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NASA NTRS2014

Impact of Satellite Viewing-Swath Width on Global and Regional Aerosol Optical Thickness Statistics and Trends

We use the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite aerosol optical thickness (AOT) product to assess the impact of reduced swath width on global and regional AOT statistics and trends. Alongtrack and across-track sampling strategies are employed, in which the full MODIS data set is sub-sampled with various narrow-swath (approximately 400-800 km) and single pixel width (approximately 10 km) configurations. Although view-angle artifacts in the MODIS AOT retrieval confound direct comparisons between averages derived from different sub-samples, careful analysis shows that with many portions of the Earth essentially unobserved, spatial sampling introduces uncertainty in the derived seasonal-regional mean AOT. These AOT spatial sampling artifacts comprise up to 60%of the full-swath AOT value under moderate aerosol loading, and can be as large as 0.1 in some regions under high aerosol loading. Compared to full-swath observations, narrower swath and single pixel width sampling exhibits a reduced ability to detect AOT trends with statistical significance. On the other hand, estimates of the global, annual mean AOT do not vary significantly from the full-swath values as spatial sampling is reduced. Aggregation of the MODIS data at coarse grid scales (10 deg) shows consistency in the aerosol trends across sampling strategies, with increased statistical confidence, but quantitative errors in the derived trends are found even for the full-swath data when compared to high spatial resolution (0.5 deg) aggregations. Using results of a model-derived aerosol reanalysis, we find consistency in our conclusions about a seasonal-regional spatial sampling artifact in AOT Furthermore, the model shows that reduced spatial sampling can amount to uncertainty in computed shortwave top-ofatmosphere aerosol radiative forcing of 2-3 W m(sup−2). These artifacts are lower bounds, as possibly other unconsidered sampling strategies would perform less well. These results suggest that future aerosol satellite missions having significantly less than full-swath viewing are unlikely to sample the true AOT distribution well enough to obtain the statistics needed to reduce uncertainty in aerosol direct forcing of climate.

remote sensing↗

NASA NTRS2014

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Crespo, Luis G.↗

NASA NTRS2014

Discovery of a Similar to 5 Day Characteristic Timescale in the Kepler Power Spectrum of Zw 229-15

We present time series analyses of the full Kepler dataset of Zw 229− 15. This Kepler light curve- with a baseline greater than three years, composed of virtually continuous, evenly sampled 30-minute measurements - is unprecedented in its quality and precision. We utilize two methods of power spectral analysis to investigate the optical variability and search for evidence of a bend frequency associated with a characteristic optical variability timescale. Each method yields similar results. The first interpolates across data gaps to use the standard Fourier periodogram. The second, using the CARMA-based time-domain modeling technique of Kelly et al., does not need evenly-sampled data. Both methods find excess power at high frequencies that may be due to Kepler instrumental effects. More importantly both also show strong bends (delta alpha is approx. 2) at timescales of approx. 5 days, a feature similar to those seen in the X-ray PSDs of AGN but never before in the optical. This observed approx. 5 day timescale may be associated with one of several physical processes potentially responsible for the variability. A plausible association could be made with light -crossing, dynamical or thermal timescales, depending on the assumed value of the accretion disk size and on unobserved disk parameters such as alpha and H¬R. This timescale is not consistent with the viscous timescale, which would be years in a approx. 10(exp7) solar mass AGN such as Zw 229− 15. However there must be a second bend on long (& 1 year) timescales, and that feature could be associated with the viscous timescale.

timescale↗

NASA NTRS2008

Microwave Radiometers from 0.6 to 22 GHz for Juno, A Polar Orbiter Around Jupiter

A compact instrument called the MWR (MicroWave Radiometer) is under development at JPL for Juno, the next NASA New Frontiers mission, scheduled to launch in 2011. It's purpose is to measure the thermal emission from Jupiter's atmosphere at six selected frequencies from 0.6 to 22 GHz, operating in direct detection mode, in order to quantify the distributions and abundances of water and ammonia in Jupiter's atmosphere. The goal is to understand the previously unobserved dynamics of the sub-cloud atmosphere, and to discriminate among models for planetary formation in our solar system. As part of a deep space mission aboard a solar-powered spacecraft, MWR is designed to be compact, lightweight, and low power. The receivers and control electronics are protected by a radiation-shielding enclosure on the Juno spacecraft that would provide a benign and stable operating temperature environment. All antennas and RF transmission lines outside the vault must withstand low temperatures and the harsh radiation environment surrounding Jupiter. This paper describes the concept of the MWR instrument and presents results of one breadboard receiver channel.

