Historical Exploration - Learning Lessons from the Past to Inform the Future

This report examines a number of exploration campaigns that have taken place during the last 700 years, and considers them from a risk perspective. The explorations are those led by Christopher Columbus, Sir Walter Raleigh, John Franklin, Sir Ernest Shackleton, the Company of Scotland to Darien and the Apollo project undertaken by NASA. To provide a wider context for investigating the selected exploration campaigns, we seek ways of finding analogies at mission, programmatic and strategic levels and thereby to develop common themes. Ultimately, the purpose of the study is to understand how risk has shaped past explorations, in order to learn lessons for the future. From this, we begin to identify and develop tools for assessing strategic risk in future explorations. Figure 0.1 (see Page 6) summarizes the key inputs used to shape the study, the process and the results, and provides a graphical overview of the methodology used in the project. The first step was to identify the potential cases that could be assessed and to create criteria for selection. These criteria were collaboratively developed through discussion with a Business Historian. From this, six cases were identified as meeting our key criteria. Preliminary analysis of two of the cases allowed us to develop an evaluation framework that was used across all six cases to ensure consistency. This framework was revised and developed further as all six cases were analyzed. A narrative and summary statistics were created for each exploration case studied, in addition to a method for visualizing the important dimensions that capture major events. These Risk Experience Diagrams illustrate how the realizations of events, linked to different types of risks, have influenced the historical development of each exploration campaign. From these diagrams, we can begin to compare risks across each of the cases using a common framework. In addition, exploration risks were classified in terms of mission, program and strategic risks. From this, a Venn diagram and Belief Network were developed to identify how different exploration risks interacted. These diagrams allow us to quickly view the key risk drivers and their interactions in each of the historical cases. By looking at the context in which individual missions take place we have been able to observe the dynamics within an exploration campaign, and gain an understanding of how these interact with influences from stakeholders and competitors. A qualitative model has been created to capture how these factors interact, and are further challenged by unwanted events such as mission failures and competitor successes. This Dynamic Systemic Risk Model is generic and applies broadly to all the exploration ventures studied. This model is an amalgamation of a System Dynamics model, hence incorporating the natural feedback loops within each exploration mission, and a risk model, in order to ensure that the unforeseen events that may occur can be incorporated into the modeling. Finally, an overview is given of the motivational drivers and summaries are presented of the overall costs borne in each exploration venture. An important observation is that all the cases - with the exception of Apollo - were failures in terms of meeting their original objectives. However, despite this, several were strategic successes and indeed changed goals as needed in an entrepreneurial way. The Risk Experience Diagrams developed for each case were used to quantitatively assess which risks were realized most often during our case studies and to draw comparisons at mission, program and strategic levels. In addition, using the Risk Experience Diagrams and the narrative of each case, specific lessons for future exploration were identified. There are three key conclusions to this study: Analyses of historical cases have shown that there exists a set of generic risk classes. This set of risk classes cover mission, program and strategic levels, and includes all the risks encountered in the cases studied. At mission level these are Leadership Decisions, Internal Events and External Events; at program level these are Lack of Learning, Resourcing and Mission Failure; at Strategic Level they are Programmatic Failure, Stakeholder Perception and Goal Change. In addition there are two further risks that impact at all levels: Self-Interest of Actors, and False Model. There is no reason to believe that these risk classes will not be applicable to future exploration and colonization campaigns. We have deliberately selected a range of different exploration and colonization campaigns, taking place between the 15th Century and the 20th Century. The generic risk framework is able to describe the significant types of risk for these missions. Furthermore, many of these risks relate to how human beings interact and learn lessons to guide their future behavior. Although we are better schooled than our forebears and are technically further advanced, there is no reason to think we are fundamentally better at identifying, prioritizing and controlling these classes of risk. Modern risk modeling techniques are capable of addressing mission and program risk but are not as well suited to strategic risk. We have observed that strategic risks are prevalent throughout historic exploration and colonization campaigns. However, systematic approaches do not exist at the moment to analyze such risks. A risk-informed approach to understanding what happened in the past helps us guard against the danger of assuming that those events were inevitable, and highlights those chance events that produced the history that the world experienced. In turn, it allows us to learn more clearly from the past about the way our modern risk modeling techniques might help us to manage the future - and also bring to light those areas where they may not. This study has been retrospective. Based on this analysis, the potential for developing the work in a prospective way by applying the risk models to future campaigns is discussed. Follow on work from this study will focus on creating a portfolio of tools for assessing strategic and programmatic risk

