Published:
This article empowers you to harness the capabilities of Python scripting for performing POD analysis on 2D slice data extracted from your OpenFOAM simulations. We’ll guide you through the process of leveraging Python’s libraries to unlock hidden patterns within your data, transforming your 2D slices from raw information to a deeper understanding of the key flow dynamics at play.
Published:
Ever wondered what secrets lurk within your CFD simulations? This article delves into the power of Proper Orthogonal Decomposition (POD), a technique for extracting key flow features from OpenFOAM data. We’ll focus on a specific POD method that utilizes Python to analyze field information directly extracted from OpenFOAM time directories. Get ready to unlock hidden patterns and gain deeper insights into your fluid dynamics simulations!
Published:
Continuing our exploration of POD, we delve into the Method of Snapshots. This powerful technique analyzes fluid behavior, like flow around a cylinder, by capturing “snapshots” of the flow field at different times. With these snapshots, POD can identify key flow features and unlock hidden patterns, providing deeper insights into seemingly chaotic fluid dynamics.
Published:
Fluid flow can be a swirling mystery, but fear not! Proper Orthogonal Decomposition (POD) can help us see through the chaos. POD acts like a magnifying glass for fluid dynamics, allowing us to extract the key, recurring patterns, or “coherent modes,” hidden within complex data. This post will explain how POD works and how it empowers us to understand and predict complex flow behavior.
Published:
Unlocking the secrets within your OpenFOAM data goes beyond just post-processing numbers. Modal decomposition methods offer a powerful lens to analyze your simulations, revealing hidden patterns and dominant flow features. This guide will equip you with the knowledge to set up an OpenFOAM case specifically designed for modal decomposition analysis, empowering you to extract deeper insights from your CFD simulations.
Published:
Now that we’ve established the importance of Verification and Validation (V&V) in CFD, buckle up! Part 2 of this series takes us on a deeper dive. We’ll explore practical best practices specifically designed for CFD research. Get ready to uncover essential strategies, tools, and methodologies to ensure the accuracy and reliability of your simulations, propelling your CFD work to the next level.
Published:
Eye-catching visualizations are just the tip of the iceberg in CFD. As researchers, we need to go beyond “Colorful Fluid Dynamics” and ensure the reliability of our simulations. This guide dives into Verification and Validation (VV) – the cornerstone of building trust in your CFD results. We’ll explore the key concepts of VV and equip you to confidently answer the crucial question: “How can I trust this?”
Published:
Embarking on the journey into CFD research with a desire to master OpenFOAM often leads to the daunting question: where to start? In this blog, I’ll guide you through the crucial ‘first contact’ protocol, offering invaluable tips and curated resources to fast-track your path from novice to OpenFOAM expert.
Published:
As a CFD researcher, harnessing computational resources efficiently is paramount. In this blog post, I’ll delve into why I opt for Windows Subsystem for Linux (WSL) as a primary environment for setting up my CFD simulations and how it streamlines my workflow, offering seamless integration of Linux tools within the Windows ecosystem.
Published:
This blog post provides a brief introduction to CFD, OpenFOAM and myself!!!.
Published:
A comprehensive guide to compilation of foam-extend on Ubuntu and Windows WSL.
Published:
Mac users, rejoice! This guide eliminates the roadblocks to running OpenFOAM on your Mac. Harnessing the power of Docker, we’ll navigate the process of compiling and running OpenFOAM, opening a world of CFD possibilities on your familiar platform.
Published:
A comprehensive guide to compilation of OpenFOAM on Ubuntu and Windows WSL.
Published:
In the world of data acquisition, the need for high-resolution measurements often comes with a cost—both in terms of time and resources. Compressed sensing offers a paradigm shift by allowing us to reconstruct high-fidelity signals from a small number of measurements, challenging traditional sampling techniques. This article explores the core principles of compressed sensing and demonstrates how sparse representations can be leveraged to recover information efficiently, making it a powerful tool in fields ranging from medical imaging to signal processing.
Published:
In the world of data-driven modeling and signal processing, compressed sensing has emerged as a powerful technique for recovering signals from limited data. At the heart of this method lie two fundamental mathematical tools: the L1 and L2 norms. While they may seem abstract, their roles in promoting sparsity and ensuring stability are crucial for the success of compressed sensing algorithms. In this article, we will explore the significance of these norms, their differences, and how they contribute to efficiently reconstructing complex signals with minimal information.
Published:
Data-driven analysis has unlocked new ways to decipher complex systems, with Dynamic Mode Decomposition (DMD) becoming a cornerstone method for identifying coherent structures in data. Yet, standard DMD techniques often fall short when it comes to capturing the multi-scale characteristics common in dynamic phenomena. This is where Multi-Resolution Dynamic Mode Decomposition (MRDMD) comes in. MRDMD is a powerful extension that deconstructs data across multiple scales, revealing patterns that are otherwise hidden. In this article, lets explore how MRDMD enhances our ability to analyze complex datasets, its unique methodology, and the insights it brings to data-driven modeling.
Published:
In the world of data-driven analysis, extracting meaningful patterns from complex fluid flows is a significant challenge. Sparsity Promoting Dynamic Mode Decomposition (SPDMD) offers a powerful approach to uncovering the most dominant features while discarding redundant information, leading to a more efficient and insightful decomposition. This method blends the mathematical elegance of Dynamic Mode Decomposition (DMD) with sparse optimization techniques, providing a robust framework to identify critical structures in high-dimensional data.
Published:
Dynamic Mode Decomposition (DMD) has revolutionized the analysis of complex systems. However, its capabilities have boundaries. This article explores two key limitations of DMD: its struggle with translational and rotational invariances, and its challenges in capturing transient phenomena. Understanding these limitations helps us choose the right tool for the job and explore alternative methods when needed.
Published:
Unraveling the mysteries of fluid flow just got easier! This article explores the powerful combination of Python scripting and Dynamic Mode Decomposition (DMD). We’ll leverage Python’s capabilities to compute DMD on 2D slice data extracted from OpenFOAM simulations. By harnessing this approach, we can extract hidden patterns and gain deeper insights into fluid dynamics phenomena.
Published:
Unveiling the secrets of complex systems often requires powerful tools. Enter OpenFOAM, a popular CFD (Computational Fluid Dynamics) software, and Dynamic Mode Decomposition (DMD), a potent data analysis method. This blog post explores the exciting intersection of these two! We’ll explore how DMD can be applied to OpenFOAM simulation data, extracting hidden patterns and dynamics within fluid flows.
