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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Model-Trained Neural Networks and Electronic Holography Demonstrated to Detect Damage in Blades

Detect Damage in Blades Electronic holography can show damaged regions in fan blades at 30 frames/sec. The electronic holograms are transformed by finite-element-model-trained artificial neural networks to visualize the damage. The trained neural networks are linked with video and graphics to visualize the bending-induced strain distribution, which is very sensitive to damage. By contrast, it is very difficult to detect damage by viewing the raw, speckled, characteristic fringe patterns. For neural-network visualization of damage, 2 frames or 2 fields are used, rather than the 12 frames normally used to compute the displacement distribution from electronic holograms. At the NASA Lewis Research Center, finite element models are used to compute displacement and strain distributions for the vibration modes of undamaged and cracked blades. A model of electronic time-averaged holography is used to transform the displacement distributions into finite-element-resolution characteristic fringe patterns. Then, a feedforward neural network is trained with the fringe-pattern/strain-pattern pairs, and the neural network, electronic holography, and video are implemented on a workstation. Now that the neural networks have been tested successfully at 30 frames/sec on undamaged and cracked cantilevers, the electronic holography and neural-network processing are being adapted for onsite damage inspection of twisted fan blades and rotormounted blades. Our conclusion is that model-trained neural nets are effective when they are trained with good models whose application is well understood. This work supports the aeromechanical testing portion of the Advanced Subsonic Technology Project.

Decker, Arthur J.↗
NASA NTRS2005

Comparison of Grammar-Based and Statistical Language Models Trained on the Same Data

This paper presents a methodologically sound comparison of the performance of grammar-based (GLM) and statistical-based (SLM) recognizer architectures using data from the Clarissa procedure navigator domain. The Regulus open source packages make this possible with a method for constructing a grammar-based language model by training on a corpus. We construct grammar-based and statistical language models from the same corpus for comparison, and find that the grammar-based language models provide better performance in this domain. The best SLM version has a semantic error rate of 9.6%, while the best GLM version has an error rate of 6.0%. Part of this advantage is accounted for by the superior WER and Sentence Error Rate (SER) of the GLM (WER 7.42% versus 6.27%, and SER 12.41% versus 9.79%). The rest is most likely accounted for by the fact that the GLM architecture is able to use logical-form-based features, which permit tighter integration of recognition and semantic interpretation.

Hockey, Beth Ann↗
NASA NTRS2015

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Leser, Patrick E.↗
NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗
NASA NTRS1997

The Design of Model-Based Training Programs

This paper proposes a model-based training program for the skills necessary to operate advance avionics systems that incorporate advanced autopilots and fight management systems. The training model is based on a formalism, the operational procedure model, that represents the mission model, the rules, and the functions of a modem avionics system. This formalism has been defined such that it can be understood and shared by pilots, the avionics software, and design engineers. Each element of the software is defined in terms of its intent (What?), the rationale (Why?), and the resulting behavior (How?). The Advanced Computer Tutoring project at Carnegie Mellon University has developed a type of model-based, computer aided instructional technology called cognitive tutors. They summarize numerous studies showing that training times to a specified level of competence can be achieved in one third the time of conventional class room instruction. We are developing a similar model-based training program for the skills necessary to operation the avionics. The model underlying the instructional program and that simulates the effects of pilots entries and the behavior of the avionics is based on the operational procedure model. Pilots are given a series of vertical flightpath management problems. Entries that result in violations, such as failure to make a crossing restriction or violating the speed limits, result in error messages with instruction. At any time, the flightcrew can request suggestions on the appropriate set of actions. A similar and successful training program for basic skills for the FMS on the Boeing 737-300 was developed and evaluated. The results strongly support the claim that the training methodology can be adapted to the cockpit.

