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A Straightforward Random Walk Model for Fast Push-Pull Tracer Test Evaluation.

Stephan Klotzsch, Martin Binder1, Falk Händel1,2

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We developed a fast random walk model for evaluating push-pull tracer tests. This adaptive algorithm significantly reduces computational time compared to traditional methods like MT3DMS.

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Computational Modeling

Background:

  • Push-pull tracer tests are crucial for characterizing subsurface solute transport.
  • Accurate simulation of these tests often requires significant computational resources.
  • Existing models may necessitate manual parameterization, increasing complexity.

Purpose of the Study:

  • To present a novel, straightforward random walk model for rapid evaluation of push-pull tracer tests.
  • To introduce an adaptive algorithm that eliminates the need for manual particle number definition.
  • To validate the model's accuracy against established methods and real-world scenarios.

Main Methods:

  • Development of a random walk particle-tracking algorithm.
  • Implementation of an adaptive approach for automatic particle number determination.
  • Validation using a push-pull tracer test incorporating a drift phase.
  • Comparison of results and computational efficiency with MT3DMS.

Main Results:

  • The random walk model provides accurate evaluations of push-pull tracer tests.
  • The adaptive algorithm successfully automates particle number selection.
  • The model achieved validation against MT3DMS for a test with a drift phase.
  • Computational time for the random walk model was less than 1% of MT3DMS.

Conclusions:

  • The proposed random walk model offers a significantly faster alternative for push-pull tracer test analysis.
  • The adaptive algorithm enhances usability by removing manual parameterization.
  • This method holds potential for efficient site characterization and contaminant transport studies.