(Advisor: Prof. Dimitri Mavris)
Reduced Order Non-INtrusive (RONIN) Modeling
for Strategic Defense Planning
Tuesday, January 18
Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST II)
The Department of Defense along with research organizations like RAND have documented a strategic gap acknowledging the need for improvements in the ability to conduct exploratory analyses to support capability development that seek to exploit both technological and doctrinal conceptual solutions. In this work, the overarching strategic gap was decomposed into more focused areas of needed research, starting with an exploration of current integration methods of different models to meet the concerns of Congress with regards to quality, accuracy, and dependability; noting that they have become too computationally prohibitive to be used to explore a large design/decision space. Along with the observation that for complex and potentially nonlinear metrics, passing expected values does not provide the needed traceability between different levels of model abstraction. Additionally, current model abstraction methods have difficulty accounting for the increasing dimensionality associated with increasingly complex models or simulations. These observations lead to the objective of this research, which is the formulation and demonstration of a methodology which leverages reduced order modeling methods for traceable model abstraction that effectively and efficiently captures complex system-of-systems behaviors within current military operations modeling and simulation methods.
From a review of current literature in order to meet this objective, reduced order models need to account for nonlinear interdependencies, underlying physical phenomena, and stochastic effects. A set of research questions, hypotheses, and experiments are posed to further understand and address identified gaps. All of which guided the formulation of the proposed Reduced Order Non-INtrusive (RONIN) modeling methodology as means to meet the stated research objective. The RONIN modeling methodology works to create and use predictive reduced order surrogate models which capture more information regarding behaviors and interactions as compared to traditional methods such as “look-up” tables or simple passing of expected values. It is proposed to demonstrate the RONIN modeling methodology through the discretization of a full order campaign scenario into specific sets of missions to be executed throughout the campaign which are then used to generate predictive reduced order surrogate models of said missions. These predictive surrogate models will then be used to generate specified mission outcomes that are used to augment the campaign scenario and the results of the augmented campaign scenario will be compared to the results of the full order campaign scenario.
- Prof. Dimitri N. Mavris – School of Aerospace Engineering (advisor)
- Prof. Daniel Schrage – School of Aerospace Engineering
- Prof. Graeme Kennedy – School of Aerospace Engineering
- Prof. John Colombi – Air Force Institute of Technology
- Dr. Alicia M. Sudol – School of Aerospace Engineering