Tag Archives: Noesis Solutions

Design space exploration and design optimization at NAFEMS 2016 Americas

2016-americas-slideDesign space exploration and design optimization will be prominent topics at the NAFEMS 2016 Americas Conference June 7-9 in Seattle. These presentation abstracts from the just-published preliminary conference agenda promise a rich and rewarding program for attendees: Continue reading

Next generation of PIDO: Noesis Solutions id8 transforms simulation-based engineering into objectives-driven engineering

Noesis Solutions NV, a pioneering developer of software technology for PIDO (process integration and design optimization), is extending its product portfolio with a series of next-generation application suites designed to transform simulation-based engineering into what the company characterizes as objectives-driven engineering. “Starting from product targets that outsmart competition,” Noesis explains, “such a process enables engineering teams to work their way back to the most critical design parameters and to engineer right first time.”

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The new id8 decide environment offers a powerful and intuitive engineering data analytics solution to complement Optimus, visualizing design space information built up from a wide range of disparate engineering data sources. Image credit: Noesis Solutions. (Click image to enlarge.)

The first of these products, called id8 decide, is a new toolset for visualizing the design space. Far more than just an incremental refinement of existing functionality, this new software multiplies the value of Noesis’ Optimus software by radically strengthening and deepening users’ ability to achieve what Noesis terms “a clear vision of the design space.” On seeing id8 decide live in operation, we were impressed with the clarity and discoverability of functionality offered by its clean, up-to-the-minute graphical user interface, as well as advances at deeper levels of functionality—responses shared by beta users.

The beginning of Noesis’ transformation from a one-product company to a multi-product solution provider, id8 decide will be followed by future id8 application suites that, in similar fashion, will complement Optimus and step up its power through the addition of entirely new capability sets. Continue reading

Smart optimization for robust optical performance in camera lens design

This information is excerpted from a case study published by Noesis Solutions. Access the complete case study here.

Besides providing the capability to shoot high-quality images, the development of new camera optics needs to deliver a product that is competitive in terms of price and weight. Geometry imperfections and clearances between the separate lens elements of photographic lenses typically reduce the quality and reliability of the manufactured product. To take into account the impact of these tolerances on product quality, engineers used Optimus from Noesis Solutions as part of their development process.

With Optimus, the engineers successfully combined both CETOL 6σ tolerance analysis software and CODE V optical design software into an automated simulation-based design process. This way the engineering team evaluated the impact of assembly tolerances for each of the individual lens tubes (defined in CETOL 6σ) on the optical performance of the camera lens (calculated by CODE V). The result is a photographic lens design which can be manufactured easily and cost-efficiently, yet reliably delivers the expected image quality. Continue reading

Multi-objective optimization of electric motor for increased drive torque and noise reduction

This article was first published by Noesis Solutions and can be downloaded here.

Optimus enabled the engineers to orchestrate ANSYS magnetic/structural simulations in exploring the design space and optimizing the motor design.

Engineers from Cybernet Systems took electric motor technology to higher quality and performance levels. Using Optimus from Noesis Solutions they optimized the design of an electric motor with interior permanent magnets (IPM) for maximum drive torque and minimum noise and vibration. Optimus enabled them to orchestrate ANSYS magnetic/structural simulations in exploring the design space and optimizing the motor design. The results of the multi-objective optimization were impressive: drive torque went up by 7% while lowering cogging torque by 35% and acoustic radiation by almost 6 dB. The approach opens up new opportunities for IPM synchronous electric motors used in (hybrid) electric vehicles, compressors and appliances. Continue reading

Maximizing aircraft economy through multidisciplinary winglet optimization

Winglets increase the lift generated at the wingtip by smoothing the airflow across the upper wing side near the tip. Winglets also reduce the lift-induced drag caused by wingtip vortices, improving the lift-to-drag ratio of the aircraft.

Executive summary—Wingtip devices are intended to reduce aircraft drag through partial recovery of the tip vortex energy. Fuji Heavy Industries (FHI) used Noesis Solutions’ Optimus to automate and couple FEM and CFD simulations to identify optimized winglet design configurations offering operational and environmental benefits. Optimus realized a 1.2% reduction in aircraft takeoff weight for a typical mission profile at 1G cruise speed. It achieved this by balancing the aerodynamic benefits and weight penalties of a passenger aircraft model integrating wing reinforcement to withstand winglet induced loads. Optimus’ design of experiments (DOE) and response surface modeling (RSM) capabilities helped FHI engineers understand the impact of winglet characteristics on aircraft performance up-front. Global design optimization enabled them to reduce the aircraft’s cruise drag by 4% and its takeoff weight by 676 kilograms, while respecting the shock induced separation (SIS) airflow constraint. Continue reading

Design space exploration: Institutionalizing the practice

EVENT NOTICE—If you’re in the Detroit area, join Noesis Solutions’ free seminar April 9 on Innovations in Process Integration & Design Optimization. You’ll hear three perspectives on how this technology can help you streamline your simulation processes and identify benchmark product designs in less time, including a presentation by me on Innovative Uses of Optimization for Engineering Design Problems. Event and registration information here.

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Achieving institutional adoption of design space exploration, design optimization and process integration is a crucial goal for engineering organizations working to establish these as strategic competencies, not just tactical. Too often implemented at only the department or workgroup level, the technologies and their attendant work processes need to be recognized and given backing as enterprise capabilities to have their greatest impact on engineering’s ability to advance corporate strategic objectives. [See also Design space exploration: Justifying the investment.] Continue reading

Geometry and beyond: Optimization taxonomy and methods

The power of numerical optimization in engineering design is its ability to rationally and rapidly search through alternatives for the best possible design(s). Parameters in a design that can be varied to search for a “best” design are design variables. Given these, design can be structured as the process of finding the minimum or maximum of some attribute, termed the objective function. For a design to be acceptable, it also must satisfy certain requirements, or design constraints. Optimization is the process of automatically changing the design variables to identify the minimum or maximum of the objective function while satisfying all the required design constraints. [This survey of design optimization methods is in complement to our recent review of design exploration techniques. See also Design exploration vs. design optimization.] Continue reading

Latin hypercubes and all that: How DOE works

Making design exploration software speak the language of engineers and not mathematicians has been a focus of development since the industry’s inception. Even so, our recent case study was typical in referencing the Latin hypercube design-of-experiments method, the radial basis function for generating a response surface model, the non-dominated sorting evolutionary algorithm to generate a Pareto front—all prompting this look into some of the quantitative methods that drive design space exploration. Continue reading

Design space exploration industry timeline

A timeline of company formations, product launches and M&A activity among design exploration and optimization software vendors maps the pace and direction of the industry’s development.

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