Risk, Reliability, and Resilience Engineering

Robust design optimization  (RDO)

• Both the mean and variance of the optimization objective are minimized
• Constraints are satisfied within specified bounds that account for uncertainty
• Pareto fronts are constructed to represent the trade-offs between conflicting objectives
• Applications:
  • Additive manufacturing process parameter optimization
  • Flight vehicle mission profile optimization
• Objective function examples: maximize bond quality, maximize part geometry accuracy, minimize part printing time

Reliability-based design optimization  (RBDO)

• Objective function is optimized such that the reliability with respect to desired performance criterion is above an acceptable threshold
• Constraints are probabilistic and require calculation of failure probability or reliability index
• Application: Flight vehicle mission and maneuver optimization
• Objective function example: minimize mean stress on critical rotorcraft components

 

Reliability-based design optimization (RBDO)

• Aircraft re-routing optimization under uncertainty
• Uncertainty in incoming aircraft state (location, heading, speed, etc.), space availability in neighboring airports, radar performance, and communication delays.
• Support vector regression model is used for constructing the distribution of the key system failure metric.
  
• Reliability-based constraint used to ensure that re-routed aircraft land before running out of fuel.

Hybrid optimization approach

• Digital twin approach for damage growth minimization / resilient system operations
• Application: Damage growth minimization in mechanical components while successfully completing assigned task(s)
• Generation of an operation-to-load map that defines loading patterns (families) for various operational regimes
• Parametrization of loading regimes and classification of parameters that define the loading patterns given an operational regime
• Hybrid approach that combines RDO with RBDO for damage growth minimization under diagnosis and prognosis uncertainty

Sensor Placement Optimization with Multi-Fidelity Models

• Fuses information from models of different fidelities
  • High fidelity model - higher accuracy, computationally expensive
  • Low fidelity model - lower accuracy, computationally cheaper

• Consider additive, multiplicative, and input correction factors to improve low-fidelity model using high-fidelity model data
• Optimize collection of high-fidelity model data with the objective of maximizing the information gain (measured using Kullback Leibler divergence)
• Multi-fidelity model trained with optimal high-fidelity simulations is used for further analysis
• Application: Sensor placement optimization on rigid panel geometries
• Objective function examples: Optimize high-fidelity model runs and optimize sensor locations to maximize the information gain regarding the parameters of the multi-fidelity model and to minimize the error between the multi-fidelity model prediction and the experimental observation

Funding

 

Current People

• Sankaran Mahadevan, Professor
• Pranav Karve, Research Assistant Professor
• Paromita Nath, Post-Doctoral Fellow
• Berkcan Kapusuzoglu, Ph.D. Student
• William Sisson, Ph.D. Student

Publications