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Modeling and Prediction of Ground Shock Induced by Penetrating Weapons in Spatially Random Geologic Media

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA1-05-P-0026
Agency Tracking Number: T041-009-0073
Amount: $99,976.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: DTRA04-009
Solicitation Number: 2004.1
Solicitation Year: 2004
Award Year: 2005
Award Start Date (Proposal Award Date): 2005-01-31
Award End Date (Contract End Date): 2005-07-30
Small Business Information
375 Hudson St FL 12
New York, NY 10014
United States
DUNS: 061226106
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 felix wong
 (650) 949-3010
Business Contact
 howard levine
Title: principal in charge
Phone: (650) 949-3010
Research Institution

We propose a stochastic-finite-element based methodology to address these requirements: (i) Characterize material variability; (ii) Quantify corresponding ground-shock variability; and (iii) Design instrumentation plan. The site is discretized into N finite-elements; rock properties over this domain are characterized as the components of an N-vector stochastic field. The probabilistic characteristics of each component are estimated from analysis of field data and encapsulated as random parameters of the Cap model; spatial variability is captured by the correlation function. Explicit joints are sampled from their distributions, and their effects modeled by reduced strength of the affected elements. Monte-Carlo simulations of the ground-shock process are performed using the finite-element site model, with each simulation corresponding to a digitally-generated (realized) random sample function of the stochastic field. Realizations are obtained using two complementary techniques: Spectral and geostatistical. An adaptive, hierarchical procedure is proposed to accommodate the wide range of data (in)completeness in practice. The statistics of the ground-shock response and their spatial variability are used to support an instrumentation plan for given test objectives/constraints. It is suggested that the same stochastic techniques used to encapsulate and realize spatial material variability may be used to encapsulate response variability, leading to a systematical instrumentation methodology.

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