Simulation assisted risk assessment applied to launch vehicle conceptual design

Mathias, Donovan; Go, Susie; Gee, Ken; Lawrence, Scott L.

doi:10.1109/rams.2008.4925773

Cited by 11 publications

(9 citation statements)

References 2 publications

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“…The debris field environment is strongly dependent on the initial fragment distribution, which is defined in terms of the total number of debris pieces as well as each piece's mass and imparted relative velocity [1]. Masses are assigned by specifying a component's total mass and a distribution type to allocate a mass value for each debris piece.…”

Section: Debris Propagationmentioning

confidence: 99%

“…The Engineering Risk Assessment (ERA) team at the NASA Ames Research Center provides simulation-based risk assessment approaches for analyzing crew launch vehicle abort scenarios during the mission ascent phase. The ERA approaches use physics-based models to characterize failure environments in terms of risks posed by blast overpressure, resulting debris field, and fireball thermal radiation [1]. The subsequent propagation of these failure environments is analyzed to evaluate the abort system's ability to escape safely.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of liquid rocket engine failure propagation using self-evolving scenarios

Mathias

Motiwala

2015

2015 Annual Reliability and Maintainability Symposium (RAMS)

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Traditional probabilistic risk assessment approaches often require failure scenarios to be explicitly defined through event sequences that are then quantified as part of the integrated analysis. This approach becomes difficult when failure propagation paths change as a function of the system operation. Additionally, if the propagation paths represent interactions among even a modest number of components, the scenario count becomes combinatorially intractable. This paper presents an alternate approach for quantifying the probability of failure propagation in such a case. Rather than explicitly defining scenario sequences, simple physical models are created for each of the components. In this way, only the physical states and rules of component interactions must be defined, rather than event sequences for each individual scenario. Initiating failures are introducted into the system, either randomly or as defined by relative likelihood, and the failures cascade through the system via the interaction rules. This process is repeated using Monte Carlo methods and, as a result, the most probable scenarios "self-evolve" in terms of both sequence path and frequency.This approach was applied to failures occurring in the engine compartment of a space launch vehicle with four liquid rocket engines and four high-pressure helium tanks. Each engine was modeled with key components, such as turbomachinery, combustion chamber, propellant lines, and additional support systems. Three test cases were conducted with different high-energy engine failures. End results of interest included an additional engine-out failure and tank burst, which represent the loss-of-mission (LOM) and loss-ofcrew (LOC) failure environments, respectively. Observations show that almost every scenario outcome is unique and that many scenarios involve complex chain reactions that are difficult to predict. This validates the usefulness of the modeling approach in assessing the overall risks to the crew during a launch vehicle abort.

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Section: Debris Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulation of liquid rocket engine failure propagation using self-evolving scenarios

Mathias

Motiwala

2015

2015 Annual Reliability and Maintainability Symposium (RAMS)

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“…LOM risk is then converted to LOC risk by applying the existing abort effectiveness (AE) models as developed by Ames Research Center (ARC) for each LOM failure bin [14].…”

Section: Mature Modeling Approachmentioning

confidence: 99%

Launch vehicle reliability growth

Mattenberger¹,

Leszczynski²,

Putney³

et al. 2012

2012 Proceedings Annual Reliability and Maintainability Symposium

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This paper addresses the importance of considering the initial reliability and reliability growth as opposed to only the mature risk estimate when making relative comparisons among developmental launch vehicle (LV) alternatives and introduces the current model used to perform this type of analysis.Probabilistic risk assessments (PRA) often focus on modeling the mature state of a system under consideration; however, in the aerospace field of LV design such an assessment can be dangerously misleading. Due to the low flight rate, a given LV may never reach maturity prior to retirement and will fly mostly in an immature state. The historical record of early LV flights suggests a risk posture well above the mature estimate predicted through the standard PRA approach. Thus, any decision based upon the mature estimates may be significantly different than a decision based upon the predicted risk during the bulk of its useful life while it is still maturing. In order to make an informed decision about the relative merits of competing LV architectures, decision makers must consider not only the mature system risk, but also the reliability growth for the system along the path to maturity.The current model described in this paper uses a reliability growth methodology, which has expanded the scope of risk influencing factors and has been able to provided Loss of Mission (LOM) and Loss of Crew (LOC) risk estimates for over 20 LVs in a period of less than two months. The advantage of employing such a methodology to conceptual LV designs is that it enables a more realistic estimate of campaign success during early flights without the need for detailed design information. This model captures the reality that element heritage and maturity are more important to early flight success than first order component reliability calculations while yielding valuable insights for designers of future vehicles.

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“…This in combination with the placement of the crew capsule on top of the launch vehicle ("where God meant them to be"), provided the foundation for a potentially high integrated abort effectiveness probability given a failure. In addition to designing the launch vehicle to use proven, heritage components, accident environment physics-based simulation during the design phase helped in the formulation of LAS requirements [15]. These requirements were derived from abort effectiveness studies which used a combination of physics simulation with Probabilistic Risk Assessment (PRA) methods.…”

Section: Constellation Programmentioning

confidence: 99%