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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Dual Microstructure Heat Treatment of a Nickel-Base Disk Alloy Assessed

Gas turbine engines for future subsonic aircraft will require nickel-base disk alloys that can be used at temperatures in excess of 1300 F. Smaller turbine engines, with higher rotational speeds, also require disk alloys with high strength. To address these challenges, NASA funded a series of disk programs in the 1990's. Under these initiatives, Honeywell and Allison focused their attention on Alloy 10, a high-strength, nickel-base disk alloy developed by Honeywell for application in the small turbine engines used in regional jet aircraft. Since tensile, creep, and fatigue properties are strongly influenced by alloy grain size, the effect of heat treatment on grain size and the attendant properties were studied in detail. It was observed that a fine grain microstructure offered the best tensile and fatigue properties, whereas a coarse grain microstructure offered the best creep resistance at high temperatures. Therefore, a disk with a dual microstructure, consisting of a fine-grained bore and a coarse-grained rim, should have a high potential for optimal performance. Under NASA's Ultra-Safe Propulsion Project and Ultra-Efficient Engine Technology (UEET) Program, a disk program was initiated at the NASA Glenn Research Center to assess the feasibility of using Alloy 10 to produce a dual-microstructure disk. The objectives of this program were twofold. First, existing dual-microstructure heat treatment (DMHT) technology would be applied and refined as necessary for Alloy 10 to yield the desired grain structure in full-scale forgings appropriate for use in regional gas turbine engines. Second, key mechanical properties from the bore and rim of a DMHT Alloy 10 disk would be measured and compared with conventional heat treatments to assess the benefits of DMHT technology. At Wyman Gordon and Honeywell, an active-cooling DMHT process was used to convert four full-scale Alloy 10 disks to a dual-grain microstructure. The resulting microstructures are illustrated in the photomicrographs. The fine grain size in the bore can be contrasted with the coarse grain size in the rim. Testing (at NASA Glenn) of coupons machined from these disks showed that the DMHT approach did indeed produce a high-strength, fatigue resistant bore and a creep-resistant rim. This combination of properties was previously unobtainable using conventional heat treatments, which produced disks with a uniform grain size. Future plans are in place to spin test a DMHT disk under the Ultra Safe Propulsion Project to assess the viability of this technology at the component level. This testing will include measurements of disk growth at a high temperature as well as the determination of burst speed at an intermediate temperature.

Gayda, John↗
NASA NTRS1988

Fatigue Lives Of Laser-Cut Metals

Fatigue lives made to approach those attainable by traditional grinding methods. Fatigue-test specimens prepared from four metallic alloys, and material removed from specimens by manual grinding, by Nd:glass laser, and by Nd:YAG laser. Results of fatigue tests of all specimens indicated reduction of fatigue strengths of laser-fired specimens. Laser machining holds promise for improved balancing of components of gas turbines.

Martin, Michael R.↗
NASA NTRS1991

The effects of multiple repairs on Inconel 718 weld mechanical properties

Inconel 718 weldments were repaired 3, 6, 9, and 13 times using the gas tungsten arc welding process. The welded panels were machined into mechanical test specimens, postweld heat treated, and nondestructively tested. Tensile properties and high cycle fatigue life were evaluated and the results compared to unrepaired weld properties. Mechanical property data were analyzed using the statistical methods of difference in means for tensile properties and difference in log means and Weibull analysis for high cycle fatigue properties. Statistical analysis performed on the data did not show a significant decrease in tensile or high cycle fatigue properties due to the repeated repairs. Some degradation was observed in all properties, however, it was minimal.

Russell, C. K.↗
NASA NTRS2012

Simulated Single Tooth Bending of High Temperature Alloys

Future unmanned space missions will require mechanisms to operate at extreme conditions in order to be successful. In some of these mechanisms, very high gear reductions will be needed to permit very small motors to drive other components at low rotational speed with high output torque. Therefore gearing components are required that can meet the mission requirements. In mechanisms such as this, bending fatigue strength capacity of the gears is very important. The bending fatigue capacity of a high temperature, nickel-based alloy, typically used for turbine disks in gas turbine engines and two tool steel materials with high vanadium content, were compared to that of a typical aerospace alloy-AISI 9310. Test specimens were fabricated by electro-discharge machining without post machining processing. Tests were run at 24 and at 490 C. As test temperature increased from 24 to 490 C the bending fatigue strength was reduced by a factor of five.

Handschuh, Robert, F.↗
NASA NTRS2010

Simulated Service and Stress Corrosion Cracking Testing for Friction Stir Welded Spun Form Domes

Damage tolerance testing development was required to help qualify a new spin forming dome fabrication process for the Ares 1 program at Marshall Space Flight Center (MSFC). One challenge of the testing was due to the compound curvature of the dome. The testing was developed on a sub-scale dome with a diameter of approximately 40 inches. The simulated service testing performed was based on the EQTP1102 Rev L 2195 Aluminum Lot Acceptance Simulated Service Test and Analysis Procedure generated by Lockheed Martin for the Space Shuttle External Fuel Tank. This testing is performed on a specimen with an induced flaw of elliptical shape generated by Electrical Discharge Machining (EDM) and subsequent fatigue cycling for crack propagation to a predetermined length and depth. The specimen is then loaded in tension at a constant rate of displacement at room temperature until fracture occurs while recording load and strain. An identical specimen with a similar flaw is then proof tested at room temperature to imminent failure based on the critical offset strain achieved by the previous fracture test. If the specimen survives the proof, it is then subjected to cryogenic cycling with loads that are a percentage of the proof load performed at room temperature. If all cryogenic cycles are successful, the specimen is loaded in tension to failure at the end of the test. This standard was generated for flat plate, so a method of translating this to a specimen of compound curvature was required. This was accomplished by fabricating a fixture that maintained the curvature of the specimen rigidly with the exception of approximately one-half inch in the center of the specimen containing the induced flaw. This in conjunction with placing the center of the specimen in the center of the load train allowed for successful testing with a minimal amount of bending introduced into the system. Stress corrosion cracking (SCC) tests were performed using the typical double beam assembly and with 4-point loaded specimens under alternate immersion conditions in a 3.5% NaCl environment for 90 days. In addition, experiments were conducted to determine the threshold stress intensity factor for SCC (K1SCC) of Al-Li 2195 which to our knowledge has not been determined previously. The successful simulated service and stress corrosion testing helped to provide confidence to continue to Ares 1 scale dome fabrication.

Stewart, Thomas J.↗
NASA NTRS1986

Corrosion fatigue of 2219-T87 aluminum alloy

Corrosion fatigue studies were conducted on bare, chemical conversion coated, and anodized 2219-T87 aluminum alloy. These tests were performed using a rotating beam machine running at a velocity of 2500 rpm. The corrosive environments tested were distilled water, 100 ppm NaCl, and 3.5 percent NaCl. Results were compared to the endurance limit in air. An evaluation of the effect of protective coatings on corrosion fatigue was made by comparing the fatigue properties of specimens with coatings to those without.

Mcmillan, V. C.↗
NASA NTRS2002

Initial Mechanical Testing of Superalloy Lattice Block Structures Conducted

The first mechanical tests of superalloy lattice block structures produced promising results for this exciting new lightweight material system. The testing was performed in-house at NASA Glenn Research Center's Structural Benchmark Test Facility, where small subelement-sized compression and beam specimens were loaded to observe elastic and plastic behavior, component strength levels, and fatigue resistance for hundreds of thousands of load cycles. Current lattice block construction produces a flat panel composed of thin ligaments arranged in a three-dimensional triangulated trusslike structure. Investment casting of lattice block panels has been developed and greatly expands opportunities for using this unique architecture in today's high-performance structures. In addition, advances made in NASA's Ultra-Efficient Engine Technology Program have extended the lattice block concept to superalloy materials. After a series of casting iterations, the nickel-based superalloy Inconel 718 (IN 718, Inco Alloys International, Inc., Huntington, WV) was successfully cast into lattice block panels; this combination offers light weight combined with high strength, high stiffness, and elevated-temperature durability. For tests to evaluate casting quality and configuration merit, small structural compression and bend test specimens were machined from the 5- by 12- by 0.5-in. panels. Linear elastic finite element analyses were completed for several specimen layouts to predict material stresses and deflections under proposed test conditions. The structural specimens were then subjected to room-temperature static and cyclic loads in Glenn's Life Prediction Branch's material test machine. Surprisingly, the test results exceeded analytical predictions: plastic strains greater than 5 percent were obtained, and fatigue lives did not depreciate relative to the base material. These assets were due to the formation of plastic hinges and the redundancies inherent in lattice block construction, which were not considered in the simplified computer models. The fatigue testing proved the value of redundancies since specimen strength was maintained even after the fracture of one or two ligaments. This ongoing test program is planned to continue through high-temperature testing. Also scheduled for testing are IN 718 lattice block panels with integral face sheets, as well as specimens cast from a higher temperature alloy. The initial testing suggests the value of this technology for large panels under low and moderate pressure loadings and for high-risk, damage-tolerant structures. Potential aeropropulsion uses for lattice blocks include turbine-engine actuated panels, exhaust nozzle flaps, and side panel structures.

