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NASA NTRS2016

Effect of Vertical Rate Error on Recovery from Loss of Well Clear Between UAS and Non-Cooperative Intruders

When an Unmanned Aircraft System (UAS) encounters an intruder and is unable to maintain required temporal and spatial separation between the two vehicles, it is referred to as a loss of well-clear. In this state, the UAS must make its best attempt to regain separation while maximizing the minimum separation between itself and the intruder. When encountering a non-cooperative intruder (an aircraft operating under visual flight rules without ADS-B or an active transponder) the UAS must rely on the radar system to provide the intruders location, velocity, and heading information. As many UAS have limited climb and descent performance, vertical position andor vertical rate errors make it difficult to determine whether an intruder will pass above or below them. To account for that, there is a proposal by RTCA Special Committee 228 to prohibit guidance systems from providing vertical guidance to regain well-clear to UAS in an encounter with a non-cooperative intruder unless their radar system has vertical position error below 175 feet (95) and vertical velocity errors below 200 fpm (95). Two sets of fast-time parametric studies was conducted, each with 54000 pairwise encounters between a UAS and non-cooperative intruder to determine the suitability of offering vertical guidance to regain well clear to a UAS in the presence of radar sensor noise. The UAS was not allowed to maneuver until it received well-clear recovery guidance. The maximum severity of the loss of well-clear was logged and used as the primary indicator of the separation achieved by the UAS. One set of 54000 encounters allowed the UAS to maneuver either vertically or horizontally, while the second permitted horizontal maneuvers, only. Comparing the two data sets allowed researchers to see the effect of allowing vertical guidance to a UAS for a particular encounter and vertical rate error. Study results show there is a small reduction in the average severity of a loss of well-clear when vertical maneuvers are suppressed, for all vertical error rate thresholds examined. However, results also show that in roughly 35 of the encounters where a vertical maneuver was selected, forcing the UAS to do a horizontal maneuver instead increased the severity of the loss of well-clear for that encounter. Finally, results showed a small reduction in the number of severe losses of well-clear when the high performance UAS (2000 fpm climb and descent rate) was allowed to maneuver vertically, and the vertical rate error was below 500 fpm. Overall, the results show that using a single vertical rate threshold is not advisable, and that limiting a UAS to horizontal maneuvers when vertical rate errors are above 175 fpm can make a UAS less safe about a third of the time. It is suggested that the hard limit be removed, and system manufacturers instructed to account for their own UAS performance, as well as vertical rate error and encounter geometry, when determining whether or not to provide vertical guidance to regain well-clear.

detect and avoid↗

DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Revisiting separation: Algorithms and complexity

Abstract Linear temporal logic (LTL) with Since and Until modalities is expressively equivalent, over the class of complete linear orders, to a fragment of first-order logic known as FOMLO (first-order monadic logic of order). It turns out that LTL, under some basic assumptions, is expressively complete if and only if it has the property, called separation, that every formula is equivalent to a Boolean combination of formulas that each refer only to the past, present or future. Herein we present simple algorithms and their implementations to perform separation of the LTL with Since and Until, over discrete and complete linear orders, and translation from FOMLO formulas into equivalent temporal logic formulas. We additionally show that the separation of a certain fragment of LTL results in at most a double exponential size growth.

Oliveira, Daniel↗

NASA NTRS1992

Multispectral observations of the Jovian aurora

The upper atmospheres of the Earth and the outer planets form a screen on which precipitating charged particles, like the electron beam in a television, trace fleeting, but revealing patterns of visible, ultraviolet, infrared, and x ray emissions that offer valuable clues to processes occurring within the planetary magnetospheres. At Earth, years of in situ measurements, as well as ground based observations, have yielded a picture (still fuzzy) where the interaction of the solar wind with the magnetosphere of the Earth provides a complex path for the storage and release of energy during magnetic substorms; the ultimate manifestation of terrestrial auroral processes. More recent global imaging of substorm events from high above the Earth (greater than 3.5 R(sub e)) by Dynamics Explorer have made a unique contribution towards understanding the global and temporal evolution of such auroral events by providing a morphological perspective and by providing the crucial observational link that allows the separation of spatial and temporal variations inherent in the interpretation of in situ data. A similar role was played by the Hubble Space Telescope (HST) during the recent encounter of Ulysses with Jupiter in helping to define a new paradigm in Jovian auroral physics. The old paradigm portrayed Jupiter's magnetosphere as totally dominated by internal processes (i.e. Io related tori, heavy ions, etc.) where energetic heavy ion precipitation in the inner magnetosphere was solely responsible for the observed auroral phenomena. Ulysses and HST portray a more Earth-like paradigm where electron acceleration in the outer magnetosphere near the boundary with the solar wind plays a distinct role in the formation of auroral hot spots, yet energetic heavy ions also enter into the picture (similar to the role of the energetic ions from the terrestrial ring current during magnetic substorms). These heavy ions as a result of excitation during their transit through the atmosphere produce the x ray emissions observed in Roentgensatellit (ROSAT) x ray energy spectra.

