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NASA NTRS2004

Computation of Sensitivity Derivatives of Navier-Stokes Equations using Complex Variables

Accurate computation of sensitivity derivatives is becoming an important item in Computational Fluid Dynamics (CFD) because of recent emphasis on using nonlinear CFD methods in aerodynamic design, optimization, stability and control related problems. Several techniques are available to compute gradients or sensitivity derivatives of desired flow quantities or cost functions with respect to selected independent (design) variables. Perhaps the most common and oldest method is to use straightforward finite-differences for the evaluation of sensitivity derivatives. Although very simple, this method is prone to errors associated with choice of step sizes and can be cumbersome for geometric variables. The cost per design variable for computing sensitivity derivatives with central differencing is at least equal to the cost of three full analyses, but is usually much larger in practice due to difficulty in choosing step sizes. Another approach gaining popularity is the use of Automatic Differentiation software (such as ADIFOR) to process the source code, which in turn can be used to evaluate the sensitivity derivatives of preselected functions with respect to chosen design variables. In principle, this approach is also very straightforward and quite promising. The main drawback is the large memory requirement because memory use increases linearly with the number of design variables. ADIFOR software can also be cumber-some for large CFD codes and has not yet reached a full maturity level for production codes, especially in parallel computing environments.

Vatsa, Veer N.↗

NASA NTRS1981

Solar pulsations and long-term solar variability

The seismology of the solar atmosphere is important in relating changes in luminosity to variations in other observables. This approach has already led to the identification of properties which were not previously observed or recognized. Equally important results from solar seismology are expected in the future.

Goode, P. R.↗

NASA NTRS1985

On the interannual variability of the middle atmosphere during winter

Reference atmospheres such as the new CIRA (COSPAR International Reference Atmosphere), which will be based on global satellite data form a very useful basis for climatological studies. When using such climatologies it is important to be aware of the interannual variability which in the middle atmosphere is particularly large during the northern winters and southern springs. For a comparison of the two polar regions, the monthly mean temperature data for 90 deg N and 90 deg S are shown in the form of frequency distributions. The main features to be noted are: (1) in the middle stratosphere the variability during the northern midwinters is much larger than during the southern midwinters due to the major midwinter warmings which take place only during the northern winters (the largest variations over Antarctics are observed during late spring, i.e., October and November when very intense final warmings bring about the transition into summer); (2) the variability in the middle stratosphere is very small in summer when the planetary waves of the troposphere cannot propagate upwards into the stratosphere due to the prevailing easterly winds (this is true for both polar regions); and (3) the annual temperature range is larger over the South Pole, because winters are colder and summers warmer than over the North Pole. In addition, the Canadian, midwinter, and final warmings occurring during northern winters are characterized and the recent cooling trend in the arctic is discussed.

Labitzke, K.↗

NASA NTRS2003

Interannual Variability in Intercontinental Transport

We have investigated the importance of intercontinental transport using source-receptor relationship. A global radon-like and seven regional tracers were used in three-dimensional model simulations to quantify their contributions to column burdens and vertical profiles at world-wide receptors. Sensitivity of these contributions to meteorological input was examined using different years of meteorology in two atmospheric simulations. Results show that Asian emission influences tracer distributions in its eastern downwind regions extending as far as Europe with major contributions in mid- and upper troposphere. On the western and eastern sides of the US, Asian contribution to annual average column burdens are 37% and 5% respectively with strong monthly variations. At an altitude of 10 km, these contributions are 75% and 25% respectively. North American emissions contribute more than 15% to annual average column burden and about 50% at 8 km altitude over the European region. Contributions from tropical African emissions are wide-spread in both the hemispheres. Differences in meteorological input cause non-uniform redistribution of tracer mass throughout the troposphere at all receptors. We also show that in model-model and model-data comparison, correlation analysis of tracer's spatial gradients provides an added measure of model's performance.

