Engineering topics

Moorhead, A. V.

Publications and source records attributed to Moorhead, A. V..

9 records

NASA NTRS2020

NASA Meteoroid Engineering Model (MEM) Version 3

The Meteoroid Engineering Model (MEM) version 3 is NASA’s most current and accurate model of the meteoroid environment. MEM 3 supersedes all previous versions of MEM, including MEM Release 2.0 (MEMR2), MEM Release 1.0c (MEMR1c), and previously internally controlled and released versions of MEMCxP v2.0 and LunarMEM v2.0. Earlier versions of MEM superseded older models of the meteoroid environment such as the Grün model and its derivative, Technical Memo 4527 (hereafter abbreviated as TM 4527) [1]. Prior to the establishment of the NASA Meteoroid Environment Office (MEO), NASA’s meteoroid environment models relied on a simple empirical expression derived from [2], as described in [3] and later in [1]. This expression describes the meteoroid flux incident on a flat plate near 1 au. TM 4527 assumes an isotropic environment, making the orientation of the plate irrelevant [4]. The flux was combined with scale factors to account for the reduction in flux occurring when the Earth shields the spacecraft from a portion of the meteoroid environment and the enhancement in flux due to the focusing effect of Earth’s gravitational field. TM 4527 also introduced a crude, piecewise meteoroid speed distribution with an average velocity of 19 km/s for an orbiting spacecraft based on [5]. Finally, TM 4527 assumed a three-step density distribution in which dust particles smaller than 10−6 g have a density of 2 g/cu cm, micrometeoroids between 10−6 g and 0.01 g have a density of 1 g/cu cm, and meteoroids larger than 0.01 g have a density of 0.5 g/cm3. Thus, the meteoroid model presented in TM 4527 was assembled from multiple independent sources. The model of TM 4527 was also used for years in Space Station risk assessments, and is described in Space Station Specification (SSP) 30425.

Moorhead, A. V.↗

NASA NTRS2017

Recent Corrections to Meteoroid Environment Models

The dynamical and physical characteristics of a meteoroid affects its behavior in the atmosphere and the damage it does to spacecraft surfaces. Accurate environment models must therefore correctly describe the speed, size, density, and direction of meteoroids. However, the measurement of dynamical characteristics such as speed is subject to observational biases, and physical properties such as size and density cannot be directly measured. De-biasing techniques and proxies are needed to overcome these challenges. In this presentation, we discuss several recent improvements to the derivation of the meteoroid velocity, directionality, and bulk density distributions. We derive our speed distribution from observations made by the Canadian Meteor Orbit Radar. These observations are de-biased using modern descriptions of the ionization efficiency and sharpened to remove the effects of measurement uncertainty, and the result is a meteoroid speed distribution that is skewed slower than in previous analyses. We also adopt a higher fidelity density distribution than that used by many older models. In our distribution, meteoroids with T(sub J) less than 2 are assigned to a low-density population, while those with T(sub J) greater than 2 have higher densities. This division and the distributions themselves are derived from the densities reported by Kikwaya et al. (2009, 2011). These changes have implications for the environment. For instance, helion and antihelion meteors have lower speeds and higher densities than apex and toroidal meteors. A slower speed distribution therefore corresponds to a sporadic environment that is more completely dominated by the helion and antihelion sources than in previous models. Finally, assigning these meteors high densities further increases their significance from a spacecraft damage perspective.

