PSAAP: Predictive Science Academic Alliance Program Verification and Validation Plan Michael Ortiz Caltech PSAAP Site Visit September 24-25, 2007

PSAAP: Predictive Science Academic Alliance Program V&V and UQ techniques validation diameter verification diameter

PSAAP: Predictive Science Academic Alliance Program UQ and priorities The Yearly Assessment Cycle

PSAAP: Predictive Science Academic Alliance Program Verification & Validation Plan SystemInput parametersPerformance measures Integrated experiment IE1 Ta plate and Ta projectile at 2-3 km/s impact velocity 1.Plate thickness. 2.Incidence angles. 3.Impact velocity. 4.Projectile mass. 5.Material properties.* 6.Numerical parameters.* 1.Residual forward momentum. 2.Rear surface particle-velocity time histories. 3.Rear surface full-field out-of-plane displacement gradients. Integrated experiment IE2 Ta plate and Ta projectile at 5-10 km/s impact velocity 1.Plate thickness. 2.Incidence angles. 3.Impact velocity. 4.Projectile mass. 5.Material properties.* 6.Numerical parameters.* 1.Residual forward momentum. 2.Rear-surface particle-velocity time histories. 3.Rear surface full-field out-of-plane displacement gradients. 4.Geometrical data of debris cloud. Integrated experiment IE3 Ta plate and Fe projectile, or Fe plate and Ta projectile, at 5-10 km/s impact velocity 1.Front late thickness. 2.Incidence angles. 3.Impact velocity. 4.Projectile mass. 5.Material properties.* 6.Numerical parameters.* 1.Residual forward momentum. 2.Rear-surface particle-velocity time histories. 3.Rear surface full-field out-of-plane displacement gradients. 4.Geometrical data of debris cloud. 5.Impact-flash spectroscopy. Table of input parameters and performance measures, integrated experiments IE1-IE3:

PSAAP: Predictive Science Academic Alliance Program Verification & Validation Plan SystemInput parametersPerformance measures Integrated experiment IE4 Ta double plate and Fe projectile, or Fe double plate and Ta projectile, at 5-10 km/s impact velocity 1.Front plate thickness. 2.Back plate thickness. 3.Back plate offset distance. 4.Incidence angles. 5.Impact velocity. 6.Projectile mass. 7.Material properties.* 8.Numerical parameters.* 1.Residual forward momentum. 2.Rear-surface particle-velocity time histories. 3.Rear surface full-field out-of-plane displacement gradients. 4.Geometrical data of debris cloud. 5.Impact-flash spectroscopy. 6.Fragment distribution on witness plate. Integrated experiment IE5 Ta double plate and Fe projectile, or Fe double plate and Ta projectile, soft gel between plates, at km/s impact velocity 1.Front plate thickness. 2.Back plate thickness. 3.Back plate offset distance. 4.Incidence angles. 5.Impact velocity. 6.Projectile mass. 7.Material properties.* 8.Numerical parameters.* 1.Residual forward momentum. 2.Rear-surface particle-velocity time histories. 3.Rear surface full-field out-of-plane displacement gradients. 4.Geometrical data of debris cloud. 5.Impact-flash spectroscopy. 6.Fragment distribution on witness plate. 7.Fragment distribution in soft gel. Table of input parameters and performance measures, integrated experiments IE4-IE5:

PSAAP: Predictive Science Academic Alliance Program Verification & Validation Plan Table of configurations, diagnostics and data acquired for component experiments CE1-CE3: Component experiment ConfigurationDiagnosticsData acquired CE1Split Hopkinson (Kolsky) pressure bar with shear compression specimen (SCS), strain rates up to 50,000 1/s. Strain gages, high- speed infrared thermography. In-situ temperature rise during dynamic deformation, fraction of plastic work converted to heat, stress-strain data. CE2Shock Wave Lens (SWL), pressures in excess of 200 GPa, with or without geo- metrical discontinuities such as wedges and notches inserted to promote melting. VISAR, biaxial gage, TEM. pressure-dependent stress-strain data, EoS, spatially and temporally resolved data on melting for validation of models in this regime of high energy densities, jetting and mixing. CE3Shock Wave Lens (SWL), pressures in excess of 200 GPa. VISAR, biaxial gage, TEM. EoS, geometry and distribution of micro-structures including dislocation structures and martensitic structures.

PSAAP: Predictive Science Academic Alliance Program Verification & Validation Plan PeriodValidation MilestoneData sources Year 1Lagrangian ballistic codeIE1 Ta strength modelCE1 Ta high P-T multiphase EoSCE3 Ta high P-T elastic moduliCE3 Year 2Eulerian hydrocodeIE2, CE2 Single-phase plasma codeIE2 Ta plasma EoSIE2 Ta plasma transport propertiesIE2 Fe high P-T multiphase EoSCE3 Fe high P-T elastic moduliCE3 Ta high P-T strength modelCE3 PeriodValidation MilestoneData sources Year 3Multiphase Eulerian hydrocodeIE3, CE2 Multiphase plasma codeIE3 Fe plasma EoSIE3 Fe plasma transport propertiesIE3 Ta high P-T transport propertiesCE2 Fe high P-T strength modelCE3 Year 4Lagrangian particle codeIE4 Ta cleavage-fracture propertiesIE4 Ta vacancy mobility modelIE4, CE1 Ta nanovoid nucleation modelIE4, CE1 Ta high P-T transport propertiesCE3 Year 5Lagrangian particle codeIE5 Fe cleavage-fracture propertiesIE5 Fe vacancy mobility modelIE5, CE1 Fe nanovoid nucleation modelIE5, CE1 Table of validation milestones and data sources:

PSAAP: Predictive Science Academic Alliance Program UQ ‘Caltech style’ Goal-oriented (certification) approach to UQ V&V = UQ: A code is verified, validated, when associated uncertainties (aleatoric, epistemic) are small enough U A = D F, U E = D F-G : rigorous quantitative measures of verification and validation When is enough enough?: U = U A +U E small enough! Uncertainties (aleatoric, epistemic) in all performance measures will be computed (verification and validation diameters) yearly, reported at the end of the Yearly Assessment cycle, to track progress towards achieving a verified and validated predictive capability (cf. V&V plan)

PSAAP: Predictive Science Academic Alliance Program UQ challenges Open research questions: Are concentration-of- measure inequalities tight enough to be useful towards certification? How can concentration-of-measure inequalities be improved upon? –Correlated inputs –General probability density functions for inputs –UQ in a multiscale, hierarchical, multicomponent setting –Use of archival data, limited ability to conduct integral tests Are uncertainties computable with present computational resources? How can UQ calculations be optimized, sped up?