A piping diagram creation device is provided configured to: generate virtual pipelines, the virtual pipelines being identified by characteristic evaluation data expressed as a sequence of numbers with an assembling angle θi of assembling portions of the respective straight pipes and cut pipes allocated between respective intersections, and an adjustment margin Lj of the cut pipe length as variables; calculate an evaluation value of each virtual pipeline based on a difference value and so on between the total number of intersections and control points and the number of intersections and control points having an error between the virtual pipeline and each intersection and between the virtual pipeline and each control point at or below a predetermined threshold, the latter number being a continuous count from the start point; and generate a pipeline assembling diagram based on one virtual pipeline with an excellent evaluation value.

Systems, methods, and media for pre-processing and post-processing in additive manufacturing are provided. A method includes receiving object geometry data. The method may further include generating a sectional snapshot and a bounding box. The method may also include performing a boundary tracing operation on the sectional snapshots. Further still, the method may include executing a contour mapping algorithm. The method may additionally include outputting slice contour points with respect to the object to be fabricated.

Definition of a design space and an objective space for conducting multi-objective design optimization of a product is received in a computer system having a design optimization application module installed thereon. Design space is defined by design variables while objective space is defined by design objectives. First set of designs in the design space is selected. Each of the first set is evaluated in the objective space for non-dominance. Design space is partitioned into first and second regions using a multi-dimensional space division scheme (e.g., SVM). The first region is part of the design space containing all of the non-dominated design alternatives while the second region contains remaining of the design space. Second set of designs is selected within the first region. Each of the second set and existing non-dominated design alternatives are evaluated for non-dominance. Multi-objective optimization repeats the partition and evaluation until an end condition is reached.

Measurement data are produced by measuring characteristics of a semiconductor device. Target parameters are selected among a plurality of parameters of a device model where the device model is configured to perform a simulation based on device data and output simulation result data indicating the characteristics of the semiconductor device. Initial value sets corresponding to different combinations of initial values of the target parameters are selected. Local minimum values are determined based on reinforcement learning. Each local minimum value corresponds to a minimum value of a difference between the measurement data and the simulation result data with respect to each initial value set. Optimal values of the target parameters are determined based on the plurality of local minimum values. The device model capable of precisely predicting characteristics of the semiconductor device is generated by determining the parameters of the device model using the optimization scheme based on the reinforcement learning.

A kirigami metamaterial with tunable auxetic property under large tensions and a design method for it. The kirigami metamaterial is composed of a plurality of square unit cells in orderly arrangement. The unit cells are arrayed in periodic, gradient and inhomogeneous layouts, corresponding to the kirigami metamaterials with homogeneous, gradient and inhomogeneous auxetic properties. The design method is as follows: Firstly, the heuristic design of the unit cell is obtained by using the structural optimization method for fully considering out-of-plane deformations. Secondly, the optimization result obtained from the above step is processed by geometric reconstruction and parametric modeling, and then the auxetic properties with different geometric parameters are obtained. Finally, the kirigami metamaterial is composed of a plurality of unit cells arrayed into a specific layout. The present invention can achieve a variety of tunable auxetic trends adjusted with the tensions by modifying the cut parameters.

A method and device for simulating a semiconductor IC is provided, which comprises generating a high level description of the IC, generating a low level description of the IC comprising a plurality of instances describing the operation of the IC, conducting a low level function analysis of the IC based on metrics values associated with the instances, and performing a design optimization scheme. The scheme comprises mapping the metric values of instances describing functional units different from standard cells, to standard cells logically connected to said instances, by dividing each of the instance metrics values between a group of standard cells logically connected to the corresponding instance and adding each resulting portion of said instance metric value to the metric value of each of the group of standard cells, respectively.

Methods and apparatus for performing profile-guided optimization of integrated circuit hardware are provided. Circuit design tools may receive a source code and compile the source code to generate a hardware description. The hardware description may include profiling blocks configured to measure useful information required for optimization. The hardware description may then be simulated to gather profiling data. The circuit design tools may then analyze the gathered profiling data to identify additional opportunities for hardware optimization. The source code may then be modified based on the analysis of the profiling data to produce a smaller and faster hardware that is better suited to the application.

Failsafe robustness of critical load carrying structures is an important design philosophy for aerospace industry. The basic idea is that a structure should be designed to survive normal loading conditions when partial damage occurred. Such damage is quantified as complete failure of a structural member, or a partial damage of a larger structural part. This paper establishes for the first time the concept and formulation of failsafe requirement within the context of topology optimization. Efficient computational scheme and computer implementation are carried out. Several examples are shown to demonstrate the impact of failsafe requirement to design concept generated by topology optimization.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes. A method includes obtaining a design space for a modeled object, one or more design criteria for the modeled object, and one or more in-use load cases; iteratively modifying a generatively designed three dimensional shape of the modeled object in the design space in accordance with the one or more design criteria and the one or more in-use load cases for the physical structure, comprising: performing numerical simulation of the modeled object in accordance the one or more in-use load cases, computing shape change velocities for an implicit surface in a level-set representation of the three dimensional shape, changing the shape change velocities in accordance with a polynomial function, and updating the level-set representation using the shape change velocities to produce an updated version of the three dimensional shape.

An optimization engine determines an optimal configuration for a solar power system projected onto a target surface. The optimization engine identifies an alignment axis that passes through a vertex of a boundary associated with the target surface and then constructs horizontal or vertical spans that represent contiguous areas where solar modules may be placed. The optimization engine populates each span with solar modules and aligns the solar modules within adjacent spans to one another. The optimization engine then generates a performance estimate for a collection of populated spans. By generating different spans with different solar module types and orientations, the optimization engine is configured to identify an optimal solar power system configuration.