Computing weak-strong uniqueness of a Mach 2000 astrophysical jet
For researchers in computational fluid dynamics and astrophysics, this work demonstrates that statistical solutions can provide a stable framework for simulating extreme compressible flows where deterministic solutions are non-unique.
This paper provides the first numerical verification of weak-strong uniqueness for a Mach 2000 astrophysical jet, showing that while individual flow realizations diverge due to instabilities, the statistical solution converges to a well-defined limit measure at a rate of 0.5 in the 1-point Wasserstein distance.
The simulation of extreme Mach astrophysical flows is traditionally viewed through the lens of deterministic positivity-preserving schemes. However, due to phenomena such as Kelvin--Helmholtz instabilities and shock anomalies, the multi-dimensional Euler equations admit a plethora of non-unique entropy solutions in turbulent regimes. For the first time, we computationally explore the weak-strong uniqueness of a Mach 2000 jet by defining the statistical solution as the pushforward of a probability measure through a vectorial lattice Boltzmann method (VLBM) operator. Utilizing highly optimized CUDA kernels, we compute an ensemble of 1000 Monte Carlo samples across a sequence of unprecedentedly refined spatial grids of up to 3.2 million cells, and subsequently post-process the empirical measures via memory-mapped CPU streaming. We contrast the strong sample-wise $L^1$ error divergence with the convergence of the probability measure in the 1-point Wasserstein distance via empirical Cauchy rates. Our mathematical results demonstrate that while individual flow realizations physically diverge due to chaotic shear-layer instabilities, the macroscopic statistical solution converges to a well-defined limit measure at a rate of 0.5. Conclusively, we provide the first numerical verification of statistical solution stability in the extreme compressible regime.