A method for modifying a component of a computer-generated model of a vehicle includes the steps of: performing a first simulation of a first accident of the model resulting in components of the model being deformed, producing a first video from the simulation, comparing frames of the first video with one another, calculating a first deformation sum from the comparison of the frames of the first video, modifying at least one of the components, performing a second simulation of a second accident of the model with the at least one modified component, producing a second video from the second simulation, comparing frames of the second video with one another, calculating a second deformation sum from the comparison of the frames of the second video, comparing the deformation sums, and rating the modification on the basis of the comparison of the deformation sums.

Embodiments of this application provide a simulation test method, apparatus, and system, and relate to the simulation test field. The method includes: obtaining a processing delay of each virtual sensor; determining whether each processing delay meets a preset condition; if any processing delay meets the preset condition, predicting a first driving state based on the processing delay to obtain a second driving state; and performing emulation based on each second driving state by using a virtual sensor corresponding to the processing delay, to obtain one or more first input signals, where each first input signal is in a one-to-one correspondence with each virtual sensor; sending the one or more first input signals to a sensor emulator. According to the method provided in embodiments of this application, behavior and performance of a sensor can be accurately simulated, accuracy of sensor simulation can be improved, and simulation test efficiency can be improved.

An embodiment for dynamically arranging vehicles on a smart crossing is provided. The embodiment may include receiving data relating to a maximum carrying capacity of a smart crossing having one or more sensors. The embodiment may also include predicting a current load carrying capacity of the smart crossing. The embodiment may further include identifying a number of vehicles traveling towards the smart crossing within a pre-defined distance of the smart crossing. The embodiment may also include identifying one or more specifications and a current arrangement of each vehicle. The embodiment may further include executing a digital twin simulation of a digital twin model of each vehicle driving across the smart crossing. The embodiment may also include in response to determining the current load carrying capacity is exceeded, assigning a priority level to each vehicle. The embodiment may further include predicting a modification of the current arrangement of each vehicle.

An embodiment for dynamically arranging vehicles on a smart crossing is provided. The embodiment may include receiving data relating to a maximum carrying capacity of a smart crossing having one or more sensors. The embodiment may also include predicting a current load carrying capacity of the smart crossing. The embodiment may further include identifying a number of vehicles traveling towards the smart crossing within a pre-defined distance of the smart crossing. The embodiment may also include identifying one or more specifications and a current arrangement of each vehicle. The embodiment may further include executing a digital twin simulation of a digital twin model of each vehicle driving across the smart crossing. The embodiment may also include in response to determining the current load carrying capacity is exceeded, assigning a priority level to each vehicle. The embodiment may further include predicting a modification of the current arrangement of each vehicle.

Apparatuses and systems and interfaces and methods implementing them including a virtual actor architecture that receives real-time, near real-time, and/or periodic data and information concerning oil/gas upstream, midstream, and downstream facilities via virtual actors, stores the real-time, near real-time, and/or periodic data and information in block of one or more blockchains, create virtual digital twins of the facility or a facility component, runs user requested or system requested simulations using either the real-time, near real-time, and/or periodic facility data directly or runs the user requested or system requested simulations on a virtual digital twin of the facility or facility component, and stores the results in blocks of the one or more blockchains.

A method for designing a spatial mode multiplexer based on diffracted light calculation is provided. A gradient of blocks of a phase plate is determined by interference patterns of forward diffracted light and backward diffracted light; and for the iteration of the blocks of the phase plate, an update step is determined using a gradient descent algorithm. According to the method, the design process utilizes the real beam transmission process, which brings higher accuracy compared to digital simulation. At the same time, diffracted light calculation is always performed at the speed of light and is highly parallel, which can improve the efficiency of optimization design.