Gupta, Mohan↗

NASA NTRSDate not supplied

Going With the Flow: Using a Particle Dispersion Model to Reveal Links Between Ocean Ecosystems and Marine Aerosols

Marine biogenic particle contributions to atmospheric aerosol concentrations are not well understood though they are important for determining cloud optical and cloud nucleating properties. Here we examine the relationship between marine aerosol measurements with satellite and model fields of ocean biology and meteorological variables during the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES). NAAMES consisted of four field campaigns between November 2015 and April 2018 that aligned with the four major phases of the annual phytoplankton bloom cycle. The FLEXPART Lagrangian particle dispersion model is used to connect these variables spatiotemporally to ship-based aerosol. We find that correlations between some aerosol measurements with satellite measured and modelled variables increase with increasing trajectory length. This indicates biological and meteorological processes over the air mass history are influential to measured particle properties. For example, results show organic aerosol mass is driven by biogenic volatile organic compound (VOC) emissions from photosynthesis by phytoplankton stocks during advection into the region. In contrast, primary marine aerosol (PMA) concentrations showed better correlations to variables weighted with shorter air mass residence times, which reflects their localized origin as primary emissions. Evidence also suggest variability in PMA concentrations likely play an important role in VOC emission removal. The removal of VOCs lessen their impact on particle number and mass concentrations. The sensitivity of marine aerosols to the main ecosystem-atmosphere drivers and its dependence on air mass history are to be further discussed.

Kevin J Sanchez↗

DOE OSTI2023

Action Effectiveness Monitoring and Research of Dredged Material Placement at Woodland Islands (Final Report)

Benthic invertebrates play critical ecosystem roles including the breakdown of organic matter, sediment mixing, and nutrient cycling. In the lower Columbia River and estuary, benthic invertebrates provide a foraging resource for threatened and endangered juvenile salmon. Driven by the goal to create low velocity, shallow water, and riparian shrub habitats to benefit juvenile salmon, the USACE placed 237,000 CY of dredged material, resulting in the deposition of 13.5 acres of sand on the off-channel margins at Woodland Islands. The Pacific Northwest National Laboratory (PNNL) designed and implemented an action effectiveness research study to understand how dredged material placement at Woodland Islands affected sediment conditions and benthic invertebrate assemblages. A Before-After-Control-Impact, or BACI, study design was used to evaluate the response of environmental conditions and benthic invertebrates to dredged material placement. The design included one impact site and two control sites. Sampling occurred for two years prior to dredged material placement and two years after. Our study found that spatial variation was a significant factor for both environmental and biological response variables which suggests local conditions are important considerations for mechanisms affecting benthic assemblages. We found that off-channel habitats across all locations sampled were producing benthic invertebrates, many of which are common prey items for juvenile salmon and steelhead—e.g., insects, chironomids, crustaceans, and corophium. There was a significant BACI effect (i.e., an effect at the dredged material placement site, relative to conditions at the control sites) for concentration of carbon and ammonium in sediment, but not for phosphorous concentrations or for percent sand. The estimated abundance for the three invertebrate response variables—total abundance, total chironomid abundance, and total corophium abundance—was significantly lower at the impact site after dredged material placement, compared to the control sites. At Woodland Island, the estimated mean abundance for all invertebrates combined decreased 28% after dredged material placement. Estimated mean abundances of chironomid and corophium decreased by 8% and 88%, respectively. While invertebrate abundances were lower after dredged placement, the composition of benthic invertebrates was similar before and after placement suggesting that as the new habitat feature evolves recolonization will likely follow. These findings provide a foundation for understanding potential benefits and consequences of repurposing dredged material for habitat creation in the LCRE.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2013

Comprehensive Modeling and Analysis of Rotorcraft Variable Speed Propulsion System With Coupled Engine/Transmission/Rotor Dynamics

This project develops comprehensive modeling and simulation tools for analysis of variable rotor speed helicopter propulsion system dynamics. The Comprehensive Variable-Speed Rotorcraft Propulsion Modeling (CVSRPM) tool developed in this research is used to investigate coupled rotor/engine/fuel control/gearbox/shaft/clutch/flight control system dynamic interactions for several variable rotor speed mission scenarios. In this investigation, a prototypical two-speed Dual-Clutch Transmission (DCT) is proposed and designed to achieve 50 percent rotor speed variation. The comprehensive modeling tool developed in this study is utilized to analyze the two-speed shift response of both a conventional single rotor helicopter and a tiltrotor drive system. In the tiltrotor system, both a Parallel Shift Control (PSC) strategy and a Sequential Shift Control (SSC) strategy for constant and variable forward speed mission profiles are analyzed. Under the PSC strategy, selecting clutch shift-rate results in a design tradeoff between transient engine surge margins and clutch frictional power dissipation. In the case of SSC, clutch power dissipation is drastically reduced in exchange for the necessity to disengage one engine at a time which requires a multi-DCT drive system topology. In addition to comprehensive simulations, several sections are dedicated to detailed analysis of driveline subsystem components under variable speed operation. In particular an aeroelastic simulation of a stiff in-plane rotor using nonlinear quasi-steady blade element theory was conducted to investigate variable speed rotor dynamics. It was found that 2/rev and 4/rev flap and lag vibrations were significant during resonance crossings with 4/rev lagwise loads being directly transferred into drive-system torque disturbances. To capture the clutch engagement dynamics, a nonlinear stick-slip clutch torque model is developed. Also, a transient gas-turbine engine model based on first principles mean-line compressor and turbine approximations is developed. Finally an analysis of high frequency gear dynamics including the effect of tooth mesh stiffness variation under variable speed operation is conducted including experimental validation. Through exploring the interactions between the various subsystems, this investigation provides important insights into the continuing development of variable-speed rotorcraft propulsion systems.

DeSmidt, Hans A.↗

NASA NTRS2010

Electrical Switching of Perovskite Thin-Film Resistors

Electronic devices that exploit electrical switching of physical properties of thin films of perovskite materials (especially colossal magnetoresistive materials) have been invented. Unlike some related prior devices, these devices function at room temperature and do not depend on externally applied magnetic fields. Devices of this type can be designed to function as sensors (exhibiting varying electrical resistance in response to varying temperature, magnetic field, electric field, and/or mechanical pressure) and as elements of electronic memories. The underlying principle is that the application of one or more short electrical pulse(s) can induce a reversible, irreversible, or partly reversible change in the electrical, thermal, mechanical, and magnetic properties of a thin perovskite film. The energy in the pulse must be large enough to induce the desired change but not so large as to destroy the film. Depending on the requirements of a specific application, the pulse(s) can have any of a large variety of waveforms (e.g., square, triangular, or sine) and be of positive, negative, or alternating polarity. In some applications, it could be necessary to use multiple pulses to induce successive incremental physical changes. In one class of applications, electrical pulses of suitable shapes, sizes, and polarities are applied to vary the detection sensitivities of sensors. Another class of applications arises in electronic circuits in which certain resistance values are required to be variable: Incorporating the affected resistors into devices of the present type makes it possible to control their resistances electrically over wide ranges, and the lifetimes of electrically variable resistors exceed those of conventional mechanically variable resistors. Another and potentially the most important class of applications is that of resistance-based nonvolatile-memory devices, such as a resistance random access memory (RRAM) described in the immediately following article, Electrically Variable Resistive Memory Devices (MFS-32511-1).

Liu, Shangqing↗

DOE OSTI2020

Wintertime Arctic Oscillation and North Atlantic Oscillation and their impacts on the Northern Hemisphere climate in E3SM

The characteristics of the wintertime Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) and their impacts on climate variability over the Northern Hemisphere are important metrics for evaluating a climate system model. Observational analyses reveal that the horizontal and vertical structures in the AO and NAO exhibit a meridional dipole and a large-scale barotropic pattern between the Arctic and mid-latitudes. Historical model simulations from the Energy Exascale Earth System Model (E3SM-HIST) are used to identify how well it captures these major climate modes. It is found that the simulated AO and NAO modes have spatial structures similar to the observed features. In addition, the observed frequency bands in the AO and NAO-related time variability are captured well in the E3SM-HIST simulation. Associated with the positive phase in wintertime AO and NAO, zonal flow and warm advection in mid-latitude continents are enhanced, along with stronger cold flow from enhanced northerly winds over high latitudes. These features are linked to the atmospheric circulation pattern reflected by lower SLP anomalies over the Arctic and higher SLP anomalies over the mid-latitudes. In E3SM-HIST, these spatial associations and main structural features are analogous to those in observations. In the time-height evolution related to winter AO and NAO modes, it can also be seen that the simulations reproduce the downward propagating patterns in observations. Nevertheless, the vertical structures associated with AO and NAO in E3SM-HIST exhibit substantial biases in the lower stratosphere. The cause of these stratospheric biases is investigated using the strength of climatological stratospheric polar vortex (SPV) and wave activity fluxes. The results herein suggest that E3SM-HIST has a reasonable skill in reproducing the observed characteristics related to the winter AO and NAO, although there exist systematic biases in the associated climate variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Variability and Diversity Load Model Tool [SWR-20-03]

The motivation for the development of this tool and the underlying algorithms and methods was to enable the development of high-temporal resolution, realistic time-series data for quasi-static time-series (QSTS) analysis of distribution systems. Often, aggregated load profile data for a distribution circuit is available (e.g. feeder loading data collected via SCADA at the utility substation) and, while this data is typically accurate it masks the considerable variability of the 100’s or 1000’s of individual loads connected on the circuit. This tool was developed to model both the increased variability expected for these individual loads (e.g. the load of a single distribution transformer connected to 8-12 houses) and the expected diversity between loads on the circuit. It is important to note that the difference in variability and diversity, in the context of this tool, is that variability modeling only adds representative variability due to disaggregated load characteristics (e.g. the presence in the load profile of loads turning off and on like an air conditioner/oven) while the average energy profile remains the same as the user supplied power profile. Diversity modeling generates multiple individual load profiles which, in aggregate, sum to the user supplied power profile. Diversity is effectively variability in the energy usage over longer periods of time than seen in the variability model. Put another way, variability modeling supplies the expected variability due to the operation of various end-use loads and diversity modeling supplies the usage differences due to human behavior, schedules, etc. This load modeling tool was developed for use in generating data for distribution systems. Modeling is summarized by two major functions: 1) taking low resolution load profiles and adding intra-seconds variability onto the profiles, and 2) taking a user supplied load profile and distribution factors and adding both diversity and variability to the user supplied profile.

Zhu, Xiangqi↗

DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Understanding the Impact of Lignocellulosic Biomass Variability on the Size Reduction Process: A Review

The importance of size reduction to the commercial deployment of biomass utilization technologies cannot be overemphasized. Size reduction is necessary to create good flowing, easily digestible lignocellulosic biomass. However, inefficiencies and inconsistencies have not been well addressed for the process of size reduction. This has motivated quite a few research studies, some of which have reported the influence of process variables and pretreatment on the size reduction performance as well as the development of novel size reduction equipment and empirical mathematical models. In this Perspective, we present a systematic and critical evaluation of the state of lignocellulosic biomass size reduction research to provide insights for engineers and scientists for future development and investigation. We first briefly discuss the structural biology and failure mode of lignocellulosic biomass. Then, we present a comprehensive, data-driven picture of the interactions among lignocellulosic biomass attributes, size reduction process variables, pretreatment processes, and size reduction process performance (as defined by product characteristics and size reduction energy consumption). The sizing equipment tool wear issues are also summarized. Finally, we highlight some existing gaps and future research opportunities for lignocellulosic biomass size reduction to inform the development of newer technologies that could effectively produce high-value lignocellulosic biomass particles.

09 BIOMASS FUELS↗

DOE OSTI2023

Ground Motion Models (GMMs) Improvements Using Earthquake Simulations on High Performance Computers

A computationally efficient simulation platform was developed that can provide representative synthetic ground motions from crustal earthquakes in the Stable Continental Regions of Central and Eastern US (CEUS), using 3D modeling and high-performance computing. The main objective was to use synthetic ground motion to provide constrains to refinements of exiting ergodic Ground Motion Models (GMMs), for large magnitude earthquakes and near-fault distances, for which these models are less reliable. Physics-based broadband (0-5Hz) ground motion simulations were used to estimate the near-fault ground motion amplitudes and within event and between-event variabilities associated with fault rupture characteristics. As part of a strategy for selecting a reginal velocity model and validation of developed rupture modeling technique, ground motions from the moment magnitude Mw5.0 November 7, 2016, Cushing Oklahoma, and Mw5.8 September 3, 2016, Pawnee Oklahoma earthquakes were simulated. In our simulations we used a 3D regional velocity model that was based on Saikia’s 1D velocity model. Saikia’s model demonstrated better performance in modelling high frequency regional wave propagation for CEUS region. The proposed 3D model includes lateral variations added to the 1D background model using the stochastic scheme of Pitarka and Mellors. Comparisons of the simulations with recordings of both earthquakes demonstrated the reliability of our deterministic simulation approach while emphasizing the importance of including small-scale variability in the regional velocity model needed to reproduce the observed high-frequency wave scattering effects. As part of validation analysis, comparisons with different GMMs for a Mw6.5 earthquake in the CESUS region resulted in a very good match between the simulated and empirical ground motion models. Initial investigations of within-event and between-event ground motion variabilities for Mw6.5 scenario earthquakes on a strike-slip fault, suggest that they are strongly related to spatial slip and slip rate variations, average rupture velocity, rupture area and rupture initiation location. For certain scenarios we found that the ground motion variability observed at near-fault distances (< 5 km) also persists at longer distances. Regardless of the rupture scenario, the simulated ground motion tends to fully saturate at short distances and for all periods. The near-fault saturation has to do with the attenuation of waves propagating along the fault and local rupture radiation pattern that also contribute to stronger ground motion variation at such distances. Analysis of effects of rupture initiation location suggest that the peak ground motion (PGV) and spectral acceleration (SA) can be quite variable due to rupture directivity effects. Such effects are stronger at periods longer than 1s. The effect of the 1D velocity models and surface topography on simulated ground motion were investigated by comparing three component synthetic seismograms computed at selected sites. Effect of surface topography was considered using the ratio between spectral accelerations simulated for two 1D models with flat surface topography and realistic model with surface topography. Overall, the topography slightly amplifies (by ~30%) the ground motion amplitude in the frequency range 1-3Hz. The effect of topography is more visible in the surface and coda waves portion of the seismograms.

