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At least 181 records · Page 10

Electrical Properties and Power Considerations of a Piezoelectric Actuator

This paper assesses the electrical characteristics of piezoelectric wafers for use in aeronautical applications such as active noise control in aircraft. Determination of capacitive behavior and power consumption is necessary to optimize the system configuration and to design efficient driving electronics. Empirical relations are developed from experimental data to predict the capacitance and loss tangent of a PZT5A ceramic as nonlinear functions of both applied peak voltage and driving frequency. Power consumed by the PZT is the rate of energy required to excite the piezoelectric system along with power dissipated due to dielectric loss and mechanical and structural damping. Overall power consumption is thus quantified as a function of peak applied voltage and driving frequency. It was demonstrated that by incorporating the variation of capacitance and power loss with voltage and frequency, satisfactory estimates of power requirements can be obtained. These relations allow general guidelines in selection and application of piezoelectric actuators and driving electronics for active control applications.

Jordan, T.↗

Soluble carbohydrate allocation to roots, photosynthetic rate of leaves, and nitrate assimilation as affected by nitrogen stress and irradiance

Upon resupply of exogenous nitrogen to nitrogen-stressed plants, uptake rate of nitrogen is enhanced relative to nonstressed plants. Absorption of nitrogen presumably is dependent on availability of carbohydrates in the roots. A buildup in soluble carbohydrates thus should occur in roots of nitrogen-stressed plants, and upon resupply of exogenous nitrogen the increased uptake rate should be accompanied by a rapid decline in carbohydrates to prestress levels. To evaluate this relationship, three sets of tobacco plants growing in a complete hydroponic solution containing 1.0 mM NO3- were either continued in the complete solution for 21 d, transferred to a minus-nitrogen solution for 21 d, or transferred to a minus-nitrogen solution for 8-9 d and then returned to the 1.0 mM NO3- solution. These nitrogen treatments were imposed upon plants growing at photosynthetic photon flux densities of 700 and 350 micromoles m-2 s-1. Soluble carbohydrate levels in roots increased during onset of nitrogen stress to levels that were fourfold greater than in roots of non-stressed plants. Following resupply of external nitrogen, a rapid resumption of nitrogen uptake was accompanied by a decline in soluble carbohydrates in roots to levels characteristic of nonstressed plants. This pattern of soluble carbohydrate levels in roots during onset of and recovery from nitrogen stress occurred at both irradiance levels. The response of net photosynthetic rate to nitrogen stress could be expressed as a nonlinear function of concentration of reduced nitrogen in leaves. The net photosynthetic rate at a given concentration of reduced nitrogen, however, averaged 10% less at the lower than at the higher irradiance. The decline in net photosynthetic rate per unit of reduced nitrogen in leaves at the lower irradiance was accompanied by an increase in the nitrate fraction of total nitrogen in leaves from 20% at the higher irradiance to 38% at the lower irradiance.

NASA Discipline Number 61-10↗

Dose-response relationships for resetting of human circadian clock by light

Since the first report in unicells, studies across diverse species have demonstrated that light is a powerful synchronizer which resets, in an intensity-dependent manner, endogenous circadian pacemakers. Although it is recognized that bright light (approximately 7,000 to 13,000 lux) is an effective circadian synchronizer in humans, it is widely believed that the human circadian pacemaker is insensitive to ordinary indoor illumination (approximately 50-300 lux). It has been proposed that the relationship between the resetting effect of light and its intensity follows a compressive nonlinear function, such that exposure to lower illuminances still exerts a robust effect. We therefore undertook a series of experiments which support this hypothesis and report here that light of even relatively low intensity (approximately 180 lux) significantly phase-shifts the human circadian pacemaker. Our results clearly demonstrate that humans are much more sensitive to light than initially suspected and support the conclusion that they are not qualitatively different from other mammals in their mechanism of circadian entrainment.