Antenna↗

NASA NTRS2014

X-Ray Spectral Components Observed in the Afterglow of GRB 130925A

We have identified spectral features in the late-time X-ray afterglow of the unusually long, slow-decaying GRB 130925A using NuSTAR, Swift/X-Ray Telescope, and Chandra. A spectral component in addition to an absorbed power law is required at greater than 4 less than 1 significance, and its spectral shape varies between two observation epochs at 2 x 10 (sup 5) and 10 (sup 6) seconds after the burst. Several models can fit this additional component, each with very different physical implications. A broad, resolved Gaussian absorption feature of several kiloelectronvolts width improves the fit, but it is poorly constrained in the second epoch. An additive blackbody or second power-law component provide better fits. Both are challenging to interpret: the blackbody radius is near the scale of a compact remnant (10 (sup 8) centimeters), while the second power-law component requires an unobserved high-energy cutoff in order to be consistent with the non-detection by Fermi/Large Area Telescope.

SPECTRAL↗

NASA NTRS2015

Investigating the Efficacy of CubeSats for Asteroid Detection

A simulation to examine the potential of a network of CubeSats for detecting Near Earth Objects is discussed, in terms of goals, methods used and initial results obtained. By designing a basic optical system and the orbital parameters of the satellites in this network, their effectiveness for detecting asteroids is examined, with a small sample of cataloged asteroids considered.The conditions to be satisfied for detection cover both the geometrical aspects of astronomy such as field of view and line of sight, along with more technical optics-based conditions such as resolution and sensitivity of our telescopes. Of special interest to us in this work is the region of the sky between 45 deg. and 90 deg. from the Sun, as seen from the Earth. This part of the sky is currently unobservable by ground-based surveys and so provides the primary reason to consider a space-based one. There exist a number of issues with the simulation which call these results into question, but an eort has been made to remove those results which exceed the possible capabilities of the satellite network, and identify those aspects of the mission which should be examined in order to provide an in-depth assessment of it's performance. With these filters applied to the overall data, a tentative result of 1458 total detections over an 85 year period has been obtained, with 14 of the 22 asteroids in the sample being detected at least once. A number of ways in which the simulation could be improved are also proposed, both in-terms of addressing the aforementioned issues, as well as how to improve on the accuracy of the simulation and capture as many aspects of a space-based optical astronomy mission as possible,with the possible nal form of the simulation being a tool for assessing the performance of any space-based optical mission to detect asteroids.

Near Earth Objects↗

NASA NTRS2016

Effects of Alternate Leading Edge Cutback on the Space Shuttle Main Engine Low Pressure Fuel Pump

A higher order cavitation oscillation observed in the SSME low pressure fuel pump has been eliminated in water flow testing of a modified subscale replica of the inducer. The low pressure pump was modified by removing the outboard sections of two opposing blades of the four-bladed inducer, blending the "cutback" regions into the blades at the leading edge and tip, and removing material on the suction sides to decrease the exposed leading edge thickness. The leading edge tips of the cutback blades were moved approximately 25 degrees from their previous locations, thereby increasing one blade to blade spacing, decreasing the second, while simultaneously moving the cutback tips downstream. The test was conducted in MSFC's inducer test loop at scaled operating conditions in degassed and filtered water. In addition to eliminating HOC across the entire scaled operating regime, rotating cavitation was suppressed while the range of both alternate blade and asymmetric cavitation were increased. These latter phenomena, and more significantly, the shifts between these cavitation modes also resulted in significant changes to the head coefficient at low cavitation numbers. Reverse flow was detected at a slightly larger flow coefficient with the cutback inducer and suction capability was reduced by approximately 1 velocity head at and above approximately 90% of the reference flow coefficient. These performance changes along with more intense reverse flow are consistent with poor flow area management and increased incidence in the cutback region. Although the test demonstrated that the inducer modification was successful at eliminating the higher order cavitation across the entire scaled operating regime, different, previously unobserved, cavitation oscillations were introduced and significant performance penalties were imposed.

