simulation multi_run optimization
In general, an optimization problem is described as a problem of minimizing or maximizing an objective function over a selection of design variables, while satisfying various constraints on the design and state variables of the system.
Format:
simulation multi_run optimization |
|---|
model_name = | existing model name |
sim_script_name = | Existing simulation script |
variable_names = | Existing variable |
objective_name = | Existing objective |
measure_name = | existing measure |
output_characteristic = | minimum/maximum/ average/ last_value/ absolute_minimum/ absolute_maximum/ rms/ standard_deviation |
constraint_names = | existing optimization constraint |
characteristic = | minimize/maximize |
Example:
simulation multi_run optimization & |
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model_name = | .MODEL_1 & |
sim_script_name = | .MODEL_1.Last_Sim & |
variable_names = | DV2 & |
objective_name = | .MODEL_1.OBJECTIVE_1 & |
characteristic = | minimize |
Description:
Parameter | Value Type | Description |
|---|
model_name | Existing Model | Specifies the name of the model. |
sim_script_name | Existing Simulation Script | Enters the name of your simulation script or uses the default. |
variable_name | Existing Variable | Specifies an existing variable name that has to be used for the optimization study. |
constraint_ names | Existing Optimization Constraint | Specifies the names of the constraints. |
characteristic | Minimize/maximize | Specifies whether to minimize or maximize the characteristic |
output_characteristic | Minimum/maximum/ Average/ Last_value/ Absolute_minimum/ Absolute_maximum/ Rms/ Standard_deviation | If you are using a measure, set the design objective’s value. For a measure, enter minimum, maximum, average, last_value, absolute_minimum,rms,standard_deviation and absolute_maximum of the measure. |
objective_name | Existing Objective | Enters the name of the design objective. |
measure_name | Existing Measure | Enters the name of an existing measure to be used for the doe. |
Extended Definition:
1. The simulation script contains options or commands to drive the simulation.
2. 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.
3. The objective function is a numerical representation of the quality, efficiency, cost, or stability of the model. You decide whether the optimization chooses to find the minimum or maximum of the function. The optimum value of this function corresponds to the best design possible using that particular mathematical model. Examples of objective functions include execution time, energy (effort) required, and total material costs.
Tips:
1. 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.
2. If you are preparing for an optimization, you can create constraint objects to limit the changes that the optimizer can make. Often an optimization finds a configuration that optimizes the objective you provided, but is unrealistic because it violates overall design constraints such as weight, size, speed, or force limits.
To avoid results that violate the design constraints, you can create constraints for the optimization. The optimization analysis improves the objective as much as possible without violating the constraints.