Free Download nTopology 5.34.3 | 1.4 Gb
nTopologyis pleased to announce the availability of
nTopology 5.34.3. This release introduces Parameter Optimization (inverse design), a robust new optimization located in its own ribbon.
This release also includes usability upgrades, such as the ability to view Description data directly from tables and new floating comments for better notebook organization.
Owner:nTopology
Product Name:nTopology
Version:5.34.3
Supported Architectures:x64
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Languages Supported:english
System Requirements:Windows *
Size:1.4 Gb
Parameter Optimization Ribbon
- Parameter optimization, also known as inverse design, determines the set of parameters that yield an optimal outcome for a specified objective function, subject to defined constraints.
- This new feature is available in its own Parameter Optimization ribbon, which gathers all the necessary blocks to set up and run an optimization study.
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Parameter Optimization
- The Parameter Optimization block runs an optimization algorithm to find the optimal collection of Design Parameters satisfying the Objective while satisfying all Constraints.
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Note: We recommend not making Parameter Optimization a variable to avoid a known issue of handling parameter group inputs while contained in a variable
- Location: Parameter Optimization > Optimization
- Inputs:
. Objective: The objective function for the optimization process.
. Constraint Type: The constraints to satisfy during the optimization process.
. Design Parameters: The collection of Parameters the algorithm will change in its search for the optimal solution.
. Algorithm: The specific optimization method to be used. This dictates how the block will search for the optimal solution.
. Max Iterations: The maximum number of iterations allowed during the optimization process.
. Tracked Parameters: Additional Parameter to be tracked and logged during the optimization process. This is useful for debugging and detailed analysis.
- Output: Parameter Optimization Result
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Parameter Objective
- The Parameter Objective block specifies a goal for Parameter Optimization. The objective guides the optimizer on the desired outcome.
- Location: Parameter Optimization > Composition
- Inputs:
. Goal: Optimization goal (Minimize or Maximize).
. Parameter: Parameter used to define the optimization objective.
- Output: Parameter Objective
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Parameter Constraint
- The Parameter Constraint block defines a condition that a Parameter must satisfy. Parameter Constraints can be used to enforce limits on the Parameter to determine a valid solution space.
- Location: Parameter Optimization > Composition
- Inputs:
. Parameter: Parameter to be constrained.
. Constraint Type: Constraint operator (Less Than or Greater Than).
. Value: Minimum or Maximum value for constraint.
- Output: Parameter Constraint Parameter Constraint
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Note: Use the Parameter Constraint Group block to combine more than one constraint.
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Constant Parameter
- The Constant Parameter block creates a parameter with a fixed value. It is used to define a constant design variable that does not change during optimization, serving as a placeholder for a specific, unchanging value in an optimization problem.
- Location: Parameter Optimization > Composition
- Inputs:
. Label: Unique identifier of the parameter.
. Value: Default value of the parameter.
- Output: Parameter Constant Parameter
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Note: Use the Parameter Group block to combine more than one parameter.
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Independent Parameter
- The Independent Parameter block creates an independent optimization parameter that can vary within optional bounds during optimization.
- Location: Parameter Optimization > Composition
- Inputs:
. Label: Unique identifier of the parameter.
. Default Value: Default value of the parameter.
. Lower Bound: Minimum allowed value of the parameter.
. Upper Bound: Maximum allowed value of the parameter.
- Output: Parameter Independent Parameter
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Note: Use the Parameter Group block to combine more than one parameter.
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Dependent Parameter
- The Dependent Parameter block creates a Dependent Parameter whose value is computed from other Parameters using a specified Function.
- Location: Parameter Optimization > Composition
- Inputs:
. Label: Unique identifier of the parameter.
. Function Object: The Function Object evaluated to produce the output Parameter's value.
. Input Parameters: Parameters defining Function Object inputs. The group elements correspond to the Function Object inputs by Index.
- Output: Parameter Dependent Parameter
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Note: Use the Parameter Group block to combine more than one parameter.
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Design Parameters from Optimization
- The Design Parameters from Optimization block extracts all design parameters at a specific iteration from a Parameter Optimization result.
- Location: Parameter Optimization > Utilities
- Inputs:
. Result: Result to extract the design parameters from.
. Iteration: The Iteration number from which the Parameter Group will be extracted. If not provided, the last iteration will be used.
- Output: Parameter Group
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Evaluate Parameter
- The Evaluate Parameter block computes the value of a Dependent Parameter for a specified collection of input parameters with constant or known values.
- Location: Parameter Optimization > Utilities
- Inputs:
. Dependent Parameter: Dependent Parameter to be evaluated.
. Input Parameters: Inputs used to evaluate the Dependent Parameter. If no inputs are provided, default values assigned during composition will be used.
- Output: Parameterizable
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Tracked Parameters from Optimization
- The Tracked Parameters from Optimization block extracts all Tracked parameters at a specific iteration from a Parameter Optimization result.
- Location: Parameter Optimization > Utilities
- Inputs:
. Result: Result to extract the tracked parameters from.
. Iteration: The Iteration number from which the Parameter Group will be extracted. If not provided, the last iteration will be used.
- Output: Parameter Group
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Usage Improvements
- You can now add a Comment that is not associated with a block by right-clicking in the notebook and selecting "Add Comment". You can also right-click an existing comment and select "Detach Comment" to remove its association from a block.
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Previously, a placeholder variable was required to add comments in the notebook.
- You can now visualize Table data as a 2D Description. To access this feature, click the View Data button in the Display tab of the Right Panel and select the Show Description button. Use the dropdown menus for the X-axis and Y-axis, which correspond to the table headers, to choose your data.
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Block Update
- We have updated the Extract Optimization History block to support Parameter Optimization as well.
nTopologyintroduced the concept of implicit modeling for mechanical design, which is an innovative, modern, and scalable way define parts and products. It has many benefits to end-users and companies, such as the elimination of model failures, speed of changes or iterations, and scalability to name a few. But implicit modeling enables so much more. In this informational session, we'll explore a topic that is redefining product development - field-driven design. In short, field-driven design is a way for design, analysis, and manufacturing teams to overlay information into one engineering model. This approach enables orders of magnitude increase in design iteration speed and greatly improves collaboration between teams.
How Field-Driven Design Allows Engineers to Design for Additive Manufacturing
Watch this information session where we'll define field-driven design, show examples of how it enables better knowledge sharing, and show how it promotes the development of more sophisticated, highly engineered products. You'll also better understand how nTopology is addressing today's engineering problems through its nTop Platform product.
nTopologywas founded in 2015 to enable engineers and designers to create any geometry - no matter how complex - and meet the requirements of high-performance products.
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