The pressure for Water Resource Recovery Facilities (WRRF) operators to efficiently treat wastewater is greater than ever because of the water crisis, produced by the climate change effects and more restrictive regulations. Technicians and researchers need to evaluate WRRF performance to ensure maximum efficiency. For this purpose, numerical techniques, such as CFD, have been widely applied to the wastewater sector to model biological reactors and secondary settling tanks with high spatial and temporal accuracy. However, limitations such as complexity and learning curve prevent extending CFD usage among wastewater modeling experts. This paper presents HydroSludge, a framework that provides a series of tools that simplify the implementation of the processes and workflows in a WRRF. This work leverages HydroSludge to preprocess existing data, aid the meshing process, and perform CFD simulations. Its intuitive interface proves itself as an effective tool to increase the efficiency of wastewater treatment. Practitioner points This paper introduces a software platform specifically oriented to WRRF, named HydroSludge, which provides easy access to the most widespread and leading CFD simulation software, OpenFOAM. Hydrosludge is intended to be used by WRRF operators, bringing a more wizard‐like, automatic, and intuitive usage. Meshing assistance, submersible mixers, biological models, and distributed parallel computing are the most remarkable features included in HydroSludge. With the provided study cases, HydroSludge has proven to be a crucial tool for operators, managers, and researchers in WRRF.

Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential equations (PDEs). Since calculating these iterative equations is highly both computationally demanding and time-consuming, data-driven methods leverage artificial intelligence (AI) techniques to alleviate that workload. Data-driven methods have to be trained in advance to provide their subsequent fast predictions; however, the cost of the training stage is non-negligible. This article presents a predictive model for inferencing future states of a specific fluid simulation that serves as a use case for evaluating different training alternatives. Particularly, this study compares the performance of only CPU, multi-GPU, and distributed approaches for training a time series forecasting deep learning model. With some slight code adaptations, results show and compare, in different implementations, the benefits of distributed GPU-enabled training for predicting high-accuracy states in a fraction of the time needed by the computational fluid dynamics solver.

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The quality of a mesh can determine the accuracy of a Computational Fluid Dynamics (CFD) simulation. In fact, meshing is not only a user highly time-consuming endeavor but also demands a lot of computational power. The need for powerful and useful tools for meshing can have a real impact on productivity and the final result. In this paper, a customizable platform as a service for meshing, named Evoker, is presented and evaluated to assist users to work over different types of geometries and accelerate the generation of meshes. Evoker is a zero-installation tool with a web Graphical User Interface (Web-GUI), which cloud-server runs OpenFOAM in order to provide a friendly interface to its meshing utilities. Evoker also manages cloud computing resources to distribute the mesh generation among different processors. Through the presented use case, Evoker demonstrates to be a versatile meshing solution that can help to save a lot of time for their users.