Published:
Fluid flows, with their mesmerizing swirls and eddies, are prime examples of complex systems. But hidden within this complexity lies order! This blog post delves into how Dynamic Mode Decomposition (DMD) can be applied to unlock the secrets of fluid flow data. We’ll explore how DMD extracts meaningful patterns, providing valuable insights for scientists and engineers.
Published:
Building upon our exploration of Dynamic Mode Decomposition (DMD), this article takes a practical turn! We’ll leverage a toy example to unveil how DMD tackles the challenge of understanding and predicting complex, nonlinear systems.
Published:
Get ready to explore the mathematical machinery behind Dynamic Mode Decomposition (DMD)! This article delves into the elegant formulations and algorithms that fuel this data-driven powerhouse. We’ll dissect the equations that unlock DMD’s ability to extract hidden patterns from complex data. By unraveling the algorithmic core, we’ll gain a deeper understanding of how DMD transforms high-dimensional chaos into interpretable insights.
Published:
The world of science is brimming with complex systems, from weather patterns to neural networks. While mountains of data exist, extracting meaningful insights remains a challenge. Enter Dynamic Mode Decomposition (DMD), a powerful tool that unlocks the secrets hidden within this data. This article delves into DMD, exploring its capabilities and how it empowers researchers across diverse fields to achieve breakthroughs.
Published:
Dynamic Mode Decomposition (DMD) has revolutionized the analysis of complex systems. However, its capabilities have boundaries. This article explores two key limitations of DMD: its struggle with translational and rotational invariances, and its challenges in capturing transient phenomena. Understanding these limitations helps us choose the right tool for the job and explore alternative methods when needed.
Published:
Unraveling the mysteries of fluid flow just got easier! This article explores the powerful combination of Python scripting and Dynamic Mode Decomposition (DMD). We’ll leverage Python’s capabilities to compute DMD on 2D slice data extracted from OpenFOAM simulations. By harnessing this approach, we can extract hidden patterns and gain deeper insights into fluid dynamics phenomena.
Published:
Unveiling the secrets of complex systems often requires powerful tools. Enter OpenFOAM, a popular CFD (Computational Fluid Dynamics) software, and Dynamic Mode Decomposition (DMD), a potent data analysis method. This blog post explores the exciting intersection of these two! We’ll explore how DMD can be applied to OpenFOAM simulation data, extracting hidden patterns and dynamics within fluid flows.
Published:
Fluid flows, with their mesmerizing swirls and eddies, are prime examples of complex systems. But hidden within this complexity lies order! This blog post delves into how Dynamic Mode Decomposition (DMD) can be applied to unlock the secrets of fluid flow data. We’ll explore how DMD extracts meaningful patterns, providing valuable insights for scientists and engineers.
Published:
Building upon our exploration of Dynamic Mode Decomposition (DMD), this article takes a practical turn! We’ll leverage a toy example to unveil how DMD tackles the challenge of understanding and predicting complex, nonlinear systems.
Published:
Get ready to explore the mathematical machinery behind Dynamic Mode Decomposition (DMD)! This article delves into the elegant formulations and algorithms that fuel this data-driven powerhouse. We’ll dissect the equations that unlock DMD’s ability to extract hidden patterns from complex data. By unraveling the algorithmic core, we’ll gain a deeper understanding of how DMD transforms high-dimensional chaos into interpretable insights.
Published:
The world of science is brimming with complex systems, from weather patterns to neural networks. While mountains of data exist, extracting meaningful insights remains a challenge. Enter Dynamic Mode Decomposition (DMD), a powerful tool that unlocks the secrets hidden within this data. This article delves into DMD, exploring its capabilities and how it empowers researchers across diverse fields to achieve breakthroughs.
Published:
Efficient data handling is a cornerstone of successful computational fluid dynamics simulations, and OpenFOAM provides powerful tools to achieve this. Among them, the writeObjects function object stands out for its ability to specify different writing frequencies for various objects registered in the simulation database. This capability allows users to tailor output schedules for volume fields and other entities, optimizing disk usage and post-processing workflows. In this blog, we will explore how configuring and utilizing writeObjects can enhance simulation efficiency. Whether you’re optimizing a large-scale simulation or managing limited resources, this guide will help you make the most of OpenFOAM’s functionality.
Published:
Simulating complex fluid flow phenomena often involves large computational domains, but the region of interest is usually much smaller. Extracting a sub-domain from OpenFOAM simulations allows for targeted analysis, reducing data processing overhead and focusing computational resources. This blog explores how to isolate and extract a sub-domain, whether to study localized flow features or to streamline data analysis workflows. We’ll cover practical steps and techniques, using OpenFOAM’s built-in tools to ensure an efficient and user-friendly process.
Published:
Efficient data handling is a cornerstone of successful computational fluid dynamics simulations, and OpenFOAM provides powerful tools to achieve this. Among them, the writeObjects function object stands out for its ability to specify different writing frequencies for various objects registered in the simulation database. This capability allows users to tailor output schedules for volume fields and other entities, optimizing disk usage and post-processing workflows. In this blog, we will explore how configuring and utilizing writeObjects can enhance simulation efficiency. Whether you’re optimizing a large-scale simulation or managing limited resources, this guide will help you make the most of OpenFOAM’s functionality.
Published:
Fluid flows, with their mesmerizing swirls and eddies, are prime examples of complex systems. But hidden within this complexity lies order! This blog post delves into how Dynamic Mode Decomposition (DMD) can be applied to unlock the secrets of fluid flow data. We’ll explore how DMD extracts meaningful patterns, providing valuable insights for scientists and engineers.
Published:
Building upon our exploration of Dynamic Mode Decomposition (DMD), this article takes a practical turn! We’ll leverage a toy example to unveil how DMD tackles the challenge of understanding and predicting complex, nonlinear systems.
Published:
Get ready to explore the mathematical machinery behind Dynamic Mode Decomposition (DMD)! This article delves into the elegant formulations and algorithms that fuel this data-driven powerhouse. We’ll dissect the equations that unlock DMD’s ability to extract hidden patterns from complex data. By unraveling the algorithmic core, we’ll gain a deeper understanding of how DMD transforms high-dimensional chaos into interpretable insights.
