Date: 12/20/2021

Dataset Title: Bounce-Averaged Quasi-Linear Diffusion Model Simulation Input/Output on Mars\'92 Crustal Magnetic Field

Dataset Creator: Shane, Alexander

Dataset Contact: adshane@umich.edu

To Cite this Dataset:
Alexander Shane. Bounce-Averaged Quasi-Linear Diffusion Model Simulation Input/Output on Mars’ Crustal Magnetic Field [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/43d3-1867

Research Overview

	To study the effect of whistler mode waves on the superthermal electron velocity space at Mars, a numerical model was built to solve the bounce-averaged quasi-linear diffusion 	equation on a crustal field. This dataset includes the input and output variables to this model for the simulations performed in Shane and Liemohn, 2022. The studies using this 	dataset were conducted by Alex Shane in the Climate and Space Sciences and Engineering Department at the University of Michigan. This research was supported by the National 		Aeronautics and Space Administration (NASA) Grant NNX16AQ04G to the University of Michigan and the Rackham Predoctoral Fellowship.


Methods

	By fixing the field strength at 90 km (500 nT) and 500 km (50 nT), an ideal dipole field can be constructed. The distance along the magnetic field can also be calculated using 	the ideal dipole field equations. An electron density profile taken from MGITM was then extrapolated along the field line. 

	Wave parameters used:
		Gaussian frequency distribution [fraction of top-of-field line electron gyrofrequency]: [0.1, 0.5], peaked at 0.25 with halfwidth 0.25
		Gaussian wave normal angle distribution [degrees]: [0, 45], peaked at 0 with halfwidth 45
		Wave power: 10^-4 nT^2/Hz
		Harmonics: n |<=| 3

	The bounce-averaged diffusion coefficients were calculated using these wave parameters and crustal field profile.

	The flux at time = 0 was taken from the output of a steady-state run using the STET model.

	The flux at all other times was calculated by solving the bounce-averaged quasi-linear diffusion equation using the Alternate Direction Implicit Method and Crank Nicolson. Only 	trust the steady-state flux. 

	The methodology can be found in Shane and Liemohn, 2022. The numerical scheme is conservative and uses the coefficients at the cell centers and cell faces to calculate the 		fluxes. This results in two energy and two equatorial pitch angle grids for the input and output.

File Inventory
	
	simulation1.p - This file contains the input and output data for Run #1 in Shane and Liemohn, 2022.
	simulation2.p - This file contains the input and output data for Run #2 in Shane and Liemohn, 2022.


Definition of Terms and Variables

	List of dictionary keys and description:

			energy_i - input energy grid [eV]
			eqPitchAngle_i - input equatorial pitch angle grid [deg]

			energy_o - output energy grid [eV]
			eqPitchAngle_o - output equatorial pitch angle grid [deg]

			altitude - altitude [km]
			bField - magnetic field strength [nT], array dims = [altitude]
			distance - distance along field line [km], array dims = [altitude]
			electronDensity - electron density [cm^-3], array dims = [altitude]
			
			S0 - normalized quarter bounce [km], array dims = [energy_i, eqPitchAngle_i]
			baDaa - bounce-averaged pitch angle diffusion coefficients [cm^2 s^-3], array dims = [energy_i, eqPitchAngle_i]
			baDee - bounce-averaged energy diffusion coefficients [cm^2 s^-3], array dims = [energy_i, eqPitchAngle_i]

			time - time series [s]
			flux - differential number flux of electrons [eV^-1 s^-1 sr^-1 cm^-2], array dims = [time, energy_o, eqPitchAngle_o] 

Use and Access 

	To import the data, open Python and enter:
		>> import pickle
		>> data = pickle.load(open('simulation1.p', 'rb'))

	To access a specific variable, use the keys given in Definition of Terms and Variables. 
		>> variable = data[key]
	For example to access the altitude array:
		>> alt = data['altitude']