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.

A system for representing attributes in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin including real-world-element digital twins embedded therein. The transportation system digital twin corresponds to a transportation system. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the transportation system. The real-world-element digital twins include mobile-element digital twins. Each mobile-element digital twin provides a digital twin of a respective mobile element within the real-world elements. The one or more processors are configured to, for each mobile element, determine, in response to an occurrence of a triggering condition, a position of the mobile element, and update, in response to determining the position of the mobile element, the mobile-element digital twin corresponding to the mobile element to reflect the position of the mobile element.

Design method for hydraulic fracturing of a reservoir is presented that maximize well production rates and minimize proppant flowback. The method comprises employing computer simulators that analyze a fracturing treatment design in the context of well properties, reservoir properties, fluids and proppants, and calculates a critical filtration velocity for a proppant pack. If the fluid flow velocity in the fracture exceeds the critical filtration velocity, there is a risk for proppant flowback. The method is applicable to wells that have not yet been fractured, as well as those that have previously undergone a fracturing treatment.

Exemplary embodiments relate to techniques for determining structural color from parameters of an array of nanopartides. The techniques include inputting structural and optical parameters and performing a probabilistic simulation to determine the structural color. An evolutionary optimization may be performed to determine parameters of the array of nanoparticles according to desired properties of structural color. The evolutionary optimization may employ the probabilistic simulation and further adjust one or more parameters of the array to approximate the desired properties of the structural color. Based on applying the probabilistic simulation, the technique may generate an output describing a value, or a range of values, for the one or more parameters of the array of nanoparticles that are selected to approximate the desired properties of the structural color.

Exemplary embodiments relate to techniques for determining structural color from parameters of an array of nanopartides. The techniques include inputting structural and optical parameters and performing a probabilistic simulation to determine the structural color. An evolutionary optimization may be performed to determine parameters of the array of nanoparticles according to desired properties of structural color. The evolutionary optimization may employ the probabilistic simulation and further adjust one or more parameters of the array to approximate the desired properties of the structural color. Based on applying the probabilistic simulation, the technique may generate an output describing a value, or a range of values, for the one or more parameters of the array of nanoparticles that are selected to approximate the desired properties of the structural color.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for simulating a concrete mixture. One of the methods includes obtaining an optical characterization of physical particles, generating a multispherical approximation of the physical particles, the multispherical approximation having reduced dimensionality compared to the optical characterization, simulating an aggregate mixture by applying the multispherical approximation of the particles to a physics simulator to obtain a predicted performance of the proposed aggregate mixture, selectively altering the aggregate mixture based on a comparison with performance metrics and simulating the altered aggregate mixture until the predicted performance satisfies the performance metrics to obtain a final aggregate mixture, and outputting the final aggregate mixture

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for simulating a concrete mixture. One of the methods includes obtaining an optical characterization of physical particles, generating a multispherical approximation of the physical particles, the multispherical approximation having reduced dimensionality compared to the optical characterization, simulating an aggregate mixture by applying the multispherical approximation of the particles to a physics simulator to obtain a predicted performance of the proposed aggregate mixture, selectively altering the aggregate mixture based on a comparison with performance metrics and simulating the altered aggregate mixture until the predicted performance satisfies the performance metrics to obtain a final aggregate mixture, and outputting the final aggregate mixture

A lithography method using a multiscale simulation includes estimating a shape of a virtual resist pattern for a selected resist based on a multiscale simulation; forming a test resist pattern by performing an exposure process on a layer formed of the selected resist; determining whether an error range between the test resist pattern and the virtual resist pattern is in an allowable range; and forming a resist pattern on a patterning object using the selected resist when the error range is in the allowable range. The multiscale simulation may use molecular scale simulation, quantum scale simulation, and a continuum scale simulation, and may model a unit lattice cell of the resist by mixing polymer chains, a photo-acid generator (PAG), and a quencher.

A lithography method using a multiscale simulation includes estimating a shape of a virtual resist pattern for a selected resist based on a multiscale simulation; forming a test resist pattern by performing an exposure process on a layer formed of the selected resist; determining whether an error range between the test resist pattern and the virtual resist pattern is in an allowable range; and forming a resist pattern on a patterning object using the selected resist when the error range is in the allowable range. The multiscale simulation may use molecular scale simulation, quantum scale simulation, and a continuum scale simulation, and may model a unit lattice cell of the resist by mixing polymer chains, a photo-acid generator (PAG), and a quencher.

A simulation device for analyzing behavior of a granular material that includes a plurality of particles includes a first parameter acquisition unit that acquires a first parameter including a parameter relating to the granular material, a second parameter calculation unit that calculates a second parameter, when a particle group including the plurality of particles is coarsely viewed as a single coarse-view particle, the second parameter relating to the coarse-view particle, and a coarse-view particle behavior analysis unit that analyzes a behavior of the coarse-view particle based on the first parameter and the second parameter. The second parameter calculation unit calculates the second parameter by solving a characteristic equation that uses a relationship between an elastic energy of the particle group and an elastic energy of the coarse-view particle.