Published:
The world of science is brimming with complex systems, from weather patterns to neural networks. While mountains of data exist, extracting meaningful insights remains a challenge. Enter Dynamic Mode Decomposition (DMD), a powerful tool that unlocks the secrets hidden within this data. This article delves into DMD, exploring its capabilities and how it empowers researchers across diverse fields to achieve breakthroughs.
Published:
Understanding the complex, dynamic behavior of fluid flows often requires more than just time-averaged statistics. Dynamic Mode Decomposition (DMD) offers a powerful, data-driven approach to uncover the temporal evolution of coherent structures within CFD datasets. In this blog, we’ll dive into the process of applying DMD to three-dimensional flow fields simulated using OpenFOAM and visualize the resulting modes with Python and ParaView. By combining these tools, we can unravel the intricate interactions that define unsteady flows, providing insights into both dominant patterns and transient dynamics.
Published:
In fluid dynamics, understanding the intricate patterns hidden within three-dimensional flow data is crucial for advancing both research and engineering applications. Proper Orthogonal Decomposition (POD) serves as a powerful tool to extract dominant flow structures, simplifying complex dynamics into comprehensible insights. This article takes you through the step-by-step application of three-dimensional POD using OpenFOAM simulation data and Python wizardry, emphasizing visualization for enhanced interpretation. Whether you’re a CFD enthusiast or a seasoned researcher, this guide will equip you with practical skills to explore and analyze multi-dimensional fluid flows effectively.
Published:
In Computational Fluid Dynamics (CFD), understanding complex flow dynamics often requires the extraction of meaningful insights from vast simulation data. This blog dives into the essential process of gathering 3D snapshots from OpenFOAM simulations, a crucial step in enabling advanced modal decomposition techniques like SPOD and DMD. By streamlining data collection, we pave the way for uncovering the hidden structures and dynamics within turbulent flows.
Published:
In recent years, data-driven modal decomposition has become an essential tool in fluid dynamics research, offering a powerful means to distill complex flow dynamics into meaningful patterns. In this blog, I’ll introduce MODULO, a versatile software developed at the von Karman Institute for Fluid Dynamics specifically for this purpose. MODULO is designed to make sophisticated decomposition techniques, like Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), accessible and highly effective, especially when working with large datasets from computational tools like OpenFOAM.
Published:
In the world of data acquisition, the need for high-resolution measurements often comes with a cost—both in terms of time and resources. Compressed sensing offers a paradigm shift by allowing us to reconstruct high-fidelity signals from a small number of measurements, challenging traditional sampling techniques. This article explores the core principles of compressed sensing and demonstrates how sparse representations can be leveraged to recover information efficiently, making it a powerful tool in fields ranging from medical imaging to signal processing.
Published:
In the world of data-driven modeling and signal processing, compressed sensing has emerged as a powerful technique for recovering signals from limited data. At the heart of this method lie two fundamental mathematical tools: the L1 and L2 norms. While they may seem abstract, their roles in promoting sparsity and ensuring stability are crucial for the success of compressed sensing algorithms. In this article, we will explore the significance of these norms, their differences, and how they contribute to efficiently reconstructing complex signals with minimal information.
Published:
In the world of data-driven analysis, extracting meaningful patterns from complex fluid flows is a significant challenge. Sparsity Promoting Dynamic Mode Decomposition (SPDMD) offers a powerful approach to uncovering the most dominant features while discarding redundant information, leading to a more efficient and insightful decomposition. This method blends the mathematical elegance of Dynamic Mode Decomposition (DMD) with sparse optimization techniques, providing a robust framework to identify critical structures in high-dimensional data.
Published:
Singular Value Decomposition (SVD) is a powerful tool for data analysis, but its output can be overwhelming due to high dimensionality. Optimal Singular Value Hard Thresholding (OSVHT) offers a streamlined approach to address this challenge. By intelligently selecting and retaining only the most significant singular values, OSVHT effectively compresses data while preserving crucial information. This data-driven technique transforms complex datasets into more manageable representations without sacrificing essential insights.
Published:
Spectral Proper Orthogonal Decomposition (SPOD) is a powerful technique for uncovering the dominant spatial patterns within a dataset at specific frequencies. By analyzing a single frequency, we can identify the key structures responsible for the oscillations at that point in the spectrum. However, to gain a comprehensive understanding of the multi-scale nature of a flow, we must consider the entire frequency range. In this article, lets explore the complete SPOD algorithm, which explores all frequencies and their associated spatial modes, using OpenFOAM simulation data.
Published:
Understanding the theory behind Spectral Proper Orthogonal Decomposition (SPOD) is essential, but practical implementation often poses challenges. This blog provides a hands-on approach by sharing a step-by-step code example. By following this guide, readers can apply SPOD to their own datasets, gaining valuable insights into complex systems.
Published:
Proper Orthogonal Decomposition (POD) has long been a cornerstone in data-driven analysis, offering valuable insights into the dominant structures of complex systems. However, POD often falls short when dealing with systems exhibiting rich temporal dynamics. This is where Spectral Proper Orthogonal Decomposition (SPOD) shines. By incorporating frequency information into the analysis, SPOD unveils a deeper understanding of the underlying mechanisms driving system behavior. In this blog, we’ll explore how SPOD transcends the limitations of POD, unlocking new avenues for exploration and discovery.
Published:
Eigenfaces, a classic application of Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), revolutionized facial recognition in the realm of computer vision. By representing faces as linear combinations of ‘eigenfaces,’ these techniques offer a powerful method for facial feature extraction, dimensionality reduction, and pattern recognition. PS, one can appreciate the versatility of SVD and PCA across diverse fields, from facial recognition to fluid dynamics, in extracting valuable insights from complex data and enhancing our understanding of underlying structures and patterns.
Published:
The world around us may seem random and unpredictable, but even in chaos, patterns tend to emerge. This is also true for large datasets. Principal Component Analysis (PCA) is a powerful tool that can help us uncover these hidden patterns.
Published:
Delving deep into the realm of Singular Value Decomposition (SVD) and its impactful applications, join me as I unravel the versatility of SVD, from its pivotal role in image compression to its effectiveness in linear and multilinear regression. Explore how SVD uncovers hidden patterns in data, enabling informed decision-making and empowering data analysis across various domains.
