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The Gmao Hybrid 4d-Envar Observing System Simulation Experiment Framework.

This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.

Data assimilation↗

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE)

NASA is planning to return to the Moon in the mid-2020s as a stepping stone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support. The software used in APACHE was created by the JSC Engineering Directorate, in partnership with Buendea, powered by a custom Unreal Engine 5 (UE5.3, Epic Games) project. APACHE currently utilizes the HTC Vive Pro Eye in a wireless configuration for VR simulations. There are two virtual environments that subjects can explore within APACHE, a Lunar and Martian surface. The virtual Lunar surface was created from LIDAR data of the Lunar South Pole to create roughly 16 sq km of explorable terrain. The virtual Martian surface contains roughly 400 sq km of explorable terrain derived from Mars Reconnaissance Orbiter LIDAR data of the Jezero Crater. The immersion and related cognitive burdens of conducting a planetary EVA is simulated through a series of EVA-relevant tasks performed in the VR environment, using these high-fidelity visual representations. Additionally, APACHE includes biosensor driven informatics, such as real-time heart rate monitoring and/or derived values from model simulations, for active monitoring by the EV crew and added cognitive demand. A “Wizard of Oz” control panel enables test operators to activate contingency events such as simulated spacesuit malfunctions, loss of communications, and/or limited visibility. Embedded performance measures such as accuracy, completeness, and execution time have been developed for various exploration tasks to objectively quantify crew performance during an EVA and compare impacts to performance when different environmental stressors, both physical and cognitive, are added to or removed from the simulation. Additionally, validated cognitive and operational performance measures such as the Digit Symbol Substitution Task have been recreated and embedded in VR for direct and relatively unobtrusive measurement of motor perception. The APACHE environment currently supports multiple research studies at NASA. Examples include the CHAPEA project, a series of simulated year-long missions on Mars by a 4-person crew; and the CO2 Contingency Walk Back Study, an investigation of elevated CO2 exposure on crew performance during a contingency EVA scenario. APACHE also provides a test environment to support the development of the Crew State and Risk Model, which is a collection of individualized, mathematical models of crew physical and cognitive state; and the Personalized EVA Informatics and Decision Support system, an operational tool for flight controllers, and eventually a self-reliant Martian crew, to make biomedically-informed decisions in real-time to optimize the EVA planning and execution with respect to crew health and performance. Some technical challenges associated with developing the APACHE environment, as well as current limitations, include VR limitless natural walking with a hybrid spacesuit simulator, optimizing performance for wireless PC VR streaming while maintaining a high degree of visual fidelity, and the integration of various physiological (metabolic masks) and psychometric (eye tracking) sensors with the VR headset.

Human Performance↗

Lattice Confinement Fusion Fast Fission – A Hybrid Power System for Accessing Icy World Oceans

Lattice Confinement Fusion (LCF) is an emerging power technology that can be combined with nuclear fission to produce a hybrid innovative power system. The proposed innovation is a compact, scalable nuclear energy source that does not use highly enriched uranium (HEU), high-assay enriched uranium (HALEU), low enriched uranium (LEU) nor plutonium-238. The nuclear energy source consists of a hybrid fusion-fast-fission method whereby neutrons generated from LCF are used to fission materials such as depleted uranium or thorium. LCF has been demonstrated by both NASA (published in Physical Review C) and by Lawrence Berkeley National Laboratory (published in the Journal of Applied Physics). Although these methods are reminiscent of Low Energy Nuclear Reactions (LENR), both methods operate at much higher energies than any attempt at cold fusion. This new hybrid energy source is sufficient to provide power and heat for melting or boring through icy caps with untethered, autonomous probes. These probes can be used for planetary (i.e., Pluto), lunar (i.e., Enceladus), or asteroid (i.e., Ceres) exploration where icy caps are encountered. Each world may have a liquid water ocean beneath their ice crust. A robotic probe exploring the oceans beneath must either melt or bore through the ice crust first. Such a mission requires a small, but robust and long lived, electrical energy and heat source such as the LCF Fast Fission hybrid power system.

