Methodology for Developing a Probabilistic Risk Assessment Model of Spacecraft Rendezvous and Dockings

In 2007 NASA was preparing to send two new visiting vehicles carrying logistics and propellant to the International Space Station (ISS). These new vehicles were the European Space Agency s (ESA) Automated Transfer Vehicle (ATV), the Jules Verne, and the Japanese Aerospace and Explorations Agency s (JAXA) H-II Transfer Vehicle (HTV). The ISS Program wanted to quantify the increased risk to the ISS from these visiting vehicles. At the time, only the Shuttle, the Soyuz, and the Progress vehicles rendezvoused and docked to the ISS. The increased risk to the ISS was from an increase in vehicle traffic, thereby, increasing the potential catastrophic collision during the rendezvous and the docking or berthing of the spacecraft to the ISS. A universal method of evaluating the risk of rendezvous and docking or berthing was created by the ISS s Risk Team to accommodate the increasing number of rendezvous and docking or berthing operations due to the increasing number of different spacecraft, as well as the future arrival of commercial spacecraft. Before the first docking attempt of ESA's ATV and JAXA's HTV to the ISS, a probabilistic risk model was developed to quantitatively calculate the risk of collision of each spacecraft with the ISS. The 5 rendezvous and docking risk models (Soyuz, Progress, Shuttle, ATV, and HTV) have been used to build and refine the modeling methodology for rendezvous and docking of spacecrafts. This risk modeling methodology will be NASA s basis for evaluating the addition of future ISS visiting spacecrafts hazards, including SpaceX s Dragon, Orbital Science s Cygnus, and NASA s own Orion spacecraft. This paper will describe the methodology used for developing a visiting vehicle risk model

Surrogate - Assisted Optimisation -Based Verification & Validation

This thesis deals with the application of optimisation based Validation and Verification (V&V) analysis on aerospace vehicles in order to determine their worst case performance metrics. To this end, three aerospace models relating to satellite and launcher vehicles provided by European Space Agency (ESA) on various projects are utilised. As a means to quicken the process of optimisation based V&V analysis, surrogate models are developed using polynomial chaos method. Surro- gate models provide a quick way to ascertain the worst case directions as computation time required for evaluating them is very small. A sin- gle evaluation of a surrogate model takes less than a second. Another contribution of this thesis is the evaluation of operational safety margin metric with the help of surrogate models. Operational safety margin is a metric defined in the uncertain parameter space and is related to the distance between the nominal parameter value and the first instance of performance criteria violation. This metric can help to gauge the robustness of the controller but requires the evaluation of the model in the constraint function and hence could be computationally intensive. As surrogate models are computationally very cheap, they are utilised to rapidly compute the operational safety margin metric. But this metric focuses only on finding a safe region around the nominal parameter value and the possibility of other disjoint safe regions are not explored. In order to find other safe or failure regions in the param- eter space, the method of Bernstein expansion method is utilised on surrogate polynomial models to help characterise the uncertain param- eter space into safe and failure regions. Furthermore, Binomial failure analysis is used to assign failure probabilities to failure regions which might help the designer to determine if a re-design of the controller is required or not. The methodologies of optimisation based V&V, surrogate modelling, operational safety margin, Bernstein expansion method and risk assessment have been combined together to form the WCAT-II MATLAB toolbox

