A flow analysis apparatus is provided. The flow analysis apparatus includes a model deriver configured to generate a flow analytic model for performing a flow analysis for a plurality of cells by using analytic data including a plurality of input signals used for performing multiple times iterations of numerical analysis by Computational Fluid Dynamics (CFD) and a plurality of output signals corresponding to each of the plurality of input signals, and a flow analyzer configured to perform the flow analysis for the plurality of cells that divide the space around a design target component by using the generated flow analytic model.

A flow analysis apparatus is provided. The flow analysis apparatus includes a model deriver configured to generate a flow analytic model for performing a flow analysis for a plurality of cells by using analytic data including a plurality of input signals used for performing multiple times iterations of numerical analysis by Computational Fluid Dynamics (CFD) and a plurality of output signals corresponding to each of the plurality of input signals, and a flow analyzer configured to perform the flow analysis for the plurality of cells that divide the space around a design target component by using the generated flow analytic model.

Disclosed are techniques for simulating a physical process and for determining boundary conditions for a specific energy dissipation rate of a k-Omega turbulence fluid flow model of a fluid flow, by computing from a cell center distance and fluid flow variables a value of the specific energy dissipation rate for a turbulent flow that is valid for a viscous layer, buffer layer, and logarithmic region of a boundary defined in the simulation space. The value is determined by applying a buffer layer correction factor as a first boundary condition for the energy dissipation rate and by applying a viscous sublayer correction factor as a second boundary condition for the energy dissipation rate.

Disclosed are techniques for simulating a physical process and for determining boundary conditions for a specific energy dissipation rate of a k-Omega turbulence fluid flow model of a fluid flow, by computing from a cell center distance and fluid flow variables a value of the specific energy dissipation rate for a turbulent flow that is valid for a viscous layer, buffer layer, and logarithmic region of a boundary defined in the simulation space. The value is determined by applying a buffer layer correction factor as a first boundary condition for the energy dissipation rate and by applying a viscous sublayer correction factor as a second boundary condition for the energy dissipation rate.

A method of identifying events includes obtaining an acoustic signal from a sensor, determining one or more frequency domain features from the acoustic signal, providing the one or more frequency domain features as inputs to a plurality of event detection models, and determining the presence of one or more events using the plurality of event detection models. The one or more frequency domain features are obtained across a frequency range of the acoustic signal, and at least two of the plurality of event detection models are different.

A method of identifying events includes obtaining an acoustic signal from a sensor, determining one or more frequency domain features from the acoustic signal, providing the one or more frequency domain features as inputs to a plurality of event detection models, and determining the presence of one or more events using the plurality of event detection models. The one or more frequency domain features are obtained across a frequency range of the acoustic signal, and at least two of the plurality of event detection models are different.

In an example, a method includes processing an input signal using a finite element model (FEM) to generate an output signal. The input signal, the output signal, and the FEM are associated with a simulated additive manufacturing process. The method also includes adjusting the input signal based on comparing the output signal to a reference signal and thereafter processing the input signal using the FEM to generate the output signal. Examples also include a computer readable medium and a computing device related to the method.

In an example, a method includes processing an input signal using a finite element model (FEM) to generate an output signal. The input signal, the output signal, and the FEM are associated with a simulated additive manufacturing process. The method also includes adjusting the input signal based on comparing the output signal to a reference signal and thereafter processing the input signal using the FEM to generate the output signal. Examples also include a computer readable medium and a computing device related to the method.

A method and system for approximating a predicted three-dimensional imbibition phase saturation profile from a measured three-dimensional drainage phase saturation profile, a derived one-dimensional drainage phase saturation profile, a measured one-dimensional imbibition phase saturation profile using a trained machine-learning algorithm are disclosed. A method for training of the machine learning algorithm is also disclosed.

A method and system for approximating a predicted three-dimensional imbibition phase saturation profile from a measured three-dimensional drainage phase saturation profile, a derived one-dimensional drainage phase saturation profile, a measured one-dimensional imbibition phase saturation profile using a trained machine-learning algorithm are disclosed. A method for training of the machine learning algorithm is also disclosed.