Theresa L. Benyo↗

Measuring the spectrum of mutation induced by nitrogen ions and protons in the human-hamster hybrid cell line A(L)C

Astronauts can be exposed to charged particles, including protons, alpha particles and heavier ions, during space flights. Therefore, studying the biological effectiveness of these sparsely and densely ionizing radiations is important to understanding the potential health effects for astronauts. We evaluated the mutagenic effectiveness of sparsely ionizing 55 MeV protons and densely ionizing 32 MeV/nucleon nitrogen ions using cells of two human-hamster cell lines, A(L) and A(L)C. We have previously characterized a spectrum of mutations, including megabase deletions, in human chromosome 11, the sole human chromosome in the human-hamster hybrid cell lines A(L)C and A(L). CD59(-) mutants have lost expression of a human cell surface antigen encoded by the CD59 gene located at 11p13. Deletion of genes located on the tip of the short arm of 11 (11p15.5) is lethal to the A(L) hybrid, so that CD59 mutants that lose the entire chromosome 11 die and escape detection. In contrast, deletion of the 11p15.5 region is not lethal in the hybrid A(L)C, allowing for the detection of chromosome loss or other chromosomal mutations involving 11p15.5. The 55 MeV protons and 32 MeV/nucleon nitrogen ions were each about 10 times more mutagenic per unit dose at the CD59 locus in A(L)C cells than in A(L) cells. In the case of nitrogen ions, the mutations observed in A(L)C cells were predominantly due to chromosome loss events or 11p deletions, often containing a breakpoint in the pericentromeric region. The increase in the CD59(-) mutant fraction for A(L)C cells exposed to protons was associated with either translocation of portions of 11q onto a hamster chromosome, or discontinuous or "skipping" mutations. We demonstrate here that A(L)C cells are a powerful tool that will aid in the understanding of the mutagenic effects of different types of ionizing radiation.

Non-NASA Center↗

Detection and quantification of Epstein-Barr virus EBER1 in EBV-infected cells by fluorescent in situ hybridization and flow cytometry

A rapid and highly sensitive fluorescent in situ hybridization (FISH) assay was developed to detect Epstein Barr virus (EBV)-infected cells in peripheral blood. Multiple fluorescein-labeled antisense oligonucleotide probes were designed to hybridize to the EBER1 transcript, which is highly expressed in latently infected cells. After a rapid (30 min) hybridization, the cells were analyzed by flow cytometry. EBER1 was detected in several positive control cell lines that have variable numbers of EBV genome copies. No EBER1 was detected in two known EBV-negative cell lines. Northern blot analyses confirmed the presence and quantity of EBER1 transcripts in each cell line. This method was used to quantify the number of EBV-infected cells in peripheral blood from a patient with chronic mononucleosis. These results indicate that EBV-infected cells can be detected at the single cell level, and that this assay can be used to quantify the number of EBV-infected cells in clinical samples.

NASA Discipline Regulatory Physiology↗

Hybrid Ground Station Technology for RF and Optical Communication Links

To support future enhancements of NASA's deep space and planetary communications and tracking services, the Jet Propulsion Laboratory is developing a hybrid ground station that will be capable of simultaneously supporting RF and optical communications. The main reason for adding optical links to the existing RF links is to significantly increase the capacity of deep space communications in support of future solar system exploration. It is envisioned that a mission employing an optical link will also use an RF link for telemetry and emergency purposes, hence the need for a hybrid ground station. A hybrid station may also reduce operations cost by requiring fewer staff than would be required to operate two stations. A number of approaches and techniques have been examined. The most promising ones have been prototyped for field examination and validation.

