Launch risk analysis

Baeker, J. B.; Collins, Jon D.; Haber, Jerold M.

doi:10.2514/3.27993

Cited by 17 publications

(9 citation statements)

References 3 publications

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“…In this paper, we extend our work to the case in which the ballistic coefficient is a random variable that is independent of the other variables. Therefore, in what follows we append the equatioṅ β = 0 (5) to (4) and consider the complete system defined by equations (4) and (5).…”

Section: A 3-d Translational Equations Of Motion-spherical Rotating mentioning

confidence: 99%

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Estimation of debris hazard areas due to a space vehicle breakup at high altitudes

Reyhanoglu

Alvarado

Carmi

2013

2013 9th Asian Control Conference (ASCC)

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With the recent developments in the Commercial Space Transportation industry, there has been a surge of interest in the analyses of the debris hazard areas due to a space vehicle breakup and the risk posed to the aircraft in the national airspace system as well as to the people and the property on the ground. The focus of this paper is to study the problem of estimation of the extent of the airspace containing falling debris due to a space vehicle breakup. A precise computation of propagation of debris to the ground is not practical for many reasons. There is insufficient knowledge of the initial state vector; ambient wind conditions; and key parameters, including the ballistic coefficient distributions. In addition, propagation of all debris pieces to the ground would require extensive computer time. In this paper, a computationally efficient covariance propagation method is employed for the estimation of debris dispersion.

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Section: A 3-d Translational Equations Of Motion-spherical Rotating mentioning

confidence: 99%

“…Depending upon the altitude of the vehicle breakup, the physical properties of the pieces and the ambient conditions, some debris could impact the ground in a few minutes, while other debris capable of damaging or destroying an aircraft could continue to fall for the next couple hours ( [5], [6]). …”

Section: Introductionmentioning

confidence: 99%

Estimation of debris hazard areas due to a space vehicle breakup at high altitudes

Reyhanoglu

Alvarado

Carmi

2013

2013 9th Asian Control Conference (ASCC)

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“…While these models are developed based on deterministic knowledge of a particular physical phenomenon, probabilistic methods are used to account for their uncertainty. In space launches, these models have been used to assess range safety involving risk of injury or loss of life in populated areas following a launch accident [14,15] due to inert debris from launch vehicle, blast effects from solid or liquid propellant explosion [16], and health effects from dispersion of toxic propellant plumes [17]. They have also been used in dealing with MM/OD risk [18], shuttle and International Space Station (ISS) subsystem component criticality assessments [19][20][21], and determination of orbiter reentry overflight hazards [22,23].…”

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confidence: 99%

Structural Health Monitoring and Risk Management of a Reusable Launch Vehicle

Yap

Macias

Kaouk³

et al. 2012

Journal of Spacecraft and Rockets

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A structural-health-monitoring system can contribute to the risk management of a structure operating under hazardous conditions. An example is the wing leading-edge impact-detection system that monitors the debris hazards to the Space Shuttle Orbiter's reinforced carbon-carbon panels. Since return to flight after the Columbia accident, the system was developed and subsequently deployed on board the orbiter to detect ascent and on-orbit debris impacts, so as to support the assessment of wing leading edge structural integrity before orbiter reentry. As structural health monitoring is inherently an inverse problem, the analyses involved, including those performed for this system, tend to be associated with significant uncertainty. The use of probabilistic approaches to handle the uncertainty has resulted in the successful implementation of many development and application milestones. Nomenclature d = damage size, in. G max = maximum acceleration response G rms = rms of acceleration response P = damage probability P C = joint probability of critical damage P C=I = conditional probability of critical damage given an impact has occurred P F = probability of multisensor corroboration being weaker than flight experience P I = probability of impact P M = probability of multisensor corroboration being stronger than model experience r diag = diagonal corroboration ratio of multisensor response r horz = horizontal corroboration ratio of multisensor response = logarithmic decay dam = damage-G max correlation factor mea = test measurement scaling factor pan = reinforced carbon-carbon panel-to-panel test scaling factor sen = sensor configuration test scaling factor tst = test-article boundary condition scaling factor

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“…The exposure effect was modeled using a debris-footprint based exposure model, developed and used for assessing the risk to the general public from space launch activities 8,9 . By this formulation, the UAV accident exposes a certain area of the ground to harm, denoted, A exp .…”

Section: B Ground Impact Modelmentioning

confidence: 99%

Safety Considerations for Operation of Different Classes of UAVs in the NAS

Weibel

Hansman

2004

AIAA 3rd "Unmanned Unlimited" Technical Conference, Workshop and Exhibit

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Recognizing the significant effort underway to integrate UAV operations in the NAS, a preliminary hazard analysis was conducted for two critical hazards of UAV operation. Models were developed to describe UAV ground impact and midair collisions. Under several assumptions, a model of ground impact was used to calculate the UAV system reliability required to meet a target level of safety, for different UAV classes differentiated by mass. The model showed a significantly higher reliability required for high-mass UAVs, and a large variation in reliability required with population density, with a two order of magnitude increase over metropolitan areas. Midair collision risk was estimated in the vicinity of airways using a model of aircraft collisions based on the density of air traffic in those regions. There is a two order of magnitude difference in risk between on-airway and onaltitude operation and operation away from airways and off major flight levels. Therefore, there are potential operating strategies that can reduce the risk of UAV operation, such as procedural separation from high population and high traffic areas. There are also additional mitigation possibilities to further reduce the risk of integrating UAVs in the NAS. Nomenclature A exp= area of exposure d = distance traveled by air traffic within airspace segment ELS = expected level of safety MTBF = mean time Between failures resulting in ground impact P fat | coll = probability of fatality given a potential collision P pen = probability of penetration P mit = probability of mitigation preventing a ground fatality ρ = population density t = time V = airspace volume

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Launch risk analysis

Cited by 17 publications

References 3 publications

Estimation of debris hazard areas due to a space vehicle breakup at high altitudes

Estimation of debris hazard areas due to a space vehicle breakup at high altitudes

Structural Health Monitoring and Risk Management of a Reusable Launch Vehicle

Safety Considerations for Operation of Different Classes of UAVs in the NAS

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