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International Journal for Multiscale Computational Engineering
Facteur d'impact: 1.016 Facteur d'impact sur 5 ans: 1.194 SJR: 0.554 SNIP: 0.68 CiteScore™: 1.18

ISSN Imprimer: 1543-1649
ISSN En ligne: 1940-4352

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.v4.i3.50
pages 351-361

Toward Coupled-Field Characterization and Design Optimization of Single-Wall Nanotube Composites for Hydrogen Storage Systems

John G. Michopoulos
Head Code 6394, FASME Computational Multiphysics Systems Lab Naval Research Laboratory Washington DC 20375
Sam G. Lambrakos
Computational Multiphysics Systems Laboratory, Code 6394, Center for Computational Material Science, Naval Research Laboratory, Washington DC 20375, USA
Nick E. Tran
Materials Science and Component Technology Directorate, U.S. Naval Research Laboratory, Washington DC, 20375


We present an examination of the feasibility for simultaneous characterization and design optimization of various aspects of reactive systems under concurrent heat and mass transport presence. The analysis described in this paper is of a carbon nanotube composite system potentially usable for hydrogen storage applications. Three distinct activities were employed for this analysis. First, the forward continuum model of a multispecies diffusing system under simultaneous exposure to mass diffusion with chemical reactivity and to heat conduction was generated. Second, the problem of hydrogen storage was defined within a pragmatic product-design context where the appropriate design parameters of the system are determined via appropriate optimization methods, utilizing extensive experimental data encoding the systemic behavior. Third, this methodology is applied on a hydrogen storage nanocomposite reactor system and appropriate systemic parameter estimation is performed. Thus, the context of the work presented is defined by a data-driven characterization of coupled heat and mass diffusion models of hydrogen storage systems from a multifield perspective at the macro length scale. In particular, a single-wall nanotube (SWNT) based composite is modeled by coupled partial differential equations representing spatiotemporal evolution of distributions of temperature and hydrogen concentration. Analytical solutions of these equations are adopted for an inverse analysis that defines a nonlinear optimization problem for determining the parameters of the model by objective function minimization. Experimentally acquired and model-produced data are used to construct this objective function. Simulations demonstrating the applicability of the methodology and a discussion of its potential extension to multiscale and manufacturing process optimization are presented.