Altair HyperWorks 2026 debuts with AI, simulation upgrades
Altair, based in Troy, Michigan, USA, has announced updates to its HyperWorks software. HyperWorks 2026 targets accelerated development and improved product performance via computer-aided engineering (CAE) design and simulation.
“HyperWorks 2026 exemplifies how Altair and Siemens are driving the future of simulation and empowering engineers to design smarter, faster, and with greater confidence in real-world outcomes by bringing AI, automation, and multi-physics into a unified ecosystem,” stated Sam Mahalingam, Chief Technology Officer, Altair, and executive vice president, Siemens Digital Industries Software. “Following the acquisition by Siemens earlier this year, our commitment is to create the world’s most complete AI-powered portfolio of product lifecycle intelligence software and further enhance the most comprehensive digital twin.”
AI-powered design and simulation
Geometric deep learning and generative algorithms and GPU-accelerated reduced order modelling (ROM) are said to enable near-real-time predictions and faster validation. Physics-based AI models can be deployed in secure, browser-based environments, reportedly producing results up to 1,000x faster than traditional solver simulations. Expanded support for vectors and smoothed-particle hydrodynamics (SPH) broadens domain coverage.
Enterprise-scale pre-processing and model assembly
HyperWorks 2026 enables engineers to simulate large, complex assemblies with speed and fluidity in an effort to shorten build and validation cycles. Enhanced navigation, batch meshing, and connector management are intended to streamline pre-processing while direct data management integration helps to ensure consistency across teams.
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Integrated multi-physics simulation
Unified solvers and domain coupling allow engineers to analyse complex interactions – such as thermal-fluid or electromagnetic-structural systems – with greater fidelity.
New workflows support e-motor optimisation, battery safety studies, and high-temperature analysis, while co-simulation standards aim to enhance digital continuity. Electromagnetic simulations reportedly run up to 40% faster and propagation modelling up to 20x faster with radar and electromagnetic compatibility (EMC) analysis expanded for next-generation applications.
Automation, collaboration and connectivity
Expanded Python and API support, intuitive no-code workflow tools, and cloud integration promote digital continuity. Enhanced visualisation and plotting tools simplify result interpretation and sharing, while interoperability with third-party software is intended to strengthen digital twin fidelity.
Realistic particle, fluid, and material behaviour
New modelling approaches are said to capture bulk flow, impact behaviour, and high-temperature effects with greater realism. Python-based automation accelerates discrete element method (DEM) workflows and coupled solvers enable advanced studies of battery safety and material response.
Intuitive design and motion exploration
Real-time updates across multi-window views aim to reduce setup time, while flexible implicit modelling and direct surface editing work to remove geometry barriers.
HyperWorks in use at JetZero
JetZero, an aviation startup targeting ultra-efficient air travel, is collaborating with Siemens on the development and production of a blended wing aircraft. The all-wing design aims to improve fuel efficiency by up to 50%, reduce noise, and advance the industry toward zero carbon emissions. According to the company, key to the pace of its development schedule is gaining engineering insights faster than using conventional high fidelity computational fluid dynamics (CFD) with FlightStream, part of the HyperWorks suite.
As John Vassberg, chief design officer at JetZero explained, “JetZero is pioneering the next step change in the aerospace industry and, to accomplish that at the scale the industry is demanding, means we need a toolset that allows us to work at pace and gain accurate insights early in design, which FlightStream does. It is easy for our engineering team to use, does not require the traditional high-performance computing resources of high-fidelity CFD, and gets us answers fast and without heavy resource demands. This is critical for companies like JetZero that need to iterate faster than ever before.”