Moorhead, A. V.↗

NASA NTRS2017

Meteoroid Bulk Density and Ceplecha Types

Determination of asteroid bulk density is an important aspect of NEO characterization, yet difficult to measure. As a fraction of meteoroids originate from asteroids (including some NEOs), a study of meteoroid bulk densities can potentially provide useful insights into the densities of NEOs and PHOs in lieu of mutual perturbations, satellite, or expensive spacecraft missions. NASA's Meteoroid Environment Office characterizes the meteoroid environment for the purpose of spacecraft risk and operations. To accurately determine the risk, a distribution of meteoroid bulk densities are needed. This is not trivial to determine. If the particle survives to the ground the bulk density can be directly measured, however only the most dense particles land on the Earth. The next best approach is to model the meteor's ablation, which is not straightforward. Clear deceleration is necessary to do this and there are discrepancies in results between models. One approach to a distribution of bulk density is to use a measured proxy for the densities, then calibrate the proxy with known densities from meteorite falls, ablation modelling, and other sources. An obvious proxy choice is the Ceplecha type, K(sub B), thought to indicate the strength of a meteoroid. KB is frequented cited as a good proxy for meteoroid densities, but we find it is poorly correlated with density. However, a distinct split by dynamical type was seen with Jovian Tisserand parameter, T(sub J), with meteoroids from Halley Type comets (T(sub J less than 2 ) exhibiting much lower densities than those originating from Jupiter and asteroids (T(sub J greater than 2).

Blaauw, R. C.↗

NASA NTRS2017

Meteoroid Bulk Density and Ceplecha Types

The determination of asteroid bulk density is an important aspect of Near Earth Object (NEO) characterization. A fraction of meteoroids originate from asteroids (including some NEOs), thus in lieu of mutual perturbations, satellites, or expensive spacecraft missions, a study of meteoroid bulk densities can potentially provide useful insights into the densities of NEOs and PHOs (Potentially Hazardous Objects). Meteoroid bulk density is still inherently difficult to measure, and is most often determined by modeling the ablation of the meteoroid. One approach towards determining a meteoroid density distribution entails using a more easily measured proxy for the densities, then calibrating the proxy with known densities from meteorite falls, ablation modelling, and other sources. An obvious proxy choice is the Ceplecha type, KB (Ceplecha, 1958), which is thought to indicate the strength of a meteoroid and often correlated to different bulk densities in literature. KB is calculated using the air density at the beginning height of the meteor, the initial velocity, and the zenith angle of the radiant; quantities more readily determined than meteoroid bulk density itself. Numerical values of K(sub B) are sorted into groups (A, B, C, etc.), which have been matched to meteorite falls or meteor showers with known composition such as the porous Draconids. An extensive survey was conducted to establish the strength of the relationship between bulk density and K(sub B), specifically looking at those that additionally determined K(sub B) for the meteors. In examining the modeling of high-resolution meteor data from Kikwaya et al. (2011), the correlation between K(sub B) and bulk density was not as strong as hoped. However, a distinct split by dynamical type was seen with Jovian Tisserand parameter (T(sub J)), with meteoroids from Halley Type comets (T(sub J) < 2) exhibiting much lower bulk densities than those originating from Jupiter Family comets and asteroids (T(sub J) > 2). Therefore, this work indicates that the dynamical classification of a meteoroid is a better indicator of the density than the strength proxy, a somewhat surprising result.

Blaauw, R. C.↗

NASA NTRS2017

The Velocity and Density Distribution of Earth-Intersecting Meteoroids: Implications for Environment Models

No abstract available

Moorhead, A. V.↗

NASA NTRS2017

The Velocity and Density Distribution of Earth-Intersecting Meteoroids: Implications for Environment Models

Meteoroids are known to damage spacecraft: they can crater or puncture components, disturb a spacecraft's attitude, and potentially create secondary electrical effects. Because the damage done depends on the speed, size, density, and direction of the impactor, accurate environment models are critical for mitigating meteoroid-related risks. Yet because meteoroid properties are derived from indirect observations such as meteors and impact craters, many characteristics of the meteoroid environment are uncertain. In this work, we present recent improvements to the meteoroid speed and density distributions. Our speed distribution is derived from observations made by the Canadian Meteor Orbit Radar. These observations are de-biased using modern descriptions of the ionization efficiency. Our approach yields a slower meteoroid population than previous analyses (see Fig. 1 for an example) and we compute the uncertainties associated with our derived distribution. We adopt a higher fidelity density distribution than that used by many older models. In our distribution, meteoroids with TJ less than 2 are assigned to a low-density population, while those with TJ greater than 2 have higher densities (see Fig. 2). This division and the distributions themselves are derived from the densities reported by Kikwaya et al. These changes have implications for the environment: for instance, the helion/antihelion sporadic sources have lower speeds than the apex and toroidal sources and originate from high-T(sub J) parent bodies. Our on-average slower and denser distributions thus imply that the helion and antihelion sources dominate the meteoroid environment even more completely than previously thought. Finally, for a given near-Earth meteoroid cratering rate, a slower meteoroid population produces a comparatively higher rate of satellite attitude disturbances.