NASA Discipline Regulatory Physiology↗

Hydrodynamic Force on a Cylinder Oscillating at Low Frequency

The hydrodynamic force on a cylinder oscillating transversely to its axis is a nonlinear function of the displacement amplitude x0. We report measurements and numerical calculations of the force at frequencies low enough that delta > R, where delta is the viscous penetration length and R is the cylinder radius. For small amplitudes, the numerically calculated Fourier transform of the force per unit length, F(sub small), agrees with Stokes' analytical calculation. For larger amplitudes, the force per unit length found by both calculation and measurement is F = F(sub small)C (x(sub 0)/delta,R/delta). The complex function C depends only weakly on R/delta, indicating that x0/delta is more appropriate as a scaling variable than the Keulegan-Carpenter number KC = pi*x(sub 0)/R. The measurements used a torsion oscillator driven at frequencies from 1 to 12 Hz while immersed in dense xenon. The oscillator comprised cylinders with an effective radius of R = 13.4 micron and oscillation amplitudes as large as x(sub 0)/delta = 4 (corresponding to KC as large as 71). The calculations used similar conditions except that the amplitudes were as large as x0/delta = 28.

Berg, Robert F.↗

Limit Cycle Analysis Applied to the Oscillations of Decelerating Blunt-Body Entry Vehicles

Many blunt-body entry vehicles have nonlinear dynamic stability characteristics that produce self-limiting oscillations in flight. Several different test techniques can be used to extract dynamic aerodynamic coefficients to predict this oscillatory behavior for planetary entry mission design and analysis. Most of these test techniques impose boundary conditions that alter the oscillatory behavior from that seen in flight. Three sets of test conditions, representing three commonly used test techniques, are presented to highlight these effects. Analytical solutions to the constant-coefficient planar equations-of-motion for each case are developed to show how the same blunt body behaves differently depending on the imposed test conditions. The energy equation is applied to further illustrate the governing dynamics. Then, the mean value theorem is applied to the energy rate equation to find the effective damping for an example blunt body with nonlinear, self-limiting dynamic characteristics. This approach is used to predict constant-energy oscillatory behavior and the equilibrium oscillation amplitudes for the various test conditions. These predictions are verified with planar simulations. The analysis presented provides an overview of dynamic stability test techniques and illustrates the effects of dynamic stability, static aerodynamics and test conditions on observed dynamic motions. It is proposed that these effects may be leveraged to develop new test techniques and refine test matrices in future tests to better define the nonlinear functional forms of blunt body dynamic stability curves.

Schoenenberger, Mark↗

On the Theoretical Framework of Magnetized Outflows from Stellar-Mass Black Holes and Related Observations

The spins of stellar-mass black holes (BHs) and the power outputs of their jets are measurable quantities. Unfortunately, the currently employed methods do not agree and the results are controversial. Two major issues concern the measurements of BH spin and beam (jet) power. The former issue can be resolved by future observations. But the latter issue can be resolved now, if we pay attention to what is expected from theoretical considerations. The question of whether a correlation has been found between the power outputs of few objects and the spins of their BHs is moot because BH beam power does not scale with the square of the spin of the BH. We show that the theoretical BH beam power is a strongly nonlinear function of spin that cannot be approximated by a quadratic relation, as is generally stated when the influence of the magnetic field is not accounted for in the Blandford & Znajek model. The BH beam power of ballistic jets should scale a lot more steeply with BH spin irrespective of the magnetic field assumed to thread the horizon and the spin range considered. This behavior may already be visible in the analyses of radio observations by Narayan & McClintock and Russell et al. In agreement with previous studies, we also find that the power output that originates in the inner regions of the surrounding accretion disks is higher than that from the BHs and it cannot be ignored in investigations of continuous compact jets from these systems.

accretion↗

Comparisons Between NO PLIF Imaging and CFD Simulations of Mixing Flowfields for High-Speed Fuel Injectors

