Aeroassisted orbital transfer is recognized as a potential technology to enhance the operational responsiveness of space with significant fuel savings. In order to use aerodynamic forces resulting from the flight through the atmosphere, however, considerable thermal protection is required, thereby potentially decreasing the savings in mass achieved by lowering the fuel consumption. In this paper the relationship between achievable fuel savings and thermal protection system (TPS) size is investigated by coupling these two disciplines through a single coupling parameter, the maximum heating rate constraint. The optimal solution that minimizes the total mass of the vehicle (fuel and TPS) is then determined. A trajectory optimization procedure and a TPS mass estimation model are then applied to a problem where it is desired to transfer a vehicle using an aeroassisted maneuver between two low earth orbits with a specified inclination change. All trajectory parameters, including de-orbit, boost, and recircularization impulses, are optimized and the thermal protection system is sized with ablative and reusable materials. It is found that the aeroassisted maneuver maintains a overall mass advantage over an all propulsive maneuver. It is also found that the minimum overall mass (fuel and TPS) is achieved when no heating rate constraint is imposed, which is also the scenario that minimizes the fuel consumption alone.

The U.S. Department of Transportation is responsible for implementing new safety improvements and regulations with the goal of ensuring limited funds are distributed to where they can have the greatest impact on safety. In this work, we conduct a study of new regulations and other reactions (such as recalls) to fatal accidents in several different modes of transportation implemented from 2002 to 2009. We find that in the safest modes of commercial aviation and bus transport, the amount of spending on new regulations is high in relation to the number of fatalities compared to the regulatory attention received by less safe modes of general aviation and private automobiles. Additionally, we study two major fatal accident investigations from commercial aviation and two major automotive recalls associated with fatal accidents. We find differences in the cost per expected fatality prevented for these reactions, with the airline accident investigations being more cost effective. Overall, we observe trends in both the automotive and aviation sectors that suggest that public transportation receives more regulatory attention than private transport. We also observe that the types of safety remedies utilized, regulation versus investigation, have varying levels of effectiveness in different transport modes. We suggest that these differences are indicative of increased public demand for safety in modes where a third party may be held responsible, even for those not participating in the transportation. These findings have important implications for the transportation industry, policymakers, and for estimating the public demand for safety in new transport modes.

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Design under uncertainty needs to account for aleatory uncertainty, such as variability in material properties, and epistemic uncertainty including errors due to imperfect analysis tools. While there is a consensus that aleatory uncertainty be described by probability distributions, for epistemic uncertainty there is a tendency to be more conservative by taking worst case scenarios or 95th percentiles. This conservativeness may result in substantial performance penalties. Epistemic uncertainty, however, is usually reduced by additional knowledge typically provided by tests. Then, redesign may take place if tests show that the design is not acceptable. This paper proposes a reliability based design optimization (RBDO) method that takes into account the effects of future tests possibly followed by redesign. We consider each realization of epistemic uncertainty to correspond to a different design outcome. Then, the future scenario, i.e., test and redesign, of each possible design outcome is simulated. For an integrated thermal protection system (ITPS) design, we show that the proposed method reduces the mass penalty associated with a 95th percentile of the epistemic uncertainty from 2.7% to 1.2% compared to standard RBDO, which does not account for the future. We also show that the proposed approach allows trading off mass against development costs as measured by probability of needing redesign. Finally, we demonstrate that the tradeoff can be achieved even with the traditional safety factor based design.

The focus of this work is on uncertainty characterization, sensitivity analysis, uncertainty propagation, and extreme-case analysis. To deal with the computationally expensive and complex NASA problem, a simpler toy problem is devised to mimic the NASA problem for which the true results were known. The toy problem helped in thoroughly testing the current methods and their repeatability. For uncertainty characterization, a novel cumulative density function matching method is proposed, which gave similar results as a standard Markov chain-Monte Carlobased Bayesian approach. An efficient reliability reanalysis-based probability-box sensitivity analysis method is employed to identify the most sensitive parameters to the risk analysis metrics. Uncertainty propagation to find extreme values for the risk analysis metrics is done using a single-loop efficient reliability reanalysis-based method. A modified version of the efficient reliability reanalysis is proposed that uses self-normalizing weights and caps on the weights; this is referred to as a capped self-normalizing efficient reliability reanalysis. This method showed considerably better performance at estimating risk analysis metrics for this application as compared to the generic efficient reliability reanalysis. The use of efficient reliability reanalysis was dictated by the cost of the black-box functions provided by NASA.Nomenclature D = modified Kolmogorov-Smirnov statistic F = cumulative density function f = probability density function g = requirement metrics I = indicator function J 1 = expected value of worst-case requirement metric J 2 = probability of failure n = given number of observations n 0 = number of aleatory samples for generating an empirical cumulative density function p = uncertain random variables q = sampling probability density function for efficient reliability reanalysis w = worst-case requirement metric x = intermediate variables θ= subparameters (epistemic uncertainty) Θ = vector of subparameters