Radio frequency (RF) communications↗

High Energy Density Additives for Hybrid Fuel Rockets to Improve Performance and Enhance Safety

We propose a conceptual study of prototype strained hydrocarbon molecules as high energy density additives for hybrid rocket fuels to boost the performance of these rockets without compromising safety and reliability. Use of these additives could extend the range of applications for which hybrid rockets become an attractive alternative to conventional solid or liquid fuel rockets. The objectives of the study were to confirm and quantify the high enthalpy of these strained molecules and to assess improvement in rocket performance that would be expected if these additives were blended with conventional fuels. We confirmed the chemical properties (including enthalpy) of these additives. However, the predicted improvement in rocket performance was too small to make this a useful strategy for boosting hybrid rocket performance.

strained hydrocarbons↗

Hybrid Guidance Control for a Hypervelocity Small Size Asteroid Interceptor Vehicle

Near-Earth Objects (NEOs) are comets and/or asteroids that have orbits in proximity with Earth's own orbit. NEOs have collided with the Earth in the past, which can be seen at such places as Chicxulub crater, Barringer crater, and Manson crater, and will continue in the future with potentially significant and devastating results. Fortunately such NEO collisions with Earth are infrequent, but can happen at any time. Therefore it is necessary to develop and validate techniques as well as technologies necessary to prevent them. One approach to mitigate future NEO impacts is the concept of high-speed interceptor. This concept is to alter the NEO's trajectory via momentum exchange by using kinetic impactors as well as nuclear penetration devices. The interceptor has to hit a target NEO at relative velocity which imparts a sufficient change in NEO velocity. NASA's Deep Impact mission has demonstrated this scenario by intercepting Comet Temple 1, 5 km in diameter, with an impact relative speed of approximately 10 km/s. This paper focuses on the development of hybrid guidance navigation and control (GNC) algorithms for precision hypervelocity intercept of small sized NEOs. The spacecraft's hypervelocity and the NEO's small size are critical challenges for a successful mission as the NEO will not fill the field of view until a few seconds before intercept. The investigation needs to consider the error sources modeled in the navigation simulation such as spacecraft initial state uncertainties in position and velocity. Furthermore, the paper presents three selected spacecraft guidance algorithms for asteroid intercept and rendezvous missions. The selected algorithms are classical Proportional Navigation (PN) based guidance that use a first order difference to compute the derivatives, Three Plane Proportional Navigation (TPPN), and the Kinematic Impulse (KI). A manipulated Bennu orbit that has been changed to impact Earth will be used as a demonstrative example to compare the three methods. In addition, a hybrid approach that is a combination between proportional navigation and kinematic impulse will be investigated to find an effective, error tolerant, and power saving approach. A 3-dimension mission scenario for both the asteroid and the interceptor spacecraft software simulator is developed for testing of the controllers. The current result demonstrates that a miss distance magnitude of less than 10m is found using the PN and TPPN guidance laws for small asteroid in the presence of error in the spacecraft states. Moreover, the paper presents these results and also the hybrid control approach simulation results.

asteroid↗

Visions of the Future: Hybrid Electric Aircraft Propulsion

The National Aeronautics and Space Administration (NASA) is investing continually in improving civil aviation. Hybridization of aircraft propulsion is one aspect of a technology suite which will transform future aircraft. In this context, hybrid propulsion is considered a combination of traditional gas turbine propulsion and electric drive enabled propulsion. This technology suite includes elements of propulsion and airframe integration, parallel hybrid shaft power, turbo-electric generation, electric drive systems, component development, materials development and system integration at multiple levels.

electric motor vehicles↗

RTAPS (Research and Technology for Aerospace Propulsion Systems): Simulation of Structural Loads within a Hybrid Gear Resulting From Loading at the Gear Teeth

This report investigates the practical usage of hybrid structures for rotorcraft gearing. The primary driver for utilizing hybrid structures for rotorcraft gearing is to reduce the drive system weight. The hybrid structure concept featured in this study for rotorcraft gearing consists of a metallic gear tooth-rim, a web section manufactured from composite materials, and a metallic hub. The metallic gear tooth-rim is manufactured from conventional gear steel alloys, such as AISI 9310. The gear tooth-rim attaches to the outer diameter of the web section made from composite materials. The inner diameter of the composite web can then attach to a metallic hub, completing the assembly. It is assumed that areas of the shafting or hub where rolling element bearings may ride must remain as gear steel alloys for this study.

