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Guest Blog: The untold CFD story of James Cameron’s Deepsea Challenger
Aug26

Guest Blog: The untold CFD story of James Cameron’s Deepsea Challenger

Phil Durbin from Finite Elements explains the untold CFD story of the design and testing of James Cameron's DeepSea Challenger, a solo manned submarine that ventured 11km down to the deepest place on earth, the Marianas Trench, in March 2012.

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How can I drive Fluent UDF Parameters directly from ANSYS Workbench?
Aug13

How can I drive Fluent UDF Parameters directly from ANSYS Workbench?

Our support team is tasked with helping our customers to extract maximum value from their CFD simulations, and we are always striving to help customers who work at the bleeding edge of CFD.  A common question is: How can I drive Fluent UDF parameters directly from ANSYS Workbench? The ANSYS Fluent User Defined Function (UDF) framework gives Fluent users an almost unlimited ability to modify the physics solved in their simulation model. Customisation can extend from simple properties such as boundary condition profiles, through to complex particle-fluid interaction laws. The ANSYS Workbench interface provides the infrastructure to specify parameters that can be used to drive any simulation inputs (such as geometric dimensions or boundary condition values). By coupling the functionality of Workbench Parameters with Fluent UDF's, we can realize the ability to perform parametric studies on any parameter imaginable. The process for coupling Workbench Parameters into Fluent UDF's is as follows: In an open Fluent Window; first initialise the scheme variables in Fluent. In the TUI, type the following line: (rp-var-define ‘leap 0.0 ‘real #f) Repeat the above line of code for every variable needed by replacing “leap” with a representative variable name. This name will be used to call the variable in the UDF. In the Fluent window, go to "Define">"Parameters". In the displayed window, go to "More" > "Use In Scheme Procedure". Click the "Select" button next to the Input Parameter box at the top and click “New Parameter” to create a new parameter. Name the parameter with a representative name. This will be the name of the parameter referenced in Workbench. Click OK on the two windows until you get back to the “Use Input Parameter in Scheme Procedure” window. In the Scheme Procedure box, type the following Scheme code: (lambda (param) (rpsetvar ‘leap param)) Click the “Define” button to link the WB parameter to the UDF accessable scheme parameter. Do the same for any subsequent parameters, choosing unique and representative names for each. The parameters are now setup. To access the value in the UDF, use the following function in the source code: RP_Get_Real("leap") Where the argument in the Scheme name of the variable as defined in the rpsetvar command. Now that the associations are properly setup, you can change the value of the parameter in Workbench, causing Fluent to notify that there has been an upstream change. When you click OK, the parameter value will be pushed through to the Scheme variable. After the Scheme variable has been updated, any UDFs will have access to the new value when the RP_Get_Real("leap") function is called. NOTE: For best performance, assign the value returned from the...

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Solving Conjugate Heat Transfer problems
Mar25

Solving Conjugate Heat Transfer problems

  For many simulations of real world engineering applications, the predictions of heat transfer properties are as important, if not more important, than the actual flow field. Such scenarios include simulations of heat exchangers, HVAC (Heating, Ventilation and Air Conditioning), combustion/burners, electronics cooling, and many more. In these applications, we are often interested in how heat moves through both the fluid and solid domains, and importantly the transfer of heat across the interface between adjacent domains. ANSYS CFD is a leader in solving all three modes of heat transfer: convection, conduction and radiation. Deciding which physics to include is critical to setting up an efficient CFD model. For instance, radiation provides a computational overhead but it is a very important heat transfer mode for bodies with high temperatures which radiate to cooler adjacent bodies or to a lower ambient temperature (since radiative heat transfer scales with Temperature4).   Conjugate Heat Transfer (CHT) is applicable whenever there are two adjacent domains and we wish to analyze the heat transfer between these domains. These domains can either be solid or fluid domains. One example is the forced or natural convective cooling of a heat-sink attached to active electronics components which generate heat. As well as heat-transfer across solid-fluid domains, we can also resolve heat transfer across solid-solid domains and fluid-fluid domains. Solid-solid interfaces are used where two solid components are in contact with each other and there is heat flowing between the objects. Although a fluid-fluid CHT system may seem unphysical, it is a valid assumption in some cases, such as a co-flow heat-exchanger where two fluids are separated by a thin wall. In this case, it can be assumed that the heat-transfer across the dividing wall is calculated in the wall normal dimension only (without explicitly meshing the wall thickness), and there is negligible heat flow along the wall. In all of the above instances, a thermal resistance can be applied to the interface in ANSYS CFD. Such resistances can be used to represent thermal coatings (often used in electronics applications) or badly mated surfaces between adjacent solids (to understand the tolerance of poorly designed connections).   For CHT simulations, it is critical to select appropriate boundary conditions that best represent the physical situation. ANSYS CFD provides a wide range of thermal boundary conditions, but also allows users to customise boundary conditions (using UDF’s or CCL Expressions) so that any heat transfer situation can be modeled. One extremely important aspect of performing accurate CHT simulations is the wall adjacent mesh sizing, as accurately resolving the thermal boundary layer is crucial for producing reliable CHT results. To resolve the thermal boundary layer, an identical approach can...