Krause, David L.↗
DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗
NASA NTRS1990

Axial-torsional fatigue: A study of tubular specimen thickness effects

A room-temperature experimental program was conducted on AISI type 316 stainless steel to determine the effect of wall thickness on the cyclic deformation behavior and fatigue life of thin-wall, tubular, axial-torsional fatigue specimens. The following experimental variables were examined in this study: the depth of the surface work-hardened layer produced in specimen machining, and the effects of strain range and axial-torsional strain phasing. Tubular fatigue specimens were fabricated with wall thicknesses of 1.5, 2.0, and 2.5 mm. One as-fabricated specimen from each wall thickness was sectioned for microstructural examination and microhardness measurement. A specimen of each wall thickness was tested at each of three conditions - high strain range in-phase, low strain range in-phase, and low strain range out-of-phase - for a total of nine axial-torsional fatigue experiments. The machining-induced work-hardened zone, as a percentage of the gage section material, was found to have a minimal effect on both deformation behavior and fatigue life. Also, little or no variation in fatigue life or deformation behavior as a function of wall thickness was observed. Out-of-phase fatigue tests displayed shorter fatigue lives and more cyclic hardening than in-phase tests.

Bonacuse, Peter J.↗
NASA NTRS1983

Mode 2 fatigue crack growth specimen development

A Mode II test specimen was developed which has potential application in understanding phemonena associated with mixed mode fatigue failures in high performance aircraft engine bearing races. The attributes of the specimen are: it contains one single ended notch, which simplifiers data gathering and reduction; the fatigue crack grous in-line with the direction of load application; a single axis test machine is sufficient to perform testing; and the Mode I component is vanishingly small.

Buzzard, R. J.↗
NASA NTRS1986

Mode II fatigue crack growth specimen development

A Mode II test specimen was developed which has potential application in understanding phemonena associated with mixed mode fatigue failures in high performance aircraft engine bearing races. The attributes of the specimen are: it contains one single ended notch, which simplifies data gathering and reduction; the fatigue crack grows in-line with the direction of load application; a single axis test machine is sufficient to perform testing; and the Mode I component is vanishingly small.

Buzzard, R. J.↗
NASA NTRS1989

A fatigue study of electrical discharge machine (EDM) strain-gage balance materials

A fatigue study was undertaken to determine how much electrical-discharge-machine (EDM) processing affected the fatigue life of balance materials: EDM and regular milling-machine (MM) samples were compared. Simulation of a typical balance stress configuration was devised for the fatigue testing in order to obtain results more closely related to balance situations. The fatigue testing of the EDM and MM specimens has indicated that the EDM technique does indeed reduce the fatigue life of 15-5PH steel, the first balance material tested. This conclusion was based on comparisons of the specimen fatigue lives with theoretical and manufacturer's data. Hence the EDM surface effects are detrimental to the fatigue life of this balance material.

Rhew, Ray D.↗
DOE OSTI2024

FY24 Integrated Results for High-Temperature Mechanical Testing of LPBF 316H Stainless Steel

This report provides the mechanical test data of laser powder bed fusion (LPBF) 316H stainless steel (SS) collected during Fiscal Year 2024 (FY 24) under the US Department of Energy, Office of Nuclear Energy’s Advanced Materials and Manufacturing Technologies program, along with the current state of microstructural-based understandings of the behaviors. Materials with variabilities in manufacturing site, machine, laser parameters, powder chemistry, porosity, specimen geometry and heat treatment were tested in high-temperature tension, creep, fatigue and creep-fatigue. Electron microscopy and optical microscopy were performed on selected materials before and after the tests to provide microstructural-based understandings to the mechanical behavior. It was discovered that the as-built (AB) and stress-relieved (SR) materials exhibit similar behaviors in tension and creep, as do the solution-annealed (SA) and hot-isostatic pressed (HIP) materials. The AB and SR materials are softer but more ductile than the SA and HIP materials in tension. In creep, the LPBF materials have comparable rupture times but lower rupture strains compared to the wrought materials. The AB and SR materials have low creep rupture strains (<10%) when tested at 720°C and 800°C, while the SA and HIP materials are much more ductile. The LPBF materials exhibit large scatter in fatigue and creep-fatigue lives. The cyclic lives of creep-fatigue tests are lower than those of fatigue tests. In fatigue and creep-fatigue, SR materials outperform SA materials. A common observation from the cyclic tests is that, given the same post-build heat treatment, a lower initial peak stress generally results in a longer cyclic life regardless of hold time. It was also discovered that the batch variation can be more impactful than heat treatment, as demonstrated by the overall better performance of one batch of material than another, regardless of the heat treatment. The results provided insights into how different factors impact the behaviors of LPBF 316H SS. An outlook to FY 25 work scope is provided.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Mode-II-Fracture Specimen And Holder

Test specimen and loading frame developed for fatigue and fracture testing of materials under mode-II (sliding-mode) loading. Assembly placed in compression-testing machine. Loads directed oppositely along centerline cause self-similar crack to propagate. Enables consistently accurate alignment of specimens before insertion of specimen/frame assemblies into compression-testing machine. Makes design attractive for testing in hostile environments in which access to machine or furnace limited. Additional feature, with little or no modification, placed horizontally into impact testing machine and subjected to loading at high speeds.

Buzzard, Robert J.↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