A method of modifying a CAD system model performed on a data processing system includes receiving a dataset of co-ordinates representing an article in 2d, or in 3d and receiving 2d or 3d constraints respectively, to be applied to any changes to the dataset of co-ordinates for the article. A modification to be applied to the dataset is received and combined with the relevant 2d and 3d constraints to produce a constrained modification for each of the article and associated article. The constrained modification is solved in 2d and in 3d to determine whether a solution exists in which all constraints are met. If the solve is successful, the constrained modification is applied to each dataset simultaneously and, updated datasets are stored. If the solve fails, the constraints may be reduced and the solve step repeated, or the process is terminated.

Systems and methods for using parametric modeling to design scenarios for autonomous vehicle simulation are provided. In particular, a computing system can obtain data identifying a plurality of parameters, each parameter associated with a particular scenario component. The computing system can determine values associated with a first set of parameters in the plurality of parameters. The computing system can determine one or more parameter relationships, such that values associated with a second set of parameters in the plurality of parameters are determined, at least in part, based on the values associated with the first set of parameters. The computing system can initiate a simulation of a scenario based, at least in part, on the values associated with the first set of the parameters and the one or more parameter relationships. The computing system can determine whether the simulated autonomous vehicle has successfully completed the scenario.

A design engine systematically explores a design space associated with a design problem related to mechanical assemblies. The design engine implements a constraint programming approach to produce mechanical assembly configurations that adhere to a set of design constraints. For each feasible configuration, the design engine then optimizes various parameters to generate design options that meet a set of design objectives. With these techniques, the design space can be explored very quickly to generate significantly more feasible design options for the mechanical assembly than possible with conventional manual approaches. Accordingly, numerous design options can be generated that may otherwise never be produced using those conventional approaches.

The present disclosure provides a method for planning a distribution network with reliability constraints based on a feeder corridor, including determining installation states of respective elements in the distribution network; determining an objective function, the objective function being an objective function of minimizing a total investment cost of the distribution network; obtaining fault-isolation-and-load-transfer time and fault recovery time in a case where the feeder segment of each feeder line that is contained in each feeder corridor fails; determining constraint conditions including reliability constraints; building a distribution network planning model according to the objective function and the constraints; and solving the distribution network planning model built to obtain optimal solutions as planning states and reliability indexes to plan the distribution network.

The present disclosure relates to a method for determination of a real subsoil geological formation. In at least one embodiment, the method includes receiving a model representing the real subsoil, determining a first stochastic trajectory of a first fluvial formation in said model, defining a constraint zone comprising said first fluvial formation, and determining a second stochastic trajectory of a second fluvial formation in said model within said constraint zone based on a stochastic process.

One or more line ends of a putative integrated circuit design are modelled using a constraint graph. A longest path algorithm is applied on subgraphs of the constraint graph. An extent minimization algorithm is carried out on the subgraphs of the constraint graph and routing on the putative integrated circuit design is carried out based on results of the longest path algorithm and the extent minimization algorithm.

A method is used to design nuclear reactors using design variables and metric variables. A user specifies ranges for the design variables and target values for the metric variables. A set of design parameter samples are selected. For each sample, the method runs three processes, which compute metric variables to thermal-hydraulics, neutronics, and stress. The method applies a cost function to each sample to compute an aggregate residual of the metric variables compared to the target values. The method trains a machine learning model using the samples and the computed aggregate residuals. The method shrinks the range for each design variable according to correlation between the respective design variable and estimated residuals using the machine learning model. These steps are repeated until a sample having a smallest residual is unchanged for multiple iterations. The method then uses the final machine learning model to assess relative importance of each design variable.

A method for optimizing orientations of an anisotropic material in a component. For example, the method overcomes the non-uniqueness and gimbal locking problems associated with using Euler angles to define the orientation by instead parameterizing the orientation using an orientation tensor that is a self-dyadic product of a direction vector. To avoid non-linear constraints in the mathematical design variables used in the optimization, isoparametric shape functions map the mathematical design variables to physical design variables, and the mapping ensures that various constraints associated with tensor invariants of the orientation tensor are satisfied even though these constraints are not directly imposed on the mathematical design variables. The physical design variables are used to model the component, whereas optimization is performed using the mathematical design variables. Thus, optimization is greatly simplified by removing the tensor-invariant constraints from the optimization step to the mapping step.

Disclosed are computer implemented techniques for correcting for numerically generated pressure waves at an inlet of a simulation space. The techniques include receiving a model of a simulation space and applying an inlet pressure to an inlet of the simulation space. The applied inlet pressure generates fluctuating velocities that produce undesired, numerically-generated pressure waves. The numerically generated pressure waves are measured to establish a measured pressure history. The measured pressure history is subtracted from the applied inlet boundary pressure history to provide a set of boundary conditions. The process conducts a fluid simulation using the set of boundary conditions. The process repeats using a subsequent set of boundary conditions, until an iteration is reached where the measured pressures near the inlet are sufficiently small to compensate for undesired, numerically-generated pressure waves, and thereafter stores that subsequent set of boundary conditions to provide a corrected set of boundary conditions.

Tools and techniques are described to attach a device to a controller, whereby the controller analyzes the device inputs, looks up information about the device in a database, and then determines which inputs on the device match the defined device inputs. It then may translate information received from the device into an intermediate language. It may also use the information received from the device, the location of the device, and information about the device to create a digital twin of the device.