The current work compares experimentally and computationally obtained nitric oxide (NO) planar laser induced fluorescence (PLIF) images of the mixing flowfields for three types of high-speed fuel injectors: a strut, a ramp, and a rectangular flushwall. These injection devices, which exhibited promising mixing performance at lower flight Mach numbers, are currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The EIMP aims to investigate scramjet fuel injection and mixing physics, and improve the understanding of underlying physical processes relevant to flight Mach numbers greater than eight. In the experiments, conducted in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF), the injectors are placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number air flow at the entrance of a scramjet combustor. Helium is used as an inert substitute for hydrogen fuel. Both schlieren and PLIF techniques are applied to obtain mixing flowfield flow visualizations. The experimental PLIF is obtained by using a UV laser sheet to interrogate a plane of the flow by exciting fluorescence from the NO molecules, which are present in the AHSTF air. Consequently, the absence of signal in the resulting PLIF images is an indication of pure helium (fuel). The computational PLIF is obtained by applying a fluorescence model for NO to the results of the Reynolds-averaged simulations (RAS) of the mixing flow field carried out using the VULCAN-CFD solver. This approach is required because the PLIF signal is a nonlinear function of not only NO concentration, but also pressure, temperature, and the flow velocity. This complexity allows additional flow features to be identified and compared with those obtained from the computational fluid dynamics (CFD) simulations, however, such comparisons are only semiquantitative. Three-dimensional image reconstruction, similar to that used in magnetic resonance imaging, is also used to obtain images in the streamwise and spanwise planes from select cross-stream PLIF plane data. Synthetic schlieren is also computed from the RAS data. Good agreement between the experimental and computational results provides increased confidence in the CFD simulations for investigations of injector performance.

Drozda, Tomasz G.↗

Precipitating Electron Energy Flux and Characteristic Energies in Jupiter's Main Auroral Region as Measured by Juno/JEDI

The relationship between electron energy flux and the characteristic energy of electron distributions in the main auroral loss cone bridges the gap between predictions made by theory and measurements just recently available from Juno. For decades such relationships have been inferred from remote sensing observations of the Jovian aurora, primarily from the Hubble Space Telescope, and also more recently from Hisaki. However, to infer these quantities, remote sensing techniques had to assume properties of the Jovian atmospheric structure - leading to uncertainties in their profile. Juno's arrival and subsequent auroral passes have allowed us to obtain these relationships unambiguously for the first time, when the spacecraft passes through the auroral acceleration region. Using Juno /Jupiter Energetic particle Detector Instrument (JEDI), an energetic particle instrument, we present these relationships for the 30-kiloelectronvolts to 1-megaelectronvolts electron population. Observations presented here show that the electron energy flux in the loss cone is a nonlinear function of the characteristic or mean electron energy and supports both the predictions from Knight (1973, https://doi.org/10.1016/0032-0633(73)90093-7) and magnetohydrodynamic turbulence acceleration theories (e.g., Saur et al., 2003, https://doi.org/10.1029/2002GL015761). Finally, we compare the in situ analyses of Juno with remote Hisaki observations and use them to help constrain Jupiter's atmospheric profile. We find a possible solution that provides the best agreement between these data sets is an atmospheric profile that more efficiently transports the hydrocarbons to higher altitudes. If this is correct, it supports the previously published idea (e.g., Parkinson et al., 2006, https://doi.org/10.1029/2005JE002539) that precipitating electrons increase the hydrocarbon eddy diffusion coefficients in the auroral regions.