Hybrid Structures↗

Multilayered Functional Insulation System (MFIS) for AC Power Transmission in High Voltage Hybrid Electrical Propulsion

High voltage hybrid electric propulsion systems are now pushing new technology development efforts for air transportation. A key challenge in hybrid electric aircraft is safe high voltage distribution and transmission of megawatts of power (>20 MW). For the past two years, a multidisciplinary materials research team at NASA Glenn Research Center has investigated the feasibility of distributing high voltage power on future hybrid electric aircraft. This presentation describes the team's approach to addressing this challenge, significant technical findings, and next steps in GRC's materials research effort for MW power distribution on aircraft.

high voltage power transmission↗

Mass Flow Rate and Isolation Characteristics of Injectors for Use with Self-Pressurizing Oxidizers in Hybrid Rockets

Self-pressurizing rocket propellants are currently gaining popularity in the propulsion community, particularly in hybrid rocket applications. Due to their high vapor pressure, these propellants can be driven out of a storage tank without the need for complicated pressurization systems or turbopumps, greatly minimizing the overall system complexity and mass. Nitrous oxide (N2O) is the most commonly used self pressurizing oxidizer in hybrid rockets because it has a vapor pressure of approximately 730 pounds per square inch (5.03 megapascals) at room temperature and is highly storable. However, it can be difficult to model the feed system with these propellants due to the presence of two-phase flow, especially in the injector. An experimental test apparatus was developed in order to study the performance of nitrous oxide injectors over a wide range of operating conditions. Mass flow rate characterization has been performed to determine the effects of injector geometry and propellant sub-cooling (pressurization). It has been shown that rounded and chamfered inlets provide nearly identical mass flow rate improvement in comparison to square edged orifices. A particular emphasis has been placed on identifying the critical flow regime, where the flow rate is independent of backpressure (similar to choking). For a simple orifice style injector, it has been demonstrated that critical flow occurs when the downstream pressure falls sufficiently below the vapor pressure, ensuring bulk vapor formation within the injector element. It has been proposed to leverage the insensitivity of critical mass flow rate to downstream pressure as a means of preventing the occurrence of feed system coupled combustion instabilities in hybrid rockets utilizing nitrous oxide. Additionally, observations indicate that the existence of two-phase flow can attenuate pressure fluctuations traveling upstream through the injector, providing a degree of isolation between the feed line and downstream pressure disturbances. In consideration of safety, carbon dioxide (CO2) has been used as an analog to nitrous oxide in many of these studies. It has been observed experimentally that carbon dioxide serves as a good analog to nitrous oxide in both the single-phase and two-phase flow regimes.

Oxidizer↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

Exploring Synoptic Display Concepts for Hybrid-Electric Airliner Flightdecks

The advent of hybrid-electric aircraft concepts with distributed propulsion has resulted in relatively complicated powertrain designs. Determining the optimal approach to display information about the hybrid system in the flightdeck has taken on great importance. At NASA Glenn Research Center, the concept SUbsonic Single Aft eNgine (SUSAN) hybrid airliner has been modeled inside a flight simulator where different flight deck displays can be evaluated. This work focused on ways to display information that reduce clutter and information overload without degrading the crew’s ability to operate the aircraft and respond properly to emergencies. The displays’ layout and symbologies were created to be intuitive to the pilots. This paper documents iterations of synoptic displays and discusses the rationale and tradeoffs behind each variation.

Pilot Displays↗