EXPERIMENTAL DATA DRIVEN SIMULATION OF CONVECTIVE TRANSPORT

2008 ◽  
Author(s):  
Yogesh Jaluria
Keyword(s):  
Experimental Data ◽  
Convective Transport ◽  
Data Driven

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.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2018 ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Model Development ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Intercellular Signaling ◽  
Growth Data ◽  
Metabolic Coupling

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

scholarly journals Data-Driven Methods in Fluid Dynamics: Sparse Classification from Experimental Data

2016 ◽  
pp. 323-342 ◽  
Author(s):  
Zhe Bai ◽  
Steven L. Brunton ◽  
Bingni W. Brunton ◽  
J. Nathan Kutz ◽  
Eurika Kaiser ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Experimental Data ◽  
Data Driven ◽  
Sparse Classification

Data-driven seismic response prediction of structural components

2021 ◽  
pp. 875529302110533
Author(s):  
Huan Luo ◽  
Stephanie German Paal
Keyword(s):  
Experimental Data ◽  
Seismic Response ◽  
Computational Cost ◽  
Data Driven ◽  
Computational Time ◽  
Lateral Stiffness ◽  
Hysteretic Model ◽  
Structural Components ◽  
Rc Columns ◽  
Detailed Model

Lateral stiffness of structural components, such as reinforced concrete (RC) columns, plays an important role in resisting the lateral earthquake loads. The lateral stiffness relates the lateral force to the lateral deformation, having a critical effect on the accuracy of the lateral seismic response predictions. The classical methods (e.g. fiber beam–column model) to estimate the lateral stiffness require calculations from section, element, and structural levels, which is time-consuming. Moreover, the shear deformation and bond-slip effect may also need to be included to more accurately calculate the lateral stiffness, which further increases the modeling difficulties and the computational cost. To reduce the computational time and enhance the accuracy of the predictions, this article proposes a novel data-driven method to predict the laterally seismic response based on the estimated lateral stiffness. The proposed method integrates the machine learning (ML) approach with the hysteretic model, where ML is used to compute the parameters that govern the nonlinear properties of the lateral response of target structural components directly from a training set composed of experimental data (i.e. data-driven procedure) and the hysteretic model is used to directly output the lateral stiffness based on the computed parameters and then to perform the seismic analysis. We apply the proposed method to predict the lateral seismic response of various types of RC columns subjected to cyclic loading and ground motions. We present the detailed model formulation for the application, including the developments of a modified hysteretic model, a hybrid optimization algorithm, and two data-driven seismic response solvers. The results predicted by the proposed method are compared with those obtained by classical methods with the experimental data serving as the ground truth, showing that the proposed method significantly outperforms the classical methods in both generalized prediction capabilities and computational efficiency.

scholarly journals Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jukka Intosalmi ◽  
Adrian C. Scott ◽  
Michelle Hays ◽  
Nicholas Flann ◽  
Olli Yli-Harja ◽  
...  
Keyword(s):  
Experimental Data ◽  
Time Course ◽  
Growth Dynamics ◽  
Colony Growth ◽  
Data Driven ◽  
Coarse Grained ◽  
Colony Development ◽  
Intercellular Signaling ◽  
Metabolic Coupling ◽  
The Impact

Abstract Background Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited. Results Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

scholarly journals Mechanisms of enhanced drug delivery in brain metastases with focused ultrasound-induced blood–tumor barrier disruption

2018 ◽  
Vol 115 (37) ◽  
pp. E8717-E8726 ◽  
Author(s):  
Costas D. Arvanitis ◽  
Vasileios Askoxylakis ◽  
Yutong Guo ◽  
Meenal Datta ◽  
Jonas Kloepper ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Experimental Data ◽  
Drug Delivery ◽  
Brain Metastases ◽  
Focused Ultrasound ◽  
Xenograft Model ◽  
Convective Transport ◽  
Pbpk Modeling ◽  
Breast Cancer Brain Metastasis ◽  
The Impact

Blood–brain/blood–tumor barriers (BBB and BTB) and interstitial transport may constitute major obstacles to the transport of therapeutics in brain tumors. In this study, we examined the impact of focused ultrasound (FUS) in combination with microbubbles on the transport of two relevant chemotherapy-based anticancer agents in breast cancer brain metastases at cellular resolution: doxorubicin, a nontargeted chemotherapeutic, and ado-trastuzumab emtansine (T-DM1), an antibody–drug conjugate. Using an orthotopic xenograft model of HER2-positive breast cancer brain metastasis and quantitative microscopy, we demonstrate significant increases in the extravasation of both agents (sevenfold and twofold for doxorubicin and T-DM1, respectively), and we provide evidence of increased drug penetration (>100 vs. <20 µm and 42 ± 7 vs. 12 ± 4 µm for doxorubicin and T-DM1, respectively) after the application of FUS compared with control (non-FUS). Integration of experimental data with physiologically based pharmacokinetic (PBPK) modeling of drug transport reveals that FUS in combination with microbubbles alleviates vascular barriers and enhances interstitial convective transport via an increase in hydraulic conductivity. Experimental data demonstrate that FUS in combination with microbubbles enhances significantly the endothelial cell uptake of the small chemotherapeutic agent. Quantification with PBPK modeling reveals an increase in transmembrane transport by more than two orders of magnitude. PBPK modeling indicates a selective increase in transvascular transport of doxorubicin through small vessel wall pores with a narrow range of sizes (diameter, 10–50 nm). Our work provides a quantitative framework for the optimization of FUS–drug combinations to maximize intratumoral drug delivery and facilitate the development of strategies to treat brain metastases.

Recent theoretical progress in understanding coherent structures in edge and SOL turbulence

2008 ◽  
Vol 74 (5) ◽  
pp. 679-717 ◽  
Author(s):  
S. I. KRASHENINNIKOV ◽  
D. A. D'IPPOLITO ◽  
J. R. MYRA
Keyword(s):  
Experimental Data ◽  
Convective Transport ◽  
Edge Plasma ◽  
Plasma Transport ◽  
Curvature Effects ◽  
Field Edge ◽  
Field Lines ◽  
Edge Plasmas ◽  
Non Gaussian ◽  
Main Ideas

AbstractIn this paper we review some theoretical aspects of the dynamics of the mesoscale filaments extending along the magnetic field lines in the edge plasma, which are often called ‘blobs’. We start with a brief historical survey of experimental data and the main ideas on edge and SOL plasma transport, which finally evolved into the modern paradigm of convective very-intermittent cross-field edge plasma transport. We show that both extensive analytic treatments and numerical simulations demonstrate that plasma blobs with enhanced pressure can be convected coherently towards the wall. The mechanism of convection is related to an effective gravity force (e.g. owing to magnetic curvature effects), which causes plasma polarization and a corresponding E× B convection. The impacts of different effects (e.g. X-point magnetic geometry, plasma collisionality, plasma beta, etc.) on blob dynamics are considered. Theory and simulation predict, both for current tokamaks and for ITER, blob propagation speeds and cross-field sizes to be of the order of a few hundred meters per second and a centimeter, respectively, which are in reasonable agreement with available experimental data. Moreover, the concept of blobs as a fundamental entity of convective transport in the scrape-off layer provides explanations for observed outwards convective transport, intermittency and non-Gaussian statistics in edge plasmas, and enhanced wall recycling in both toroidal and linear machines.

Sign in / Sign up

Export Citation Format

Share Document