A physics-based and data-driven hybrid modeling method for accurately simulating complex contact phenomenon

2020 ◽  
Vol 50 (1) ◽  
pp. 97-117 ◽  
Author(s):  
Qian Liu ◽  
Jianxun Liang ◽  
Ou Ma
Keyword(s):  
Modeling Method ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Contact Phenomenon

Operation Optimization Approaches of Thermal Power Units with Big Data-Driven Hybrid Modeling

2014 ◽  
Vol 631-632 ◽  
pp. 362-366
Author(s):  
Ning Ling Wang ◽  
Yong Zhang ◽  
Long Fei Zhu ◽  
Zhi Ping Yang
Keyword(s):  
Energy Consumption ◽  
Thermal Power ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Fuzzy Rough Set ◽  
Operation Optimization ◽  
Operation Conditions ◽  
Energy Consumption Model ◽  
Practical Guidelines ◽  
Data Driven Modeling

An accurate and reliable energy-consumption model is the key to operation optimization and energy-saving diagnosis of thermal power units especially under different operation conditions and boundaries. Conventional mathematical and data-driven modeling methods were overviewed and compared in this paper. A hybrid modeling based on thermodynamic theory and fuzzy rough set (FRS) method was proposed to process the great volume of operation data and describe the energy-consumption behavior of thermal power units. On this basis, the operation optimization was performed with intelligent computation methods to derive the realizable benchmark state with the whole set of operation parameters. The resultant optimum operation state reflects the exterior factors and system behavior, taking practical guidelines for the modeling and optimization of large thermal power units.

scholarly journals Data-driven modeling reveals cell behaviors controlling self-organization during Myxococcus xanthus development

2017 ◽  
Vol 114 (23) ◽  
pp. E4592-E4601 ◽  
Author(s):  
Christopher R. Cotter ◽  
Heinz-Bernd Schüttler ◽  
Oleg A. Igoshin ◽  
Lawrence J. Shimkets
Keyword(s):  
Cell Tracking ◽  
Myxococcus Xanthus ◽  
Cell Movement ◽  
Modeling Method ◽  
Data Driven ◽  
Self Organization ◽  
Emergent Properties ◽  
Developmental Cycle ◽  
Fluorescent Cell ◽  
Cell Behaviors

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, embryogenesis, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Known for its social developmental cycle, the bacterium Myxococcus xanthus uses coordinated movement to generate three-dimensional aggregates called fruiting bodies. Despite extensive progress in identifying genes controlling fruiting body development, cell behaviors and cell–cell communication mechanisms that mediate aggregation are largely unknown. We developed an approach to examine emergent behaviors that couples fluorescent cell tracking with data-driven models. A unique feature of this approach is the ability to identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms. The fluorescent cell tracking revealed large deviations in the behavior of individual cells. Our modeling method indicated that decreased cell motility inside the aggregates, a biased walk toward aggregate centroids, and alignment among neighboring cells in a radial direction to the nearest aggregate are behaviors that enhance aggregation dynamics. Our modeling method also revealed that aggregation is generally robust to perturbations in these behaviors and identified possible compensatory mechanisms. The resulting approach of directly combining behavior quantification with data-driven simulations can be applied to more complex systems of collective cell movement without prior knowledge of the cellular machinery and behavioral cues.

scholarly journals A data-driven modeling method to analyze cardiomyocyte impedance data

2018 ◽  
Vol 93 ◽  
pp. 140
Author(s):  
Levy Batista ◽  
Thierry Bastogne ◽  
Franck Atienzar ◽  
Annie Delaunois ◽  
Jean-Pierre Valentin
Keyword(s):  
Modeling Method ◽  
Data Driven ◽  
Impedance Data ◽  
Data Driven Modeling

Data-driven adaptive modeling method for industrial processes and its application in flotation reagent control

2020 ◽  
Author(s):  
Jin Zhang ◽  
Zhaohui Tang ◽  
Yongfang Xie ◽  
Mingxi Ai ◽  
Guoyong Zhang ◽  
...  
Keyword(s):  
Modeling Method ◽  
Data Driven ◽  
Industrial Processes ◽  
Flotation Reagent ◽  
Adaptive Modeling

Hybrid Modeling Approach for Melt Pool Prediction in Laser Powder Bed Fusion Additive Manufacturing

2020 ◽  
Author(s):  
Tesfaye Moges ◽  
Zhuo Yang ◽  
Kevontrez Jones ◽  
Shaw Feng ◽  
Paul Witherell ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Hybrid Model ◽  
Hybrid Models ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Powder Bed Fusion ◽  
Cfd Model ◽  
Laser Powder Bed Fusion ◽  
Powder Bed ◽  
Melt Pool

Abstract Multi-scale, multi-physics, computational models are a promising tool to provide detailed insights to understand the process-structure-property-performance relationships in additive manufacturing (AM) processes. To take advantage of the strengths of both physics-based and data-driven models, we propose a novel, hybrid modeling framework for laser powder bed fusion (L-PBF) processes. Our unbiased, model integration method combines physics-based data and measurement data for approaching more accurate prediction of melt-pool width. Both a high-fidelity computational fluid dynamics (CFD) model and experiments utilizing optical images are used to generate a combined dataset of melt-pool widths. From this aggregated dataset, a hybrid model is developed using data-driven modeling techniques, including polynomial regression and Kriging methods. The performance of the hybrid model is evaluated by computing the average relative error and comparing it with the results of the simulations and surrogate models constructed from the original CFD model and experimental measurements. It is found that the proposed hybrid model performs better in terms of prediction accuracy and computational time. Future work includes a conceptual introduction on the use of an AM ontology to support improved model and data selection when constructing hybrid models. This study can be viewed as a significant step towards the use of hybrid models as predictive models with improved accuracy and without the sacrifice of speed.

scholarly journals Simulation on Cooperative Changeover of Production Team Using Hybrid Modeling Method

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 204
Author(s):  
Xiaodong Zhang ◽  
Yiqi Wang ◽  
Bingcun Xu
Keyword(s):  
Simulation Modeling ◽  
Simulation Method ◽  
Modeling Method ◽  
Hybrid Modeling ◽  
Test Results ◽  
Multi Agent Systems ◽  
Discrete Events ◽  
Cooperative Strategies ◽  
Multi Agent ◽  
Production Cell

In the multi-variety and small-quantity manufacturing environment, changeover operation occurs frequently, and cooperative changeover method is often used as a way to shorten the changeover time and balance the workload. However, more workers and tasks will be affected by cooperative changeover. As such, the effectiveness of the cooperative changeover is dependent on other factors, such as the scope of cooperation and the proportion of newly introduced products. For this reason, this paper proposes a hybrid modeling method to support the simulation study of the production team's cooperative changeover strategies under various environments. Firstly, a hybrid simulation modeling method consisting of multi-agent systems and discrete events is introduced. Secondly, according to the scope of cooperation, this paper puts forward four kinds of cooperative changeover strategies. This paper also describes the cooperative line-changing behavior of operators. Finally, based on the changeover strategies, the proposed simulation method is applied to a production cell. Four production scenarios are considered according to the proportion of newly introduced part. The performance of various cooperative strategies in different production scenarios is simulated, and the statistical test results show that the optimal or satisfactory strategy can be determined in each production scenario. Additionally, the effectiveness and practicability of the proposed modeling method are verified.

Sign in / Sign up

Export Citation Format

Share Document