Divvflow Systems
ABOUT US
We specialize in Computational Fluid Dynamics (CFD), mixing, and process development. Our global clients benefit from optimized product designs and streamlined processes. Our team, with diverse industrial and academic expertise, delivers innovative and practical solutions.
Our mission
Delivering fast, innovative engineering solutions to meet your process challenges
Explore our solutions
Innovative Solutions in Oil & Gas, Petrochemicals, Pharmaceuticals and Food Processing
Optimization of Cyclone Separators
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Engineering Problem
Cyclones are extensively utilized in manufacturing, pharmaceuticals, chemicals, and HVAC systems for their high efficiency in separating particles from gas or liquid streams. Optimizing parameters such as cone aperture size, cyclone height, and vortex finder length is essential for maximizing separation efficiency and reducing pressure loss. However, traditional prototyping for cyclone design is impractical due to high costs, limited time, and difficulty in accurately measuring complex interactions.
Simulation of cylone separator
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Our Solution
Using advanced simulation tools like OpenFOAM and our in-house simulation tools, we at Divvflow Systems model the fluid dynamics and particle-particle interactions within cyclones to enhance their performance (see the video). Our approach allows us to visualize and optimize separation efficiency, reduce pressure loss, and minimize environmental impact by simulating the effects of geometric and operational factors.
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Key Takeaways
Our optimized design resulted in a significant improvement in cyclone performance, with a 20% increase in separation efficiency and a notable 15% reduction in pressure drop compared to conventional designs leading to significant cost reductions for our clients.
Modeling and Scale-up of Crystallization Processes
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Engineering Problem
Crystallization is essential for the separation and purification of fine chemicals and pharmaceuticals, playing a critical role in drug substance manufacturing. The performance of downstream processes, bioavailability, and shelf life of the final products are intimately linked to the crystallization step. However, the scale-up of crystallization processes can be particularly challenging due to the interplay of crystallization kinetics, thermodynamics, and mixing (see figure below). Mixing often becomes a limiting step, especially during scale-up.
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Our Approach
At Divvflow Systems, we leverage our extensive experience in crystallization process development including experimentation, population balance modeling, and CFD simulations to develop efficient Quality by Design (QbD) and Quality by Control (QbC) strategies for our clients. These strategies ensure that critical quality attributes such as crystal size distribution (CSD), morphology, polymorphic form, and chemical purity are met at all scales, from lab to industrial production. Our approach guarantees consistent product quality and process reliability throughout the crystallization scale-up process.
Key parameters influencing crystallization scale-up
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Key Takeaways
Our comprehensive approach led to decreased costs and ensured reliable process performance at large scales. Additionally, we significantly reduced the process development timelines for our clients.
Particle trajectory in a stirred tank crystallizer
Optimization of Fluidized Beds
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Engineering Problem
Fluidized beds are essential in various industrial operations, including chemical, petrochemical, metallurgical, and energy production, due to their excellent solid mixing and near-isothermal conditions. Efficient design and optimization of fluidized beds are crucial for effective operation, maximized production rates, and high product quality.
We recently tackled a problem that involved developing efficient designs and strategies for the disposal of hazardous materials with the potential for detonation. This complex problem presented several key engineering challenges:
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Safety and containment to prevent detonation during disposal.
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Heat and mass transfer efficiency to ensure complete decomposition of the hazardous materials.
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Robust process design to maintain stable operation under varying feedstock and operating conditions.
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Our Approach
At Divvflow Systems, we tackled these challenges using advanced simulation tools and innovative engineering solutions. Due to inherent safety concerns, experimentation was infeasible. Instead, we focused on developing rigorous first-principles models. The accuracy of these models was critical, particularly in developing multiphase models to identify optimal reactor geometry and operating conditions.
Bubbling fluidized bed with rectangular geometry
Fluidization in an optimized geometry
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Key Takeaways
Utilizing our experience in model development and advanced simulation tools, we designed a safe and efficient fluidized bed reactor for hazardous materials disposal, overcoming significant safety and operational challenges.
Turbulence Modeling for Industrial Applications
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Scientific Challenge
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Our Approach
Turbulence is generally desirable in many engineering applications and results in increased diffusion and energy dissipation. Current computer technologies limit the feasibility of direct numerical simulations (DNS) of the Navier-Stokes equations for turbulent fluid flow problems, making Reynolds Averaged Navier-Stokes (RANS) models the most popular despite their numerous shortcomings. The development of accurate reduced order models for turbulent fluid flows remains complex due to the difficulty of accurately capturing the wide range of scales and interactions present. As a result, efforts are focused on refining RANS models and exploring alternative approaches like Large Eddy Simulations (LES) to enhance the accuracy and reliability of turbulence predictions in various engineering applications.
At Divvflow Systems, we have developed and tested LES models, which offer significant advantages including improved accuracy, better resolution of turbulence, enhanced capability for complex flows, deeper insights into flow physics, and versatile applications across various engineering problems.
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Key Takeaways
When compared with conventional RANS models (top video) used in popular CFD packages, our newly developed LES model (bottom) accurately captures the intricate details of turbulent flow structures. The LES model provides a dynamic representation of turbulence flows. This results in enhanced predictive capabilities, particularly for complex and unsteady flow conditions where conventional RANS models fail.
