We’re currently in an era witnessing a surge in every technology, propelled by advancements in computation resources. The potential of technology-driven systems is skyrocketing because of the acceleration delivered by data centers. Various engineering disciplines, such as computational fluid dynamics and automotive systems, are harnessing new-age technologies like accelerated computing and artificial intelligence to address increasingly complex challenges.
Computational Fluid Dynamics (CFD) is one domain that extensively employs GPU servers for speeding up simulation throughput. Though various CFD accelerators have been around for years, in recent years, GPUs have altogether revolutionized the field of fluid engineering. Let’s read more about it.
What is Computational Fluid Dynamics?
Beginning with a primer first. Fluid mechanics is a scientific discipline dedicated to studying the behavior of fluids and how external forces influence their motion. One of its subsets is fluid dynamics which is exclusively concerned with understanding how fluids (liquids and gases) move.
Computer simulations have played crucial roles in enabling these inter-related fields factor in fluid dynamics applications across industries such as oil and gas exploration and extraction, automotive and aerodynamics, wind turbines, air conditioning systems, etc. Vis-a-vis conventional testing systems, CFD has been instrumental in helping enterprises and research organizations reduce the time and money spent on designing and engineering.
What are the Applications of Computational Fluid Dynamics?
CFD software simulate diverse aspects of multi-physics systems involving fluids and their thermodynamic properties. These software produce visual simulations and heatmaps. Various industries have begun leveraging CFD for visualizing the effects of various forces on fluids and for calculating the numerical characteristics of fluid flow. Examples include –
- Aerodynamics: The most widespread employer of CFD, the aerospace sector uses CFD software suites for simulations, assessment of the effects of laminar-turbulent transitions, and calculations pertaining to aircraft drag, stall angle and lift. s
- Automotive: Another sector making heavy use of CFD simulations, the automotive sector uses it to tackle challenges such as combustion, propulsion, turbulence, acoustic noise modeling, water particle tracking, and flow interactions with structural mechanics. Simulating these concerns within the software and resolving them prior to structural fabrication saves time and money.
- HVAC systems: Heating, Ventilation and Air conditioning systems (HVAC) are among the most overlooked fixtures of our daily lives. Malls, offices, indoor stadiums and congregation halls use HVAC pumps. Air conditioning across large spaces also requires fluid dynamics analysis. CFD software help perform rigorous testing to facilitate effective air circulation in line with IAQ standards.
- Thermal management in electronics: Electronic equipment, semiconductor processors, and even the laptop/ phone you’re reading this blog on require an inbuilt heat management system. CFD software assist in developing effective approaches for managing vents, heatsinks, thermal components, fans, or their combinations within devices and machinery.
What are the Challenges associated with Computing Fluid Dynamics?
Despite widespread utilization of CFD across various domains, several challenges persist that must be addressed –
- Measuring internal fluid flow: Accurately measuring the flow of fluids within structures is a complex proposition, especially when additional physical variables such as external forces and thermodynamics are involved. Calculating such multi-disciplinary equations with traditional computing systems can lead to inaccuracies, cost overruns and exorbitant increase in time to market.
- Delays in prototyping and design: Enterprises’ dependency on manual testing and associated turnaround times can lead to delays in fixing issues and deploying commercial projects.
- Geometrical miscalculations: Geometry plays a critical role in fluid dynamics, but accurately factoring it in via simulations can be challenging. This is because fluid dynamics geometry operates in real-time, and delays in calculations can result in changes in operating parameters and thus, misalignment with the results gathered.
Optimizing Computational Fluid Dynamics Simulations with GPUs
CFD research and commercial utilization today is bottlenecked primarily by the daunting complexity of calculations and the humongous timeline required to factor in the numerous variables and reach a cost-efficient resolution. This also has the propensity to rocket the computation and prototyping costs involved to the stratosphere!
For instance, the automotive industry, while testing out new vehicle design, cannot exclusively focus on aerodynamic design, but must also incorporate the effect of resulting airflow and acoustics on the passengers seated within.
GPU acceleration can provide the seamless performance required for complex calculations simulating free flow of fluids. CFD engineers are increasingly opting for robust GPU-based systems to augment the various visualization software they use to generate fluid simulation scenarios, such as turbulent or transonic flows. There are several advantages of doing so –
1. Reduced hardware cost:
CFD simulations are computation resource-intensive, thereby posing one of two challenges for traditional CPU-based systems – (a) increased hardware costs to accommodate the scale of calculations, or (b) unacceptably high development time.
Many fluid dynamics simulations use the Incompressible Navier-Stokes Equation (INSE) or the more advanced Lattice Boltzmann Method (LBM) which are challenging to solve with low-performance processors. Furthermore, many simulations and CFD software suites use rich visualizations that CPUs inherently take a long time to render.
The incorporation of GPU computing has allowed researchers to utilize parallel computing and resolve fluid dynamics problems within realistic simulations rather than depending on prototypes and extrapolating from thereon. GPUs, such as Nvidia’s A100, have set benchmarks in optimizing these resource-intensive simulations, delivering superior performance with consistent accuracy.
Researchers at simulation enterprise Ansys reveal that using CPUs for CFD leads to 7x increase in high-end processing costs! GPUs thus guarantee a better performance-to-price ratio while remaining within lower Total Cost of Ownership (TCO). This is motivating engineers across domains to migrate their fluid dynamics projects from CPUs to GPU-oriented servers.
2. Enhanced accuracy:
As a scientific principle, CFD demand precision. Utilizing mixed computing systems (CPU+GPU) allows the CPU to focus on computation load distribution and other operations while the GPU takes over the calculations using its dedicated cores optimized for performing floating-point operations with greater accuracy. This minimizes the likelihood of project failure or cost overruns.
3. Improved performance:
Time is of the essence in any business or research endeavor. Enterprises employing CFD software suites conduct high-end simulations that are resource intensive. Running these simulations with GPUs not only enhances optimization potential but also reduces the time-to-market manifolds.
It has been demonstrated that a simulation that takes an entire day on a 100x CPU server can be completed in approx. one hour on a single-node server equipped with 8x Nvidia A100 GPUs!
4. Diminished power expenditure:
Legacy CPU systems consume substantial power vis-a-vis GPUs delivering equivalent computational thoroughput. supplies. Transitioning to GPU-accelerated systems or HPC clusters minimize power consumption through optimized hardware. Plus, these need to operate for significantly less time, thereby further reducing the power consumption. The afore-mentioned Ansys research report highlights that enterprises spend 4x more on power expenditure when they use CPUs for CFD instead of GPUs.
5. GPU-acceleration libraries:
GPU manufacturers like Nvidia and AMD are prioritizing the development of an ecosystem of optimized tools, frameworks, and software suites to complement their hardware. Several CFD-specific acceleration frameworks are now available in the market including Prometec Particleworks, FUN3D, Dassault’s Simulia XFlow, and Cascade Technologies’ CharLES. Many of these tools make available multiple variations of the same benchmark such as INSE.
Conclusion
GPUs are revolutionizing the research space across domains, and CFD is no exception. GPU-accelerated CFD simulations offer superior performance at affordable cost, making these highly lucrative for enterprises. For CFD projects involving study of internal fluid flow, aerodynamics, fluid/ gas mass transfer and thermodynamics, GPUs are highly recommended. Besides the enhanced efficiency and the reduction in time-to-market, the reduction in environmental footprint makes one rest easy knowing that their ESG compliances are also in check!
Want to learn how AceCloud can support your CFD workloads? Connect with our consultant at+91-789-789-0752 today.
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