Features of the implementation of an efficient parallel computation algorithm for modeling the icing of a swept wing with a GLC-305 airfoil

The paper considers the possibility of the ICELIB library, developed at ISP RAS, for modeling ice formation processes on the surface of aircraft. As a test example to compare the accuracy of modeling the physical processes arising during the operation of the aircraft, the surface of a swept wing with a GLC-305 profile was studied. The possibilities of an efficient parallelization algorithm using a liquid film model, a dynamic mesh, and the geometric method of bisectors are discussed. The developed ICELIB library is a collection of three solvers. The first solver iceFoam1 is intended for preliminary estimation of the icing zones of the fuselage surface and aircraft’s swept wing. The change in the geometric shape of the investigated body is neglected, the thickness of ice formation is negligible. This version of the solver has no restrictions on the number of cores when parallelizing. The second version of solver iceDyMFoam2 is designed to simulate the formation of two types of ice, smooth (“Glaze ice”) and loose (“Rime ice"), for which the shape of ice often takes on a complex and bizarre appearance. The effect of changing the shape of the body on the icing process is taken into account. The limitations are related to the peculiarities of the construction of the mesh near the boundary layer of the streamlined body. Different algorithms are used to move the front and back edges of the film, which are optimized for their cases. The performance gain is limited and is achieved with a fixed number of cores. The third version of solver iceDyMFoam3 also allows you to take into account the effect of changes in the surface of a solid during the formation of ice on the icing process itself. For the case of smooth ice formation, the latest version of the solver is still inferior in its capabilities to the second one with complex ice surface shapes. In the third version, a somewhat simplified and more uniform approach is still used to calculate the motion of both boundaries of the ice film. The estimation of the calculation results with the data of the experiment from M. Papadakis for various airfoils and swept wing for the case of “Rime ice” is carried out. Good agreement with the experimental results was obtained.

1. Кошелев К.Б., Мельникова В.Г., Стрижак С.В. Разработка решателя iceFoam для моделирования процесса обледенения. Труды ИСП РАН, том 32, вып. 4, 2020 г., стр. 217–234 / Koshelev K.B., Melnikova V.G. Strijhak S.V. Development of iceFom solver for modeling ice accretion. Trudy ISP RAN/Proc. ISP RAS, vol. 32, issue 4, 2020. pp. 217–234 (in Russian). DOI: 10.15514/ISPRAS-2020-32(4)-16.

2. Bourgault Y., Beaugendre H., Habashi W. Development of a shallow-water icing model in FENSAP-ICE. Journal of Aircraft, vol. 37, no. 4, 2000, pp. 640-646.

3. Wright W., Rutkowski A. Validation results for LEWICE 2.0. Technical report CR–1999-208690, NASA, 1999, 674 p.

4. Addy, Harold E. Ice Accretions and Icing Effects for Modern Airfoils. Technical report TP-2000-210031, NASA, 2000

5. Papadakis M., Yeong H-W. et al. Experimental investigation of ice accretion effects on a swept wing. Technical report PB2005-110681, NASA, 2005, 205 p.

6. Zocca M., Gori G., and Guardone A. Blockage and Three-Dimensional Effects in Wind-Tunnel Testing of Ice Accretion over Wings. Journal of Aircraftm, vol. 54, no. 1, 2016, pp. 759-767.

7. Pena D., Hoarau Y., Laurendeau E. A single step ice accretion model using Level-Set method. Journal of Fluids and Structures, vol. 65, 2016, pp. 278-294.

8. Chang S., Tang H. et al. Three-Dimensional Modelling and Simulation of the Ice Accretion Process on Aircraft Wings. International Journal of Astronautics and Aeronautical Engineering, vol. 3, issue 2, 2018, pp. 1-25.

9. Гергель В.П. Высокопроизводительные вычисления для многопроцессорных многоядерных систем. М., Изд-во МГУ, 2010 г., 544 стр. / Gergel V.P. High performance computing for multiprocessor multicore systems. M., Publishing house of Moscow State University, 2010, 544 p. (in Russian).

10. Гергель В.П., Сысоев А.В. Высокопроизводительные параллельные вычисления. 100 заданий для расширенного лабораторного практикума. М., ФИЗМАТЛИТ, 2018 г., 248 стр. / Gergel V.P., Sysoev A.V. High performance parallel computing. 100 tasks for an extended laboratory practice. M., FIZMATLIT, 2018, 248 p.