For device groups (3) each including devices (31) included in a processing plant (1) processing fluid and each having an occupied area (30) set therefor, connection information representing two specific device groups (3) are connected and paired via a pipe (4) and pipe information required for calculating use amount of a pipe material are set. Then, a first step, including arranging the device groups (3) in an installation area (10) of the processing plant (1) so that an outer edge of the occupied area (30) and a long side of a pipe-rack arrangement area (20) contact with each other, and a second step, including calculating a total use amount of the pipe material of the pipe (4) supported by a pipe rack (21, 22, 23), are repeated. From results obtained by changing the arrangement of the device groups (3), arrangements having small total use amounts of pipe materials are selected.

One or more methods of designing microchannel fluid flow networks in a fuel cell bipolar plate includes executing one or more programs on one or more computing devices having one or more processors to optimize the spatially varying orientations of homogenized anisotropic porous media by iteratively executing a gradient-based algorithm that incorporates objective functions of reaction uniformity and flow resistance, and then generate, in response to the homogenized anisotropic porous media optimization, one or more microchannel fluid flow networks by dehomogenizing the optimized anisotropic porous media.

An evaluation apparatus includes a processor that performs operations including reading a simulation parameter of a topography simulator and first range information or second range information that are associated with each other, the simulation parameter being calculated to cause the topography simulator output topography information of a processed target object that is to be obtained by processing the unprocessed target object under a predetermined processing condition, providing topography information of a new unprocessed target object and the simulation parameter to the topography simulator to cause the topography simulator to predict topography information of a new processed target object that is processed under the predetermined processing condition, and outputting a result of comparing the topography information of the new unprocessed target object with the first range information or a result of comparing the topography information of the new processed target object with the second range information.

A design engine for designing an object using structural analysis. The design engine generates a lattice structure for the object comprising a plurality of nodes and a plurality of lines connecting the nodes. The lattice structure is optimized to remove one or more lines using structural analysis based on at least one load-related design requirement. Several design options are provided for generating and optimizing the lattice structure. The design engine then generates a 3D model of the object by thickening each line of the lattice structure into a pipe volume. The thickness of each pipe is determined using structural analysis based on the at least one load-related design requirement. The 3D model represents the volume of the object and is exportable to a fabrication device.

Methods are provided for designing a structure with developable surfaces using a surface developability constraint. The surface developability constraint is developed based on the discovery of a sufficient condition for surface piecewise developability, namely surface normal directions lie on a small, finite number of planes. Automated methods and algorithms may include providing a design domain and a characteristic function of a material in the design domain to be optimized. The methods include defining a nodal density of the material, and determining surface normal directions of a plurality of planes. A density gradient that describes the surface normal directions is then determined. The methods include performing a topology optimization process on the design domain using a surface developability constraint that is based, at least in part, on the characteristic function. A geometric domain is then created for the structure using results from the topology optimization.

Embodiments include herein are directed towards a method for use in an electronic design environment is provided. Embodiments may include receiving a netlist associated with an electronic design and performing genetic optimization on a portion of the netlist to identify and place one or more capacitors on a printed circuit board to minimize an impedance associated with a power plane. Embodiments may further include displaying, at a graphical user interface, a placement of the one or more capacitors, wherein the placement is based upon, at least in part, the performing.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes include: obtaining one or more design criteria, a model of an object for which a physical structure is to be manufactured, and a set of eigenmodes from a modal analysis of the model of the object; extracting a proper subset of non-zero eigenmodes from the set of eigenmodes, wherein the proper subset of non-zero eigenmodes include at least three lowest valued, non-zero eigenmodes; combining data of the proper subset of non-zero eigenmodes to form a strain energy field for the model of the object; iteratively modifying a generatively designed shape of the model of the object using the strain energy field to drive changes to the generatively designed shape of the model; and providing the generatively designed shape of the model of the object.

A layered-composite-member shape optimization analysis method includes: setting, as a design space, an optimization target part of a structural body model of an automotive body; generating a layered block model in the set design space, the layered block model including layers, each layer being a three-dimensional element and having material properties different from each other; connecting the generated layered block model to the part of the structural body model of the automotive body; and inputting an analysis condition, performing optimization analysis on the layered block model as an optimization analysis target, and determining an optimum shape of the layered block model.

An arithmetic processing apparatus includes a memory; and a processor coupled to the memory and the processor configured to execute a prediction process and a search process in an evolutionary calculation process for searching an optimum value of inputs by calculating an objective function based on eigen solutions for inputs and repeatedly calculating the objective function, wherein the prediction process includes predicting a range of an eigen solution for a second input, which satisfies a predetermined eigen solution condition, based on a first eigen solution for a first input when searches an optimum value of inputs by calculating an objective function based on eigen solutions for inputs and repeatedly calculating the objective function, and the search process includes searching a second eigen solution for the second input, which satisfies the eigen solution condition, in the predicted range of the eigen solution.

An XY model calculation apparatus of the present disclosure includes a resonator unit that amplifies a plurality of optical pulses, a measurement unit that measures phases and amplitudes of the plurality of optical pulses to obtain a measurement result, and a feedback configuration that calculates and feeds back an interaction related to a certain optical pulse of the plurality of optical pulses by using a coupling coefficient of an Ising model in response to the measurement result. The feedback configuration is configured to perform a feedback input of a correlation to be determined by a coupling coefficient of two optical pulses of the plurality of optical pulses and is configured so that only one component of pulsed light is to be measured.