Moorhead, A. V.↗

NASA NTRS2016

Spacecraft Risk Posed by the 2016 Perseid Outburst

The Perseids are one of the more prolific annual showers, known for high rates and for producing bright meteors. Outbursts of this shower have been noted in the 1860s, the early 1990s, 2004, and 2009, with the 1993 outburst being especially active (peak ZHR above 300). The 1993 Perseids also affected the space-faring nations, as the launch of the STS-51 mission was delayed by NASA until after the shower maximum due to an inability to predict the shower intensity, and the ESA telecommunications satellite Olympus suffered a mission-ending anomaly attributed to a static discharge caused by a Perseid impact [1]. Rates were again high (peak ZHR around 200) in 2009, when the NASA/USGS imaging satellite Landsat-5 experienced a gyro anomaly just before the shower peak; however in this case, the satellite was recovered and normal operations resumed one week later [2]. It is interesting to note that both spacecraft anomalies were not what is typically expected from meteoroid strikes, i.e., physical damage or an attitude displacement due to transfer of momentum. It would appear that the very fast Perseids (59 km s(sup -1) have a marked ability to produce plasma upon impact, which can then serve as a conductive path for discharge currents. The shower is expected to outburst again in 2016, and we present the results from the MSFC Meteoroid Stream Model [4], which predicts enhanced activity on a level similar to that of 2009 as the Earth passes through several debris trails on the night of August 11-12 (UT). We then compare our results to those of other modelers.

Cooke, W. J.↗

NASA NTRS2016

Optical and Radar Measurements of the Meteor Speed Distribution

The observed meteor speed distribution provides information on the underlying orbital distribution of Earth-intersecting meteoroids. It also affects spacecraft risk assessments; faster meteors do greater damage to spacecraft surfaces. Although radar meteor networks have measured the meteor speed distribution numerous times, the shape of the de-biased speed distribution varies widely from study to study. Optical characterizations of the meteoroid speed distribution are fewer in number, and in some cases the original data is no longer available. Finally, the level of uncertainty in these speed distributions is rarely addressed. In this work, we present the optical meteor speed distribution extracted from the NASA and SOMN allsky networks [1, 2] and from the Canadian Automated Meteor Observatory (CAMO) [3]. We also revisit the radar meteor speed distribution observed by the Canadian Meteor Orbit Radar (CMOR) [4]. Together, these data span the range of meteoroid sizes that can pose a threat to spacecraft. In all cases, we present our bias corrections and incorporate the uncertainty in these corrections into uncertainties in our de-biased speed distribution. Finally, we compare the optical and radar meteor speed distributions and discuss the implications for meteoroid environment models.

Moorhead, A. V.↗

NASA NTRS2015

NASA Meteoroid Engineering Model Release 2.0

The Meteoroid Engineering Model release 2.0 (MEMR2) software is NASA's most current and accurate model of the meteoroid environment. It enables the user to generate a trajectory-specific meteoroid environment for spacecraft traveling within the inner solar system. In addition to the total meteoroid flux, MEMR2 provides the user with meteoroid directionality and velocity information. Users have the ability to make a number of analysis and output choices that tailor the resulting environment to their needs. This Technical Memorandum outlines the history of MEMR2, the meteoroid environment it describes, and makes recommendations for the correct use of the software and interpretation of its results.

Moorhead, A. V.↗