Robustness analysis of linear time-varying systems with application to aerospace systems

In recent years significant effort was put into developing analytical worst-case analysis tools to supplement the Verification \& Validation (V\&V) process of complex industrial applications under perturbation. Progress has been made for parameter varying systems via a systematic extension of the bounded real lemma (BRL) for nominal linear parameter varying (LPV) systems to IQCs. However, finite horizon linear time-varying (LTV) systems gathered little attention. This is surprising given the number of nonlinear engineering problems whose linearized dynamics are time-varying along predefined finite trajectories. This applies to everything from space launchers to paper processing machines, whose inertia changes rapidly as the material is unwound. Fast and reliable analytical tools should greatly benefit the V\&V processes for these applications, which currently rely heavily on computationally expensive simulation-based analysis methods of full nonlinear models. The approach taken in this thesis is to compute the worst-case gain of the interconnection of a finite time horizon LTV system and perturbations. The input/output behavior of the uncertainty is described by integral quadratic constraints (IQC). A condition for the worst-case gain of such an interconnection can be formulated using dissipation theory. This utilizes a parameterized Riccati differential equation, which depends on the chosen IQC multiplier. A nonlinear optimization problem is formulated to minimize the upper bound of the worst-case gain over a set of admissible IQC multipliers. This problem can then be efficiently solved using custom-tailored meta-heuristic (MH) algorithms. One of the developed algorithms is initially benchmarked against non-tailored algorithms, demonstrating its improved performance. A second algorithm's potential application in large industrial problems is shown using the example of a touchdown constraints analysis for an autolanded aircraft as was as an aerodynamic loads analysis for space launcher under perturbation and atmospheric disturbance. By comparing the worst-case LTV analysis results with the results of corresponding nonlinear Monte Carlo simulations, the feasibility of the approach to provide necessary upper bounds is demonstrated. This comparison also highlights the improved computational speed of the proposed LTV approach compared to simulation-based nonlinear analyses

Closed-loop nonlinear optimal control design for flapping-wing flying robot (1.6 m wingspan) in indoor confined space: Prototyping, modeling, simulation, and experiment

This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).The flapping-wing technology has emerged recently in the application of unmanned aerial robotics for autonomous flight, control, inspection, monitoring, and manipulation. Despite the advances in applications and outdoor manual flights (open-loop control), closed-loop control is yet to be investigated. This work presents a nonlinear optimal closed-loop control design via the state-dependent Riccati equation (SDRE) for a flapping-wing flying robot (FWFR). Considering that the dynamic modeling of the flapping-wing robot is complex, a proper model for the implementation of nonlinear control methods is demanded. This work proposes an alternative approach to deliver an equivalent dynamic for the translation of the system and a simplified model for orientation, to find equivalent dynamics for the whole system. The objective is to see the effect of flapping (periodic oscillation) on behavior through a simple model in simulation. Then the SDRE controller is applied to the derived model and implemented in simulations and experiments. The robot bird is a 1.6 m wingspan flapping-wing system (six-degree-of-freedom robot) with four actuators, three in the tail, and one as the flapping input. The underactuated system has been controlled successfully in position and orientation. The control loop is closed by the motion capture system in the indoor test bed where the experiments of flight have been successfully done

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

Robustness analysis of linear time-varying systems with application to aerospace systems

