Altair Engineering has developed a system for optimizing prototype and machine design using three-dimensional fluid modeling. The system includes a modeling component, machine learning component, and graphical user interface component to predict characteristics and generate physics modeling data for mechanical devices. The graphical user interface provides a design and simulation environment. GlobalData’s report on Altair Engineering gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Altair Engineering, Hydrogen fuel dispensers was a key innovation area identified from patents. Altair Engineering's grant share as of May 2024 was 35%. Grant share is based on the ratio of number of grants to total number of patents.
Optimization of prototype and machine design using fluid modeling
A recently granted patent (Publication Number: US11947882B2) outlines a system designed to generate three-dimensional models of mechanical devices using stored data elements for both mechanical and electrical components. The system employs machine learning processes to predict characteristics of the mechanical device, such as fluid behavior, thermal properties, and combustion dynamics. It then generates physics modeling data based on these characteristics, representing fluid dynamics, thermal dynamics, and combustion dynamics. The system presents a visual representation of the mechanical device through a graphical user interface, showcasing the three-dimensional model along with visual representations of the fluid dynamics, thermal dynamics, and combustion dynamics.
Furthermore, the system includes features for probabilistic simulation environments associated with optimizing the three-dimensional model. It allows for modifications to the physics modeling data based on a second machine learning process, offering a set of components from the stored data elements library based on physical or thermal characteristics. The system can modify the three-dimensional model based on selected components and perform additional machine learning processes using techniques like Latin hypercube sampling or Monte Carlo sampling. Overall, this patented system provides a comprehensive approach to generating, analyzing, and visualizing complex mechanical devices, particularly suited for applications like aviation systems, with a focus on fluid dynamics, thermal properties, and combustion characteristics.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
