ITR - (ASE) - (sim+dmc): Computational Toolbox for the Investigation of Multiscale Surface Processes

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

This project focuses on the development of a computational toolbox for investigation of multiscale surface processes that are central to nanotechnology as well as other current technologies. Two physical systems will be studied that span from nano-scale phenomena to large-scale deterministic transport phenomena. The algorithms and software, developed to simulate and extract information from multiscale systems, are generic over a broad class of problems, and will contribute well beyond the applications used in their development. The physical systems include electrodeposition of metallic nanoclusters with additives to achieve specific shapes, and environmental degradation through interaction of pits, crevices and cracks. The physical systems, chosen for their computational structure, are characteristic of a large class of systems where controlled shape evolution is exploited to produce desired structures. Key issues are to understand how small-scale surface interactions guide spontaneous self-organization, how to extract insight from noisy data and uncertain fundamental understanding, and how to insure quality control at multiple scales in manufacturing. Computational tools will be developed for simulation and sensitivity analysis in multi-phenomena multiscale systems that require methods for coupling of stochastic and deterministic models. Challenges for deterministic simulation include the effective use of parallel computers, and dealing with moving boundaries, ill-conditioning and stiffness. We will explore classes of preconditioners for the iterative methods that solve large linear systems of equations at each time step, in particular a newly-developed multigrid method that is well-suited to moving boundary problems. Challenges for stochastic simulation include stiffness, which has only recently been recognized as a barrier to efficiency for stochastic simulation. Sensitivity analysis is an important part of this effort. For the deterministic computations, we will make use of recently developed methods that are adaptive in space and time. We will develop new sensitivity analysis methods and software for stochastic systems, and couple them to deterministic sensitivity analysis for the physical systems of interest. We will facilitate the use of our toolkit by extension to larger-scale software systems of a recently-developed environment for the rapid creation of GUIs for scientific and numerical software. This project addresses the National Priority Area of Advanced Science and Engineering (ASE), and the Technical Focus Areas of Innovation in computational modeling or simulation in research or education (sim) (primary), and of Innovative approaches to the integration of data, models, communications, analysis and/or control systems, including dynamic, data-driven applications for use in prediction, risk-assessment and decision-making (dmc) (secondary). Broader Impacts The proposed project will impact the National Priority Area of ASE through the development of algorithms and software to enhance the use of high performance computers in the investigation of multiscale surface processes. The availability of such a toolbox will accelerate fundamental scientific research and engineering design in an area with the potential for large economic impact. Software developed as a result of this project will be widely distributed in the scientific and engineering, computer science and mathematical sciences communities. The educational activities feature a multidisciplinary, cross-institutional approach to graduate education. Students will work in multidisciplinary teams, with joint thesis advisors from a primary and a secondary discipline. This approach has recently been undertaken at UCSB with some success; we plan to institutionalize this approach to graduate education in Computational Science and Engineering (CSE) at UCSB, and to export the model to UIUC. The model also includes industrial internships, career development workshops, and mentoring of undergraduates. Both UIUC and UCSB have been pioneers in developing graduate programs in CSE and have programs with a similar structure which will facilitate the sharing of educational ideas and innovations across the institutions.