Auroral↗

Direct Numerical Simulation of Receptivity to Roughness in a Swept-Wing Boundary Layer at High Reynolds Numbers

Direct numerical simulations (DNS) are performed to examine the receptivity to roughness in a spatially developing three-dimensional boundary layer over an in finite-swept natural-laminar-flow wing at a free stream Mach number of 0:75 and a chord Reynolds number of approximately 25 million based on the long, swept chord. Stationary cross ow disturbances are excited by applying either critically spaced discrete cylinders of micron size or naturally occurring distributed roughness in the leading-edge region. The DNS data show that the spanwise spectral content of the excited cross ow disturbances is highly dependent upon the shape of roughness elements, and the initial growth of the cross ow structures is a nonlinear function of the element height. The linear growth rate of the excited cross ow disturbances predicted by DNS shows good agreement with linear parabolized stability equations. The receptivity study lays the foundation for investigating the stabilization of the naturally most unstable steady cross ow mode by using spanwise periodic DREs.

Nicholson, Gary↗

Linear Analysis of a Two-Parachute System Undergoing Pendulum Motion

Motion resembling that of a pendulum undergoing large-amplitude oscillation was ob- served during a series of flight tests of an unoccupied Orion Capsule Parachute Assembly System (CPAS) drop-test vehicle. Large excursions away from vertical by the capsule could cause it to strike the ground or ocean at a large angle with respect to vertical, with an undesirable attitude with respect to heading, or with a large horizontal or vertical speed. These conditions are to be avoided because they would endanger the occupants of the capsule in an actual mission. Pendulum motion is intimately related to a parachute’s aerodynamic normal force coefficient, which is a nonlinear function of angle of attack. An analytical investigation of the dynamics of pendulum motion is undertaken with the aid of a simplified model of the physical system and the assumption that the normal force coefficient is a linear function of angle of attack in the neighborhood of a value corresponding to stable equilibrium. The analysis leads to a simple relationship for the location of a pivot point, which provides insights that are consistent with previous studies.

Roithmayr, Carlos M.↗

Comparisons Between NO PLIF Imaging and CFD Simulations of Mixing Flowfields for High-Speed Fuel Injectors

The current work compares experimentally and computationally obtained nitric oxide (NO) planar laser-induced fluorescence (PLIF) images of the mixing flowfields for three types of high-speed fuel injectors: a strut, a ramp, and a rectangular flushwall. These injection devices, which exhibited promising mixing performance at lower flight Mach numbers, are currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The EIMP aims to investigate scramjet fuel injection and mixing physics, and improve the understanding of underlying physical processes relevant to flight Mach numbers greater than eight. In the experiments conducted in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF), the injectors are placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number air flow at the entrance of a scramjet combustor. Helium is used as an inert substitute for hydrogen fuel. Both schlieren and PLIF techniques are applied to obtain mixing flowfield flow visualizations. The experimental PLIF is obtained by using a UV laser sheet to interrogate a plane of the flow by exciting fluorescence from the NO molecules that are present in the AHSTF air. Consequently, the absence of signal in the resulting PLIF images is an indication of pure helium (fuel). The computational PLIF is obtained by applying a fluorescence model for NO to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield carried out using the VULCAN-CFD solver. This approach is required because the PLIF signal is a nonlinear function of not only NO concentration, but also pressure, temperature, and the flow velocity. This complexity allows additional flow features to be identified and compared with those obtained from the computational fluid dynamics (CFD) simulations, however, such comparisons are only semiquantitative. Three-dimensional image reconstruction, similar to that used in magnetic resonance imaging, is also used to obtain images in the streamwise and spanwise planes from select cross-stream PLIF plane data. Synthetic schlieren is also computed from the RAS data. Good agreement between the experimental and computational results provides increased confidence in the CFD simulations for investigations of injector performance.

