A method for generating a fault tree of a multi-component system is provided. The multicomponent system includes a logical-functional system layer and a physical system layer as different layers of abstraction The physical system layer may correspond, for example, to software and/or hardware implementing the functional aspects of the logical-functional system layer. The method first provides a logical-functional fault tree for the logical-functional system layer and a physical fault tree for the physical system layer, the latter having elements corresponding to elements in the logical-functional fault tree. Next, a mixed-layer fault tree is generated by combining aspects of both fault trees in a systematic way. The disclosed is particularly relevant for analyzing safety-critical systems. However, the present concepts are not limited to these applications and may be applied to general use cases where fault tree analysis is applicable.

A method for calculating a valve closure time includes performing a computational fluid dynamics model simulation of the valve. The method also includes performing multiple functional performance analysis model simulations of the valve based on the computational fluid dynamics model simulation of the valve to calculate the valve closure time. The functional performance analysis model simulations are based on a numerical solution of a second order differential equation according to an equation of motion given by: (I), where m.sub.L is a mass of translating components, y(t) is a piston displacement at a given time t, F.sub.τ is a force on the valve due to fluid flow, Eμ is a friction force, F.sub.D is a hydraulic damping force on the piston, F.sub.D is a spring force, FPPA is a hydraulic piston pressure assist force, F.sub.BPA is a hydraulic bore pressure assist force, and F.sub.G is a force due to gravity.

A computer-implemented method for providing a machine learning algorithm for determining similar scenarios based on scenario data of a data set of sensor data, wherein an optimization algorithm is applied to the feature representation, output by the first machine learning algorithm, of the first augmentation of the data set of sensor data, wherein the optimization algorithm approximates the feature representation, output by the second machine learning algorithm, of the second augmentation of the data set of sensor data. The invention further relates to a method for determining similar scenarios based on scenario data of a data set of sensor data and to a training controller.

A computer-implemented method of assessing roughness in metallic lines of an integrated circuit (IC) is provided. The computer-implemented method includes developing a library of roughness characterizations for metal lines of varying characteristics and dimensions. The computer-implemented method further includes fabricating a testable IC and identifying, in the testable IC, a location of interest (LoI) at which roughness of a metal line within the LoI could impact IC performance. Also, the computer-implemented method includes developing, from the library, a roughness model using the roughness characterizations for those metal lines having characteristics and dimensions corresponding to those of the metal line within the LoI and refining the library and the roughness model based on a comparison of the roughness model and an actual roughness of the metal line within the LoI to obtain a final roughness model.

Disclosed herein are systems, devices, and methods related to a determination of whether an asset has a fluid issue. In particular, examples involve a platform defining a predictive model for outputting an indicator of whether an asset is likely to have a fluid issue based at least on historical fluid data for one or more assets. The historical fluid data may comprise at least one of a plurality of fluid reports for the one or more assets and an indication of a fluid issue for each fluid report. The platform may receive at least one fluid report associated with a given asset and based at least on the predictive model and the received at least one fluid report, make a determination that the given asset is likely to have a fluid issue. The platform may cause a computing device to output an indication of the determination.

Creation and use of a digital twin instance (DTI) for a physical instance of the part. The DTI may be created by a model inversion process such that model parameters are iterated until a convergence criterion related to a physical resonance inspection result and a digital resonance inspection result is satisfied. The DTI may then be used in relation to part evaluation including through simulated use of the part. The physical instance of the part may be evaluated by way of the DTI or the DTI may be used to generate maintenance schedules specific to the physical instance of the part.

A vehicle response predicting device comprising: a memory; and a processor coupled to the memory, wherein the processor is structured so as to, by using a convolutional neural network model that has been trained in advance, and that is for predicting response of a vehicle, and whose input is input data expressing a time series of an input to the vehicle and a characteristic of the vehicle, predict response of a vehicle that is an object of prediction, from input data expressing a time series of an input to the vehicle that is the object of prediction and a characteristic of the vehicle that is the object of prediction.

A meshless method for solid mechanics simulation, which includes the following steps: first constructing a trial function and a test function by distributing points according to a geometric shape of a structure; then introducing numerical flux to correct the traditional Galerkin weak form; and finally substituting the trial and test functions into the weak form to construct a global stiffness matrix of the structure and a system of algebraic equations, and solving the system of algebraic equations to obtain displacement and stress of each point, thus completing simulation and analysis of the structure deformation and stress.

A notch treatment method for flaw simulation including providing the specimen with the notch, the notch having a re-melt material layer; isolating the notch; and selectively etching the notch to provide an etched surface of the notch; wherein at least a portion of the re-melt material layer has been removed from the notch. In one aspect, there is provided a notch treatment method for flaw simulation including providing the specimen with the notch, the notch having a re-melt material layer, the specimen includes steel or an alloy thereof; isolating the notch; and selectively etching the notch with a first etching solution and a second etching solution to provide an etched surface on the notch; wherein at least a portion of the re-melt material layer has been removed from the notch.

The invention relates to the technical field of vehicle design, and aims to solve the problem that an existing vehicle body design method is likely to cause a high scrap rate of a stamped part constituting a vehicle body in a manufacturing process. To this end, the invention provides a method for improving the design of a vehicle-body stamped part, the method including: obtaining thinned portions with thinning rates exceeding a standard rate on a stamped part that has been formed; screening a first portion with a defect rate and/or a defect type not meeting a preset requirement from the thinned portions; and adjusting a shape, a size, and/or a material of a second portion that is the same as the first portion on an original design model of the stamped part to obtain a target design model. In a process of improving the design of vehicle body styling, the improvement is made based on an original design model of a previous version of vehicle-body stamped part and actual information of the previous version of vehicle-body stamped part after mass production, and this can not only ensure a fast research and development speed, but also avoid a high scrap rate of a newly designed vehicle-body stamped part in a manufacturing phase, thereby greatly reducing research and development and manufacturing costs.