Design variables

Plot
Total pressure ratio
Inlet flow coefficient
Inlet relative Mach
Inlet hub-to-tip ratio
Rotor de Haller
Outlet relative yaw
Blade circulation
Tip clearance
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Inlet conditions


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Efficiency $\etatt = $ , $\etats = $

Total-to-total Pressure Ratio, $\PRtt$ Inlet Relative Mach, $\Main$ Inlet Flow Coefficient, $\phiin$ Hub-to-tip ratio, $\HTRin$ Outlet Relative Yaw, $\Alout$ Blade Circulation Coefficient, $\Cga$ Tip Clearance, $\tipg$ Rotor De Haller, $\DHin$

Output mean line

$\Omega \, [\mathrm{rpm}] =$ Beans $\dot{W}_\mathrm{x} \, [\mathrm{kW}] =$ Beans
$r_\mathrm{h1} \, [\mathrm{m}] =$ Beans $r_\mathrm{c1} \, [\mathrm{m}] =$ Beans
$r_{2} \, [\mathrm{m}] =$ Beans $\mathrm{span}_{2} \, [\mathrm{m}] =$ Beans
$\mathit{R\kern-.1emR} =$ Beans $r_\mathrm{c1}/r_2 =$ Beans

Usage instructions

How it works

First, a database of 3708 radial compressors is generated using the TURBIGEN design code. For mean-line designs sampled from the full eight-dimensional design space, geometry is created and a Reynolds-averaged Navier--Stokes solution obtained. Then, polynomial surface regression is used to create a continous function mapping efficiency as a function of the design variables. By quantifying the influence of different loss sources, the physical mechanisms that cause drops in efficiency at the boundaries of the design space can be deduced. See the paper for more details:
Brind, J. "Data-driven radial compressor design space mapping." Proc. ASME GT2024, paper No. GT2024-123250.
This online demonstration makes the following assumptions: