Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Meta-Model to Predict the Drag Coefficient of a Particle Translating in Viscoelastic Fluids: A Machine Learning Approach.

ABSTRACT: This study presents a framework based on Machine Learning (ML) models to predict the drag coefficient of a spherical particle translating in viscoelastic fluids. For the purpose of training and testing the ML models, two datasets were generated using direct numerical simulations (DNSs) for the viscoelastic unbounded flow of Oldroyd-B (OB-set containing 12,120 data points) and Giesekus (GI-set containing 4950 data points) fluids past a spherical particle. The kinematic input features were selected to be Reynolds number, 0

SUBMITTER: Faroughi SA

PROVIDER: S-EPMC8838701 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

A Meta-Model to Predict the Drag Coefficient of a Particle Translating in Viscoelastic Fluids: A Machine Learning Approach.

Faroughi Salah A SA Roriz Ana I AI Fernandes Célio C

Polymers 20220121 3

This study presents a framework based on Machine Learning (ML) models to predict the drag coefficient of a spherical particle translating in viscoelastic fluids. For the purpose of training and testing the ML models, two datasets were generated using direct numerical simulations (DNSs) for the viscoelastic unbounded flow of Oldroyd-B (OB-set containing 12,120 data points) and Giesekus (GI-set containing 4950 data points) fluids past a spherical particle. The kinematic input feature ...[more]

PMID: 35160419

Similar Datasets

Multiplex particle focusing via hydrodynamic force in viscoelastic fluids.

Project description:We introduce a multiplex particle focusing phenomenon that arises from the hydrodynamic interaction between the viscoelastic force and the Dean drag force in a microfluidic device. In a confined microchannel, the first normal stress difference of viscoelastic fluids results in a lateral migration of suspended particles. Such a viscoelastic force was harnessed to focus different sized particles in the middle of a microchannel, and spiral channel geometry was also considered in order to take advantage of the counteracting force, Dean drag force that induces particle migration in the outward direction. For theoretical understanding, we performed a numerical analysis of viscoelastic fluids in the spiral microfluidic channel. From these results, a concept of the 'Dean-coupled Elasto-inertial Focusing band (DEF)' was proposed. This study provides in-depth physical insight into the multiplex focusing of particles that can open a new venue for microfluidic particle dynamics for a concrete high throughput platform at microscale.

| S-EPMC3832872 | biostudies-literature

Dynamic control of particle separation in deterministic lateral displacement separator with viscoelastic fluids.

Project description:We proposed an innovative method to achieve dynamic control of particle separation by employing viscoelastic fluids in deterministic lateral displacement (DLD) arrays. The effects of shear-thinning and elasticity of working fluids on the critical separation size in DLD arrays are investigated. It is observed that each effect can lead to the variation of the critical separation size by approximately 40%. Since the elasticity strength of the fluid is related to the shear rate, the dynamic control can for the first time be easily realized through tuning the flow rate in microchannels.

| S-EPMC5827740 | biostudies-literature

Machine learning approach to predict body weight in adults.

Project description:BackgroundObesity is an established risk factor for non-communicable diseases such as type 2 diabetes mellitus, hypertension and cardiovascular disease. Thus, weight control is a key factor in the prevention of non-communicable diseases. A simple and quick method to predict weight change over a few years could be helpful for weight management in clinical settings.MethodsWe examined the ability of a machine learning model that we constructed to predict changes in future body weight over 3 years using big data. Input in the machine learning model were three-year data on 50,000 Japanese persons (32,977 men) aged 19-91 years who underwent annual health examinations. The predictive formulas that used heterogeneous mixture learning technology (HMLT) to predict body weight in the subsequent 3 years were validated for 5,000 persons. The root mean square error (RMSE) was used to evaluate accuracy compared with multiple regression.ResultsThe machine learning model utilizing HMLT automatically generated five predictive formulas. The influence of lifestyle on body weight was found to be large in people with a high body mass index (BMI) at baseline (BMI ≥29.93 kg/m2) and in young people (<24 years) with a low BMI (BMI <23.44 kg/m2). The RMSE was 1.914 in the validation set which reflects ability comparable to that of the multiple regression model of 1.890 (p = 0.323).ConclusionThe HMLT-based machine learning model could successfully predict weight change over 3 years. Our model could automatically identify groups whose lifestyle profoundly impacted weight loss and factors the influenced body weight change in individuals. Although this model must be validated in other populations, including other ethnic groups, before being widely implemented in global clinical settings, results suggested that this machine learning model could contribute to individualized weight management.

| S-EPMC10308016 | biostudies-literature

A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-01-26 | GSE161019 | GEO

Machine learning models to predict onset of dementia: A label learning approach.

Project description:IntroductionThe study objective was to build a machine learning model to predict incident mild cognitive impairment, Alzheimer's Disease, and related dementias from structured data using administrative and electronic health record sources.MethodsA cohort of patients (n = 121,907) and controls (n = 5,307,045) was created for modeling using data within 2 years of patient's incident diagnosis date. Additional cohorts 3-8 years removed from index data are used for prediction. Training cohorts were matched on age, gender, index year, and utilization, and fit with a gradient boosting machine, lightGBM.ResultsIncident 2-year model quality on a held-out test set had a sensitivity of 47% and area-under-the-curve of 87%. In the 3-year model, the learned labels achieved 24% (71%), which dropped to 15% (72%) in year 8.DiscussionThe ability of the model to discriminate incident cases of dementia implies that it can be a worthwhile tool to screen patients for trial recruitment and patient management.

| S-EPMC6920083 | biostudies-literature

Nondestructive Estimation of Hazelnut (Corylus avellana L.) Terminal Velocity and Drag Coefficient Based on Some Fruit Physical Properties Using Machine Learning Algorithms.

