more cleanup of tutorials

jmvegh · Apr 12, 2016 · Apr 17, 2016 · Apr 17, 2016 · Apr 17, 2016 · Apr 17, 2016
commit 72bc70f1a05c142e64fbda7cbf0554adbeb7fa3c
diff --git a/_posts/2015-06-14-Boeing_737-800.md b/_posts/2015-06-14-Boeing_737-800.md
@@ -133,7 +133,7 @@ turbofan.nacelle_diameter  = 2.05
 turbofan.working_fluid = SUAVE.Attributes.Gases.Air()
 </code></pre>
 
-Next the components of the turbofan are initialized and added to the engine as shown in the energy networks section. The turbofan is composed of an inlet diffuser(nozzle), low and high pressure compressors, a combustor, a fan, a low and high pressure turbine and exit nozzles for the core and the fan. 
+Next the components of the turbofan are initialized and added to the engine as shown in the energy networks section. The turbofan is composed of an inlet diffuser (nozzle), low and high pressure compressors, a combustor, a fan, a low and high pressure turbine and exit nozzles for the core and the fan. 
 
 Finally the network is added to the aircraft as shown below.
 
@@ -158,7 +158,7 @@ The data structure associated with the Boeing 737-800 can be visualized [here](/
 This visualization shows the hierarchy of data in a SUAVE vehicle. Some of the values are defaulted, for example costs. This is because we are not performing cost analysis on the Boeing 737-800. Additionally, this snapshot of the data structure was taken prior to analysis. Therefore weights have not been calculated and are not part of the data. During the mission analysis component weights will be determined and weights will be populated.
 
 #### configs_setup()
-The aircraft geometry created is converted into configurations that can be used to make simple modifications to the geometric paramaters while not modifying the aircraft geometry completely. An example of the use of this feature is when high-lift devices need to be deployed during landing and takeoff. Thus a landing and takeoff configuration can be added as shown below and then during say the flaps and slats settings can be set differently for each of the configurtaions.
+The aircraft geometry created is converted into configurations that can be used to make simple modifications to the geometric parameters while not modifying the aircraft geometry completely. An example of the use of this feature is when high-lift devices need to be deployed during landing and takeoff. Thus a landing and takeoff configuration can be added as shown below and then during say the flaps and slats settings can be set differently for each of the configurations.
 
 <pre><code class="python"># ------------------------------------------------------------------
 #   Cruise Configuration

diff --git a/_posts/2015-06-15-Lithium_Air_Regional_Jet_Sizing.md b/_posts/2015-06-15-Lithium_Air_Regional_Jet_Sizing.md
@@ -17,16 +17,9 @@ permalink: /guides/lithium_air_regional_jet_analysis.html
 ## Introduction
 The purpose of this tutorial is to highlight some of SUAVE's more exotic propulsion system capabilities. This tutorial assumes that the user has completed the Boeing 737-800 tutorial, and has some familiarity with SUAVE's propulsion system data structures.
 
-<<<<<<< HEAD:_posts/2015-06-15-Lithium_Air_Regional_Jet_Analysis.md
 ## Baseline Case
- Open the file called "tut_lithium_air_jet.py" in 
-=======
-##Baseline Case
  Open the file called "tut_lithium_air_jet_sizing.py" in a text editor or IDE.
 
-
- Run the program (cd to the folder, then type python tut_lithium_air_jet_sizing.py). 
-
  Look over the plots, to gain a feel for the various idiosyncrasies of the design. Note the significant rise in aircraft mass, as a result of the lithium-air batteries.
 
 ![li air mass](../images/li_air_mass.png)
@@ -37,9 +30,7 @@ The purpose of this tutorial is to highlight some of SUAVE's more exotic propuls
     battery.specific_power =.67*Units.kW/Units.kg
 </code></pre>
 
-run the script (python tut_lithium_air_jet.py).
-
-
+Run the script (python tut_lithium_air_jet.py).
 
 Now try changing the motor efficiency from .95 to .9 and running the script (line 513).
 

diff --git a/_posts/2015-06-15-Turbofan_Network.md b/_posts/2015-06-15-Turbofan_Network.md
@@ -49,7 +49,7 @@ The basic components used to model the turbofan are described below.
 
 #### Ram
 
-The 'Ram' component is used to convert the freestream quantities that are passed into the Turbofan network into stagnation quantities. As the turbofan network as based on a 1D gasdynamic analysis, most of the energy transfer across the different components are modelled as changes in the stagnation quantities. Thus the Ram component acts as a preprocessor converting the input 'Conditions' into quantities required by the network.
+The 'Ram' component is used to convert the freestream quantities that are passed into the Turbofan network into stagnation quantities. As the turbofan network is based on a 1D gasdynamic analysis, most of the energy transfer across the different components are modelled as changes in the stagnation quantities. Thus the Ram component acts as a preprocessor converting the input 'Conditions' into quantities required by the network.
 
 <pre><code class="python"># ------------------------------------------------------------------
 #   Component 1 - Ram

diff --git a/_posts/2015-06-17-Payload_Range_Diagram.md b/_posts/2015-06-17-Payload_Range_Diagram.md
@@ -9,7 +9,7 @@ permalink: /guides/payload_range_diagram.html
 ---
 
 ### Embraer E-190 Payload Range Diagram Tutorial
-1) Locate the tutorial script folder, SUAVE/scripts/tutorial. If necessary cd to this directory.
+1) Locate the tutorial script folder. If necessary cd to this directory.
 
 2) Open the test_payload_range.py script in your favorite editor or IDE.
 

diff --git a/_posts/2015-07-15-Concorde.md b/_posts/2015-07-15-Concorde.md
@@ -37,7 +37,7 @@ However, the supersonic aerodynamics can still handle the subsonic portions of t
 Some of the calculations are changed to account for different properties that are not automatically captured in the model.
 
 * Vortex lift is set to true. This causes the vortex lift module to add a Cl increment to the aircraft.
-* High mach is set to true. This changes the way that high subsonic compressibility drag is calculated and brings it more in line with Concorde behavior.
+* High Mach is set to true. This changes the way that high subsonic compressibility drag is calculated and brings it more in line with Concorde behavior.
 
 ```
     wing.vortex_lift             = True
@@ -65,8 +65,8 @@ nozzle = SUAVE.Components.Energy.Converters.Expansion_Nozzle()
 
 ### Mission Segments:
 
-The mission typically flown by Concorde calls for mission segments that are not utilized in the Boeing 737 case. These are mach number dependent, and require different inputs from the segments previously given. 
-The new mission segments are linear mach constant rate (climb and descent). Both require a starting and ending mach number, which will vary linearly in time over the descent. For example check the third climb segment (line 894):
+The mission typically flown by Concorde calls for mission segments that are not utilized in the Boeing 737 case. These are Mach number dependent, and require different inputs from the segments previously given. 
+The new mission segments are linear Mach constant rate (climb and descent). Both require a starting and ending Mach number, which will vary linearly in time over the descent. For example check the third climb segment (line 894):
 
 
     segment = Segments.Climb.Linear_Mach_Constant_Rate(base_segment)

diff --git a/_posts/2016-02-112-Regional_Jet_Optimization.md b/_posts/2016-02-112-Regional_Jet_Optimization.md
@@ -19,7 +19,7 @@ Some important files for the optimization problem can be seen below
 #### Optimize.py:
 Defines the optimization framework of the problem, wherein one minimizes an assigned objective, subject to certain constraints, by altering some design variables.
 
- In order to ensure that the subfunctions can communicate with eachother, as well as that SUAVE can communicate with the external optimizer, a special data object called the "nexus" is used. The nexus object contains all of the vehicles, missions, as well as results, altering them at each optimizer iteration, depending on the input parameters defined in Optimize.py.
+ In order to ensure that the subfunctions can communicate with eachother, as well as that SUAVE can communicate with the external optimizer, a special data object called the "Nexus" is used. The nexus object contains all of the vehicles, missions, as well as results, altering them at each optimizer iteration, depending on the input parameters defined in Optimize.py.
 
 In this particular setup, there are two design variables: wing area and cruise altitude. The objective is fuel burn, while there is only one constraint: fuel margin.. The default inputs are defined in the following lines.
 
@@ -52,7 +52,7 @@ Note that in this case, only a single constraint is used; Multiple constraints m
 
 
 
- This file also defines the "aliasing," i.e. how the design variables, constraints, and objective "map" to the variables used in Procedure.py (which is runs the problem). The aliases for this problem are defined in the lines below.  Note that the first entry refers to the tag defined in either problem.inputs, problem.objective, or problem.constraints.
+This file also defines the "aliasing," i.e. how the design variables, constraints, and objective "map" to the variables used in Procedure.py (which is runs the problem). The aliases for this problem are defined in the lines below.  Note that the first entry refers to the tag defined in either problem.inputs, problem.objective, or problem.constraints.
 
 <pre><code class="python">
     problem.aliases = [
@@ -65,18 +65,14 @@ Note that in this case, only a single constraint is used; Multiple constraints m
 
 </code></pre>
 
- Note that, sometimes, a single input can map to multiple outputs, such as the "wing_area" design variable; in this case, use a list for the outputs, as seen above.
-
-
-
-
+Note that, sometimes, a single input can map to multiple outputs, such as the "wing_area" design variable; in this case, use a list for the outputs, as seen above. The use of a wild card "*", can also allow values to map to multiple outputs. Values to be outputted cannot contain wild cards as that would be ambiguous to an optimizer.
 
 #### Procedure.py:
 Links everything together, defining the steps you would use to size and analyze the aircraft at each optimizer iteration.
 
 This file contains a number of subfunctions to alter the vehicle and mission. The function setup() instantiates the procedure, defining the functions that are called at each step of the optimizer in their order of execution. 
 
-1. simple_sizing defines the geometry of the aircraft based on the input parameters (in this case, wing area and cruise altitude). 
+1. simple_sizing() defines the geometry of the aircraft based on the input parameters (in this case, wing area and cruise altitude). 
 
 2. weights() determines the weight breakdown of the aircraft
 
@@ -90,35 +86,33 @@ Each step of the procedure takes as the nexus object as an input, and returns th
 #### Vehicles.py:
  Initializes whatever vehicles are used in the optimization problem. This includes two subfunctions: base_setup(), where the vehicle structure is itself defined, including the fuselage, wing, vertical and horizontal tail, as well as the propulsion system.
 
- configs_setup() takes in the vehicle that was defined in base_setup() and defines other configurations (such as takeoff and landing, which include different flap settings). This may be used to define other parameters, such as changing the sweep angle of a variable-sweep-wing at higher Mach Numbers, or the use of afterburners.
+configs_setup() takes in the vehicle that was defined in base_setup() and defines other configurations (such as takeoff and landing, which include different flap settings). This may be used to define other parameters, such as changing the sweep angle of a variable-sweep-wing at higher Mach Numbers, or the use of afterburners.
 
 #### Missions.py:
  Initializes the missions that are run at each iteration. In this case, only a single mission is run.
 
 #### Analyses.py:
  Defines the set of features that are used in this particular problem (e.g. weights correlations, aerodynamics correlations, etc.).
 
-
-
 #### Plot_Mission.py:
 Plots the mission outputs using matplotlib.
 
 ### Running the Problem:
 1. Locate the tutorial script folder "regional_jet_optimization." If necessary cd to this directory.
-2. Open the Optimize.py script in a text editor or IDE
 
+2. Open the Optimize.py script in a text editor or IDE
 
 3. Run it with the default set of inputs; uncomment out the following line in Optimize.py;
 
 <pre><code class="python">
 output = problem.objective()
 </code></pre>
 
-open up a terminal, and type "python Optimize.py." You should see a set of output plots.
+Open up a terminal, and type "python Optimize.py." You should see a set of output plots.
 
 
 ### Running a Sweep of the Inputs
-Now try running a 2D sweep of the problem to observe the shape of the design space: recomment  "output = problem.objective" then uncomment the following.
+Now try running a 2D sweep of the problem to observe the shape of the design space: re-comment  "output = problem.objective" then uncomment the following.
 
 <pre><code class="python">
 variable_sweep(problem)

diff --git a/_posts/2016-05-11-solar_uav_optimization_tutorial.md b/_posts/2016-05-11-solar_uav_optimization_tutorial.md
@@ -12,7 +12,7 @@ permalink: /guides/solar_uav_optimization.html
 
 The purpose of this tutorial is to illustrate a different type of problem. We assume you have gone through the first optimization tutorial: Regional Jet Optimization. This tutorial will illustrate a little more complex setup that modifies a mission parameter.
 
-Your objective is simple; get an airplane to fly from here to there. In fact you can pose that as an optimization problem, with some constraints that govern how it works. There is no requirement to minimize this or maximize that. Of course you could try to minimize something, but you just want something that works for now. Later iterations can do fancier things.
+Your objective is simple: get a small UAV to fly from San Francisco to San Diego. In fact you can pose that as an optimization problem, with some constraints that govern how it works. There is no requirement to minimize this or maximize that. Of course you could try to minimize something, but you just want something that works for now. Later iterations can do fancier things.
 
 Next we will go into detail about some of the required files. *Analyses.py* and *Plot_mission.py* are straightforward from prior tutorials. So we will not go into those in detail, except to say that we are using a UAV weight model in *Analyses.py*.