aPriori: a Python package to process direct numerical simulations

TL;DR

aPriori is an open-source Python package designed to efficiently analyze large direct numerical simulation datasets in fluid dynamics, enabling advanced post-processing and model development on standard hardware.

Contribution

It introduces a memory-efficient, user-friendly framework that unifies various analysis workflows for turbulence and combustion simulations, improving accessibility and reproducibility.

Findings

Enables processing of large datasets on standard workstations.

Supports workflows like a priori model assessment and chemical analysis.

Reduces technical barriers for high-fidelity data analysis.

Abstract

In the field of computational fluid dynamics, direct numerical simulations generate highly detailed data for the analysis of turbulent flows by resolving all relevant physical scales. Yet their large size, complexity, and heterogeneity make systematic post-processing and data reuse increasingly challenging. Despite the growing availability of high-fidelity simulations through public repositories, extracting meaningful physical insight often requires substantial technical effort, specialized workflows, and access to high-performance computing resources. In this article we introduce \texttt{aPriori}, an open-source Python package developed to address these limitations by providing a dedicated, memory-efficient, and user-oriented framework for the analysis of direct numerical simulation data. The software enables streamlined handling of three-dimensional fields, including filtering, scale separation, gradient evaluation, thermochemical analysis, and visualization, using concise and reproducible scripts. Its pointer-based data management strategy allows very large datasets to be processed on standard workstations without excessive memory usage, significantly lowering the barrier to advanced analysis. Beyond basic post-processing, \texttt{aPriori} supports workflows central to modern turbulence and combustion research, such as a priori model assessment, data-driven closure development, and detailed chemical analyses that include computational singular perturbation. By unifying these capabilities within a coherent and extensible software architecture, \texttt{aPriori} enhances productivity, promotes reproducibility, and facilitates broader and more effective use of high-fidelity simulation data within the computational fluid dynamics community.