Source record↗

NASA NTRS2001

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗

NASA NTRS2006

Tropospheric and stratospheric ozone from assimilation of Aura data

Ozone is an atmospheric trace gas with multiple impacts on the environment. Global ozone fields are needed for air quality predictions, estimation of the ultraviolet radiation reaching the surface, climate-radiation studies, and may also have an impact on longer-term weather predictions. We estimate global ozone fields in the stratosphere and troposphere by combining the data from EOS Aura satellite with an ozone model using data assimilation. Ozone exhibits a large temporal variability in the lower stratosphere. Our previous work showed that assimilation of satellite data from limb-sounding geometry helps constrain ozone profiles in that region. We assimilated ozone data from the Aura Microwave Limb Sounder (MLS) and the Ozone Monitoring Instrument (OMI) into the ozone system at NASA's Global Modeling and Assimilation Office (GMAO). Ozone is transported within a general circulation model (GCM) which includes parameterizations for stratospheric photochemistry, tropospheric chemistry, and a simple scheme for heterogeneous ozone loss. The focus of this study is on the representation of ozone in the lower stratosphere and tropospheric ozone columns. We plan to extend studies of tropospheric ozone distribution through assimilation of ozone data from the Tropospheric Emission Spectrometer (TES). Comparisons with ozone sondes and occultation data show that assimilation of Aura data reproduces ozone gradients and variability in the lower stratosphere well. We proceed by separating the contributions to temporal changes in the ozone field into those that are due to the model and those that are due to the assimilation of Aura data. The impacts of Aura data are illustrated and their role in the representation of ozone variability in the lower stratosphere and troposphere is shown.

Stajner, I.↗

NASA NTRS1998

Baseline Observations of Hemispheric Sea Ice with the Nimbus 7 Scanning Multichannel Microwave Radiometer

The Scanning Multichannel Microwave Radiometer (SMMR) on board the NASA Nimbus 7 satellite was designed to obtain data for sea surface temperatures (SSTs), near-surface wind speeds, sea ice coverage and type, rainfall rates over the oceans, cloud water content, snow water equivalent, and soil moisture. In this paper, I shall emphasize the sea ice observations and mention briefly some important SST observations. A prime factor contributing to the importance of SMMR sea ice observations lies in their successful integration into a long-term time series, presently being extended by observations from the series of Special Sensor Microwave/Imager (SSMI) on board the DOD/DMSP F8, Fl1, and F12 satellites. This currently constitutes a 19-year data set. Almost half of this was provided by the SMMR. Unfortunately, the 4-year data set produced earlier by the single-channel Electrically Scanned Microwave Radiometer (ESMR) was not successfully integrated into the SMMR/SSMI data set. This resulted primarily from the lack of an overlap period to provide intersensor adjustment, but also because of the large difference between the algorithms to produce ice concentrations and large temporal gaps in the ESMR data. The lack of overlap between the SeaSat and Nimbus 7 SMMR data sets was an important consideration for also excluding the SeatSat one, but the spatial gaps especially in the Southern Hemisphere daily SeaSat observations was another. The sea ice observations will continue into the future by means of the Advanced Microwave Scanning Radiometer (AMSR) on board the ADEOS II and EOS satellites due to be launched in mid- and late-2000, respectively. Analysis of the sea ice data has been carried out by a number of different techniques. Long-term trends have been examined by means of ordinary least squares and band-limited regression. Oscillations in the data have been examined by band-limited Fourier analysis. Here, I shall present results from a novel combination of Principal Component analysis and the recently-developed Empirical Mode Decomposition (EMD). In this method, the data are first separated into spatial and temporal parts, and then the temporal parts of the first few PCs are broken into intrinsic modes by the EMD method.