Published:
Fluid simulations generate a mountain of data, but how do you extract the gems of information hidden within? Enter Singular Value Decomposition (SVD)! This powerful tool acts like a decoder ring for complex datasets. In this blog, we’ll delve into the world of SVD, exploring how it tackles challenges in fluid dynamics, data analysis, and machine learning. We’ll uncover how SVD helps researchers sift through the noise and discover the key patterns driving fluid behavior.
Published:
Mac users, rejoice! This guide eliminates the roadblocks to running OpenFOAM on your Mac. Harnessing the power of Docker, we’ll navigate the process of compiling and running OpenFOAM, opening a world of CFD possibilities on your familiar platform.
Published:
In the realm of OpenFOAM post-processing, navigating the intricacies becomes far simpler with the aid of a built-in utilities known as Function-objects. In this article, delve into the realm of Function-objects: discover what they are, how to effectively utilize them, and explore strategies for streamlining your post-processing tasks with the power of Python scripting.
Published:
Mac users, rejoice! This guide eliminates the roadblocks to running OpenFOAM on your Mac. Harnessing the power of Docker, we’ll navigate the process of compiling and running OpenFOAM, opening a world of CFD possibilities on your familiar platform.
Published:
Eigenfaces, a classic application of Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), revolutionized facial recognition in the realm of computer vision. By representing faces as linear combinations of ‘eigenfaces,’ these techniques offer a powerful method for facial feature extraction, dimensionality reduction, and pattern recognition. PS, one can appreciate the versatility of SVD and PCA across diverse fields, from facial recognition to fluid dynamics, in extracting valuable insights from complex data and enhancing our understanding of underlying structures and patterns.
Published:
The world around us may seem random and unpredictable, but even in chaos, patterns tend to emerge. This is also true for large datasets. Principal Component Analysis (PCA) is a powerful tool that can help us uncover these hidden patterns.
Published:
Delving deep into the realm of Singular Value Decomposition (SVD) and its impactful applications, join me as I unravel the versatility of SVD, from its pivotal role in image compression to its effectiveness in linear and multilinear regression. Explore how SVD uncovers hidden patterns in data, enabling informed decision-making and empowering data analysis across various domains.
Published:
Fluid simulations generate a mountain of data, but how do you extract the gems of information hidden within? Enter Singular Value Decomposition (SVD)! This powerful tool acts like a decoder ring for complex datasets. In this blog, we’ll delve into the world of SVD, exploring how it tackles challenges in fluid dynamics, data analysis, and machine learning. We’ll uncover how SVD helps researchers sift through the noise and discover the key patterns driving fluid behavior.
Published:
Understanding the complex, dynamic behavior of fluid flows often requires more than just time-averaged statistics. Dynamic Mode Decomposition (DMD) offers a powerful, data-driven approach to uncover the temporal evolution of coherent structures within CFD datasets. In this blog, we’ll dive into the process of applying DMD to three-dimensional flow fields simulated using OpenFOAM and visualize the resulting modes with Python and ParaView. By combining these tools, we can unravel the intricate interactions that define unsteady flows, providing insights into both dominant patterns and transient dynamics.
Published:
In fluid dynamics, understanding the intricate patterns hidden within three-dimensional flow data is crucial for advancing both research and engineering applications. Proper Orthogonal Decomposition (POD) serves as a powerful tool to extract dominant flow structures, simplifying complex dynamics into comprehensible insights. This article takes you through the step-by-step application of three-dimensional POD using OpenFOAM simulation data and Python wizardry, emphasizing visualization for enhanced interpretation. Whether you’re a CFD enthusiast or a seasoned researcher, this guide will equip you with practical skills to explore and analyze multi-dimensional fluid flows effectively.
Published:
In Computational Fluid Dynamics (CFD), understanding complex flow dynamics often requires the extraction of meaningful insights from vast simulation data. This blog dives into the essential process of gathering 3D snapshots from OpenFOAM simulations, a crucial step in enabling advanced modal decomposition techniques like SPOD and DMD. By streamlining data collection, we pave the way for uncovering the hidden structures and dynamics within turbulent flows.
Published:
Dynamic mesh techniques in Computational Fluid Dynamics (CFD) are pivotal for capturing fluid-structure interactions and other phenomena involving motion. In OpenFOAM, the prescribed motion method allows users to simulate complex scenarios using deformable meshes while maintaining computational efficiency. This article further explores the prescribed mesh motion technique, with a specific focus on simulating pure pitching and pure heaving motions. By the end of this guide, you’ll gain insights into implementing these motions in OpenFOAM and understanding their impact on flow dynamics.
Published:
Simulating real-world fluid dynamics problems often requires the ability to handle dynamic changes in the computational domain. OpenFOAM’s dynamic mesh capabilities provide the necessary tools for this purpose. This blog explores the fascinating concept of dynamic mesh motion in OpenFOAM, specifically the mesh deformation method using prescribed motion technique. I will showcase the case setup and provide a general overview of simulating mesh motion in OpenFOAM.
Published:
Simulating flows with moving boundaries and deforming geometries requires specialized computational techniques, and OpenFOAM offers robust tools to tackle such challenges. Dynamic meshes enable mesh motion and deformation, allowing simulations to accurately capture phenomena like valve operations, moving pistons, or deforming structures. In this blog, we will explore the foundational concepts of dynamic meshes in OpenFOAM, covering the principles of mesh motion and their application in CFD. Whether you are a beginner or looking to deepen your understanding, this article sets the stage for harnessing OpenFOAM’s dynamic meshing capabilities.
Published:
Data-driven analysis has unlocked new ways to decipher complex systems, with Dynamic Mode Decomposition (DMD) becoming a cornerstone method for identifying coherent structures in data. Yet, standard DMD techniques often fall short when it comes to capturing the multi-scale characteristics common in dynamic phenomena. This is where Multi-Resolution Dynamic Mode Decomposition (MRDMD) comes in. MRDMD is a powerful extension that deconstructs data across multiple scales, revealing patterns that are otherwise hidden. In this article, lets explore how MRDMD enhances our ability to analyze complex datasets, its unique methodology, and the insights it brings to data-driven modeling.
Published:
Singular Value Decomposition (SVD) is a powerful tool for data analysis, but its output can be overwhelming due to high dimensionality. Optimal Singular Value Hard Thresholding (OSVHT) offers a streamlined approach to address this challenge. By intelligently selecting and retaining only the most significant singular values, OSVHT effectively compresses data while preserving crucial information. This data-driven technique transforms complex datasets into more manageable representations without sacrificing essential insights.
