Computational Fluid Dynamics (CFD) relies heavily on the efficiency of linear solvers based on sparse linear algebra kernels. Widely used frameworks like OpenFOAM exploit parallelism primarily at the domain decomposi-tion level via MPI. Support for vector/SIMD architectures is limited to compiler auto-vectorization. Furthermore, support for such architectures is limited by OpenFOAM’s internal matrix data format, which is intrinsically ill-suited for the contiguous memory accesses required for efficient execution on vector processors. In this work, we focused on two very different RISC-V architectures: the prototype long-vector EPAC accelerator and the commercial short-vector CPU Sophon SG2044. On these platforms, we optimized the Sparse Matrix-Vector multiplication (SpMV) using RISC-V vector intrinsics and integrated it into a custom smoother, performing a runtime conversion of internal data into a vector-friendly format. Experimental results on the EPAC test chip show a 6× speedup for the smoother; benchmarks on Monte Cimone (MCv2) cluster with the Sophon SG2044 processor achieve a 1.5× smoother speedup, proving that legacy CFD codes can be effectively accelerated on both research and commercial emerging hardware.