Abstract:
A simulation computer device for securely executing a model includes at least one processor in communication with at least one memory device. The simulation computer device is configured to store a smart container including a model and a usage policy. The simulation computer device is also configured to receive a plurality of inputs for the model and determine whether to validate the model based on the usage policy. The simulation computer device is further configured to execute the model with the plurality of inputs if the model was validated. Moreover, the simulation computer device is configured to transmit at least one output.
Abstract:
A simulation computer device for securely executing a model includes at least one processor in communication with at least one memory device. The simulation computer device is configured to store a smart container including a model and a usage policy. The simulation computer device is also configured to receive a plurality of inputs for the model and determine whether to validate the model based on the usage policy. The simulation computer device is further configured to execute the model with the plurality of inputs if the model was validated. Moreover, the simulation computer device is configured to transmit at least one output.
Abstract:
A method implemented using a processor based device for simulation based testing of materials, includes selecting a first set of points from a data generated from a design space and generating a stochastic metamodel based on the first set of points. The method also includes determining an uncertainty value based on the stochastic metamodel. The method also includes identifying a second set of points different from the first set of points, from the data generated from the design space, based on the uncertainty value. The method further includes combining the second set of points with the first set of points to generate a third set of points, assigning the third set of points to the first set of points. The method also includes iteratively generating, determining, identifying, combining, and assigning steps till the uncertainty value is less than or equal to a predetermined threshold value.
Abstract:
According to some embodiments, system, apparatus and methods are provided comprising a platform hosting one or more elements; an application programming interface (API) wrapper associated with each of the one or more elements, the API wrapper including input information to the one or more elements, output information to the one or more elements, and at least one instruction defining a function of the element; and wherein the one or more elements and the API wrapper form a self-aware element. Numerous other aspects are provided.
Abstract:
A system for similarity analysis-based information augmentation for a target component includes an information augmentation (IA) computer device. The IA computer device identifies a target component input variable with unavailable data. The IA computer device executes a similarity analysis function, identifying at least two test components with data for the input variable exceeding a threshold. The IA computer device generates parameter distributions for test data for each test component. The IA computer device generates model coefficients using the parameter distributions, determining a proportional mix of the parameter distributions. The IA computer device authors a predictive model configured to generate at least one predicted value for the target data for the at least one input variable for the target component by including the at least one model coefficient in the predictive model. The IA computer device generates, using the predictive model, the at least one predicted value.
Abstract:
A method implemented using at least one processor includes receiving a plurality of measured operational parameters of a turbo machine having a rotor and a stator. The plurality of measured operational parameters includes a plurality of real-time operational parameters and a plurality of stored operational parameters. The method further includes generating a finite element model of the turbo machine and generating a plurality of snapshots based on the finite element model and the plurality of stored operational parameters. The method further includes generating a reduced order model based on the plurality of snapshots. The method also includes determining an estimated clearance between the rotor and the stator during operation of the turbo machine, based on the reduced order model and the plurality of real-time operational parameters.
Abstract:
An industrial asset item definition data store may contain at least one electronic record defining the industrial asset item. An automated support structure creation platform may include a support structure optimization computer processor. The automated support structure optimization computer processor may be adapted to automatically create support structure geometry data associated with an additive printing process for the industrial asset item. The creation may be performed via an iterative loop between a build process simulation engine and a topology optimization engine.
Abstract:
According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.
Abstract:
A unit cell structure is provided. The unit cell structure includes a first section and a second section. The first section defines a first load path and includes a first plurality of first unit cells joined together. The second section defines a second load path separate from the first load path and includes a second plurality of second unit cells joined together, each second unit cell of the second plurality of second unit cells nested within and spaced apart from each first unit cell of the first plurality of first unit cells of the first section.
Abstract:
A testing system computer device for dynamically updating a test plan of an apparatus includes at least one processor in communication with at least one memory device. The testing system computer device is configured to store a plurality of historical data and generate a simulation model of the apparatus based in part on the historical data. The simulation model includes a plurality of inputs and a plurality of outputs of the apparatus. The testing system computer device is also configured to determine a plurality of tests to perform on the apparatus based on the simulation model and the plurality of historical data. The testing system computer device is further configured to receive a plurality of desirability ratings from a user, rank the plurality of tests to perform based on the plurality of desirability ratings, and present the ranked plurality of tests to the user.