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Systems Engineering and Multidisciplinary Design Optimization Commons™
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- Tradespace exploration (2)
- Aerodynamics (1)
- Automation (1)
- Autonomy (1)
- Collaborative vehicles (1)
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- Computer Aided Engineering (1)
- Conceptual design (1)
- Connectivity (1)
- Decomposition and coordination; multiobjective optimization; reinforcement learning; constrained MDPs; presentation bias; Mars navigation; sequential relaxation (1)
- Digital engineering. (1)
- Diversity (1)
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- MATLAB (1)
- Machine Learning (1)
- Model-based systems engineering (1)
- Multi Objective Optimization (1)
- Multi-objective optimization (1)
- Parent-child architecture (1)
- Pareto analysis (1)
- Resilience (1)
- System-of-Systems (SoS) (1)
- UAV (1)
- Unmanned Aerial Vehicles (1)
Articles 1 - 4 of 4
Full-Text Articles in Systems Engineering and Multidisciplinary Design Optimization
Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh
Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh
All Theses
Modern military operations demand systems that adapt to uncertain, rapidly changing missions across diverse terrains. Traditional single-platform vehicle design is insufficient for such complexity. This research introduces a hierarchical tradespace exploration framework for designing and evaluating families of heterogeneous ground vehicles under a System-of-Systems (SoS) architecture. The framework treats vehicle design as a co-optimization problem, where a “parent” vehicle (e.g., a Squad Multipurpose Equipment Transport) coordinates specialized “child” vehicles for reconnaissance, amphibious tasks, terrain traversal, and stealth missions. Unlike conventional approaches that optimize vehicles individually, this study emphasizes collaborative performance, resource sharing, and adaptability at the family level. Central to …
Understanding Policy Transfer Under Decomposition And Coordination: Sequential Reinforcement Learning With Relaxation-Based Feasibility Control, Aannand Lal
All Theses
This thesis investigates how decomposition and coordination (D&C) choices influence exploration, learning, and transfer in reinforcement learning (RL) based design frameworks. A unified methodology is developed to evaluate generality under decomposition and to demonstrate sequential coordination on a representative physical task. The generality study holds the underlying physics constant while varying problem presentation - specifically scalarization weights, objective orientations, and feasibility handling. A train-evaluate matrix is constructed in which each trained policy is assessed across all alternative decompositions. Set-based diagnostics quantify generality through exact-match identity, Jaccard similarity of distinct feasible states, total feasible coverage, and Pareto-front quality. Results show that …
Mission-To-Designs: Automating Uav Conceptual Design Generation, Nana A. Adjei
Mission-To-Designs: Automating Uav Conceptual Design Generation, Nana A. Adjei
All Theses
The conceptual design stage of Unmanned Aerial Vehicles (UAVs) is an exploratory phase where designers are tasked with developing and evaluating a wide range of viable design concepts, considering different geometries and configurations based on mission requirements. The goal is to identify the most suitable design after the exploration for further development and progression to the next phase. Several tools and frameworks have been developed to assist designers in this stage, each offering unique capabilities, but all with the common aim of aiding in the efficient development and evaluation of conceptual designs. This is important in the UAV design process …
Tradespace Exploration Of A Uav Conceptual Design Using Model-Based Systems Engineering, Ayan Srivastava
Tradespace Exploration Of A Uav Conceptual Design Using Model-Based Systems Engineering, Ayan Srivastava
All Theses
The objective of this research is to apply model-based systems engineering approaches to the conceptual design of unmanned aerial vehicles. This is accomplished by evaluating the models of aircraft performance, extracting input and output parameters from the models, creating chains of models, and implementing the models in the MATLAB programming language. By following this process, it is possible to identify the global parameters that remain constant across models, the shared input parameters, the dependencies between models, and the feedback loops with the systems models.
The models are currently implemented in two files using Microsoft MS Excel, one is focused on …