3 years ago

Inference of Gas-liquid Flowrate using Neural Networks. (arXiv:2003.08182v1 [physics.flu-dyn])

Akshay J. Dave (1), Annalisa Manera (2) ((1) Massachusetts Institute of Technology, (2) University of Michigan Ann-Arbor)
The metering of gas-liquid flows is difficult due to the non-linear relationship between flow regimes and fluid properties, flow orientation, channel geometry, etc. In fact, a majority of commercial multiphase flow meters have a low accuracy, limited range of operation or require a physical separation of the phases. We introduce the inference of gas-liquid flowrates using a neural network model that is trained by wire-mesh sensor (WMS) experimental data. The WMS is an experimental tool that records high-resolution high-frequency 3D void fraction distributions in gas-liquid flows. The experimental database utilized spans over two orders of superficial velocity magnitude and multiple flow regimes for a vertical small-diameter pipe. Our findings indicate that a single network can provide accurate and precise inference with below a 7.5% MAP error across all flow regimes. The best performing networks have a combination of a 3D-Convolution head, and an LSTM tail. The finding indicates that the spatiotemporal features observed in gas-liquid flows can be systematically decomposed and used for inferring phase-wise flowrate. Our method does not involve any complex pre-processing of the void fraction matrices, resulting in an evaluation time that is negligible when contrasted to the input time-span. The efficiency of the model manifests in a response time two orders of magnitude lower than the current state-of-the-art.

Publisher URL: http://arxiv.org/abs/2003.08182

DOI: arXiv:2003.08182v1

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.