Polson, Peter↗
NASA NTRS2020

Medics: Medical Decision Support System for Long-Duration Space Exploration

The Autonomous Medical Operations (AMO) group at NASA Ames is developing a “medical decision support system” to enable astronauts on long-duration exploration missions to operate autonomously. The system will support clinical actions by providing medical interpretation advice and procedural recommendations during emergent care and clinical work performed by crew. The current state of development of the system, called MedICS (Medical Interpretation Classification and Segmentation) includes two separate aspects: a set of machine learning diagnostic models trained to analyze organ images and patient health records, and an interface to ultrasound diagnostic hardware and to medical repositories. Three sets of images of different organs and medical records were utilized for training machine learning models for various analyses, as follows: 1. Pneumothorax condition (collapsed lung). The trained model provides a positive or negative diagnosis of the condition. 2. Carotid artery occlusion. The trained model produces a diagnosis of 5 different occlusion levels (including “normal”). 3. Ocular retinal images. The model extracts optic disc pixels (image segmentation). This is a precursor step for advanced autonomous fundus clinical evaluation algorithms to be implemented in FY20. 4. Medical health records. The model produces a differential diagnosis for any particular individual, based on symptoms and other health and demographic information. A probability is calculated for each of 25 most common conditions. The same model provides the likelihood of survival. All results are provided with a confidence level. Item 1 images were provided by the US Army and were part of a data set for the clinical treatment of injured battlefield soldiers. This condition is relevant to possible space mishaps, due to pressure management issues. Item 2 images were provided by Houston Methodist Hospital, and item 3 health records were acquired from the MIT laboratory of computational physiology. The machine learning technology utilized is deep multilayer networks (Deep Learning), and new models will continue to be produced, as relevant data is made available and specific health needs of astronaut crews are identified. The interfacing aspects of the system include a GUI for running the different models, and retrieving and storing data, as well as support for integration with an augmented reality (AR) system deployed at JSC by Tietronix Software Inc. (HoloLens). The AR system provides guidance for the placement of an ultrasound transducer that captures images to be sent to the MedICS system for diagnosis. The image captured and the associated diagnosis appear in the technician’s AR visual display.

Colombano, Silvano↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
NASA NTRSDate not supplied

Humans to Mars, but How Many? Using Training Requirements Modeling to Inform Crew Size

Missions to Mars will differ from previous human spaceflight missions in that the onboard crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays. Without a systematic, repeatable process to determine the number and composition of crew necessary to successfully accomplish these missions, NASA increases the risk that crew sizes may be too small to meet primary mission objectives under nominal conditions and, more consequentially, that crewmembers may not have the expertise needed to successfully respond to unforeseen failures without the real-time expertise of the Mission Control Central (MCC) team NASA currently relies upon. The NASA Engineering and Safety Center (NESC) is developing a methodology for assessing the trade space of factors that affect the number of crew for future missions. This methodology includes the consideration of results from three human performance models developed using the Improved Performance Research and Integration Tool (IMPRINT) modeling platform as well as a custom-built model on expertise trained within the crew. The IMPRINT results will be presented in the modeling and simulation sub-tag. Here we present results of a model based on NASA’s crew qualification and responsibility matrix (CQRM), a tool used to identify the crew qualifications for each area of responsibility (operation, system, and payload) for a mission. The model outputs an optimized allocation of training assignments along with a flight-assigned CQRM that can be used to consider the expertise that can be trained within a crew of a given size. We discuss the CQRM model result implications on the trade space for Mars mission crew size.

Mars↗
NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗
NASA NTRS2024

Humans to Mars, But How Many? Using Training Qualifications Modeling to Inform Number of Crew

Missions to Mars will differ from all previous human spaceflight missions in that the crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays, making the decision on crew size critical to mission success. To support National Aeronautics and Space Administration (NASA) decision makers, NASA’s Engineering and Safety Center (NESC) developed a quantitative methodology for assessing the number of crew necessary to meet primary mission objectives and respond to unforeseen failures based on modeling of NASA’s crew training qualifications. We present results of a custom-built model of a Mars crew qualification and responsibility matrix (CQRM) and discuss the implications on trade space analysis of Mars mission crew size.