Cross Entropy-based Analysis of Spacecraft Control Systems

Space missions increasingly require sophisticated guidance, navigation and control algorithms, the development of which is reliant on verification and validation (V&V) techniques to ensure mission safety and success. A crucial element of V&V is the assessment of control system robust performance in the presence of uncertainty. In addition to estimating average performance under uncertainty, it is critical to determine the worst case performance. Industrial V&V approaches typically employ mu-analysis in the early control design stages, and Monte Carlo simulations on high-fidelity full engineering simulators at advanced stages of the design cycle. While highly capable, such techniques present a critical gap between pessimistic worst case estimates found using analytical methods, and the optimistic outlook often presented by Monte Carlo runs. Conservative worst case estimates are problematic because they can demand a controller redesign procedure, which is not justified if the poor performance is unlikely to occur. Gaining insight into the probability associated with the worst case performance is valuable in bridging this gap. It should be noted that due to the complexity of industrial-scale systems, V&V techniques are required to be capable of efficiently analysing non-linear models in the presence of significant uncertainty. As well, they must be computationally tractable. It is desirable that such techniques demand little engineering effort before each analysis, to be applied widely in industrial systems. Motivated by these factors, this thesis proposes and develops an efficient algorithm, based on the cross entropy simulation method. The proposed algorithm efficiently estimates the probabilities associated with various performance levels, from nominal performance up to degraded performance values, resulting in a curve of probabilities associated with various performance values. Such a curve is termed the probability profile of performance (PPoP), and is introduced as a tool that offers insight into a control system's performance, principally the probability associated with the worst case performance. The cross entropy-based robust performance analysis is implemented here on various industrial systems in European Space Agency-funded research projects. The implementation on autonomous rendezvous and docking models for the Mars Sample Return mission constitutes the core of the thesis. The proposed technique is implemented on high-fidelity models of the Vega launcher, as well as on a generic long coasting launcher upper stage. In summary, this thesis (a) develops an algorithm based on the cross entropy simulation method to estimate the probability associated with the worst case, (b) proposes the cross entropy-based PPoP tool to gain insight into system performance, (c) presents results of the robust performance analysis of three space industry systems using the proposed technique in conjunction with existing methods, and (d) proposes an integrated template for conducting robust performance analysis of linearised aerospace systems

Robustness analysis of VEGA launcher model based on effective sampling strategy

An efficient robustness analysis for the VEGA launch vehicle is essential to minimize the potential system failure during the ascending phase. Monte Carlo sampling method is usually considered as a reliable strategy in industry if the sampling size is large enough. However, due to a large number of uncertainties and a long response time for a single simulation, exploring the entire uncertainties sufficiently through Monte Carlo sampling method is impractical for VEGA launch vehicle. In order to make the robustness analysis more efficient when the number of simulation is limited, the quasi-Monte Carlo(Sobol, Faure, Halton sequence) and heuristic algorithm(Differential Evolution) are proposed. Nevertheless, the reasonable number of samples for simulation is still much smaller than the minimal number of samples for sufficient exploration. To further improve the efficiency of robustness analysis, the redundant uncertainties are sorted out by sensitivity analysis. Only the dominant uncertainties are remained in the robustness analysis. As all samples for simulation are discrete, many uncertainty spaces are not explored with respect to its objective function by sampling or optimization methods. To study these latent information, the meta-model trained by Gaussian Process is introduced. Based on the meta-model, the expected maximum objective value and expected sensitivity of each uncertainties can be analyzed for robustness analysis with much higher efficiency but without loss much accuracy

Development of a biomechanical surrogate for the evaluation of commotio cordis protection

Commotio Cordis (CC) has proven to be life threatening for young athletes as it is the second leading cause of mortality in youth sports. In the past 15 years, researchers have been working to understand the pathophysiology of this event. It has been proven that impacts directly over the cardiac silhouette during a vulnerable period of the cardiac cycle can cause CC. In order to reduce the occurrence of CC in sports, chest protectors need to be tested for efficacy. Currently there is no biofidelic surrogate to serve this purpose. In order to test equipment to a given standard of protection, a biomechanical surrogate is needed that models the human response to impacts observed in sports. The goal of this dissertation was to develop and validate a biomechanical surrogate that can predict the risk of CC. The first step in developing a biomechanical surrogate is the identification of an effective injury criterion that can predict the injury outcome. Porcine specimens were impacted directly over the heart during the vulnerable portion of their cardiac cycle. Impacts were conducted with a lacrosse ball at four speeds that have been proven effective to induce CC in a porcine model (30, 40, 50, and 60mph). Ten injury criteria were evaluated, and impact force proved to be the most effective injury criterion (Somer\u27s D = 0.52). Human response corridors were developed for the same impact conditions using Post Mortem Human Specimens (PMHS). These data were used to evaluate existing thoracic biomechanical surrogates. Three surrogates were tested in the same impact conditions and none were found to be biofidelic (Biofidelity Rank \u3e 2). A new Sports Specific Thoracic Surrogate (SSTS) was developed and validated using the human response corridors (Biofidelity Rank = 1.2). The SSTS was used to evaluate 70 lacrosse chest protectors from ten (10) manufacturers. These data provided a broad survey of the current level of CC protection commercially available. A statistical analysis was conducted on ten (10) pieces of equipment from seven of the manufacturers. All of the equipment proved to be effective in limiting impact force and reducing the risk of CC. Equipment efficacy could be improved by utilizing this surrogate as a development tool to evaluate new chest protector designs. It could also be used in certification testing by an organization such as National Operating Committee on Standards for Athletic Equipment (NOCSAE). The development of a NOCSAE certification standard would encourage manufacturers to improve the CC protection offered

