Thermo-physical properties prediction of carbon-based magnetic nanofluids based on an artificial neural network
Nanostructured magnetic suspensions have superior thermophysical properties, which have
attracted widespread attention owing to their industrial applications for heat transfer …
attracted widespread attention owing to their industrial applications for heat transfer …
[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
[HTML][HTML] Emission quantification via passive infrared optical gas imaging: A review
Passive infrared optical gas imaging (IOGI) is sensitive to toxic or greenhouse gases of
interest, offers non-invasive remote sensing, and provides the capability for spatially …
interest, offers non-invasive remote sensing, and provides the capability for spatially …
Laser ultrasonics and machine learning for automatic defect detection in metallic components
This paper develops an automatic and reliable nondestructive evaluation (NDE) technique
that enables quantification of the width and depth of subsurface defects of metallic …
that enables quantification of the width and depth of subsurface defects of metallic …
Deep neural network inversion for 3D laser absorption imaging of methane in reacting flows
Mid-infrared laser absorption imaging of methane in flames is performed with a learning-
based approach to the limited view-angle inversion problem. A deep neural network is …
based approach to the limited view-angle inversion problem. A deep neural network is …
An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion
Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization
control and environmental protection. This study proposes a novel ensemble deep learning …
control and environmental protection. This study proposes a novel ensemble deep learning …
Machine learning based technique for outlier detection and result prediction in combustion diagnostics
With the utilization of data correlation, this work proposed a novel machine learning-based
technique for outlier detection and result prediction to cover the deficiency and economize …
technique for outlier detection and result prediction to cover the deficiency and economize …
U-Net applied to retrieve two-dimensional temperature and CO2 concentration fields of laminar diffusion flames
Direct absorption spectroscopy measurement of the 4. 3 μ m CO 2 using interband cascade
laser with a high spectral resolution, provides theoretical feasibility to retrieve the spatial …
laser with a high spectral resolution, provides theoretical feasibility to retrieve the spatial …
Efficient two-dimensional scalar fields reconstruction of laminar flames from infrared hyperspectral measurements with a machine learning approach
The latest hyperspectral measurements of combustion flames by Rhoby et al.(2014)
provided extensive spatially and spectrally resolved information of flame radiation, which …
provided extensive spatially and spectrally resolved information of flame radiation, which …
Use of machine learning with temporal photoluminescence signals from cdte quantum dots for temperature measurement in microfluidic devices
C Lewis, JW Erikson, DA Sanchez… - ACS applied nano …, 2020 - ACS Publications
Because of the vital role of temperature in many biological processes studied in microfluidic
devices, there is a need to develop improved temperature sensors and data analysis …
devices, there is a need to develop improved temperature sensors and data analysis …