Density and Electrical Conductivity for Aqueous Mixtures of Monoethylene Glycol and Sodium Chloride: Experimental Data and Data-Driven Modeling for Composition Determination

2021 ◽  
Author(s):  
Mário H. Moura-Neto ◽  
Mateus F. Monteiro ◽  
Fedra A. V. Ferreira ◽  
Dannielle J. Silva ◽  
Camila S. Figueiredo ◽  
...  
Keyword(s):  
Experimental Data ◽  
Electrical Conductivity ◽  
Sodium Chloride ◽  
Data Driven ◽  
Aqueous Mixtures ◽  
Composition Determination ◽  
Data Driven Modeling ◽  
Monoethylene Glycol

scholarly journals Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

2020 ◽  
Author(s):  
He Tan ◽  
Vladimir Tarasov ◽  
Vasileios Fourlakidis ◽  
Attila Dioszegi
Keyword(s):  
Experimental Data ◽  
Fuzzy Logic ◽  
Cast Iron ◽  
Materials Science ◽  
Fatigue Behavior ◽  
Relevant Literature ◽  
Data Driven ◽  
Fuzzy Logic Systems ◽  
Data Driven Approach ◽  
Data Driven Modeling

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

Electrical Conductivity of Supercooled Aqueous Mixtures of Trehalose with Sodium Chloride

2000 ◽  
Vol 104 (44) ◽  
pp. 10419-10425 ◽  
Author(s):  
Danforth P. Miller ◽  
Paul B. Conrad ◽  
Silvana Fucito ◽  
Horacio R. Corti ◽  
Juan J. de Pablo
Keyword(s):  
Electrical Conductivity ◽  
Sodium Chloride ◽  
Aqueous Mixtures

Rheology of Ice Slurry in Circular Pipe at Different Freezing-Point Depressants

2020 ◽  
Vol 28 (01) ◽  
pp. 2050002
Author(s):  
Fajri Ashfi Rayhan ◽  
Agus Sunjarianto Pamitran ◽  
Yanuar
Keyword(s):  
Experimental Data ◽  
Sodium Chloride ◽  
Mass Fraction ◽  
Freezing Point ◽  
Circular Pipe ◽  
Viscosity Data ◽  
Ice Slurry ◽  
Initial Concentration ◽  
Mass Fractions ◽  
Monoethylene Glycol

The utilization of ice slurry as a pumpable phase-change material has been getting a lot of attention in research and development discourses. This study attempted to investigate the rheological characteristics of ice slurry at different freezing-point depressants through experiments. Ice slurry was formed by mixing water and different freezing-point depressants (monoethylene glycol, ethanol, and sodium chloride) at a 20% initial concentration. Rheology tests were conducted on the transition from laminar to turbulent flows in a circular pipe. The inner dimension of the pipe was 12.7[Formula: see text]mm in diameter, while the ice mass fraction varied in between 0–28% depending on storage time of ice slurry. Experimental results showed sodium chloride ice slurry to have a higher pressure drop and friction factor compared to those of monoethylene glycol and ethanol ice slurry ones at the same initial concentration and ice mass fraction level. In general, ice slurry was discovered to behave as a Newtonian fluid at 10–15% ice mass fractions, as a shear thinning fluid at 15–20% ice mass fractions, and a shear thickening fluid at 20–28% ice mass fractions. Later, experimental data of shear stress were compared to Ostwald–deWaele and Herschel–Bulkley models based on Mellari method. In fact, a modified Herschel–Bulkley model for monoethylene glycol ice slurry showed a close agreement with experimental data with 4.7% mean deviation. In addition, the experimental viscosity data were compared to the Einstein, Jeffrey, Kunitz, Guth, Steimour, Vand, Mooney, Simha, Happel, Ford, Thomas dan Morio–Ototake models. The best fit was only produced with Morio model for values at low ice mass fraction ([Formula: see text]%).

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

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