Published:
Dynamic mesh techniques in Computational Fluid Dynamics (CFD) are pivotal for capturing fluid-structure interactions and other phenomena involving motion. In OpenFOAM, the prescribed motion method allows users to simulate complex scenarios using deformable meshes while maintaining computational efficiency. This article further explores the prescribed mesh motion technique, with a specific focus on simulating pure pitching and pure heaving motions. By the end of this guide, you’ll gain insights into implementing these motions in OpenFOAM and understanding their impact on flow dynamics.
Published:
Simulating real-world fluid dynamics problems often requires the ability to handle dynamic changes in the computational domain. OpenFOAM’s dynamic mesh capabilities provide the necessary tools for this purpose. This blog explores the fascinating concept of dynamic mesh motion in OpenFOAM, specifically the mesh deformation method using prescribed motion technique. I will showcase the case setup and provide a general overview of simulating mesh motion in OpenFOAM.
Published:
Simulating flows with moving boundaries and deforming geometries requires specialized computational techniques, and OpenFOAM offers robust tools to tackle such challenges. Dynamic meshes enable mesh motion and deformation, allowing simulations to accurately capture phenomena like valve operations, moving pistons, or deforming structures. In this blog, we will explore the foundational concepts of dynamic meshes in OpenFOAM, covering the principles of mesh motion and their application in CFD. Whether you are a beginner or looking to deepen your understanding, this article sets the stage for harnessing OpenFOAM’s dynamic meshing capabilities.
Published:
Efficient data handling is a cornerstone of successful computational fluid dynamics simulations, and OpenFOAM provides powerful tools to achieve this. Among them, the writeObjects function object stands out for its ability to specify different writing frequencies for various objects registered in the simulation database. This capability allows users to tailor output schedules for volume fields and other entities, optimizing disk usage and post-processing workflows. In this blog, we will explore how configuring and utilizing writeObjects can enhance simulation efficiency. Whether you’re optimizing a large-scale simulation or managing limited resources, this guide will help you make the most of OpenFOAM’s functionality.
Published:
Simulating complex fluid flow phenomena often involves large computational domains, but the region of interest is usually much smaller. Extracting a sub-domain from OpenFOAM simulations allows for targeted analysis, reducing data processing overhead and focusing computational resources. This blog explores how to isolate and extract a sub-domain, whether to study localized flow features or to streamline data analysis workflows. We’ll cover practical steps and techniques, using OpenFOAM’s built-in tools to ensure an efficient and user-friendly process.
Published:
Dynamic Mode Decomposition (DMD) has revolutionized the analysis of complex systems. However, its capabilities have boundaries. This article explores two key limitations of DMD: its struggle with translational and rotational invariances, and its challenges in capturing transient phenomena. Understanding these limitations helps us choose the right tool for the job and explore alternative methods when needed.
Published:
Unraveling the mysteries of fluid flow just got easier! This article explores the powerful combination of Python scripting and Dynamic Mode Decomposition (DMD). We’ll leverage Python’s capabilities to compute DMD on 2D slice data extracted from OpenFOAM simulations. By harnessing this approach, we can extract hidden patterns and gain deeper insights into fluid dynamics phenomena.
Published:
Unveiling the secrets of complex systems often requires powerful tools. Enter OpenFOAM, a popular CFD (Computational Fluid Dynamics) software, and Dynamic Mode Decomposition (DMD), a potent data analysis method. This blog post explores the exciting intersection of these two! We’ll explore how DMD can be applied to OpenFOAM simulation data, extracting hidden patterns and dynamics within fluid flows.
Published:
This article empowers you to harness the capabilities of Python scripting for performing POD analysis on 2D slice data extracted from your OpenFOAM simulations. We’ll guide you through the process of leveraging Python’s libraries to unlock hidden patterns within your data, transforming your 2D slices from raw information to a deeper understanding of the key flow dynamics at play.
Published:
Ever wondered what secrets lurk within your CFD simulations? This article delves into the power of Proper Orthogonal Decomposition (POD), a technique for extracting key flow features from OpenFOAM data. We’ll focus on a specific POD method that utilizes Python to analyze field information directly extracted from OpenFOAM time directories. Get ready to unlock hidden patterns and gain deeper insights into your fluid dynamics simulations!
Published:
Unlocking the secrets within your OpenFOAM data goes beyond just post-processing numbers. Modal decomposition methods offer a powerful lens to analyze your simulations, revealing hidden patterns and dominant flow features. This guide will equip you with the knowledge to set up an OpenFOAM case specifically designed for modal decomposition analysis, empowering you to extract deeper insights from your CFD simulations.
Published:
Embarking on the journey into CFD research with a desire to master OpenFOAM often leads to the daunting question: where to start? In this blog, I’ll guide you through the crucial ‘first contact’ protocol, offering invaluable tips and curated resources to fast-track your path from novice to OpenFOAM expert.
Published:
Mac users, rejoice! This guide eliminates the roadblocks to running OpenFOAM on your Mac. Harnessing the power of Docker, we’ll navigate the process of compiling and running OpenFOAM, opening a world of CFD possibilities on your familiar platform.
Published:
Ready to take your OpenFOAM post-processing skills to the next level? This guide completes our post-processing triology, delving into conventional post-processing: extracting and analyzing data after your simulation finishes. Unlock the power of command-line techniques and discover how to effortlessly visualize your data using Python. Learn to extract specific data points, analyze results, and create compelling visuals to gain deeper insights from your OpenFOAM simulations.
Published:
Craving deeper insights from your OpenFOAM simulations? Explore the wonders of Python post-processing with fluidfoam. Learn how to effortlessly extract and visualize key data points like force coefficients and velocity probes, turning complex data into clear and interpretable results. This guide equips you with the tools to unlock the true potential of your simulations and gain valuable knowledge from every run.
Published:
In the realm of OpenFOAM post-processing, navigating the intricacies becomes far simpler with the aid of a built-in utilities known as Function-objects. In this article, delve into the realm of Function-objects: discover what they are, how to effectively utilize them, and explore strategies for streamlining your post-processing tasks with the power of Python scripting.
Published:
Tired of slow OpenFOAM simulations?
Dive into the world of parallel computing with MPI and accelerate your cases to blazing speeds. This guide will equip you with the knowledge and tools to unlock the full potential of your multi-core machine or cluster, significantly reducing simulation times and boosting your research productivity. Get ready to witness the magic of parallel processing in OpenFOAM!
Published:
Continuing the Exploration: Advanced Simulation Setup in OpenFOAM
Published:
Introducing the complexities of setting up advanced simulations in OpenFOAM.
Published:
Guide for coupling PETSc into OpenFOAM.
Published:
Explore OpenFOAM with our step-by-step guide to getting your first simulation up and running. This easy-to-follow tutorial covers everything you need to know about setting up and running a basic OpenFOAM case. I break down the jargon into simple terms, explaining each step in both everyday language and plain fluid dynamics terms, so you can understand the ins and outs of your OpenFOAM case.
Published:
Guide for coupling PETSc into OpenFOAM.
Published:
This article empowers you to harness the capabilities of Python scripting for performing POD analysis on 2D slice data extracted from your OpenFOAM simulations. We’ll guide you through the process of leveraging Python’s libraries to unlock hidden patterns within your data, transforming your 2D slices from raw information to a deeper understanding of the key flow dynamics at play.
Published:
Ever wondered what secrets lurk within your CFD simulations? This article delves into the power of Proper Orthogonal Decomposition (POD), a technique for extracting key flow features from OpenFOAM data. We’ll focus on a specific POD method that utilizes Python to analyze field information directly extracted from OpenFOAM time directories. Get ready to unlock hidden patterns and gain deeper insights into your fluid dynamics simulations!
Published:
Continuing our exploration of POD, we delve into the Method of Snapshots. This powerful technique analyzes fluid behavior, like flow around a cylinder, by capturing “snapshots” of the flow field at different times. With these snapshots, POD can identify key flow features and unlock hidden patterns, providing deeper insights into seemingly chaotic fluid dynamics.
Published:
Fluid flow can be a swirling mystery, but fear not! Proper Orthogonal Decomposition (POD) can help us see through the chaos. POD acts like a magnifying glass for fluid dynamics, allowing us to extract the key, recurring patterns, or “coherent modes,” hidden within complex data. This post will explain how POD works and how it empowers us to understand and predict complex flow behavior.
Published:
Tired of slow OpenFOAM simulations?
Dive into the world of parallel computing with MPI and accelerate your cases to blazing speeds. This guide will equip you with the knowledge and tools to unlock the full potential of your multi-core machine or cluster, significantly reducing simulation times and boosting your research productivity. Get ready to witness the magic of parallel processing in OpenFOAM!
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Explore OpenFOAM with our step-by-step guide to getting your first simulation up and running. This easy-to-follow tutorial covers everything you need to know about setting up and running a basic OpenFOAM case. I break down the jargon into simple terms, explaining each step in both everyday language and plain fluid dynamics terms, so you can understand the ins and outs of your OpenFOAM case.
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Ready to take your OpenFOAM post-processing skills to the next level? This guide completes our post-processing triology, delving into conventional post-processing: extracting and analyzing data after your simulation finishes. Unlock the power of command-line techniques and discover how to effortlessly visualize your data using Python. Learn to extract specific data points, analyze results, and create compelling visuals to gain deeper insights from your OpenFOAM simulations.
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Craving deeper insights from your OpenFOAM simulations? Explore the wonders of Python post-processing with fluidfoam. Learn how to effortlessly extract and visualize key data points like force coefficients and velocity probes, turning complex data into clear and interpretable results. This guide equips you with the tools to unlock the true potential of your simulations and gain valuable knowledge from every run.
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Understanding the complex, dynamic behavior of fluid flows often requires more than just time-averaged statistics. Dynamic Mode Decomposition (DMD) offers a powerful, data-driven approach to uncover the temporal evolution of coherent structures within CFD datasets. In this blog, we’ll dive into the process of applying DMD to three-dimensional flow fields simulated using OpenFOAM and visualize the resulting modes with Python and ParaView. By combining these tools, we can unravel the intricate interactions that define unsteady flows, providing insights into both dominant patterns and transient dynamics.
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In fluid dynamics, understanding the intricate patterns hidden within three-dimensional flow data is crucial for advancing both research and engineering applications. Proper Orthogonal Decomposition (POD) serves as a powerful tool to extract dominant flow structures, simplifying complex dynamics into comprehensible insights. This article takes you through the step-by-step application of three-dimensional POD using OpenFOAM simulation data and Python wizardry, emphasizing visualization for enhanced interpretation. Whether you’re a CFD enthusiast or a seasoned researcher, this guide will equip you with practical skills to explore and analyze multi-dimensional fluid flows effectively.
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In Computational Fluid Dynamics (CFD), understanding complex flow dynamics often requires the extraction of meaningful insights from vast simulation data. This blog dives into the essential process of gathering 3D snapshots from OpenFOAM simulations, a crucial step in enabling advanced modal decomposition techniques like SPOD and DMD. By streamlining data collection, we pave the way for uncovering the hidden structures and dynamics within turbulent flows.
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Python has emerged as a powerful tool for Computational Fluid Dynamics (CFD) simulations due to its open-source nature, extensive libraries, and ease of use. This blog post will guide you through the essential steps to set up your Python environment for CFD, including the installation of key libraries and the configuration of necessary tools.
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In recent years, data-driven modal decomposition has become an essential tool in fluid dynamics research, offering a powerful means to distill complex flow dynamics into meaningful patterns. In this blog, I’ll introduce MODULO, a versatile software developed at the von Karman Institute for Fluid Dynamics specifically for this purpose. MODULO is designed to make sophisticated decomposition techniques, like Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), accessible and highly effective, especially when working with large datasets from computational tools like OpenFOAM.
Published:
Ready to take your OpenFOAM post-processing skills to the next level? This guide completes our post-processing triology, delving into conventional post-processing: extracting and analyzing data after your simulation finishes. Unlock the power of command-line techniques and discover how to effortlessly visualize your data using Python. Learn to extract specific data points, analyze results, and create compelling visuals to gain deeper insights from your OpenFOAM simulations.
Published:
Craving deeper insights from your OpenFOAM simulations? Explore the wonders of Python post-processing with fluidfoam. Learn how to effortlessly extract and visualize key data points like force coefficients and velocity probes, turning complex data into clear and interpretable results. This guide equips you with the tools to unlock the true potential of your simulations and gain valuable knowledge from every run.
