TGSF-DA

Motivation

‍Interstellar turbulence, coupled with self-gravity, governs how dense pre-stellar cores emerge within molecular clouds - but understanding this process remains one of the most challenging problems in astrophysics. The difficulty arises from the enormous range of scales and the complex interplay of forces involved, making accurate simulation and analysis technically demanding. Traditional Smoothed Particle Hydrodynamics (SPH) methods struggle in this domain: they introduce excessive dissipation, fail to resolve subtle turbulent structures, and do not correctly capture energy cascades in subsonic regimes. Although recent methodological and computational advances in SPH-EXA have enabled the largest and most physically accurate simulations to date, they now produce an unprecedented volume of data. Without dedicated tools for data extraction, performance analysis, and scientific interpretation, much of the potential insight remains inaccessible. The TGSF-DA project is motivated by the need to bridge this gap - turning cutting-edge simulation outputs into a deeper physical understanding of star formation and turbulence.

Proposed Approach / Solution

‍TGSF-DA builds on the extreme-scale simulations produced within the SPH-EXA ecosystem, which model interstellar turbulence and self-gravity with unmatched resolution. This project focuses on transforming that raw data into actionable scientific knowledge through advanced and scalable data-analysis workflows. Key objectives include: (1) characterizing how pre-stellar cores form and determining their initial mass function in relation to local and global turbulence properties; (2) investigating turbulent mixing to clarify the origin of chemical homogeneity observed in stellar clusters; (3) advancing the broader theory of turbulence by computing Lagrangian Lyapunov exponents using the intrinsic advantages of SPH; (4) analyzing performance traces from high-end computing runs to identify causes of load imbalance, inefficient resource use, and energy waste; and (5) enabling efficient visualization and in-situ analysis on modern heterogeneous architectures. Through these steps, the project tightly integrates astrophysics, turbulence theory, data science, and high-performance computing.

Impact

‍The TGSF-DA project will generate unique scientific outcomes because no other effort has simulated turbulent, self-gravitating environments at comparable scale, nor collected such extensive performance and energy-consumption data. The resulting discoveries will reshape our understanding of how stars form, how turbulence behaves in extreme astrophysical conditions, and how large-scale HPC simulations can be optimized for sustainability. These insights will benefit diverse fields including cosmology, computational fluid dynamics, and exascale computing. Reducing computational overhead directly translates into lower carbon emissions from future simulation campaigns.