Our team has expertise in a variety of CFD applications, including subsonic, transonic, and supersonic/hypersonic simulations. We offer a range of solutions, including our own in-house developed software, for complex projects in fields such as high speed, plasma, low density, and more.

Our team has expertise in dynamic modeling, control system design, and UAV design for a variety of air and space vehicles including helicopters, airplanes, UAVs, satellites, and rockets. Our services include flight dynamics modeling, the creation of simulation environments, and the design and optimization of autonomous flight control systems, as well as detailed modeling of aerodynamics, aerothermodynamics, and flight dynamics.

Our team has expertise in various space applications, including space mission design, space propulsion, and space environment quantification. We have in-house developed and open source software capabilities that allow us to tackle problems such as preliminary mission design, propulsion plume effects and interactions, contamination, and electrical charging.

Our team has developed novel optimization techniques using high performance computing to address questions such as optimization, sensitivity analysis, uncertainty quantification, and calibration. These capabilities can provide solutions for applications requiring features such as parameter studies, shape optimization, multi-constraint/goal function problems, response surface generation, and adaptive sampling.

Open source software development provides a wider range of design perspectives than any single company can develop and maintain over the long term. At Rotorbit, we strive to use and contribute to open source software, and offer customized solutions for applications including aerodynamics, aerothermodynamics, flight mechanics, optimization, uncertainty quantification, space mission and propulsion, and high performance computing.

Physics-based machine learning is a field that combines machine learning techniques with principles from physics to analyze data and make predictions. It is used to build models that can make accurate predictions based on physical laws and principles, rather than just on patterns in the data. This approach can be particularly useful in domains where the underlying physical processes are well understood, such as in engineering and scientific applications.