PLIF↗

Comparisons Between NO PLIF Imaging and CFD Simulations of Mixing Flowfields for High-Speed Fuel Injectors

The current work compares experimentally and computationally obtained nitric oxide (NO) planar laser-induced fluorescence (PLIF) images of the mixing flowfields for three types of high-speed fuel injectors: a strut, a ramp, and a rectangular flushwall. These injection devices, which exhibited promising mixing performance at lower flight Mach numbers, are currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The EIMP aims to investigate scramjet fuel injection and mixing physics, and improve the understanding of underlying physical processes relevant to flight Mach numbers greater than eight. In the experiments conducted in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF), the injectors are placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number air flow at the entrance of a scramjet combustor. Helium is used as an inert substitute for hydrogen fuel. Both schlieren and PLIF techniques are applied to obtain mixing flowfield flow visualizations. The experimental PLIF is obtained by using a UV laser sheet to interrogate a plane of the flow by exciting fluorescence from the NO molecules that are present in the AHSTF air. Consequently, the absence of signal in the resulting PLIF images is an indication of pure helium (fuel). The computational PLIF is obtained by applying a fluorescence model for NO to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield carried out using the VULCAN-CFD solver. This approach is required because the PLIF signal is a nonlinear function of not only NO concentration, but also pressure, temperature, and the flow velocity. This complexity allows additional flow features to be identified and compared with those obtained from the computational fluid dynamics (CFD) simulations, however, such comparisons are only semiquantitative. Three-dimensional image reconstruction, similar to that used in magnetic resonance imaging, is also used to obtain images in the streamwise and spanwise planes from select cross-stream PLIF plane data. Synthetic schlieren is also computed from the RAS data. Good agreement between the experimental and computational results provides increased confidence in the CFD simulations for investigations of injector performance.

PLIF↗

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↗

LOFTID Heat Flux Gauge Calibration: What is Truth?

The Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) is a demonstration of Hypersonic Inflatable Aerodynamic Decelerator (HIAD) technology, which may enable the delivery of heavy payloads to Mars, Venus, and Titan, as well as return to Earth. Unlike rigid aeroshells that are constrained by the size of the rocket’s shroud, inflatable aeroshells can be deployed to a much larger scale, thus allowing a spacecraft to begin its deceleration earlier and experience less heating. On LOFTID, there will be 4 total heat flux gauges (HFG) with a range of 70 W/cm2 and 1 radiometer with a range of 3 W/cm2, arranged as shown in Fig. 1. Both the radiometer and total HFGs are Schmidt-Boelter gauges purchased from an external vendor. Radiative calibrations were performed in-house at NASA Ames’ Sensors and TPS Advanced Research Laboratories (STAR Labs) before and after environmental testing to investigate how the testing affected the sensors' response. Additional rounds of radiative calibration at STAR Labs were also performed in order to investigate the large uncertainties associated with these tests. For example, a survey of multiple calibration facilities concluded that the uncertainty within a given facility was +/-3% [1]. An additional NIST study that calibrated heat flux gauges at 7 different facilities also found the variation in calibration coefficients to be up to ~3% within a given facility, but up to 15% between facilities, suggesting systematic differences between test setups [2]. Finally, the response of heat flux gauges to radiative versus convective heat flux has shown to differ by up to 20% [3],[4]. Because the heat flux gauges on LOFTID will predominantly experience convective heat flux during flight, a convective calibration study was performed at Boeing's Large-Core Arc Tunnel (LCAT) facility. Radiative Calibration Procedure The calibrations performed at STAR Labs utilize a quartz lamp bank (QLB) that provides a maximum heat flux of 50 W/cm2, which bounds the expected LOFTID flight environment. The calibration involves exposing a water-cooled Gardon gauge (reference) and then the unit-under-test (UUT) to 5 different heat fluxes multiple times for 10 seconds each, and then calculating a linear fit. The test setup is shown in Fig. 2. The total HFGs were calibrated at STAR Labs 3 times, denoted as STAR 1 (before environmental testing), STAR 2 (after protoflight vibration and thermal-vacuum testing), and STAR 3 (no change from previous test). All 8 flight-lot total HFGs showed a decrease in full-scale output from STAR 1 to STAR 2 by between 0.5% and 10. The first portion of this investigation was to determine whether the change could be due to differences in temperature between the two calibration runs. A typical linear fit to the calibration data was performed using Eq. 1 where q’ is the heat flux in W/cm2, c is the calibration coefficient, and mV is the sensor output. To account for temperature, the data were fit to a nonlinear function that included both the sensor output (mV) and the temperature from the thermocouple embedded inside the HFG near the surface (T): q'=mV/(c1* T + c0}. The residuals between the fits and the actual data points were calculated for every point, and proven to be much smaller for the temperature-compensated fits than for the linear fits for all sensors. An example is shown in Fig. 3. When the temperature-compensated fits from STAR 1 were applied to the STAR 2 data, the residuals did not improve, suggesting that the change in sensitivity between these two calibration runs was not due to temperature. A third round of calibration (STAR 3) was conducted to further address the temperature dependence of the total HFGs, and the resulting sensitivities matched closely to STAR 2 (within 2%). Temperature-compensated calibration curves were once again fit to the data. In this case, when the temperature-compensated fits from STAR 3 were applied to STAR 2 data, the residuals between the fits and STAR 2 data were much lower than the residuals due to the linear fits. This suggests that the changes seen between STAR 1 and STAR 2 were likely due to actual changes in the sensors caused by the environmental testing between the two calibrations. A modification of the original calibration process, in which the UUT was exposed to each heat flux for just 3 seconds (instead of 10) to reduce the temperature increase during the test, was additionally performed on several of the HFGs. In general, the sensitivities were 1-1.5% lower than from the 10-second tests, but the temperatures were also significantly lower. When the temperature-compensated fits from the 10-second tests were applied to the 3-second test data, the residuals were greatly improved than when just using the linear fits, further suggesting that the temperature-compensated fits may lead to better accuracy than the linear fits in flight. Convective Calibration The second portion of this study was to create a mapping between the radiative and convective calibration coefficients. The majority of the heating during flight will be convective, so it is important to understand how the HFG response differs under these conditions. However, there are no standardized methods for convective calibration [5]. Because the TPS aerothermal response models were validated at LCAT, the same facility was chosen for convective calibration of two of the total HFGs (Fig. 4). Preliminary results showed that the full-scale output was 3% and 8% higher in convective heat flux as compared to radiative heat flux. However, tunnel variation may have contributed to noise and uncertainty in the measurements, and more testing and analysis remains to be done. Scope of Presentation The presentation will include an overview of the changes seen in HFG calibration before and after environmental testing, differences between radiative and convective calibrations, the modeling work done to aid in understanding the sensor response to varying environments, and recommended future work.

H S Alpert↗

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. 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. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗

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↗

The M-dwarf Ultraviolet Spectroscopic Sample. I. Determining Stellar Parameters for Field Stars

Accurate stellar properties are essential for precise stellar astrophysics and exoplanetary science. In the M-dwarf regime, much effort has gone into defining empirical relations that can use readily accessible observables to assess physical stellar properties. Often, these relations for the quantity of interest are cast as a nonlinear function of available data; in Bayesian modeling, however, the reverse is needed. In this article, we introduce a new Bayesian framework to self-consistently and simultaneously apply multiple empirical calibrations to fully characterize the mass, luminosity, radius, and effective temperature of a field age M-dwarf. This framework includes a new M-dwarf mass–radius relation with a scatter of 3.1% at fixed mass. We further introduce the M-dwarf Ultraviolet Spectroscopic Sample (MUSS), and apply our methodology to provide consistent stellar parameters for these nearby low-mass stars, selected as having available spectroscopic data in the ultraviolet. These targets are of interest largely as either exoplanet hosts or benchmarks in multiwavelength stellar activity. We use the field MUSS stars to define a low-mass main sequence in the solar neighborhood through Gaussian Process (GP) regression. These results enable us to empirically measure a feature in the GP derivative at M ⊙ that indicates where the MUSS transitions from fully to partly convective interiors.

J. Sebastian Pineda↗

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↗