Skip to main navigation
Skip to search
Skip to main content
Aston Research Explorer Home
Help & FAQ
Home
Research units
Profiles
Research Outputs
Datasets
Student theses
Activities
Press/Media
Prizes
Equipment
Search by expertise, name or affiliation
Dynamic thermal model development of direct methanol fuel cell
Mohammad Biswas, Tabbi Wilberforce
*
*
Corresponding author for this work
Mechanical, Biomedical & Design Engineering
College of Engineering and Physical Sciences
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Dynamic thermal model development of direct methanol fuel cell'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Model-driven Development
100%
Dynamic Thermal Model
100%
Direct Methanol Fuel Cell
100%
Artificial Neural Network
40%
Anode Temperature
40%
Learning Algorithm
20%
Hidden Neurons
20%
Control Approach
20%
Environmentally Friendly
20%
Coefficient of Determination
20%
Operating Conditions
20%
Characteristic Performance
20%
Carbon Dioxide
20%
Mean Square Error
20%
Artificial Neural Network Method
20%
Artificial Neural Network Model
20%
Operational Environment
20%
Cell Development
20%
Liquid Methanol
20%
Inlet Flow Rate
20%
Optimum Control
20%
Methanol Concentration
20%
Transient Characteristics
20%
Direct Methanol Fuel Cell Stack
20%
Dynamic Thermal Characteristics
20%
Engineering
Thermal Model
100%
Direct Methanol Fuel Cell
100%
Artificial Neural Network
50%
Learning Algorithm
16%
Transients
16%
Performance Characteristic
16%
Hidden Neuron
16%
Thermal Characteristic
16%
Optimal Control
16%
Operational Condition
16%
Artificial Neural Network Model
16%
Mean Square Error
16%
Fuel Cell Stack
16%
Methanol Concentration
16%
Anodes and Cathode
16%
Inlet Flowrate
16%
Liquid Methanol
16%
Material Science
Methanol Fuels
100%
Anode
33%
Cathode
33%
Carbon Dioxide
16%
Chemical Engineering
Methanol
100%
Neural Network
50%
Carbon Dioxide
12%