simulation multi_run doe
Allows you to use a set of simulations that help you adjust parameters related to doe.
Format:
simulation multi_run doe |
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model_name = | an existing model |
sim_script_name = | an existing sim_script |
variable_names = | an existing variable |
number_of_levels = | integer |
objective_names = | an existing objective |
measure_name = | an existing measure |
output_characteristic = | output_characteristic |
number_of_user_trials = | integer |
user_matrix = | integer |
technique = | doe techinque |
matrix_file_name = | string |
Example:
simulation multi_run doe & |
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model_name = | ATV_4poster & |
sim_script_name = | Last_Sim & |
technique = | full_factorial & |
variable_names = | var__1 & |
number_of_levels = | 2 & |
objective_names = | objective__1 & |
measure_name = | measure__1 & |
output_characteristic = | average & |
number_of_user_trials = | 2 |
Description:
Parameter | Value Type | Description |
|---|
model_name | An Existing Model | Specifies an existing model. |
sim_script_name | An Existing Sim_script | Enters the name of your simulation script or use the default. |
variable_names | An Existing Var | Enters the name of the design variable that you want to vary. |
number_of_levels | Integer | Specifies the number of levels of the variable |
objective_names | An Existing Objective | Enters the name of the design objective. |
measure_name | An Existing Measure | Specifies the name of an existing measure. |
output_characteristic | Output_characteristic | If you are using a measure set the design objective’s value. |
number_of_user_trials | Integer | Enters the number of trials (simulations) and the trial matrix. |
user_matrix | Integer | Specifies indexes to the levels for each variable. |
technique | | Selects a DOE technique. |
rows_in_table | | |
table_of_values | | |
matrix_file_name | String | Specifies the file in which the DOE trials are described. |
Extended Definition:
1. DOE is an active approach to quality improvement. DOE is a methodology by which you design a set of runs or trials and extract from the results a pattern of behavior for the system. DOEs can:
■Provide up-front optimization
■Reduce sensitivity to manufacturing variation
■Enable you to balance conflicting designs
■Link physical and computer tests
■Correlate computer models
■Transfer up-front information downstream
■Communicate results to non-computer literate individuals
2. You may identify a model by typing its name or by picking it from the screen. If the model is not visible on the screen, you must type the name. You may also find it convenient to type the name even if the model is displayed. You must separate multiple model names by commas. If the model is visible in one of your views, you may identify it by picking on any of the graphics associated with it. You need not separate multiple model picks by commas.
3. You can also browse for the script name in the Database Navigator. Right-click in the text box, point to Simulation_Script, and then select Browse. Select the model in the Database Navigator, and then select OK. The simulation script contains options or commands to drive the simulation.
4. Before you run a design study or design of experiment (DOE), you must specify a range of values or list of values for each design variable used in the analysis. These determine the values that Adams View uses during the design study or DOE simulations. These values are called the levels of the variable. Specify the number of levels to use when you run the design study or DOE.
5. Objectives usually involve simulation results, but they are not required to do so. You can create an objective that depends only on the model data, such as overall weight or size. You can then use Adams View to vary, or even optimize, the design variables and immediately see the results on the model. Typical objectives include time, energy, or displacement from a path.
6. The DOE technique or trial matrix controls the number of simulations and the combination of variable values to use for each simulation. For example, the Full Factorial technique simulates every possible combination of levels. If you use two variables with three levels each, Adams View runs nine simulations. The DOE technique or trial matrix selects values for a variable based on the range or list of values you defined for the variable.
■If you specified only a range for a design variable, Adams View selects from equally spaced values across the range. You enter the number of values in the Default Levels text box.
■If you specified a list of values for a design variable, Adams View selects directly from those values, ignoring the value in the Default Levels text box.
7. The matrix_file_name parameter specifies the file in which the DOE trials are described. The first line of the file contains three numbers. The first is the number of variables for this DOE. The second number is the number of levels for each variable. The third number is the number of trials to be found in the subsequent lines of the file. Each line that follows, contains indexes to the levels for each variable. These indexes should be centered. This means that for a two-level variable, the only possible values are -1 and +1; for three-levels, -1, 0 and +1; for four-levels, -2, -1, +1, +2; and so on.
The following is an example of a file which could be used for an experiment with two variables with three levels each. There would be four trials in the experiment.
2 3 4
0 +1
-1 0
+1 -1
+1 +1
8. The USER_MATRIX parameter specifies indexes to the levels for each variable. These indexes should be centered. This means that for a two-level variable, the only possible values are -1 and +1; for three-levels, -1, 0 and +1; for four-levels, -2, -1, +1, +2; and so on.
The following is an example which could be used for an experiment with two variables with three levels each. There would be four trials in the experiment.
...
NUMBER_OF_USER_TRIALS=4 USER_MATRIX=0,+1, -1, 0, +1,-1, +1,+1