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In the realm of OpenFOAM post-processing, navigating the intricacies becomes far simpler with the aid of a built-in utilities known as Function-objects. In this article, delve into the realm of Function-objects: discover what they are, how to effectively utilize them, and explore strategies for streamlining your post-processing tasks with the power of Python scripting.
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Spectral Proper Orthogonal Decomposition (SPOD) is a powerful technique for uncovering the dominant spatial patterns within a dataset at specific frequencies. By analyzing a single frequency, we can identify the key structures responsible for the oscillations at that point in the spectrum. However, to gain a comprehensive understanding of the multi-scale nature of a flow, we must consider the entire frequency range. In this article, lets explore the complete SPOD algorithm, which explores all frequencies and their associated spatial modes, using OpenFOAM simulation data.
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Understanding the theory behind Spectral Proper Orthogonal Decomposition (SPOD) is essential, but practical implementation often poses challenges. This blog provides a hands-on approach by sharing a step-by-step code example. By following this guide, readers can apply SPOD to their own datasets, gaining valuable insights into complex systems.
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Proper Orthogonal Decomposition (POD) has long been a cornerstone in data-driven analysis, offering valuable insights into the dominant structures of complex systems. However, POD often falls short when dealing with systems exhibiting rich temporal dynamics. This is where Spectral Proper Orthogonal Decomposition (SPOD) shines. By incorporating frequency information into the analysis, SPOD unveils a deeper understanding of the underlying mechanisms driving system behavior. In this blog, we’ll explore how SPOD transcends the limitations of POD, unlocking new avenues for exploration and discovery.
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Singular Value Decomposition (SVD) is a powerful tool for data analysis, but its output can be overwhelming due to high dimensionality. Optimal Singular Value Hard Thresholding (OSVHT) offers a streamlined approach to address this challenge. By intelligently selecting and retaining only the most significant singular values, OSVHT effectively compresses data while preserving crucial information. This data-driven technique transforms complex datasets into more manageable representations without sacrificing essential insights.
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Eigenfaces, a classic application of Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), revolutionized facial recognition in the realm of computer vision. By representing faces as linear combinations of ‘eigenfaces,’ these techniques offer a powerful method for facial feature extraction, dimensionality reduction, and pattern recognition. PS, one can appreciate the versatility of SVD and PCA across diverse fields, from facial recognition to fluid dynamics, in extracting valuable insights from complex data and enhancing our understanding of underlying structures and patterns.
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The world around us may seem random and unpredictable, but even in chaos, patterns tend to emerge. This is also true for large datasets. Principal Component Analysis (PCA) is a powerful tool that can help us uncover these hidden patterns.
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Delving deep into the realm of Singular Value Decomposition (SVD) and its impactful applications, join me as I unravel the versatility of SVD, from its pivotal role in image compression to its effectiveness in linear and multilinear regression. Explore how SVD uncovers hidden patterns in data, enabling informed decision-making and empowering data analysis across various domains.
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Fluid simulations generate a mountain of data, but how do you extract the gems of information hidden within? Enter Singular Value Decomposition (SVD)! This powerful tool acts like a decoder ring for complex datasets. In this blog, we’ll delve into the world of SVD, exploring how it tackles challenges in fluid dynamics, data analysis, and machine learning. We’ll uncover how SVD helps researchers sift through the noise and discover the key patterns driving fluid behavior.
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Python has emerged as a powerful tool for Computational Fluid Dynamics (CFD) simulations due to its open-source nature, extensive libraries, and ease of use. This blog post will guide you through the essential steps to set up your Python environment for CFD, including the installation of key libraries and the configuration of necessary tools.
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In the world of data-driven analysis, extracting meaningful patterns from complex fluid flows is a significant challenge. Sparsity Promoting Dynamic Mode Decomposition (SPDMD) offers a powerful approach to uncovering the most dominant features while discarding redundant information, leading to a more efficient and insightful decomposition. This method blends the mathematical elegance of Dynamic Mode Decomposition (DMD) with sparse optimization techniques, providing a robust framework to identify critical structures in high-dimensional data.
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Dynamic mesh techniques in Computational Fluid Dynamics (CFD) are pivotal for capturing fluid-structure interactions and other phenomena involving motion. In OpenFOAM, the prescribed motion method allows users to simulate complex scenarios using deformable meshes while maintaining computational efficiency. This article further explores the prescribed mesh motion technique, with a specific focus on simulating pure pitching and pure heaving motions. By the end of this guide, you’ll gain insights into implementing these motions in OpenFOAM and understanding their impact on flow dynamics.
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Simulating real-world fluid dynamics problems often requires the ability to handle dynamic changes in the computational domain. OpenFOAM’s dynamic mesh capabilities provide the necessary tools for this purpose. This blog explores the fascinating concept of dynamic mesh motion in OpenFOAM, specifically the mesh deformation method using prescribed motion technique. I will showcase the case setup and provide a general overview of simulating mesh motion in OpenFOAM.
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Simulating flows with moving boundaries and deforming geometries requires specialized computational techniques, and OpenFOAM offers robust tools to tackle such challenges. Dynamic meshes enable mesh motion and deformation, allowing simulations to accurately capture phenomena like valve operations, moving pistons, or deforming structures. In this blog, we will explore the foundational concepts of dynamic meshes in OpenFOAM, covering the principles of mesh motion and their application in CFD. Whether you are a beginner or looking to deepen your understanding, this article sets the stage for harnessing OpenFOAM’s dynamic meshing capabilities.
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Python has emerged as a powerful tool for Computational Fluid Dynamics (CFD) simulations due to its open-source nature, extensive libraries, and ease of use. This blog post will guide you through the essential steps to set up your Python environment for CFD, including the installation of key libraries and the configuration of necessary tools.
Published:
In recent years, data-driven modal decomposition has become an essential tool in fluid dynamics research, offering a powerful means to distill complex flow dynamics into meaningful patterns. In this blog, I’ll introduce MODULO, a versatile software developed at the von Karman Institute for Fluid Dynamics specifically for this purpose. MODULO is designed to make sophisticated decomposition techniques, like Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD), accessible and highly effective, especially when working with large datasets from computational tools like OpenFOAM.
