CASCADE TECHNOLOGIES, INC.

Company Information
Address 2445 FABER PL STE 100
PALO ALTO, CA, 94303-3347

http://www.cascadetechnologies.com

Information

DUNS: 179576715

# of Employees: 11


Ownership Information

HUBZone Owned: N

Socially and Economically Disadvantaged: N

Woman Owned: N



Award Charts




Award Listing

  1. High-Fidelity Design Tools and Technologies for High-Pressure Heavy Fuel Injectors

    Amount: $499,988.53

    The objectives of the project are to develop and demonstrate a high-fidelity simulation tool to predict and optimize high-pressure heavy fuel injectors. During Phase I research, Cascade Technologies d ...

    SBIRPhase II2019Department of Defense Army
  2. Massively-parallel, low-dissipation, compressible, two-phase discretization for high-speed flows

    Amount: $149,935.00

    The objectives of the project are to develop and demonstrate a high-fidelity simulation tool to predict atomization of liquid jet in supersonic environment targeting for high-speed air-breathing syste ...

    SBIRPhase I2019Department of Defense Air Force
  3. Conjugate heat transfer for LES of gas turbine engines

    Amount: $139,989.00

    Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approx ...

    STTRPhase I2019Department of Defense Navy
  4. Fast, Parallel, High-Quality Voronoi Mesh Generator

    Amount: $749,997.00

    Cascade Technologies has developed a massively parallel Voronoi-based mesh generation tool called ``Stitch#39;#39;. Given a water-tight surface triangulation of arbitrary complexity and a set of gener ...

    SBIRPhase II2019National Aeronautics and Space Administration
  5. Fast, Parallel, High-Quality Voronoi Mesh Generator

    Amount: $124,704.00

    In this work we propose to further develop and demonstrate a mesh generation approach based on clipped Voronoi diagrams. This approach to mesh generation has the potential to significantly improve per ...

    SBIRPhase I2018National Aeronautics and Space Administration
  6. Prediction and Modal Analysis of Supersonic Jet Noise From Complex Nozzles in an Integrated Simulation Environment

    Amount: $124,733.00

    Our long-term vision is to seamlessly integrate resolvent operator-based spectral analysis into Cascade’s high-fidelity large eddy simulation (LES) environment, for accurate prediction and effic ...

    SBIRPhase I2018National Aeronautics and Space Administration
  7. Highly scalable large-eddy simulations of oxy-fuel combustors for direct-fired supercritical CO2 power cycles

    Amount: $999,989.00

    Direct-fired supercritical CO2 cycles are an emerging energy conversion technology that promises to increase power plant efficiency while capturing all CO2 emissions. However, given the novelty of the ...

    SBIRPhase II2018Department of Energy
  8. Machine-Learning & QMU for Multi-Fidelity Analysis of Scramjet Operability

    Amount: $124,941.00

    Dual-mode scramjets have the potential to operate efficiently in a variety of flight conditions without requiring complicated variable configurations, thus providing cost-effective access to space and ...

    SBIRPhase I2017National Aeronautics and Space Administration
  9. A Massively Parallel Framework for Low-Dissipation, Multiphysics Simulations of Rocket Engines

    Amount: $124,952.00

    In this proposal, researchers from Cascade Technologies and Stanford University outline a multi-year research plan to develop large-eddy simulation (LES) tools to predict and understand combustion ins ...

    STTRPhase I2017National Aeronautics and Space Administration
  10. High-Fidelity Design Tools and Technologies for High-Pressure Heavy Fuel Injectors

    Amount: $149,938.83

    The objective of the proposed SBIR project is to develop a computational tool capable of predicting the atomization of liquid injected through a practical I.C. Engine fuel injector under realistic ope ...

    SBIRPhase I2017Department of Defense Army
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