Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The Jet Propulsion Laboratory Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team. The Architecture Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an A-Team study. In these studies, Concept Maturity Levels are used to group methods. These levels include idea generation and capture (Concept Maturity Level 1), initial feasibility assessment (Concept Maturity Level 2), and trade space exploration (Concept Maturity Level 3). Methods used for exploring the science objectives, feasibility, and scope will be described including the use of a new technique for understanding the most compelling science, called a Science Return Diagram. In the process of developing the Science Return Diagram, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid toward developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions, and reveal the mission concept's most compelling science. This article will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique

A Robust and Optimal Multidisciplinary Approach For Space Systems Conceptual Design

L'abstract è presente nell'allegato / the abstract is in the attachmen

Understanding the Development and Implementation of Heuristics and Biases in Design

Heuristics are rules of thumb used by designers to save time and resources in exchange for satisfactory, but not necessarily optimal, solutions. However, there is a large knowledge gap in understanding how heuristics are developed, retrieved, employed, and modified by designers. Having a better awareness of one’s own set of heuristics can be beneficial for relaying to other team members, improving a team’s training processes, and aiding others on their path to design expertise. Similarly, awareness of heuristics used by other team members could aid a designer’s understanding of decisions outside of their own expertise and the collective vision for the team’s final design. Ultimately, describing how heuristics are used may lead to a more normative approach to heuristics, through determining how one heuristic may add more value to the design process over another. This justification should lead to more effective decision making in design. To do this, the heuristics and their characteristics must be extracted using a repeatable scientific research methodology. This dissertation presents four exploratory case studies aimed at identifying improvements to heuristic extraction methodology, with participants ranging from space mission concept design, advanced manufacturing, and graduate student design teams. A framework for documenting and updating heuristic knowledge over time is formed based on statistically significant correlations of heuristic attributes, specifically in regards to how often a heuristic is used, how the reliable the heuristic is perceived, and how often the heuristic evolves. Lastly, an alternate perspective of heuristics as an error management bias is highlighted and discussed.Ph.D