Olivia J. Pinon-Fischer

A Design Space Exploration Methodology to Support Decisions under Evolving Requirements’ Uncertainty and its Application to Suborbital Vehicles

This research aims at supporting the development of emerging markets such as suborbital vehicles by establishing a methodology that enables a broad design space exploration at a conceptual level to select the best concepts against unclear objectives and under evolving requirements’ uncertainty. To bridge the gap in current design space exploration techniques, a new architecture-based morphological matrix is developed to generate all feasible concepts. Then, a new evolutionary algorithm based on architecture fitness is implemented that drives multi-objective optimization algorithms to simultaneously compare and optimize all configurations. To support decisions under evolving uncertainty, requirements are modeled by membership functions and are propagated using fuzzy set theory. The new methodology is expected to reduce the risk of missing promising concepts and help designers with challenging go/no-go decisions. It will also provide more flexibility by allowing decision makers to develop scenarios and support more analytic decisions.

A Conceptual Design Framework for Performance, Life-Cycle Cost, and Safety Evaluation of Suborbital Vehicles

Recent technological advancements along with a growing demand for space tourism is supporting the development of new manned suborbital vehicles. This market is characterized yet by a lack of both an optimized baseline and clearly-defined requirements so that a methodology that explores the entire design space is needed. In particular, this research focuses on the development of a multi-objective design framework that provides the capabilities to rapidly evaluate the flying, economic, and safety performance of all suborbital vehicles at a conceptual level. For development purposes, the modeling and simulation environment is broken down into six modules: weight/size, aerodynamics, trajectory, propulsion, economics, and safety. By leveraging empirical models, physics-based approaches, and surrogate modeling techniques, it enables the rapid and parametric assessment and optimization of a multitude of design concepts. Finally, it is the first environment of this sort to provide economic and safety assessment capabilities for all architectures of suborbital vehicles.

An Evolutionary Multi-Architecture Multi-Objective Optimization Algorithm for Design Space Exploration

The increasing complexity of future aerospace vehicles gives rise to large combinatorial spaces of possible configurations for which no baseline has been established. To ensure that the best concept is selected, the entire design space has to be explored. In addition, the presence of evolving requirements’ uncertainty due to the lack of experience and established regulations requires flexible decision-making techniques to be implemented to alleviate the risks inherent to the launch of new programs. To address these challenges, a new evolutionary multi-architecture multi-objective optimization algorithm is presented. The proposed approach allows designers to efficiently and exhaustively generate variable-oriented architectures that can be further optimized and compared. It provides a dynamic decision-making environment able to identify trends and trade-offs, while also prioritizing designs. The application of the proposed methodology on suborbital vehicles highlights key promising technological enablers, which can be leveraged to design high-performance and robust concepts.

Identification of Key Factors in Integrating Aircraft and the Associated Supply Chains during Early Design Phases

The aerospace supply chain network has evolved over the years to become more complex and essential to a company’s success. Traditionally, the supply chain considerations are brought in after the design has been finalized. However, this no longer represents a sufficient practice to satisfy today’s market demand and meet the challenges inherent to the design and production of a new aircraft. This paper discusses the need to bring supply chain considerations and constraints earlier in the design process. It presents a new framework to establish and analyze the interrelationship between aircraft design and supply chain design. In particular, this paper aims to identify the significant factors linking the two disciplines during the early design phases. To do so, the authors leverage the Manufacturing-Influenced Design (MInD) methodology that generates and integrates manufacturing knowledge early into aircraft design. They further extend this methodology by integrating a supply chain model in the form of a mixed-integer programming with linear optimization that minimizes the total time-discounted network costs. The supply chain costs in turn provide additional criteria and constraints to the selection of aircraft designs. The aircraft design traits of the work breakdown structure (WBS), material selections, and the bill of materials (BoM) are all inputs to the supply chain model. Demand, production years, material unit cost, transportation unit cost, and labor rate are supply chain related inputs. Design of Experiments (DoE) and surrogate modeling techniques are then leveraged to generate aircraft performance as well as supply chain outputs. Finally, a sensitivity analysis is conducted to gain more insight into the key factors and their implications on real-world design decisions. Areas for future research are outlined at the end of the paper.

Design Methodology for the Performance, Weight, and Economic Assessment of Chemical Rocket Engines

AbstractA design methodology is presented that supports the design of future aerospace rocket-powered vehicles. In particular, it provides the capabilities to rapidly evaluate the performance, weight, size, and lifecycle costs of all chemical rocket engines at a conceptual level. By leveraging cycle-based approaches and surrogate modeling techniques, the performance of all chemical rocket engines can be evaluated with an accuracy of 3%, whereas it divides the execution time by a factor of 105 compared to current physics-based models. New mass-estimating relationships are developed for estimating the weight and the size of solid engines with an improved accuracy compared to existing models. Physics-based models built around the key design drivers are used for the weight and size estimation of liquid and hybrid engines. Although existing cost-estimating relationships are used to evaluate the lifecycle costs of solid and liquid engines, a more physics-based model is developed for hybrid engines. Although it ...

An Evaluation Framework for Unmanned Aircraft Systems Integration in the National Airspace System

The National Airspace System (NAS) is a complex system defined by the interactions between aircraft and Air Traffic Control (ATC). As Unmanned Aircraft Systems (UAS) come to play an increasing role in domestic civil applications, the NAS will face unprecedented challenges to safely integrate these new systems and technologies into the existing regulatory framework. The focus of this research is to develop an evaluation framework that will aid in the future integration of UAS into the NAS. An Agent Based Modeling & Simulation approach is used to aid in the development of the evaluation framework. The primary objective is to aid decision makers so they may rigorously and transparently assess the impact of regulatory and technological requirements on UAS/NAS performance and safety. This objective is accomplished through the development of key metrics that enable both safety analysis and technology assessment to be conducted. These metrics can then be used to construct a visual analytic framework to further aid decision makers in setting new regulatory requirements.