North/South) suffer from a higher solder bond damage and have a shorter lifetime, suggesting a conservative design. This model should serve as a building block of a comprehensive reliability framework that can predict the lifetime of a module that experiences simultaneous and correlated degradation mechanisms involving yellowing, corrosion, and potential-induced degradation.

14 SOLAR ENERGY↗

DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Predictive Analytics for Hydropower Fleet Intelligence

A primary challenge in hydropower industry is the ability to maintain cost-competitiveness, reliability, and security of hydropower assets through evolving power system contexts and aging of the fleet. Maintaining cost-effective and reliable operations under these conditions is expected to require new modernization and maintenance paradigms for changing contexts. Changes in existing practices for O&M will require an understanding of the current state and health of hydropower assets, and the impact of changing paradigms on asset health and reliability. The Hydropower Fleet Intelligence project is developing and evaluating standardized methodologies and analysis tools for data-driven asset reliability and management technologies for hydropower, leading to eventual predictive maintenance planning, repair/replacement decision making, and asset-reliability and cost-optimized operations. A key question is the feasibility of using existing data sets at hydropower facilities to perform assessments of asset reliability. This document uses data from hydropower facilities to assess the potential for using available analytics methods for asset reliability estimates. In addition to reliability assessments, the feasibility of using existing analytics techniques for several other potential applications is discussed. Finally, a case study that a data-driven model is trained to learn nominal operations via vibration data from an asset of a certain plant, and then utilized to identify anomalies on a similar asset from a different plant, highlighting the generic use of proposed Prognostics and Health Management (PHM) approaches.

Yucesan, Yigit↗

DOE OSTI2024

Integration and validation of some modules for modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures

Three modules are integrated into the built-in OpenFOAM rhoCentralFoam solver towards accurate and efficient modelling of high-speed chemically reactive flows in two-phase gas-droplet mixtures within the OpenFOAM 10.0 framework. The first module is the mixture-averaged diffusion model. The second module is the built-in OpenFOAM Lagrangian solver coupled with optimised droplet drag coefficient and convective heat transfer coefficient sub-models. The last module is a sparse stiff chemistry solver based on dynamic adaptive hybrid integration (AHI-S). The optimised droplet sub-models are first verified in correct implementation for subsequent simulations in this work. Further, they show good accuracy against experimental and analytical data in the modelling of ammonia droplet acceleration and cooling in the flowing and/or low-temperature air. The accuracy and efficiency gains related to the mixture-averaged diffusion model and the AHI-S chemistry solver are examined by simulating 1-D detonation propagation in ammonia droplet-free/laden ammoniaoxygen mixtures. Numerical results of detonation propagation speed, gaseous temperature, density, and species distributions around the induction zone show good agreement with experimental data and analytical solutions. Compared to the built-in OpenFOAM diffusion model, the mixture-averaged diffusion model provides different numerical predictions of pulsating instabilities in detonation propagation. It shows better accuracy in depicting the detonation structure within the droplet-free section attributed to improved multi-component diffusion modelling. Compared to the built-in OpenFOAM solver EulerImplicit (backward Euler), the AHI-S chemistry solver reduces the computational cost by around 50%. It achieves satisfactory accuracy in calculating detonation propagation speed within the droplet-free section with the optimal efficiency when the safety factor, β, equals 0.5.

42 ENGINEERING↗

DOE OSTI2022

Thermal conductivity tensor of γ and ε -hexanitrohexaazaisowurtzitane as a function of pressure and temperature

