Addressing Deep Uncertainty in Space System Development through Model-based Adaptive Design

When developing a space system, many properties of the design space are initially unknown and are discovered during the development process. Therefore, the problem exhibits deep uncertainty. Deep uncertainty refers to the condition where the full range of outcomes of a decision is not knowable. A key strategy to mitigate deep uncertainty is to update decisions when new information is learned. In this paper, the spacecraft development problem is modeled as a dynamic, chance-constrained, stochastic optimization problem. The Model-based Adaptive Design under Uncertainty (MADU) framework is presented, in which conflict-directed search is combined with reuse of information to solve the development problem efficiently in the presence of deep uncertainty. The framework is built within a Model-based Systems Engineering (MBSE) paradigm in which a SysML model contains the design, the design space, and information learned during search. The development problem is composed of a series of optimizations, each different than the previous. Changes between optimizations can be the addition or removal of a design variable, expansion or contraction of the domain of a design variable, addition or removal of constraints, or changes to the objective function. These changes are processed to determine which search decisions can be preserved from the previous optimization. The framework is illustrated on a case study drawn from the thermal design of the REgolith X-ray Imaging Spectrometer (REXIS) instrument. This case study demonstrates the advantages of the MADU framework with the solution found 30% faster than an algorithm that doesn't reuse information. With this framework, designers can more efficiently explore the design space and perform updates to a design when new information is learned. Future work includes extending the framework to multiple objective functions and continuous design variables.