Federated Space Systems Tradespace Exploration for Strategic Robustness

Motivated by the growth of the commercial space economy and renewed focus on the disaggregation of military space systems, this work develops a method for conceptual design of federated satellite systems as a collaborative system-of-systems (SoS). Objectives seek to improve the likelihood of successful SoS formation and pursue constituent system utility robustness. The proposed metaheuristic optimization tradespace exploration method accounts for technical and economic design variables and multi-decision maker strategy dynamics. Constituent system designs are ranked on their simulated net present value. A game-theoretic measure of risk dominance is used in concert with net present value to assess robustness and utility of candidate SoS designs. The method is validated with a notional application case that assesses potential collaboration between Earth observing and telecommunications systems. Results demonstrate a fundamental trade between system efficiency and robustness and highlight the importance of accounting for strategy dynamics when designing systems for collaboration.

A Model-Based System Metaheuristic Engineering MBSME Approach in the Conceptual Selection of Offshore Production Units

The objective of this paper is to present and discuss the philosophy behind the integration of "Model-Based Systems Engineering" (MBSE) with metaheuristic algorithms, referred to as "Model-Based Systems Metaheuristic Engineering" (MBSME), which has demonstrated high potential of techno-economic optimization of large capital projects in oil and gas industry, notably in the automatic and integrated conceptual design and selection of offshore systems architectures. Virtual modeling has always been an important part of systems engineering to support functional, performance and other engineering analysis. The so-called MBSME allows the simulation of several specific System-of-Systems physically addressed in offshore field development, bringing all the benefits of the traditional MBSE approach, and set a stochastic characteristic in the analysis, allowing the project team to focus on a Model-Centric approach, as well as to quickly understand the influence of several combined project strategies and application of different technologies, communicated through a Tradespace exploration map. Due to the characteristics associated with and the countless number of variables of the multidimensional problem addressed in an offshore field development, the integration of "Meta-Heuristic" algorithms with "Model-Based Systems Engineering" has demonstrated a remarkable efficiency and powerful applicability in the search for optimized design solutions in oil and gas industry, especially considering the processes of generation of conceptual alternatives of offshore production systems. This method leads to a reduction of more than 2/3 of the average time currently observed, with an increase in the number of conceptual alternatives evaluated in the order of tens to an order of thousands of options, in an automatic and integrated approach. Although the digital MBSME already developed addresses the combination of all technical disciplines associated with a complete offshore field development, the current work emphasizes the latest R&D achievements, addressing the automatic design and specification of Topside Facilities architecture, combined with the automatic selection of fitting for purpose Production Unit, based on internal requirements, such as the required capacity to support total weight and footprint imposed by the topside facilities’ modules, as well as external requirements, like water depth, surface metocean, type of well completion and oil storage requirements. An example of the MBSME application is presented, demonstrating a three-dimensional Tradespace exploration, relating Net Present Value (NPV), Capital Expenditure (CAPEX) and Breakeven Oil Price, through the application of a computational package in a hypothetical project, reflecting the design conditions of an offshore development in the Brazilian Pre-Salt region. The paper communicates an efficient method to increase the scope and accuracy of conceptual analyses, leading to the identification of the most favorable techno-economic conditions to the particularities of each project, supporting significant increases of return on investments.

Quantitative Set-Based Design to Inform Design Teams

System designers, analysts, and engineers use various techniques to develop complex systems. A traditional design approach, point-based design (PBD), uses system decomposition and modeling, simulation, optimization, and analysis to find and compare discrete design alternatives. Set-based design (SBD) is a concurrent engineering technique that compares a large number of design alternatives grouped into sets. The existing SBD literature discusses the qualitative team-based characteristics of SBD, but lacks insights into how to quantitatively perform SBD in a team environment. This paper proposes a qualitative SBD conceptual framework for system design, proposes a team-based, quantitative SBD approach for early system design and analysis, and uses an unmanned aerial vehicle case study with an integrated model-based engineering framework to demonstrate the potential benefits of SBD. We found that quantitative SBD tradespace exploration can identify potential designs, assess design feasibility, inform system requirement analysis, and evaluate feasible designs. Additionally, SBD helps designers and analysts assess design decisions by providing an understanding of how each design decision affects the feasible design space. We conclude that SBD provides a more holistic tradespace exploration process since it provides an integrated examination of system requirements and design decisions.

Demonstrating set-based design techniques: an unmanned aerial vehicle case study

The Engineered Resilient Systems research program seeks to improve decision making in the Analysis of Alternatives process by leveraging model-based engineering (MBE) early in the design process to develop more resilient systems. Traditional tradespace exploration using point-based design often converges quickly to an initial baseline design concept with subsequent engineering changes to modify the design. However, this process can lead to significant cost growth if the initial concept is not able to meet requirements or if the revised design is not affordable. Enabled by MBE, set-based design (SBD) considers sets of all possible design concepts and down-selects design concepts to converge to a final design using insights into design trade-off analysis, modeling and simulation, and test data. Using a notional unmanned aerial vehicle case study with low-fidelity physics-based models and an open source Excel® add-in called SIPmath©, this research implements an integrated MBE trade-off analytics framework that simultaneously generates numerous SBDs using parametric performance and cost models and evaluates the designs in the value and cost tradespace. In addition, this research explores incorporating resilience quantification and uncertainty into SBD trade-off analysis. Future research is needed to validate the use of SBD with low-fidelity models for tradespace exploration in early system design.