Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

Englander, Jacob; Vavrina, Matthew

Home/Archive/NASA NTRS

NASA NTRS · 20150021060

Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

Englander, Jacob·Vavrina, Matthew

View original source ↗Export metadata ↓

Abstract

The customer (scientist or project manager) most often does not want just one point solution to the mission design problem Instead, an exploration of a multi-objective trade space is required. For a typical main-belt asteroid mission the customer might wish to see the trade-space of: Launch date vs. Flight time vs. Deliverable mass, while varying the destination asteroid, planetary flybys, launch year, etcetera. To address this question we use a multi-objective discrete outer-loop which defines many single objective real-valued inner-loop problems.

Explore related subjects

Design Optimization Trajectory

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Englander, Jacob, Vavrina, Matthew. 2015-08-10. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion. https://ntrs.nasa.gov/citations/20150021060

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

NASA NTRS2024

Usage of ChatGPT for Engineering Design and Analysis Tool Development

ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.

Design↗

NASA NTRS2019

Failure of Nd:YVO4 Amplifier Crystals

Brittle single crystals are used in NASA applications ranging from lenses to centrally heated laser slabs. Despite standard procedures to design such components, unexpected failures occasionally occur. The ICEsat-2 (Ices,Cloud, and Elevation satellite-2) employs Yttrium orthovanadate single crystals in laser amplifiers and oscillators. Although the systems are currently flying and successfully operating, some unexpected crystal fractures occurred just prior to flight. The failures were traced to poor crystal quality along with time-dependent chemical reaction within the system assembly that increased stress beyond expectations and promoted both fast fracture and stress corrosion. This presentation will discuss failure analysis of the crystals and the chemical reactions that promoted failures, along with corrective actions taken to reduce likelihood of fracture over the three-year mission. Re-designed lasers were stored for 12 months and operated for a total of 1,000 hours without signs of degradation.

Design↗

NASA NTRS2019

Optimizing a Coupled Solution for the Orion MPCV Using DPLR and NEQAIR

The goal of this project is to study the convective and radiative heat flux predictions using computational fluid dynamics (CFD) on a smooth body representation of the NASA Orion Multi-Purpose Crew Vehicle (MPCV). The Orion MPCV is designed for long duration missions in space and will play a crucial role in transporting astronauts in the upcoming Artemis missions. With Orion's aim for cost-effective reusability comes a need to study its Thermal Protection System (TPS), which allows for safe entry into the atmospheres of the Earth, Moon, and Mars. In order to study the effects of the TPS on test flights and better understand the aerothermal environment, CFD is a necessary tool used to create simulations of entry vehicles and calculate the heat load and heat flux. However, solutions are often performed using an uncoupled approach between the flow and the radiation solver. This leads to an overestimation of the overall heating effects because they do not consider radiative cooling. Furthermore, the effects of radiation on the aft-body is an area of research with significant uncertainty. Through the use of codes developed at NASA Ames, DPLR and NEQAIR, a coupled solution can be attained for the Orion capsule which will more accurately solve for the effects of radiation in both the fore and aft-body regions. This yields more accurate design margins for the TPS, which produces a better estimate of the thickness required. By developing an accurate coupled solution of the TPS on the Orion MPCV, planners will have greater flexibility in carrying out future missions to the Moon and beyond.

Design↗