Using reverse non-equilibrium molecular dynamics simulations, we have determined the dependences on temperature and pressure of the thermal conductivity tensors for the monoclinic γ and ε polymorphs of hexanitrohexaazaisowurtzitane (HNIW or CL20). In this work, a recently developed non-reactive force field [X. Bidault and S. Chaudhuri, RSC Adv. 9, 39649–39661 (2019)], designed to study polymorphism and phase transitions in CL20, is employed. The effects of temperature and pressure are investigated between 200 and 500 K and up to 0.5 GPa for γ-CL20 and 2 GPa for ε-CL20. In order to obtain the full thermal conductivity tensor, $κ_{ij}$, for the monoclinic crystals, four distinct heat propagation directions are used. We find that $κ_{ij}$ for both polymorphs is more isotropic than for other energetic molecular crystals, including α- and γ-RDX, β-HMX, and PETN, with a maximum difference of 9.8% between orientations observed at 300 K and 0 GPa for γ-CL20 and a maximum difference of 4.8% for ε-CL20. The average thermal conductivity, $\bar{κ}$, of ε-CL20 is 6.4% larger than that of γ-CL20 at 300 K and 0 GPa. Analytic linear functions of the inverse temperature and the pressure are provided, which fit the data well and can be used to predict the thermal conductivity of both polymorphs for any orientation, pressure, and temperature in and around the fitting range. Our predictions agree reasonably well with the limited available experimental data, for which the polymorph type is unknown.

36 MATERIALS SCIENCE↗

NASA NTRS1980

Capillary acquisition devices for high-performance vehicles: Executive summary

Technology areas critical to the development of cryogenic capillary devices were studied. Passive cooling of capillary devices was investigated with an analytical and experimental study of wicking flow. Capillary device refilling with settled fluid was studied using an analytical and experimental program that resulted in successful correlation of a versatile computer program with test data. The program was used to predict Centaur D-1S LO2 and LH2 start basket refilling. Comparisons were made between the baseline Centaur D-1S propellant feed system and feed system alternatives including systems using capillary devices. The preferred concepts from the Centaur D-1S study were examined for APOTV and POTV vehicles for delivery and round trip transfer of payloads between LEO and GEO. Mission profiles were determined to provide propellant usage timelines and the payload partials were defined.

Blatt, M. H.↗

NASA NTRS1980

ET LOX modal survey analysis and test assessment

The results are presented of the analysis and modal test of the liquid oxygen tank and intertank of the space shuttle external tank. The analytical models, test article, and support hardware are described. Frequency and damping are compared on a mode-by-mode basis. The data assessment clearly confirms the validity of the analytical model and establishes a high level of confidence that the methodology will accurately predict modal characteristics of flight configurations.

Mccomas, R. L.↗

NASA NTRS1982

Benchmark notch test for life prediction

Aircraft gas turbine engine components are subjected to severe stress, temperature, and environmental conditions. Economic and reliabilty demands have prompted inordinate effort in development of analytic methods to predict stresses and strains in aircraft engines. There remains, however, the need to check or verify these analytical methodologies against actual experimental data measurements. The laser interferometric strain displacement gage was recognized as having the potential to accomplish this task and was employed in this program. The actual strains incurred at the root of a discontinuity in cyclically loaded test samples subjected to inelastic deformation at high temperature where creep deformation readily occur were measured. The steady-state, cyclic stress-strain response at the root of the discontinuity in the tested samples was analyzed for comparison with the measured results. A comprehensive set of local notch root strain measurements for a variety of load patterns in an Inconel 718 notch specimen at 649 C (1200 F) was obtained and documented using the laser interferometric strain displacement gage.

Domas, P. A.↗

NASA NTRS1987

Modeling of joints for the dynamic analysis of truss structures

An experimentally-based method for determining the stiffness and damping of truss joints is described. The analytical models use springs and both viscous and friction dampers to simulate joint load-deflection behavior. A least-squares algorithm is developed to identify the stiffness and damping coefficients of the analytical joint models from test data. The effects of nonlinear joint stiffness such as joint dead band are also studied. Equations for predicting the sensitivity of beam deformations to changes in joint stiffness are derived and used to show the level of joint stiffness required for nearly rigid joint behavior. Finally, the global frequency sensitivity of a truss structure to random perturbations in joint stiffness is discussed.

Belvin, W. Keith↗