Gloersen, Per↗

DOE OSTI2021

Performance Validation of Pulsed Thermal Imaging System for In-Service Applications

42 ENGINEERING↗

DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗

NASA NTRS1981

Planetary-scale wave structure in the Martian atmosphere

Wave-like perturbations are found in the Mariner 9 IRIS atmospheric temperature data during late Northern Hemisphere winter in a latitude band between 45 deg N and 65 deg N. The nature of the data base prevents a unique separation of spatial and temporal behavior, but Fourier analysis of the data constrains the waves to discrete combinations of planetary wavenumber and period. One major spectral component possesses a meridional amplitude cross section with a maximum near the 1 mbar level at 60 deg N and is strongly correlated with the circumpolar jet observed in thermal winds calculated from the mean meridional temperature cross section. This feature is consistent with the low-wavenumber baroclinic waves observed in Viking Lander data, and the vertical structure reflects the behavior anticipated for a vertically penetrating quasi-geostrophic disturbance. Other possible origins for the wave cannot be ruled out, however. Among these is a stationary wave forced by wavenumber-2 topographic relief.

Conrath, B. J.↗

NASA NTRS1982

Voyager observations of solar wind proton temperature - 1-10 AU

Simultaneous measurements are made of the solar wind proton temperatures by the Voyager 1 and 2 spacecraft, far from earth, and the IMP 8 spacecraft in earth orbit. This technique permits a separation of radial and temporal variations of solar wind parameters. The average value of the proton temperature between 1 and 9 AU is observed to decrease as r (the heliocentric radius) to the -(0.7 + or - 0.2). This is slower than would be expected for adiabatic expansion. A detailed examination of the solar wind stream structure shows that considerable heating occurs at the interface between high and low speed streams.

Gazis, P. R.↗

NASA NTRS1993

Finite-Difference Algorithms For Computing Sound Waves

Governing equations considered as matrix system. Method variant of method described in "Scheme for Finite-Difference Computations of Waves" (ARC-12970). Present method begins with matrix-vector formulation of fundamental equations, involving first-order partial derivatives of primitive variables with respect to space and time. Particular matrix formulation places time and spatial coordinates on equal footing, so governing equations considered as matrix system and treated as unit. Spatial and temporal discretizations not treated separately as in other finite-difference methods, instead treated together by linking spatial-grid interval and time step via common scale factor related to speed of sound.

Davis, Sanford↗

NASA NTRS2002

In Situ Measurements of N2O and CH4 During SOLVE on the ER-2 Using a New Tunable Diode Laser Instrument

This report is the final report for Cooperative Agreement NCC2-1105: 'In Situ Measurements of N2O and CH4 during SOLVE on the ER-2 using a new tunable diode laser instrument.' The tasks outlined in the proposal are listed below with a brief comment. The publications and the conference presentations are listed. Finally the important publications are attached. The Cooperative Agreement made possible a research effort to produce high precision and high accuracy in situ measurements of methane and nitrous oxide on the ER-2 during the SOLVE field campaign and to analyze these measurements. These measurements of CH4 and N2O were of utmost importance to studies of the ozone losses in the Arctic winter and spring. The concentrations measured over a large spatial and temporal range allowed the separation of the dynamical and chemical ozone loss. The most important results of the SOLVE program were contained in two scientific papers. This Cooperative Agreement allowed the participation of the Argus instrument in the program and the analysis of the data.

Jost, Hansjurg↗

NASA NTRS2012

Science Objectives and Rationale for the Radiation Belt Storm Probes Mission

The NASA Radiation Belt Storm Probes (RBSP) mission addresses how populationsof high energy charged particles are created, vary, and evolve in space environments,and specifically within Earths magnetically trapped radiation belts. RBSP, with a nominallaunch date of August 2012, comprises two spacecraft making in situ measurements for atleast 2 years in nearly the same highly elliptical, low inclination orbits (1.1 5.8 RE, 10).The orbits are slightly different so that 1 spacecraft laps the other spacecraft about every2.5 months, allowing separation of spatial from temporal effects over spatial scales rangingfrom 0.1 to 5 RE. The uniquely comprehensive suite of instruments, identical on the twospacecraft, measures all of the particle (electrons, ions, ion composition), fields (E and B),and wave distributions (dE and dB) that are needed to resolve the most critical science questions.Here we summarize the high level science objectives for the RBSP mission, providehistorical background on studies of Earth and planetary radiation belts, present examples ofthe most compelling scientific mysteries of the radiation belts, present the mission design ofthe RBSP mission that targets these mysteries and objectives, present the observation andmeasurement requirements for the mission, and introduce the instrumentation that will deliverthese measurements. This paper references and is followed by a number of companionpapers that describe the details of the RBSP mission, spacecraft, and instruments.

