Abstract: One or more physical constraints are selected from a plurality of physical constraints for a part. The physical constraints are for use by a physics solver and define a physical performance of the part. One or more connectivity constraints are defined for use by the physics solver. The connectivity constraints enforce connectivity to or from at least one region over a complement space of the part. The connectivity constraints include locally differentiable violation measures that are modeled after at least one of the physical constraints. A topology of the part is optimized in the physics solver by enforcing the physical constraints and the connectivity constraints while satisfying a primary objective function that optimizes the physical performance of the part. A computer-aided design of the part is produced based on the optimized topology.

Abstract: This disclosure provides techniques for providing or facilitating automatic generation or conversion to geometric designs based on system-level designs. An example method may include receiving a system model from a system modeling environment. The system model describes, represents, or reflects a typology of two or more system components. The topology describes connectivity and performance by the two or more system components. A processing device extracts the topology of the two or more system components from the system model. The topology is represented by two or more connected nodes. The topology describes connectivity and performance by two or more system components. The processing device generates design spaces for the two or more connected nodes. The processing device further generates geometric representations in the design spaces for each of the two or more connected nodes to form the geometric assembly based on the system model.

Abstract: This disclosure provides techniques for automatically generating a geometric representation based on a system-level model (e.g., a lumped parameter model, or LPM). The geometric representation may include a three-dimensional (3D) or cross-sectional shape, resulting from topology optimization within a design space automatically generated without human intervention. An example method may include identifying one or more constraints for each of two or more components of an LPM. One or more conditions are generated for the LPM. The one or more conditions are mapped to the one or more constraints. A processing device may generate a design space for a geometric representation to perform functions represented by the LPM. The geometric representation is subject to the generated one or more conditions. The processing device may then perform topology optimization of the geometric representation in the design space to generate an optimized geometry (e.g., a converged and/or final output).

Abstract: The present disclosure provides techniques for automatically generating shapes (e.g., geometric representations) of two or more interacting objects based on arbitrary motions specified for the two or more interacting objects. In an example system. the system receives specifications of motions or movements of two or more objects and the associated design spaces and generates collision-free shapes for the two or more objects while the two or more objects undergo the specified motions. As such, the system solves for unknown or undefined shapes based on desired motion profiles. For example, given respective movement or motion characterizations of two or more objects, such as at least one of a function translation or a function rotation over time in at least one of the three axes in a Euclidean space, topology optimizations may be performed to generate the respective shapes or geometric representations of the two or more objects.

Abstract: A target system is coupled to a diagnosis engine that uses a lumped parameter model of the system for diagnosis. A proximity search in is performed in a computer-aided design model of the system to find groups of components that may be affected by resistive or parasitic interactions between the individual components in the groups. The lumped parameter model is augmented by adding elements that emulate the resistive or parasitic interactions between the individual components in the groups. The augmented lumped model is used by the diagnosis engine to perform diagnosis on the system.

Abstract: A target system is coupled to a diagnosis engine that uses a lumped parameter model of the system for diagnosis. A proximity search in is performed in a computer-aided design model of the system to find groups of components that may be affected by resistive or parasitic interactions between the individual components in the groups. The lumped parameter model is augmented by adding elements that emulate the resistive or parasitic interactions between the individual components in the groups. The augmented lumped model is used by the diagnosis engine to perform diagnosis on the system.