Credibility/Expertise

· Our portfolio of projects with the U.S. Air Force, NASCAR, Honda, P&G and many more companies has allowed us to validate our work across industries and develop a refined process combined with the best tools to help our clients solve the toughest engineering challenges.

· At TotalSim we have gained our decades of CFD expertise by applying our professional engineering and aerodynamics knowledge within automotive, motorsports, aerospace, consumer products and other industries.

· In working side-by-side with teams at Honda Racing or Worthington Industries we have a true understanding of real-world constraints and business objectives and help you to simplify complex CFD processes or engineering challenges to accelerate design and development.

Increase Efficiency and Time Savings

· Because we have expertise across diverse industries such as aerospace to consumer goods, this allows us to apply learnings that have shared physics to different systems or components increasing efficiency and savings.

· In the thousands of aerodynamic projects, we’ve worked on, we have probably already experienced your challenge, allowing us to help you solve it more quickly and efficiently saving you time and money or keeping you from having to develop a process we have already established.

Full-Service Capabilities

· From CFD consulting, CAE automation, training to customized software, we have the knowledge and technology needed to manage all your CFD needs.

· We are always learning and innovating and recently commercialized an end-to-end solution to help ourselves and global clients to collect data from most CFD solvers and put it into a standardized format, provide repeatability of post processing and an archival view of results over the long-term. Automating workflows is the ultimate way to increase efficiency and repeatability while reducing costs, especially around R&D.

· With our high-performance computing capabilities, backed by our unmatched hardware, TotalSim has the capability to model your problems without having to scale down to an approximate sample, which only leaves room for error.