Towards Industrial Adoption of High-Order Methods Towards Industrial Adoption of High-Order Methods PyFR Symposium 19 th June 2020 Mark Allan 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods About Zenotech Meet the team Founded in 2012 Based in the Bristol Business Park, Bristol, UK Background in industrialisation of Computational Engineering research for Aerospace Oliver David James Sharpe Jamil Appa Mike Turner Mark Allan Darbyshire Standingford Lead CFD Engineer Director Director Product Lead, EPIC Product Lead, Acoustics Lead, Big Data & Security 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods Software Products Delivering simulation at scale • High performance computational fluid • Scalable, secure & simple HPC on demand dynamics solver • Connect with multiple providers, find the • Best in class algorithms and numerics for resource that matches your requirements accuracy and industrial performance • Pay-per-use model, no capital expenditure • Energy efficiency and high turnaround • Manage computing clusters, jobs and data using the latest in manycore hardware https://epic.zenotech.com/ https://zcfd.zenotech.com/ 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Overview • zCFD solver features • CFD Applications • CAA Applications • Summary • Aim • Provide an industrial perspective on the use of HO methods 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • zCFD solver features • High fidelity, density based, compressible FV-CFD solver on CPU/GPU • MG, local time-stepping, low Mach preconditioning, dual time-stepping, RANS turbulence modelling, LES, DES • Arbitrary polyhedral • Pressure based incompressible solver under development • High fidelity, density based, compressible DG-CFD solver on CPU/GPU • PMG, local time-stepping, low Mach preconditioning, dual time-stepping, shock capturing, 2-eqn turbulence modelling via SST k-ln( ⍵ ), LES, DES • Known cell types + hanging faces • Benefited from working with PyFR team (Innovate UK Project - Hyperflux) • DG-CAA solver on CPU/GPU • APE-4 equations • Stochastic noise source generator • Flow properties and turbulence statistics based on RANS • Runs on host CPU 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • CFD Applications • Automotive example • Using HO, can we maintain a ”similar” level of accuracy but reduce simulation time with coarse grids and higher spatial order? • Built environment (building) example • Using HO, turbulence modelling and GPU, can we observe extreme events for an ”affordable” cost? 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Automotive example 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Automotive example • Generic wing mirror Re = 520000, M = 0.11, 4M / 17M solution points + PMG + dual time stepping SST-DES model “The sub-critical flow past a generic side mirror and it’s impact on sound generation and propagation”, Ask. J, and Davidson, L., AIAA-2006-2558, May 2006. 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Automotive example • Generic wing mirror 1 2 3 Approximately equivalent number of core hours (CPU) Cost to converge inner dual time-stepping iterations prohibitive 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Built environment (building) example • Aim of study to assess potential of high order methods to simulate extreme events at affordable cost • Utilise DES modelling for high Reynolds number • GPU + high order to reduce run time 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. • Model dimensions: 1 m x 0.3m x 2m (1:50 scale) • Experiment 𝑣 #$% ~ 10 m/s @ 1m height • Building chord length, 𝑀 #$% = 1 m • Reynolds number, 𝑆𝑓 $-. ~ 800,000 • Varying wind incidence A B D C From L. Amerio, PhD Thesis, 2017 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. • Spikes Cp ~ -10 to -15 observed in top corner of leeward side for oblique angle of incidence • Hypothesis is that these may be due to a vortical structure that originates off the top of the model A From L. Amerio, PhD Thesis, 2017 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. Key: Inlet – inlet profile Building at 20°incidence Outlet – farfield pressure Building – no slip Ground – symmetry plane Side walls – free slip NB. Top hidden – free slip 4 m 2 m 1 m 4.5 Trendline Error Trendline Error Trendline Error 4.0 3.5 3.0 20 m 14 m 2.5 H (m) 2.0 Flow direction 1.5 1.0 0.5 0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 u/u ref Experiment CFD 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. Blue edges: mesh Red edges: high-order solution points e.g. P4 • Structured mesh • Near wall refinement 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. • Simulation details • DGX-1 system with 8x Tesla V100 GPUs • SST k-ln( ⍵ ) DES model • Dual time-stepping (dt = 1.0E-04s), PMG, local time-stepping • 30M solution points (P2) • 160 hours to simulate ~0.7s • Resolved issue with inter GPU data transfer (~100 hours) 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. 𝑣 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods “Affordable modelling of complex extreme events in the built environment using GPU accelerated CFD”, Saeed, T., et. Al., GTC 2020. • Validation – Tile A Reduction in PSD amplitude potentially due to RANS wall modelling at corner Insufficient amount of time simulated to adequately resolve spectrum / observe extreme events 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Conclusions – HO CFD for low speed flows • In terms of spatial accuracy, HO far more efficient • Time-marching limitations reducing competitiveness of method • Challenges - HO CFD for low speed flows • For many engineers accuracy is secondary to turnaround time / cost • Strong competition from LBM! • Fast • Easy meshing 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • Computational Aero-Acoustics • Nose landing gear example • Using HO and stochastic source modelling, can we predict broadband turbulence generated noise from a complex geometry? 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods URANS DES LES RANS FRPM APE DG Solver Farassat 1A Radiated noise 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • FRPM • Generates a divergence free turbulent velocity field by convecting a random white noise field through the domain following mean flow streamlines • Discrete random field is achieved by “particles” with random values (1 in 2D, 3 in 3D) • Required statistics achieved by filtering the convecting random field to give a desired correlation ; 58 ⃗ #59 : 6 5 6 ; • 𝑠, 𝜐 = 4 6 7 𝑓 <= 7 𝑆 ⃗ 𝑆𝑓 We obtain a streamfunction from which we can derive turbulent velocities via ψ 𝑦, 𝑢 = A 𝐵𝐻 𝑦 − 𝑧 𝑉 𝑧, 𝑢 𝑒𝑧 B 3. Ewert, R., Dierke, J., Siebert, J., Neifeld, A., Appel, C., Siefert, M., and Kornow, O., “CAA broadband noise prediction for aeroacoustic design,” Journal of Sound and Vibration, Vol. 330, No. 17, 2011, pp. 4139–4160. 19 th June 2020 Presenter Mark Allan [ Product Lead ]
Towards Industrial Adoption of High-Order Methods • PDCC-NLG • M = 0.166, Wheel diameter = 0.1397m • zCFD Mesh • 30 M cells • Menter SST • 3.5 hours on 24 x V100 • CAA • 26.4M solution points (4 th order spatial accuracy) • < 1.2 hours on 24 x V100 19 th June 2020 Presenter Mark Allan [ Product Lead ]
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