pyMAISE.datasets.load_heat

pyMAISE.datasets.load_heat()

Load the heat conduction data. This data consists of 1000 samples of 7 inputs:

• qprime: linear heat generation rate \([W/m]\),

• mdot: mass flow rate \([g/s]\),

• Tin: temperature of the fuel boundary \([K]\),

• R: fuel radius \([m]\),

• L: fuel length \([m]\),

• Cp: heat capacity \([J/(g\cdot K)]\),

• k: thermal conductivity \([W/(m\cdot K)]\),

with one output:

• T: fuel centerline temperature \([K]\).

The data set was constructed through Latin hypercube sampling of the seven input parameters for heat conduction through a fuel rod. These samples were then used to solve for the fuel centerline temperature analytically. We assume volumetric heat generation is uniform radially. The problem is defined by

\[\frac{1}{r}\frac{d}{dr}\Big(kr\frac{dT}{dr}\Big) + q''' = 0\]

with two boundary conditions: \(\frac{dT}{dr}\Big|_{r=0}=0\) and \(T(R) = T_{in}\). Therefore, the temperature profile in the fuel is

\[T(r) = \frac{q'}{4\pi k}(1 - (r/R)^2) + T_{in}.\]

Returns:

• data (xarray.DataArray) – Raw heat conduction data.

• inputs (xarray.DataArray) – Seven inputs.

• outputs (xarray.DataArray) – Fuel centerline temperature.