Mulder, Andrew↗

NASA NTRS2016

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO (Tropospheric Emissions: Monitoring of Pollution) was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (approximately 2.1 kilometers N/S by 4.4 kilometers E/W at 36.5 degrees N, 100 degrees W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

tropospheric measurement suite↗

NASA NTRS2017

Laboratory Investigations into the Spectra and Origin of Propylene Oxide: A Chiral Interstellar Molecule

Propylene oxide was recently identified in the interstellar medium, but few laboratory results are available for this molecule to guide current and future investigations. To address this situation, here we report infrared spectra, absorption coefficients, and band strengths of solid propylene oxide along with the first measurement of its refractive index and a calculation of its density, all for the amorphous solid form of the compound. We present the first experimental results showing a low-temperature formation pathway for propylene oxide near 10 K in interstellar ice analogs. Connections are drawn between our new results and the interstellar molecules propanal and acetone, and predictions are made about several as yet unobserved vinyl alcohols and methylketene. Comparisons are given to earlier laboratory work and a few applications to interstellar and solar system astrochemistry are described.

molecular processes↗

NASA NTRS2017

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.

CDA↗

NASA NTRS2019

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

radar backscatter↗

NASA NTRS2019

Laser Rayleigh Scattering for Flow Density and Condensation Measurements in the Supercooled Mach-14 Free-Stream at AEDC Wind Tunnel No. 9

Laser Rayleigh scattering was used to investigate clusters in the free-stream flow at Arnold Engineering Development Center’s Tunnel 9 (T9). The facility was run at Mach-14, with a pure-N2 flow medium, and at several total pressures and temperatures. Using an excimer laser operating at 248 nm, the Rayleigh instrument imaged scattering from the focused laser beam in the free-stream. As a wind-tunnel flow is accelerated, it cools and approaches the condensation boundary. As a precursor to condensation, small clusters of molecules are first formed, but the individual clusters are too small to be spatially resolved in typical images of the beam. Thus clusters effectively add a spatially smooth background signal to the pure diatomic-molecule Rayleigh signal. The main result of the present work is that clustering was not significant. After correcting for interference by small particles imbedded in the T9 flow, cluster scattering was unobservable or smaller than one standard deviation (1-sigma) of the uncertainties for almost all tunnel runs. The total light scattering level was measured to be 1.05 +/- 0.15 (1-sigma) of the expected diatomic scattering, when averaged over the entire usable data set. This result included flow conditions that were supercooled to temperatures of ~ 20 K, about 25 K below the condensation limit of ~ 45 K. Thus the Mach-14 nozzle flow is essentially cluster-free for many supercooled conditions that might be used to extend the facility operating range to larger Reynolds numbers.

Herring, Gregory C.↗

NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗

NASA NTRS2020

Dynamic Controllability of Partially Observable Temporal Plans

The formalism of Simple Temporal Networks provides methods for evaluating the feasibility of temporal plans. The basic formalism deals with the consistency of quantitative temporal requirements on scheduled events. Over time, the formalism has been extended to handle exogenous events with varying degrees of observability.A major problem that has only been partially solved before now involves a combination of observable and unobservable events. In this paper, we present a sound and complete solution to this problem.

Arthur Bit-Monnot↗

NASA NTRS2020

Prognostics for Systems Health Management - Model and Hybrid Based Approaches. Where are We Heading?

To facilitate and solve the prediction problem, awareness of the current state and health of the system is key, since it is necessary to perform condition-based system health predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditional. In case of next generation electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current health state of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operational conditions and flight profiles for accurate estimation of end-of-discharge (EOD) for the batteries. Similar framework can be implemented to other complex systems and subsystems. Our research approach is to develop a system level health monitoring safety indicator which runs estimation and prediction algorithms to estimate remaining useful life predictions at system, subsystem swell as component levels. Given models of the current and future system behavior, a general approach of model-based prognostics is discussed as a solution to the prediction problem and further for decision making. Data driven prognostics approaches have been equally used with good results in the past, where respective approaches have their own challenges to tackle. This limits their applicability to complex real-world domains: (a) high complexity or incompleteness of physics-based models and (b) limited representativeness of the training dataset for data-driven models. With the advent of internet of things for data collection and increased use of ML algorithms, hybrid approaches are the next avenue to reduce the challenges and achieve better results. An 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, we use physics-based performance models to infer unobservable model parameters related to the system's components health solving a calibration problem.

Prognostics↗

NASA NTRS2020

Structure and Parameter Uncertainty in Centennial Projections of Forest Community Structure and Carbon Cycling

Secondary forest regrowth shapes community succession and biogeochemistry for decades, including in the Upper Great Lakes region. Vegetation models encapsulate our understanding of forest function, and whether models can reproduce multi‐decadal succession patterns is an indication of our ability to predict forest responses to future change. We test the ability of a vegetation model to simulate C cycling and community composition during 100 years of forest regrowth following stand‐replacing disturbance, asking (a) Which processes and parameters are most important to accurately model Upper Midwest forest succession? (b) What is the relative importance of model structure versus parameter values to these predictions? We ran ensembles of the Ecosystem Demography model v2.2 with different representations of processes important to competition for light. We compared the magnitude of structural and parameter uncertainty and assessed which sub‐model–parameter combinations best reproduced observed C fluxes and community composition. On average, our simulations underestimated observed net primary productivity (NPP) and leaf area index (LAI) after 100 years and predicted complete dominance by a single plant functional type (PFT). Out of 4,000 simulations, only nine fell within the observed range of both NPP and LAI, but these predicted unrealistically complete dominance by either early hardwood or pine PFTs. A different set of seven simulations were ecologically plausible but under‐predicted observed NPP and LAI. Parameter uncertainty was large; NPP and LAI ranged from ~0% to >200% of their mean value, and any PFT could become dominant. The two parameters that contributed most to uncertainty in predicted NPP were plant–soil water conductance and growth respiration, both unobservable empirical coefficients. We conclude that (a) parameter uncertainty is more important than structural uncertainty, at least for ED‐2.2 in Upper Midwest forests and (b) simulating both productivity and plant community composition accurately without physically unrealistic parameters remains challenging for demographic vegetation models.

canopy radiative transfer↗

NASA NTRS2021

Small Volcanic Vents of the Tharsis Volcanic Province, Mars

Distributed-style volcanism is an end member of terrestrial volcanism that produces clusters of small volcanoes when isolated magma bodies ascend from broad magma source regions. Volcano clusters can develop over millions of years, one volcano at a time, and can be used to infer unobserved geologic phenomena, including subsurface stresses and cracks during eruption periods. The Tharsis Volcanic Province covers approximately one quarter of the Martian surface and hosts a large concentration of small volcanoes that formed from distributed volcanism. We present a catalog of 1106 small volcanic vents identified within Tharsis Volcanic Province. This catalog includes morphologic measurements for each cataloged vent. Vent lengths range from 71 m to 51 km, widths range from 40 m to 3.1 km, and 90% of vents have lengths at least 1.5 times their widths. Additionally, 90% of edifices associated with vents have topographic prominences <100 m. Vents are found throughout Tharsis, though they generally form clusters near large volcanoes or among large graben sets. Older regions with volcanic eruption ages of >1 Ga are found at the Tharsis periphery in the Tempe-Mareotis region and Syria Planum. Vents in the Tharsis interior have reported ages <500 Ma. Regional trends in vent orientation and intervent alignment are dependent on nearby central volcanoes and fossae. We use these findings to hypothesize that within the most recent 500 Ma, magma was present under and to the east of the Tharsis Montes and that some of this magma erupted and built hundreds of small volcanoes in this region.

Distributed volcanism↗

NASA NTRSDate not supplied

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↗