58 GEOSCIENCES↗

DOE OSTI2022

Contribution of Atmospheric Rivers to Antarctic Precipitation

Abstract Atmospheric rivers (ARs) are efficient mechanisms for transporting atmospheric moisture from low latitudes to the Antarctic Ice Sheet (AIS). While AR events occur infrequently, they can lead to extreme precipitation and surface melt events on the AIS. Here we estimate the contribution of ARs to total Antarctic precipitation, by combining precipitation from atmospheric reanalyses and a polar‐specific AR detection algorithm. We show that ARs contribute substantially to Antarctic precipitation, especially in East Antarctica at elevations below 3,000 m. ARs contribute substantially to year‐to‐year variability in Antarctic precipitation. Our results highlight that ARs are an important component for understanding present and future Antarctic mass balance trends and variability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Causal Interactions between Southern Ocean Polynyas and High-Latitude Atmosphere–Ocean Variability

Abstract Weddell Sea open-ocean polynyas have been observed to occasionally release heat from the deep ocean to the atmosphere, indicating that their sporadic appearances may be an important feature of high-latitude atmosphere–ocean variability. Yet, observations of the phenomenon are sparse and many standard-resolution models represent these features poorly, if at all. We use a fully coupled, synoptic-scale preindustrial control simulation of the Energy Exascale Earth System Model (E3SMv0-HR) to effectively simulate open-ocean polynyas and investigate their role in the climate system. Our approach employs statistical tests of Granger causality to diagnose local and remote drivers of, and responses to, polynya heat loss on interannual to decadal time scales. First, we find that polynya heat loss Granger causes a persistent increase in surface air temperature over the Weddell Sea, strengthening the local cyclonic wind circulation. Along with responding to polynyas, atmospheric conditions also facilitate their development. When the Southern Ocean experiences a rapid poleward shift in the circumpolar westerlies following a prolonged negative phase of the southern annular mode (SAM), Weddell Sea salinity increases, promoting density destratification and convection in the water column. Finally, we find that the reduction of surface heat fluxes during periods of full ice cover is not fully compensated by ocean heat transport into the high latitudes. This imbalance leads to a buildup of ocean heat content that supplies polynya heat loss. These results disentangle the complex, coupled climate processes that both enable the polynya’s existence and respond to it, providing insights to improve the representation of these highly episodic sea ice features in climate models.

Kaufman, Zachary S.↗

DOE OSTI2022

Influence of Aerosol Embedded in Shallow Cumulus Cloud Fields on the Surface Solar Irradiance

Ubiquitous shallow cumulus clouds are associated with complex variability in surface solar irradiance (SSI). Aerosol embedded in the cloud field typically has a much smaller overall radiative effect, but can significantly perturb the shape of the SSI probability density function (PDF). These perturbations have important implications for several applications that utilize SSI, but are poorly quantified and are the subject of this study. Multiple cases of shallow cumulus cloud fields with embedded aerosol are simulated at the Southern Great Plains Atmospheric Observatory using large eddy simulation (LES). The LES-derived cloud and aerosol fields are then ingested into Monte Carlo three-dimensional (3D) radiative transfer to simulate SSI. We find a variety of perturbations to the SSI PDF that depend on aerosol presence and optical properties. The processes leading to these perturbations include extinction of the direct beam that often increases from the clear-sky region toward cloud edge due to aerosol hygroscopic growth, and scattering of radiation by aerosol into cloud shadows. The ability to predict the SSI PDF in the presence of aerosol is assessed by adding three representative aerosol optical properties into an existing machine learning framework. We show that machine learning accurately predicts the SSI PDF across a wide range of conditions with negligible computational expense. Importance metrics reveal the relatively high influence of aerosol optical properties in making the predictions. These new findings highlight the important role that aerosol plays in SSI variability for highly 3D cloud-aerosol environments and provides a computationally efficient route forward for its simulation.

3D radiative transfer↗

DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