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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Multiscale Materials Modeling in an Industrial Environment.

Horst Weiß1, Peter Deglmann1, Pieter J In 't Veld1

  • 1BASF SE - Materials and Systems Research, Materials Modeling Group, 67056 Ludwigshafen, Germany; email: horst.weiss@basf.com , peter.deglmann@basf.com , pieter.intveld@basf.com , murat.cetinkaya@basf.com , eduard.schreiner@basf.com.

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|March 2, 2016
PubMed
Summary
This summary is machine-generated.

Predictive and fast materials modeling is crucial for the chemical industry. Stable, automated workflows for complex systems, especially soft matter, significantly improve new material development efficiency.

Keywords:
COSMO-RSMaterials Genome Initiativedissipative particle dynamicsformulation polymerspredictive modelingsoft matter modeling

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Chemical Engineering

Background:

  • The chemical industry relies on efficient research and development (R&D) for new materials.
  • Predictive and rapid modeling are essential for competitive R&D.
  • Handling complex systems requires robust and automated computational workflows.

Purpose of the Study:

  • To review the industrial materials modeling process.
  • To highlight the importance of predictive and fast modeling.
  • To discuss approaches for building and parameterizing soft matter systems.

Main Methods:

  • Review of existing materials modeling techniques in industry.
  • Focus on approaches for soft matter system construction and parameterization.
  • Integration of computational modeling with experimental validation.

Main Results:

  • Predictive and fast modeling is a prerequisite for R&D success in the chemical industry.
  • Stable, highly automated workflows are necessary for complex systems.
  • Improved efficiency in new material development, particularly in polymer formulation, is achievable.

Conclusions:

  • Intelligent combination of existing modeling techniques enhances product development.
  • Materials modeling, when integrated with experimental work, offers significant value.
  • Alignment with initiatives like the Materials Genome Initiative is beneficial.