Mars↗
NASA NTRS2024

Humans to Mars, But How Many? Using Training Qualification Modeling to Inform Number of Crew

Missions to Mars will differ from all previous human spaceflight missions in that the crew of astronauts will be required to operate in an Earth-independent manner due to the long communication delays, making the decision on crew size critical to mission success. To support National Aeronautics and Space Administration (NASA) decision makers, NASA’s Engineering and Safety Center (NESC) developed a quantitative methodology for assessing the number of crew necessary to meet primary mission objectives and respond to unforeseen failures based on modeling of NASA’s crew training qualifications. We present results of a custom-built model of a Mars crew qualification and responsibility matrix (CQRM) and discuss the implications on trade space analysis of Mars mission crew size.

Mars↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This paper describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

machine learning↗
NASA NTRS2021

Real-time Unimpeded Taxi Out Machine Learning Service

This presentation describes a study on the estimation of the unimpeded taxi out time using Machine Learning (ML) tools and proposes an implementation that can be used to make real-time predictions at any airport in the National Airspace System. Kedro, an open-source pipeline framework, is used to develop the model definition and training. Models are stored in scikit-learn containers on a MLFlow server where they can be retrieved and served to make predictions in the live system. These open source frameworks provide common structures between ML services, allow for easier maintenance and updates, and overall deliver an easier CI/CD (Continuous Integration/Continuous Deployment) process. The current models were trained on data acquired at KCLT and KDFW from June 1st to December 31st, 2019 and compute taxi time in the ramp, airport movement area (AMA) and total (from gates to runways). The current versions of the models achieve relatively low uncertainties of about 10 to 15% for the total and AMA taxi times and about 20% for the ramp taxi time at both KCLT and KDFW. Initial tests on offline data from 2020 and 2021 show a small degradation (10 to 15%) in accuracy performance indicating the model’s resilience to operational changes over time.

Machine Learning↗
NASA NTRS1985

WEST-3 wind turbine simulator development. Volume 2: Verification

The details of a study to validate WEST-3, a new time wind turbine simulator developed by Paragib Pacific Inc., are presented in this report. For the validation, the MOD-0 wind turbine was simulated on WEST-3. The simulation results were compared with those obtained from previous MOD-0 simulations, and with test data measured during MOD-0 operations. The study was successful in achieving the major objective of proving that WEST-3 yields results which can be used to support a wind turbine development process. The blade bending moments, peak and cyclic, from the WEST-3 simulation correlated reasonably well with the available MOD-0 data. The simulation was also able to predict the resonance phenomena observed during MOD-0 operations. Also presented in the report is a description and solution of a serious numerical instability problem encountered during the study. The problem was caused by the coupling of the rotor and the power train models. The results of the study indicate that some parts of the existing WEST-3 simulation model may have to be refined for future work; specifically, the aerodynamics and procedure used to couple the rotor model with the tower and the power train models.

Sridhar, S.↗
NASA NTRS2021

Predicting Arrival and Departure Runway Assignments with Machine Learning

Runway assignments at major airports are made by air traffic controllers subject to various constraints, and to achieve various objectives. In this research, we describe our efforts training machine learning (ML) models to predict both departure and arrival runway assignments using an entirely data-driven approach. This approach is compared to existing rule-based approaches developed in previous research using input from Subject Matter Experts. The models have features derived from various FAA data feeds, and leverage multiple machine learning algorithms. Results for models trained for nine major U.S. airports are described and compared to one another across various important dimensions. Particular attention was paid to developing a repeatable framework for training these models so the approach could be scaled to other airports, and to developing models that are useful in a real-time environment. In addition, the models were designed to be functional in a real-time environment to support NASA’s ATD-2 project, as part of an ML-powered shadow system to compare against the performance of the fielded system.

machine learning↗