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Sunswift leads race to Adelaide in new "Cruiser" class of 2013 World Solar Challenge
Oct11

Sunswift leads race to Adelaide in new "Cruiser" class of 2013 World Solar Challenge

In the past, posters of cars such as the KTM Crossbow and Infiniti Red Bull F1 would adorn the bedroom walls of teenagers who were passionate about motorsport, while solar cars were banished to science fairs as a mere curiosity. Enter the UNSW Sunswift Solar Car Team, who are looking to radically change this status quo by building eVe, a next-generation solar car that the team has described as both “sporty, efficient and beautiful”. Sunswift’s eVe is entered into the newly created “Cruiser” class in the 2013 World Solar Challenge, which concludes today in Adelaide. Behind the flowing aerodynamic curves and shiny carbon fibre, eVe is a complex feat of engineering, combining the best technologies across a range of engineering disciplines (including students studying electrical, mechanical, automotive and aerospace degrees). The new “Cruiser” class is a departure from the typical solar car concepts which are often driven entirely by aerodynamic concerns (see 2011 Sunswift car at right). Instead, teams participating in the new “Cruiser” class are aiming for the optimum combination of both speed and practicality, with the cars being required to meet all requirements for road registration in their country of origin.  To do so, the UNSW Sunswift team had to carefully refine every component on the car in order to achieve maximum overall performance, whilst maintaining a number of ‘practical’ features that every-day motorists would typically expect such as 4 wheels and at least 2 forward facing seats (and struggling with the challenges that these additional constraints bring; such as greater frontal area, frictional drag and weight). Avid readers of this blog will know that even with the most efficient solar cells and powertrain, a solar car will struggle in the World Solar Challenge if much of this power is consumed overcoming aerodynamic drag. At speeds typically exceeding 100 km/hr (and reaching up to 127 km/hr in optimum conditions during the race), the aerodynamic drag profile of these solar cars becomes the largest contributing factor to overall efficiency. Ultimately, the first “Cruiser” class car to reach Adelaide will no doubt be the design that is able to match other teams on speed during the race, whilst preserving sufficient battery power to maintain their speed through any extended cloudy periods. There is no doubt that the engineering challenges (and compromises) are significant, and it is also worth noting that university organisations, such as UNSW Sunswift, often lack the financial resources and time to conduct elaborate physical prototyping and testing. As such, simulation-driven product development strategies with ANSYS are used widely to provide detailed engineering insight into the car’s performance, allowing the team to rapidly consider tens or even...

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Turbulence Part 5 - Overview of Scale-Resolving Simulations (SRS)
Sep26

Turbulence Part 5 - Overview of Scale-Resolving Simulations (SRS)

An increasing number of industrial CFD users are recognising the need to move away from RANS modelling and resolve a greater spectrum of turbulence (particularly in cases involving large-scale separation, strongly swirling flows, acoustics, etc.). Here we present an overview of Scale Resolving Simulation techniques and important considerations when considering applying SRS to your project.

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