Description: CU Aerospace proposes the development of a new parallel nonlinear program (NLP) solver software package. NLPs allow the solution of complex optimization problems, and there exist today a number of capable NLP solvers typically used within the astrodynamics community. Unfortunately, none of them take into account the capabilities of a high performance computer with thousands of processors, nor use the recent advances in graphics processing unit (GPU) calculation. Many NASA optimal trajectory packages (OTIS, MALTO, EMTG) have already streamlined and optimized their own code to run as fast as possible, and it is now the serial-execution nature of the existing packages that represents the largest bottleneck. The new parallel NLP solver to be developed by CU Aerospace in partnership with the University of Illinois will represent a novel, ground-up redesign of this kind of solver. It will be built to be transparently usable by the existing optimal trajectory solvers used at NASA; however, it will also take advantage of high performance computing and/or GPU processing to reduce the run time by orders of magnitude. This has strong implications for NASA's mission design groups, as the time to solution for the trajectory teams will be significantly improved. Further, an improved optimization tool will have applications across all fields at NASA which user optimization, from engineering design to cost analysis. CU Aerospace is uniquely positioned to develop this new parallel NLP solver, with a team of experts, and the nearby proximity to the high performance computing resources at the National Center for Super Computing.

Benefits: NASA currently utilizes SNOPT, IPOPT, and WORHP software packages for non-linear optimization as a plug in for tools like OTIS, EMTG and MALTO. These astrodynamics applications allow solving complex spacecraft trajectories and optimal control problems, but could greatly benefit from introducing a new parallel large-scale, nonlinear, sparse optimization solution. This new parallelized NLP technique, as introduced in this proposal, would result in a reduction in execution time, thereby reducing the optimization's turn-around time and improve communications between both designers and scientists. The integration of this new NLP solver technique will be transparent, and NASA trajectory optimization tools can be easily augmented to take advantage of this approach. Further, many non-astrodynamics NASA research initiatives depend on non-linear optimization. This technique has applications across the entire space program, and also to much of the aviation design and research. Optimization is a tool used in almost all design processes, and this new tool will reduce the design time spent on running simulations, using existing NASA high performance computers, saving overhead and improving the speed at which new designs can occur.

Optimization is a general tool used across government, academia, and industry alike. The potential non-NASA applications are boundless as everyone can benefit from improved time-to-solution. Within the astrodynamics community, companies such as Boeing, Aerospace Corp, AGI, etc. will be able to immediately replace their current non-linear optimizers with this new approach. They will have few reasons not to, given it will be demonstrably faster and competitively priced against their current solutions, with very little necessary hardware improvements to run the solver. Further, the optimization is not a unique field to the aerospace community, and this software will have applications in the other engineering disciplines, in economics, in marketing, in agriculture, allowing for solutions to trajectories, design problems, network configurations, portfolio analysis, etc. This is the kind of technology that will be developed because of cutting edge space-related needs, but will have far reaching benefits and appeal to all aspects of society.