Published:
Data-driven analysis has unlocked new ways to decipher complex systems, with Dynamic Mode Decomposition (DMD) becoming a cornerstone method for identifying coherent structures in data. Yet, standard DMD techniques often fall short when it comes to capturing the multi-scale characteristics common in dynamic phenomena. This is where Multi-Resolution Dynamic Mode Decomposition (MRDMD) comes in. MRDMD is a powerful extension that deconstructs data across multiple scales, revealing patterns that are otherwise hidden. In this article, lets explore how MRDMD enhances our ability to analyze complex datasets, its unique methodology, and the insights it brings to data-driven modeling.
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In the world of data acquisition, the need for high-resolution measurements often comes with a cost—both in terms of time and resources. Compressed sensing offers a paradigm shift by allowing us to reconstruct high-fidelity signals from a small number of measurements, challenging traditional sampling techniques. This article explores the core principles of compressed sensing and demonstrates how sparse representations can be leveraged to recover information efficiently, making it a powerful tool in fields ranging from medical imaging to signal processing.
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In the world of data-driven modeling and signal processing, compressed sensing has emerged as a powerful technique for recovering signals from limited data. At the heart of this method lie two fundamental mathematical tools: the L1 and L2 norms. While they may seem abstract, their roles in promoting sparsity and ensuring stability are crucial for the success of compressed sensing algorithms. In this article, we will explore the significance of these norms, their differences, and how they contribute to efficiently reconstructing complex signals with minimal information.
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Spectral Proper Orthogonal Decomposition (SPOD) is a powerful technique for uncovering the dominant spatial patterns within a dataset at specific frequencies. By analyzing a single frequency, we can identify the key structures responsible for the oscillations at that point in the spectrum. However, to gain a comprehensive understanding of the multi-scale nature of a flow, we must consider the entire frequency range. In this article, lets explore the complete SPOD algorithm, which explores all frequencies and their associated spatial modes, using OpenFOAM simulation data.
Published:
Understanding the theory behind Spectral Proper Orthogonal Decomposition (SPOD) is essential, but practical implementation often poses challenges. This blog provides a hands-on approach by sharing a step-by-step code example. By following this guide, readers can apply SPOD to their own datasets, gaining valuable insights into complex systems.
Published:
Proper Orthogonal Decomposition (POD) has long been a cornerstone in data-driven analysis, offering valuable insights into the dominant structures of complex systems. However, POD often falls short when dealing with systems exhibiting rich temporal dynamics. This is where Spectral Proper Orthogonal Decomposition (SPOD) shines. By incorporating frequency information into the analysis, SPOD unveils a deeper understanding of the underlying mechanisms driving system behavior. In this blog, we’ll explore how SPOD transcends the limitations of POD, unlocking new avenues for exploration and discovery.
Published:
Continuing our exploration of POD, we delve into the Method of Snapshots. This powerful technique analyzes fluid behavior, like flow around a cylinder, by capturing “snapshots” of the flow field at different times. With these snapshots, POD can identify key flow features and unlock hidden patterns, providing deeper insights into seemingly chaotic fluid dynamics.
Published:
Fluid flow can be a swirling mystery, but fear not! Proper Orthogonal Decomposition (POD) can help us see through the chaos. POD acts like a magnifying glass for fluid dynamics, allowing us to extract the key, recurring patterns, or “coherent modes,” hidden within complex data. This post will explain how POD works and how it empowers us to understand and predict complex flow behavior.
Published:
Unlocking the secrets within your OpenFOAM data goes beyond just post-processing numbers. Modal decomposition methods offer a powerful lens to analyze your simulations, revealing hidden patterns and dominant flow features. This guide will equip you with the knowledge to set up an OpenFOAM case specifically designed for modal decomposition analysis, empowering you to extract deeper insights from your CFD simulations.
Published:
Tired of slow OpenFOAM simulations?
Dive into the world of parallel computing with MPI and accelerate your cases to blazing speeds. This guide will equip you with the knowledge and tools to unlock the full potential of your multi-core machine or cluster, significantly reducing simulation times and boosting your research productivity. Get ready to witness the magic of parallel processing in OpenFOAM!
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Continuing the Exploration: Advanced Simulation Setup in OpenFOAM
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Introducing the complexities of setting up advanced simulations in OpenFOAM.
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A comprehensive guide to compilation of foam-extend on Ubuntu and Windows WSL.
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A comprehensive guide to compilation of OpenFOAM on Ubuntu and Windows WSL.
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Now that we’ve established the importance of Verification and Validation (V&V) in CFD, buckle up! Part 2 of this series takes us on a deeper dive. We’ll explore practical best practices specifically designed for CFD research. Get ready to uncover essential strategies, tools, and methodologies to ensure the accuracy and reliability of your simulations, propelling your CFD work to the next level.
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Eye-catching visualizations are just the tip of the iceberg in CFD. As researchers, we need to go beyond “Colorful Fluid Dynamics” and ensure the reliability of our simulations. This guide dives into Verification and Validation (VV) – the cornerstone of building trust in your CFD results. We’ll explore the key concepts of VV and equip you to confidently answer the crucial question: “How can I trust this?”
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Simulating complex fluid flow phenomena often involves large computational domains, but the region of interest is usually much smaller. Extracting a sub-domain from OpenFOAM simulations allows for targeted analysis, reducing data processing overhead and focusing computational resources. This blog explores how to isolate and extract a sub-domain, whether to study localized flow features or to streamline data analysis workflows. We’ll cover practical steps and techniques, using OpenFOAM’s built-in tools to ensure an efficient and user-friendly process.
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As a CFD researcher, harnessing computational resources efficiently is paramount. In this blog post, I’ll delve into why I opt for Windows Subsystem for Linux (WSL) as a primary environment for setting up my CFD simulations and how it streamlines my workflow, offering seamless integration of Linux tools within the Windows ecosystem.
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A comprehensive guide to compilation of foam-extend on Ubuntu and Windows WSL.
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A comprehensive guide to compilation of OpenFOAM on Ubuntu and Windows WSL.
Published:
Ready to take your OpenFOAM post-processing skills to the next level? This guide completes our post-processing triology, delving into conventional post-processing: extracting and analyzing data after your simulation finishes. Unlock the power of command-line techniques and discover how to effortlessly visualize your data using Python. Learn to extract specific data points, analyze results, and create compelling visuals to gain deeper insights from your OpenFOAM simulations.
Published:
A comprehensive guide to compilation of foam-extend on Ubuntu and Windows WSL.