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A universal framework for non-deteriorating time-domain numerical algorithms in…

Award Information

Department of Defense
Award ID:
Program Year/Program:
2013 / STTR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
8000 Madison Blvd., Suite D102-351 Madison, AL -
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2013
Title: A universal framework for non-deteriorating time-domain numerical algorithms in Maxwell's electrodynamics
Agency / Branch: DOD / ARMY
Contract: W911NF-13-P-0020
Award Amount: $149,946.00


The project will remove a key difficulty that currently hampers many existing methods for computing unsteady electromagnetic waves on unbounded regions. Numerical accuracy and/or stability may deteriorate over long times due to the treatment of artificial outer boundaries. We propose to develop a universal algorithm and software that will correct this problem by employing the Huygens'principle and quasi-lacunae of Maxwell's equations. The algorithm will provide a guaranteed error bound, uniform in time (no deterioration at all), and the software will enable robust electromagnetic simulations in a high-performance computing environment. The methodology will apply to any geometry, any scheme, and any boundary condition. It will eliminate the long-time deterioration regardless of its origin and how it manifests itself. Dr. Tsynkov who co-invented this method is the Academic partner on the project. Phase I includes development of an innovative numerical methodology for high fidelity error-controlled modeling of a broad variety of electromagnetic and other wave phenomena. Proof-of-concept 3D computations will be conducted and verified against benchmarks, to demonstrate efficiency of the proposed approach. In Phase II our algorithms will be implemented as robust commercial software tools in a standalone module that can be combined with existing numerical schemes in computational electromagnetic codes.

Principal Investigator:

Edward J. Kansa
Staff Scientist
(256) 270-0956

Business Contact:

Tatiana Shvetsova
Business Officer
(256) 270-0956
Small Business Information at Submission:

Computational Sciences, LLC
8000 Madison Blvd., Suite D102-351 Madison, AL -

EIN/Tax ID: 900191003
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
North Carolina State University
2701 SullivanDrive Admin III,
Admin III, Suite 240
Raleigh, NC 27695-7514
Contact: Matt Ronning
Contact Phone: (919) 515-2444