scholarly journals Semi‐automated Data‐driven FE Mesh Generation and Inverse Parameter Identification for a Multiscale and Multiphase Model of Function‐Perfusion Processes in the Liver

PAMM ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lena Lambers ◽  
André Mielke ◽  
Tim Ricken
Keyword(s):  
Parameter Identification ◽  
Mesh Generation ◽  
Data Driven ◽  
Multiphase Model

scholarly journals Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

2020 ◽  
Author(s):  
Ziqiang Cheng ◽  
Kevin M. Tenny ◽  
Alberto Pizzolato ◽  
Antoni Forner-Cuenca ◽  
Vittorio Verda ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Rank Correlation ◽  
Cell Voltage ◽  
Cell Polarization ◽  
Vanadium Redox Flow Battery ◽  
Data Driven ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Kinetic Rate Constant ◽  
Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

scholarly journals Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods

2020 ◽  
Vol 279 ◽  
pp. 115530
Author(s):  
Ziqiang Cheng ◽  
Kevin M. Tenny ◽  
Alberto Pizzolato ◽  
Antoni Forner-Cuenca ◽  
Vittorio Verda ◽  
...  
Keyword(s):  
Numerical Methods ◽  
Parameter Identification ◽  
Data Driven ◽  
Redox Flow Batteries ◽  
Flow Batteries ◽  
Vanadium Redox

scholarly journals Impact of Data Sampling Methods on the Performance of Data-driven Parameter Identification for Lithium ion Batteries

2021 ◽  
Vol 54 (20) ◽  
pp. 534-539
Author(s):  
Gyouho Cho ◽  
Youngki Kim ◽  
Jaerock Kwon ◽  
Wencong Su ◽  
Mengqi Wang
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Sampling Methods ◽  
Lithium Ion ◽  
Data Driven ◽  
Data Sampling

scholarly journals Data-Driven Electrode Parameter Identification for Vanadium Redox Flow Batteries Through Experimental and Numerical Methods

2020 ◽  
Author(s):  
Ziqiang Cheng ◽  
Kevin M. Tenny ◽  
Alberto Pizzolato ◽  
Antoni Forner-Cuenca ◽  
Vittorio Verda ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Rank Correlation ◽  
Cell Voltage ◽  
Cell Polarization ◽  
Vanadium Redox Flow Battery ◽  
Data Driven ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Kinetic Rate Constant ◽  
Vanadium Redox

The vanadium redox flow battery (VRFB) is a promising energy storage technology for stationary applications (e.g., renewables integration) that offers a pathway to cost-effectiveness through independent scaling of power and energy as well as longevity. Many current research efforts are focused on improving battery performance through electrode modifications, but high-throughput, laboratory-scale testing can be time- and material-intensive. Advances in multiphysics-based numerical modeling and data-driven parameter identification afford a computational platform to expand the design space by rapidly screening a diverse array of electrode configurations. Herein, a 3D VRFB model is first developed and validated against experimental results. Subsequently, a new 2D model is composed, yielding a computationally-light simulation framework, which is used to span bounded values of the electrode thickness, porosity, volumetric specific surface area, fiber diameter, and kinetic rate constant across six cell polarization voltages. This generates a dataset of 7350 electrode property combinations for each cell voltage, which is used to evaluate the effect of these structural properties on the pressure drop and current density. These structure-performance relationships are further quantified using Kendall $\tau$ rank correlation coefficients to highlight the dependence of cell performance on bulk electrode morphology and to identify improved property sets. This statistical framework may serve as a general guideline for parameter identification for more advanced electrode designs and redox flow battery (RFB) stacks.

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