This paper examines the current state of machine learning and related techniques with regard to their application to propulsion applications with an emphasis on rockets. A survey of the current research literature will be provided, examining the different machine learning tools and how they are applied to propulsive applications. In addition to this review, challenges to adoption of machine learning tools will be examined both in general and in propulsion-specific contexts. The availability of high-fidelity data is a challenge of critical importance, especially in the area of propulsion-related research, as machine learning algorithms require extensive training data to be effective. Finally, a review of potential areas for expansion will also be evaluated for their suitability for machine learning and related tools. Of particular interest are the areas of generative design and physics-informed machine learning which may provide increased propulsion system performance due to alternate designs that may not have been considered and additional physical insight into the complex physics of propulsive flows. 

A Survey of Machine Learning in Rocket Propulsion Applications

Finite-rate chemistry combustion simulations require the computationally expensive direct integration of the chemical source term. In order to reduce the calculation time of such simulations, an efficient artificial neural network approach is developed. The artificial neural network consists of a classification methodology to subdivide the thermochemical state space into smaller clusters and a subsequent multi-layer perceptron for each of the clusters. Due to the subdivision approach, the multi-layer perceptron size can be kept small, which guarantees a computationally efficient artificial neural network. For the classification two different approaches are investigated, namely a self-organizing map and a k-means binary decision tree. Both methods are tested with respect to their clustering quality and their performance. Chemical reactors are used to generate training and validation data. The multi-layer perceptron prediction includes all species of the original chemical mechanism. A priori comparison with direct integration proved the ability of both methods to give accurate results even for minor species. A posteriori calculations of ignition delay are conducted over an initial temperature range of 1100 to 1700 K and an equivalence ratio range of φ = 0.7 to φ = 1.4. While species concentrations and temperature profiles are reproduced well for most of the initial conditions, the prediction quality of the artificial neural networks decreases for a few calculations starting at low temperatures. Performance benchmarks confirmed that the artificial neural network approaches are superior to direct source term integration in terms of computational costs. The benchmarks also revealed that the k-means binary decision tree-based approach is three times faster than the self-organizing map approach.

/pdf/application-of-artificial-neural-networks-for-the-simulation-41jbyjxgav.pdf

Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Timo Seitz

Author Tools

Chat about Author

Papers

Application of Artificial Neural Networks for the Simulation of a Perfectly Premixed Chemical Reactor