Geomagnetic storms↗

NASA NTRS2014

Challenges in Measuring External Currents Driven by the Solar Wind-Magnetosphere Interaction

In studying the Earth's geomagnetism, it has always been a challenge to separate magnetic fields from external currents originating from the ionosphere and magnetosphere. While the internal magnetic field changes very slowly in time scales of years and more, the ionospheric and magnetospheric current systems driven by the solar wind -magnetosphere interaction are very dynamic. They are intimately controlled by the ionospheric electrodynamics and ionospheremagnetosphere coupling. Single spacecraft observations are not able to separate their spatial and temporal variations, and thus to accurately describe their configurations. To characterize and understand the external currents, satellite observations require both good spatial and temporal resolutions. This paper reviews our observations of the external currents from two recent LEO satellite missions: Space Technology 5 (ST-5), NASA's first three-satellite constellation mission in LEO polar orbit, and Communications/Navigation Outage Forecasting System (C/NOFS), an equatorial satellite developed by US Air Force Research Laboratory. We present recommendations for future geomagnetism missions based on these observations.

WIND-MAGNETOSPHERE↗

NASA NTRS2012

Science Objectives and Rationale for the Radiation Belt Storm Probes Mission

The NASA Radiation Belt Storm Probes (RBSP) mission addresses how populations of high energy charged particles are created, vary, and evolve in space environments, and specifically within Earths magnetically trapped radiation belts. RBSP, with a nominal launch date of August 2012, comprises two spacecraft making in situ measurements for at least 2 years in nearly the same highly elliptical, low inclination orbits (1.1 x 5.8 RE, 10 deg.).The orbits are slightly different so that 1 spacecraft laps the other spacecraft about every 2.5 months, allowing separation of spatial from temporal effects over spatial scales ranging from 0.1 to 5 RE. The uniquely comprehensive suite of instruments, identical on the two spacecraft, measures all of the particle (electrons, ions, ion composition), fields (E and B),and wave distributions (dE and dB) that are needed to resolve the most critical science questions. Here we summarize the high level science objectives for the RBSP mission, provide historical background on studies of Earth and planetary radiation belts, present examples of the most compelling scientific mysteries of the radiation belts, present the mission design of the RBSP mission that targets these mysteries and objectives, present the observation and measurement requirements for the mission, and introduce the instrumentation that will deliver these measurements. This paper references and is followed by a number of companion papers that describe the details of the RBSP mission, spacecraft, and instruments.

magnetosphere↗

NASA NTRS2017

Maven Observations of Tail Current Sheet Flapping at Mars

The Martian magnetotail is a complex regime through which atmospheric particles are lost to space. Our current understanding of Mars' tail continues to develop with the comprehensive particle and field data collected by Mars Atmosphere and Volatile EvolutioN (MAVEN). In this work, we identify periods when MAVEN encounters multiple current sheet crossings through a single tail traversal in order to understand tail dynamics. We apply an analysis technique that has been developed and validated by using multipoint measurements in order to separate the spatial and temporal properties associated with current sheet flapping. Events are classified into periods of steady flapping, due to a global motion of the current sheet, and kink-like flapping, resulting from localized wave propagation along the tail current sheet. Out of 106 periods during which multiple current sheet crossings were observed, 20 were due to steady flapping and 10 from kink-like flapping. A majority of the kink-like events resulted from waves propagating in the opposite direction of the solar wind convection electric field, regardless of their location in the tail, unlike at Earth and Venus. This finding suggests that possible magnetosphere energy sources, whereby plasma is accelerated and removed from the Martian environment, are not located in the central magnetotail; rather, these waves may be driven by a source located at the tail flank based on the direction of the solar wind electric field. Therefore, by identifying potential sources of impulsive energy release in the tail, we may better understand mechanisms that drive atmospheric loss at Mars.

Atmospheric Particles↗