Research
Software
BBIPED
CFD Industrial Platform for Engineering Design
News! BBIPED platform beta has been released !. Download
If you have windows, you can still try it by means of VirtualBox. Here, you can download a virtualbox with everything ready. Try it! BBIPED-VirtualBox
BBIPED is an engineering simulation software for fluid dynamics industrial applications. BBIPED can handle a wide range of engineering applications in sectors such as turbomachinery, aeronautics, renewable energy, biosciences, advanced manufacturing, automotive, water and heat management; providing ad-hoc solutions for specific industry needs.
The BBIPED Graphical User Interface allows an automated CFD (Computational Fluid Dynamics) simulation process from CAD generation, meshing, solving and post-processing.
The idea of developing this platform was proposed by BCAM and Baltogar, who joined efforts to overcome current industrial challenges in turbomachinery design by means of extensive research. Thus, BBIPED is the result of a fruitful collaboration between science and industry. This work has been developed within the research project "Development of an efficient, flexible and innovative CFD Computational Platform to optimally simulate and design industrial products and processes", co-funded by the Department of Economic Promotion of the Biscay Foral Council and the Department of Education, Language Policy and Culture of the Basque Government.
BBIPED Key Features
- A friendly user GUI (easily customizable on demand) is provided to better manage the CFD workflow process from CAD, to solving and post-processing
- Ad-hoc solutions to fit industrial needs keeping the same interface, reducing engineers learning curve
- Flexible easy to use for engineers, showing only what they need (customizable configuration variables views)
BBIPED Extra Features
- Highly customizable eXtended SU2 (XSU2) library for future developments
- Multi-zone computation
- Multiple rotating reference frame: using multizone or virtual zone approaches
- Improved boundary conditions for internal flows.
- Customized CAD parametrization for automatic optimization
- CAD-free flow simulation
BBIPED Research Roadmap
- Multiphysics
- Proper Orthogonal Decomposition (POD) and Genetic Algorithms (GA) for optimization
- New turbulent models
- Medical Applications
Our partner
We would like to thanks the effort and support of our main partner in this project, Baltogar
Co-funded by
Acknowledgments
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LBM-HPC
LBM-HPC is a CFD tool optimized for distributed memory architectures. Based on Lattice-Boltzmann method and the so-called pull approach, LBM-HPC is parallelized using a hybrid MPI/OpenMP approach with parallel I/O.
The most common LBM standards for bidimensional (D2Q9) and tridimensional (D3Q19) simulations are included into the tool.
The implementation is portable and has been verified to work on several different supercomputers such as MareNostrum-BSC, Lindgren-PDC (Cray XE6) and Hornet-HLRS (Cray XC40) systems.
npROCRegression: Kernel-Based Nonparametric ROC Regression Modelling
Implements several nonparametric regression approaches for the inclusion of covariate information on the receiver operating characteristic (ROC) framework.
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PROreg: Patient Reported Outcomes Regression Analysis
Offers a variety of tools, such as specific plots and regression model approaches, for analyzing different patient reported questionnaires. Especially, mixed-effects models based on the beta-binomial distribution are implemented to deal with binomial data with over-dispersion (see Najera-Zuloaga J., Lee D.-J. and Arostegui I. (2017).
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SpATS: Spatial Analysis of Field Trials with Splines
Allows for the use of two-dimensional (2D) penalised splines (P-splines) in the context of agricultural field trials. Traditionally, the modelling of the spatial or environmental effect in the expression of phenotypes has been done assuming correlated random noise (Gilmour et al, 1997). We, however, propose to model the spatial variation explicitly using 2D P-splines (Rodriguez-Alvarez et al., 2016; arXiv:1607.08255). Besides the existence of fast and stable algorithms for estimation (Rodriguez-Alvarez et al., 2015; Lee et al., 2013), the direct and nice interpretation of the spatial trend that this approach provides makes it attractive for the analysis of field experiments.
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