Engineering topics
Luo, Gang
Publications and source records attributed to Luo, Gang.
Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog
Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.
Design and Synthesis of Phenylpyrrolidine Phenylglycinamides As Highly Potent and Selective TF-FVIIa Inhibitors
Abstract not provided
Nonbenzamidine acylsulfonamide tissue factor–factor VIIa inhibitors
Aminoisoquinoline and isoquinoline groups have successfully replaced the more basic P1 benzamidine group of an acylsulfonamide factor VIIa inhibitor. Inhibitory activity was optimized by the identification of additional hydrophobic and hydrophilic P' binding interactions. The molecular details of these interactions were elucidated by X-ray crystallography and molecular modeling. We also show that decreasing the basicity of the P1 group results in improved oral bioavailability in this chemotype.
Simulation and validation of liquid water transport in fuel cells from neutron imaging experiments.
Abstract Not Provided
A Three-Dimensional Two-Phase Model for Simulating PEM Fuel Cell Performance.
Abstract Not Provided
Validation of PEMFC Computer Models Using Segmented Current and Temperature Data.
Abstract not provided.
VALIDATION AND UNCERTAINTY QUANTIFICATION OF A TWO-PHASE MULTIDIMENSIONAL PEMFC COMPUTER MODEL USING HIGH-RESOLUTION SEGMENTED CURRENT COLLECTOR DATA.
Abstract not provided.
Systematic parameter estimation and sensitivity analysis using a multidimensional PEMFC model coupled with Dakota.
Abstract Not Provided
Toward developing a computational capability for PEM* fuel cell design and optimization.
Abstract Not Provided
Systematic parameter estimation and sensitivity analysis using a multidimensional PEMFC model coupled with DAKOTA.
Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated in order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.
Toward developing a computational capability for PEM fuel cell design and optimization.
In this paper, we report the progress made in our project recently funded by the US Department of Energy (DOE) toward developing a computational capability, which includes a two-phase, three-dimensional PEM (polymer electrolyte membrane) fuel cell model and its coupling with DAKOTA (a design and optimization toolkit developed and being enhanced by Sandia National Laboratories). We first present a brief literature survey in which the prominent/notable PEM fuel cell models developed by various researchers or groups are reviewed. Next, we describe the two-phase, three-dimensional PEM fuel cell model being developed, tested, and later validated by experimental data. Results from case studies are presented to illustrate the utility of our comprehensive, integrated cell model. The coupling between the PEM fuel cell model and DAKOTA is briefly discussed. Our efforts in this DOE-funded project are focused on developing a validated computational capability that can be employed for PEM fuel cell design and optimization.
11 micrometer emissivities and droplet radii for marine stratocumulus
The results of a new multispectral infrared retrieval scheme for obtaining fractional cloud cover and 11 micrometer emissivity with those of the spatial coherence method which obtains fractional cloud cover assuming that the clouds are opaque at infrared wavelengths. Both methods are applied to 4-km NOAA advanced very high resolution radiometer global area coverage data for 250-km-scale regions containing single-layered marine stratocumulus off the coast of South America. The average 11 micrometer emissivity for low level clouds is found to be between 0.70 and 0.85. The low emissivity is evidently due to the thinning of clouds at their edges. Semitransparent cloud edges evidently make up a substantial portion of the area covered by such clouds. This result indicates that cloud cover obtained using the spatial coherence method is underestimated by 0.1 to 0.2, as has been claimed in a previous study. The fractional cloud cover for the ensemble of 250-km-scale regions studied here increased slightly from 0.60 for daytime observations to 0.63 for nighttime observations. The 11 micrometer emissivity also increased slightly, but about half of the increase was related to the increase in cloud cover and a decrease in the relative area covered by cloud edge material. Presumably, the other half was due to an increase in cloud liquid water. Cloud height showed no significant change. The average effective droplet radius increased from 9.3 micrometers for daytime observations to 10.2 micrometers at night.