In recent years significant effort was put into developing analytical worst-case analysis tools to supplement the Verification \& Validation (V\&V) process of complex industrial applications under perturbation. Progress has been made for parameter varying systems via a systematic extension of the bounded real lemma (BRL) for nominal linear parameter varying (LPV) systems to IQCs. However, finite horizon linear time-varying (LTV) systems gathered little attention. This is surprising given the number of nonlinear engineering problems whose linearized dynamics are time-varying along predefined finite trajectories. This applies to everything from space launchers to paper processing machines, whose inertia changes rapidly as the material is unwound. Fast and reliable analytical tools should greatly benefit the V\&V processes for these applications, which currently rely heavily on computationally expensive simulation-based analysis methods of full nonlinear models. The approach taken in this thesis is to compute the worst-case gain of the interconnection of a finite time horizon LTV system and perturbations. The input/output behavior of the uncertainty is described by integral quadratic constraints (IQC). A condition for the worst-case gain of such an interconnection can be formulated using dissipation theory. This utilizes a parameterized Riccati differential equation, which depends on the chosen IQC multiplier. A nonlinear optimization problem is formulated to minimize the upper bound of the worst-case gain over a set of admissible IQC multipliers. This problem can then be efficiently solved using custom-tailored meta-heuristic (MH) algorithms. One of the developed algorithms is initially benchmarked against non-tailored algorithms, demonstrating its improved performance. A second algorithm's potential application in large industrial problems is shown using the example of a touchdown constraints analysis for an autolanded aircraft as was as an aerodynamic loads analysis for space launcher under perturbation and atmospheric disturbance. By comparing the worst-case LTV analysis results with the results of corresponding nonlinear Monte Carlo simulations, the feasibility of the approach to provide necessary upper bounds is demonstrated. This comparison also highlights the improved computational speed of the proposed LTV approach compared to simulation-based nonlinear analyses

Multi-Objective Multidisciplinary Design Optimization Approach for Partially Reusable Launch Vehicle Design

International audienceReusability of the first stage of launch vehicles may offer new perspectives to lower the cost of payload injection into orbit if sufficient reliability and efficient refurbishment can be achieved. One possible option that may be explored is to design the vehicle first stage for both reusable and expendable uses, in order to increase the flexibility and adaptability to different target missions. This paper proposes a multilevel multidisciplinary design optimization (MDO) approach to design aerospace vehicles addressing multimission problems. The proposed approach is focused on the design of a family of launchers for different missions sharing commonalities using multi-objective MDO to account for the computational cost associated with the discipline simulations. The multimission problem addressed considers two missions: 1) a reusable configuration for a sun synchronous orbit with a medium payload range and recovery of the first stage using a gliding-back strategy; 2) an expendable configuration for a medium payload injected into a geostationary transfer orbit. A dedicated MDO formulation introducing couplings between the missions is proposed in order to efficiently solve such a coupled problem while limiting the number of calls to the exact multidisciplinary analysis thanks to the use of Gaussian processes and multi-objective efficient global optimization

System of Systems conceptual design methodology for space exploration

The scope of the research is to identify and develop a design methodology for System-of-System (a set of elements and sub-elements able to interact and cooperate in order to complete a mission), based on models, methods and tools, to support the decision makers during the space exploration scenarios design and evaluation activity in line with the concurrent design philosophy. Considering all combinations of system parameters (such as crew size, orbits, launchers, spacecraft, ground and space infrastructures), a large number of mission concept options are possible, even though not all of them are optimal or even feasible. The design methodology is particularly useful in the first phases of the design process (Phase 0 and A) to choose rationally and objectively the best mission concepts that ensure the higher probability of mission success in compliance with the high level requirements deriving from the “user needs”. The first phases of the project are particularly critical for the success of the entire mission because the results of this activity are the starting point of the more costly detailed design phases. Thus, any criticality in the baseline design will involve inevitably into undesirable and costly radical system redesigns during the advanced design phases. For this reason, it is important to develop reliable mathematical models that allow prediction of the system performances notwithstanding the poorly defined environment of very high complexity. In conjunction with the development of the design methodology for system-of-systems and in support of it, a software tool has been developed. The tool has been developed into Matlab environment and provides users with a useful graphical interface. The tool integrates the model of the mission concept, the models of the space elements at system and subsystem level, the cost-effectiveness model or value, the sensitivity and multi-objective optimization analysis. The tool supports users to find a system design solution in compliance with requirements and constraints, such as mass budgets and costs, and provides them with information about cost-effectiveness of the mission. The developed methodology has been applied for the design of several space elements (Man Tended Free Flyer, Cargo Logistic Vehicle, Rover Locomotion System) and several mission scenarios (Moon surface infrastructure support, Cis-Lunar infrastructure delivering, Cis-Lunar infrastructure logistic support), in order to assess advantages and disadvantages of the proposed method. The results of the design activity have been discussed and accepted by the European Space Agency (ESA) and have also been compared and presented to the scientific community. Finally, in a particular case, the study of the locomotion system of a lunar rover, the results of the methodology have been verified through the production and testing of the same system

Compilation of Abstracts for SC12 Conference Proceedings

1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets