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DEM simulation helps consumer durable goods companies accelerate product development

Consumer goods companies use discrete element methods (DEM) to continually innovate and develop higher quality and more-efficient products with must-have features, in a shorter time frame, and for better consumer experience. These products are mechanical and electromechanical systems. Designers, engineers and developers working on these equipment must understand the underlying physics that drives the equipment reliability and performance. Often a deep insight is needed, for example, of the mechanical, fluid-flow, electronics, and particles and their interaction to arrive at an optimal design, and efficient operation.

But, you might ask, how does discrete element modeling (DEM), which deals with particle behavior, add to the consumer goods product development story? Any appliance that involves particulate material — hair, dirt/dust, powder, agricultural fiber, fabric, natural/manufactured strands — can benefit from proven solutions that significantly optimize engineering projects. 

Personal care (up), coffee grinder (middle) and lawnmower (down) design can benefit from DEM simulation.

DEM software solves general-motion equations throughout the domain no matter the application. Simulation results allow you to see how particles behave around and inside a device. Sometimes — but not always — engineers can measure particle behavior through bench tests. But DEM provides insight into the movement of every single particle throughout the simulation over the course of time.

Vacuum cleaner development

Consider the vacuum cleaner. This appliance sucks up debris from different surfaces (wooden floor, ceramic tile, carpet), and we consumers expect that it will operate well every time, on every surface for all types of debris. R&D and designers engineers developing floorcare products, however, note that it’s difficult to predict how a unit may work on a carpet with different types of fibers and need to understand the interaction between the vacuum cleaner and the rug. They also need to analyze the interaction between the particle and the rug fibers. They know that one can’t apply simple friction equations, because both the vacuum’s moving body and the carpet comprise multiple components. To illustrate this, consider the case study below. One of the objectives was to predict a robot vacuum’s wheel behavior without conducting lab tests. 

Using Rocky DEM, we applied a constant rotation and a vertical normal load (in which the wheel is free to move in the vertical direction) to measure two important parameters related to how the wheel and carpet interact: torque, which is required to rotate at a given speed over the carpet, and tow force, which holds the carpet sample in place. The model enables evaluation of different wheel designs using simulation. 

Rocky DEM software analyzed prescribed rotation and free-body motion to measure carpet tow force and wheel torque (left). A variety of carpet samples were created to virtually test wheel concepts.
Rocky DEM software analyzed prescribed rotation and free-body motion to measure carpet tow force and wheel torque (left). A variety of carpet samples were created to virtually test wheel concepts.

Rocky DEM software analyzed prescribed rotation and free-body motion to measure carpet tow force and wheel torque (top). A variety of carpet samples were created to virtually test wheel concepts.

The virtual carpet is built by placing single filaments together, depending on the fiber’s characteristics: spacing, length, diameter, uniformity, pattern. We modeled isotropic and anisotropic effects to compare how wheels and fibers interact. 

We developed a few wheel designs to test virtually: one with a smooth surface, the other with grooves. The goal was to determine which wheel would operate best on all carpet surfaces without sacrificing efficiency. Rocky DEM showed that the smooth wheel exhibits slipping behavior, since it doesn’t have as tight a grip on carpet fibers as the grooved wheel. Overall, the grooved wheel showed better traction.

Smooth and grooved vacuum cleaner wheels.
Smooth (left) and grooved (right) vacuum cleaner wheels.

One of the best-known names in consumer goods is Dyson, which develops high-quality vacuum cleaners as well as environmental control equipment (air purifiers, humidifiers, fans, lighting solutions) and personal care products (hand dryers, hair dryers, hair straighteners). The company considers simulation-driven product development an integral part of its R&D effort. Dyson pushes virtual engineering to its limits to discover what is possible.

How does the consumer-goods giant go from scratch to a robust new product? “In research, we use simulation to gain deep insight into very complex but fundamental processes,” says Stefan Koch, research manager of the aerodynamics and separation systems research team at Dyson. “My own expectations of software include high resolution, maximum accuracy, and the ability to realistically capture physics.” The company also leverages simulation tools to prove a concept, optimize a design, and accelerate product development to launch in as short a time frame as possible.

Dyson’s challenges related to vacuum cleaner product development
Dyson’s challenges related to vacuum cleaner product development.

As an example of how it turns to multiphysics software for answers, Koch says that a successful vacuum cleaner head must handle large particles and fibers, particle−particle interactions, fiber−fiber interactions, and rotating and moving boundaries.

This is one of the most complex physics problems that I have come across. We apply DEM simulation to gain insight into particle dynamics. Studies have shown us what particles get stuck, what particles move quicker or slower, what particles pose a particular challenge for a specific design. It’s possible to see this only using simulation.

Stefan Koch, research manager of the aerodynamics and separation systems research team at Dyson
Dyson uses Rocky DEM to study the fundamentals of dust removal, capturing particles that bounce off walls, which provides details and force data that cannot be observed with high-speed camera bench tests.
Dyson uses Rocky DEM to study the fundamentals of dust removal, capturing particles that bounce off walls, which provides details and force data that cannot be observed with high-speed camera bench tests.

For the future, Dyson expects to perform faster and better simulation, notably on complete systems rather than single components.

Summary

Leon White Nogueira

CAE Application Specialist, DEM at ESSS

Leon White Nogueira is a CAE application specialist at ESSS, which develops Rocky DEM. He is a mechanical engineer with an M.Sc. from the Polytechnic School of the University of São Paulo (POLI-USP). He joined ESSS in 2012, originally in the computational fluid dynamics (CFD) group and now in the discrete element method (DEM) group on the Rocky technical team.


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