Tianfang Xie
Chemical Engineering · Purdue University West Lafayette
Publications
10
Citations
73
Est. group size
—
Recurring co-author estimate
Active years
12
Publishing since 2013
Tianfang Xie works on combustion science and engine performance, studying how fuels like ammonia, hydrogen, and dimethyl ether burn and ignite under engine-relevant conditions, and how to model these combustion processes mathematically. Some recent work also applies machine learning to predict engine power output and to generate computer interfaces, suggesting a secondary interest in applying data-driven methods beyond combustion. This research is relevant to students interested in cleaner or alternative fuels for engines and computational modeling of combustion physics.
Publication output was minimal or absent for much of the past decade but increased sharply in 2024 after a slow, sporadic pace in earlier years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Investigating the Effect of Ammonia Addition on the Performance of a Heavy-Duty Natural Gas Spark Ignition Engine Operated at Stoichiometric Conditions
Journal of energy resources technology. · 2024
- Characteristics of Fuel-Borne Nitrogen Pollutants in Hydrogen–Ammonia Mixtures Using Argon–Oxygen Atmosphere Under Engine Conditions
Journal of energy resources technology. · 2024
- Stage-Wise Kinetic Analysis of Ammonia Addition Effects on Two-Stage Ignition in Dimethyl Ether
Journal of energy resources technology. · 2024
- Opposed jet burner and Tsuji burner for representative laminar flamelet with differential molecular diffusion for non-premixed combustion modeling
Combustion and Flame · 2024
- Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
2024
- Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
arXiv (Cornell University) · 2024
- Stoichiometry preservation and generalization of Bilger mixture fraction for non-premixed combustion with differential molecular diffusion
arXiv (Cornell University) · 2023
- The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines
Energies · 2022
- A priori analysis of a power-law mixing model for transported PDF model based on high Karlovitz turbulent premixed DNS flames
Proceedings of the Combustion Institute · 2020
- Journal of energy resources technology.×3
- arXiv (Cornell University)×2
- Energies×1
- Combustion and Flame×1
- Proceedings of the Combustion Institute×1
- Tianyu Gai
Chemical Engineering · Purdue University West Lafayette
- Christopher M. Atkinson
Chemical Engineering · The Ohio State University
- Gregory M. Shaver
Chemical Engineering · Purdue University West Lafayette
- Alex Alberts
Chemical Engineering · Purdue University West Lafayette
- Greg Shaver
Chemical Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile