1. 1 Prognosis 2030 – A Vision February 19 2008 T.A. Cruse, DARPA/DSO Consultant Dayton, Ohio 45459

2. 19 February 2008DCT WorkshopSlide 2 The Perfect World for Prognosis Damage progression is deterministic Perfect knowledge of microstructural features, chemistry, initial strains, etc. Perfect physics-based damage models appropriate to microstructures Ignorance of any = uncertainty Usually seen as scatter in actual/predicted Processing deviation = new population Design error = major shift in new population Prognosis is more than physics/chemistry

3. 19 February 2008DCT WorkshopSlide 3 Prognosis is a 3-Legged Stool Leg 1: Physics-based system state models Response to defined/controlled processing Response to defined/recorded environments Prediction of future states, given initial state Leg 2: System state “fingerprint” Initial state definition and recording Leg 3: State validation – “virtual sensor” Linking Leg 1 with new NDE characterizations to provide updated system state “fingerprint”

4. 19 February 2008DCT WorkshopSlide 4 What is New NDE? What it is not Finding cracks Finding delaminations Stiffness change What it will include Change detection New signal processing Witness mapping Ping-ring response mode Focus on state awareness What it will be Integral part of 3-legs

5. 19 February 2008DCT WorkshopSlide 5 State Awareness Attributes Real-time environmental loads & history E.g., loads, thermal loads, humidity, chemistry, etc. Real-time microstructural response E.g., strain, temperature, diffusion, oxidation, etc. Initial state “fingerprint” Micro/macro state definition at service intro Critical processing history state contributions Current state “fingerprint”

6. 19 February 2008DCT WorkshopSlide 6 Prognosis 2030 – the vision Vast computational power in the system Real-time state assessment updating Real-time integration of initial state and environmental history Real-time forensics (space systems/avionics) Real-time communications to user for prognosing future state capabilities “Columbia safe-return” scenario capability System operates as integrated, virtual sensor All three legs are fully integrated State awareness through “new NDE” Analytical system certification revolution Fully risk based design/deployment with processing histories Fully adaptive to individual part tracking up to the “tail number”

7. 19 February 2008DCT WorkshopSlide 7 Prognosis 2030 - Challenges Close the “scatter band” High fidelity modeling – system response High fidelity damage models  Complex 3D material systems  High heat flux loading environments/responses Revolutionize material state awareness Neural materials and systems Ping-ring state updating Global-local communications within system Remote diagnostics/forensics Future material system complexities Hybrids, 3D and tailored systems, Complete material processing All things to be probabilistically integrated

8. 19 February 2008DCT WorkshopSlide 8 Recommendations to Workshop Assess balance between the three legs High fidelity, probabilistic system modeling Complex materials – damage state modeling  Microstructurally physics-based state processes  Cumulative history, fully-integrated mechanisms Integrated, new NDE concepts  Self-assessing materials; automated reporting  Characterize the material state; integrate with models Define the most important basic research enablers for each Where are the greatest discovery potentials? What are the most critical challenge problems?