Smoking meat (and other food) in a barbecue smoker doesn’t sound complicated, but there are more factors at work in producing delicious food than you would expect. Barbecue enthusiast Travis Jacobs, president of Jacobs Analytics, was aware that in windy conditions the air flow through the bottom inlets and the top outlet vents of a smoker can be variable, leading to internal temperature gradients and swirling air that removes smoke and makes a less savory product. He wanted to make a smoker that could smoke food to perfection in any conditions. Unlike most of us non-engineer weekend barbecuers, he turned to computational fluid dynamics (CFD) simulations to solve this problem.
LEDs are increasingly used in automobile headlights because of their small size and reduced energy consumption. But, though they are much more energy efficient than traditional headlights, most of the energy required is converted to heat rather than light — 70 percent, in fact. This presents a challenge to engineers and designers because, since they are semiconductor-based, the diode junction of LEDs must be kept below 120 C. Maintaining temperature below this limit typically involves cooling airflow from an electric fan combined with heat sink fins.
EnSight, the leading post-processor for Computational Fluid Dynamics (CFD) data is now part of ANSYS. In the two decades since its launch, EnSight has taken off like a multistage rocket. Here is the story.
I grew up in that magical era when NASA used multi-stage rockets to carry Apollo astronauts to the moon and back. As a toddler I learned to count backwards from 10, 9, 8, 7, 6 … because that’s what I heard Mission Control say. I dreamt of being an astronaut, studied aerospace engineering and started my career at NASA’s Johnson Space Center in Houston, Texas. I met my lovely wife there, blocks from the NASA gates. Her parents still live next door to Buzz Aldrin’s Apollo era house. I used to store my lunch in the Mission Control fridge while working on my space shuttle aerodynamic simulations in the support room next door. So maybe it’s natural for me to think in rocket terms. Continue reading
In my last blog entry, I discussed the positive ROI for CFD simulation. The metrics used were very business-oriented such as reduction in lead time, costs, etc. A new example of this ROI is the fresh victory of Red Bull Racing in the 2011 Formula One season. For the second year in a row the Red Bull team has won both the Driver and Constructors’ titles for the 2011 Formula One Championship, thanks to a great Formula One design and a driver with extraordinary talent, Sebastian Vettel. Because of speed limits I cannot drive faster than 65 miles per hour (104 kilometers by hour) so I cannot even start to explain how Sebastian Vettel performs so well at the high speeds of F1 races. However, I would venture to claim his performance is partly due to a great car — a car designed using CFD simulation.
After performing a conjugate heat transfer (CHT) simulation in ANSYS Fluent or ANSYS CFX software, you may be interested in the thermal stresses generated in your model. ANSYS Workbench 13.0 makes it easy to perform this type of analysis by transferring the volumetric temperature field from your CFD solution and applying it as a body temperature load in a static structural system. Continue reading
Engineers on the front lines of product development know that CFD analysis can be extremely helpful in reducing time to market, designing a better quality product, etc. More important, and perhaps less well know, is the positive impact that fluid dynamics analysis can have on return on investment (ROI). The benefits of CFD far outweigh its initial costs.
When simulating particulate flows using the dense discrete phase model (DDPM) or discrete element method (DEM) in ANSYS Fluent software, you might want to initialize the case with a certain region filled with particles. Examples of this include an initial static bed in a bubbling fluidized bed simulation and a partially filled rotary drum in a powder mixing simulation. Although it is possible to use standard DPM injection options to continuously inject particles until the desired quantity is reached, this approach is computationally expensive and impractical when the amount of particulate mass to be injected is large. Additionally surface injection from “interior” cell facets is not recommended as this option does not provide good control over injection and may lead to numerical instability.
So what are the options currently available?