Project description:Hazelnut culture originated in Turkey, which has the highest volume and area of hazelnut production in the world. For the design and sizing of equipment and structures in agricultural operations for the hazelnut industry, especially harvesting operations and post-harvest operations, it is essential that an understanding of hazelnuts' aerodynamic properties, i.e., terminal velocity and drag coefficient, is acquired. In this study, the moisture, mass, density, projected area, surface area, and geometric diameter were used as independent variables in the data set, and the dependent variables terminal velocity and drag coefficient estimation were determined. In this study, logistic regression (LR), support vector regression (SVR), and artificial neural networks (ANNs) were used based on machine learning methods. When the results were evaluated according to R2 (determination coefficient), MSE (mean squared error), and MAE (mean absolute error) metrics, it was seen that the most successful models were the ANN, SVR, and LR, respectively. According to the R2 metric, the ANN method achieved 91.5% for the terminal velocity of hazelnuts and 85.9% for the drag coefficient of hazelnuts. Using the independent variables in the study, it was seen that the terminal velocity and drag coefficient value of hazelnuts could be successfully estimated.

| S-EPMC10417351 | biostudies-literature

Machine learning approach to predict medication overuse in migraine patients.

Project description:Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. Thus, to understand the benefits of ML in MO prediction, we explored an automated predictor to estimate MO risk in migraine. To achieve this objective, a study was designed to analyze the performance of a customized ML-based decision support system that combines support vector machines and Random Optimization (RO-MO). We used RO-MO to extract prognostic information from demographic, clinical and biochemical data. Using a dataset of 777 consecutive migraine patients we derived a set of predictors with discriminatory power for MO higher than that observed for baseline SVM. The best four were incorporated into the final RO-MO decision support system and risk evaluation on a five-level stratification was performed. ROC analysis resulted in a c-statistic of 0.83 with a sensitivity and specificity of 0.69 and 0.87, respectively, and an accuracy of 0.87 when MO was predicted by at least three RO-MO models. Logistic regression analysis confirmed that the derived RO-MO system could effectively predict MO with ORs of 5.7 and 21.0 for patients classified as probably (3 predictors positive), or definitely at risk of MO (4 predictors positive), respectively. In conclusion, a combination of ML and RO - taking into consideration clinical/biochemical features, drug exposure and lifestyle - might represent a valuable approach to MO prediction in migraine and holds the potential for improving model precision through weighting the relative importance of attributes.

| S-EPMC7327028 | biostudies-literature

A machine learning approach to predict in vivo skin growth.

Project description:Since their invention, tissue expanders, which are designed to trigger additional skin growth, have revolutionised many reconstructive surgeries. Currently, however, the sole quantitative method to assess skin growth requires skin excision. Thus, in the context of patient outcomes, a machine learning method which uses non-invasive measurements to predict in vivo skin growth and other skin properties, holds significant value. In this study, the finite element method was used to simulate a typical skin expansion protocol and to perform various simulated wave propagation experiments during the first few days of expansion on 1,000 individual virtual subjects. An artificial neural network trained on this dataset was shown to be capable of predicting the future skin growth at 7 days (avg. R2=0.9353 ) as well as the subject-specific shear modulus ( R2=0.9801 ), growth rate ( R2=0.8649 ), and natural pre-stretch ( R2=0.9783 ) with a very high degree of accuracy. The method presented here has implications for the real-time prediction of patient-specific skin expansion outcomes and could facilitate the development of patient-specific protocols.

| S-EPMC11286771 | biostudies-literature

An unexpected particle oscillation for electrophoresis in viscoelastic fluids through a microchannel constriction.

Project description:Electrophoresis plays an important role in many applications, which, however, has so far been extensively studied in Newtonian fluids only. This work presents the first experimental investigation of particle electrophoresis in viscoelastic polyethylene oxide (PEO) solutions through a microchannel constriction under pure DC electric fields. An oscillatory particle motion is observed in the constriction region, which is distinctly different from the particle behavior in a polymer-free Newtonian fluid. This stream-wise particle oscillation continues until a sufficient number of particles form a chain to pass through the constriction completely. It is speculated that such an unexpected particle oscillating phenomenon is a consequence of the competition between electrokinetic force and viscoelastic force induced in the constriction. The electric field magnitude, particle size, and PEO concentration are all found to positively affect this viscoelasticity-related particle oscillation due to their respective influences on the two forces.

| S-EPMC3987105 | biostudies-other

Sheathless Microflow Cytometry Using Viscoelastic Fluids.

Project description:Microflow cytometry is a powerful technique for characterization of particles suspended in a solution. In this work, we present a microflow cytometer based on viscoelastic focusing. 3D single-line focusing of microparticles was achieved in a straight capillary using viscoelastic focusing which alleviated the need for sheath flow or any other actuation mechanism. Optical detection was performed by fiber coupled light source and photodetectors. Using this system, we present the detection of microparticles suspended in three different viscoelastic solutions. The rheological properties of the solutions were measured and used to assess the focusing performance both analytically and numerically. The results were verified experimentally, and it has been shown that polyethlyene oxide (PEO) and hyaluronic acid (HA) based sheathless microflow cytometer demonstrates similar performance to state-of-the art flow cytometers. The sheathless microflow cytometer was shown to present 780 particles/s throughput and 5.8% CV for the forward scatter signal for HA-based focusing. The presented system is composed of a single capillary to accommodate the fluid and optical fibers to couple the light to the fluid of interest. Thanks to its simplicity, the system has the potential to widen the applicability of microflow cytometers.

| S-EPMC5617843 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data