Multidisciplinary Design Analysis of Reusable European VTHL and VTVL Booster Stages

While initially met with skepticism, launch vehicles with reusable stages are now an established and successful part of the global launch market. Thus, there is a need to analyze and assess the possibility of such a system being designed and built in Europe. Accordingly, in 2016 the German Aerospace Center (DLR) initiated a study on reusable first stages named ENTRAIN (European Next Reusable Ariane). Within this study two return method categories, respectively vertical take-off, vertical landing (VTVL) and vertical take-off, horizontal landing (VTHL) with winged stages, were investigated. First, preliminary design tools were used to identify promising configurations and in the second phase more specialized and extensive analyses were conducted for subsystems of special interest. From this second phase, the results of the evaluation of two areas are presented: Structure as well as system dynamics, guidance and control. The results of these analyses together with previously published results from other subsystems increase the confidence in the designs proposed and evaluated within the ENTRAIN study as well as in the general understanding of the technical factors driving the design of reusable stages

Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy

The global war on terror emphasizes the need for a weapon system that can improve the self-defense capability of the U.S Navy ship against small surface craft threats. This MSSE Capstone Project investigated the feasibility of integrating the Non-Line of Sight Launching System (NLOS-LS) onto U.S. Navy ships. In particular, the focus of the project was on the DDG-51 class ships. The NLOS-LS was originally designed to provide support to Army ground forces against over the horizon threats. The U.S. Navy recognizes the prospect of this weapon in an at-sea environment. The capability of the system has been proven through its developmental testing to date and illustrates the potential to the U.S. Navy for ship defense. System integration involves incorporating a stand-alone, land-based system onto a ship with an existing shipboard combat system. This report addresses the top-level integration issues, such as the physical installation and combat system integration, and provides recommendations related to some important concerns that include interface analysis, functional analysis, system behavior, and physical installation. This analysis concludes with a notional implementation for many issues and provides a risk analysis for those issues. It also identifies many integration areas requiring further research.http://archive.org/details/integratingnonli10945692

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices

Decoupled UMDO formulation for interdisciplinary coupling satisfaction under uncertainty

International audienceAt early design phases, taking into account uncertainty for the optimization of a multidisciplinary system is essential to establish the optimal system characteristics and performances. Uncertainty Multidisciplinary Design Optimization (UMDO) formulations have to eciently organize the dierent disciplinary analyses, the uncertainty propagation, the optimization, but also the handling of interdisciplinary couplings under uncertainty. A decoupled UMDO formulation (Individual Discipline Feasible - Polynomial Chaos Expansion) ensuring the coupling satisfaction for all the instantiations of the uncertain variables is presented in this paper. Ensuring coupling satisfaction in instantiations is essential to ensure the equivalence between the coupled and decoupled UMDO problem formulations. The proposed approach relies on the iterative construction of surrogate models based on Polynomial Chaos Expansion in order to represent at the convergence of the optimization problem, the coupling functional relations as a coupled approach under uncertainty does. The performances of the proposed formulation is assessed on an analytic test case and on the design of a new Vega launch vehicle upper stage