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Geosciences, Multidisciplinary
Yashar Mehmani et al.
Summary: Shales will play a key role in the transition to renewable energy but face challenges due to their nanoporous structure and extreme heterogeneity. Challenges include understanding fluid flow and phase behavior in shales, and the lack of scale separation for reliable physics descriptions. Advances in computational power, imaging technology, and machine learning are helping to address these challenges through scale translation methods.
EARTH-SCIENCE REVIEWS
(2021)
Article
Thermodynamics
Fabian Jirasek et al.
Summary: This article discusses the role of machine learning in research on thermophysical properties, providing an overview of developments and new directions in the field. By linking the perspectives of chemical engineers and computer scientists, the discussion highlights the importance of merging physical modeling with machine learning to create hybrid approaches for future advancements.
FLUID PHASE EQUILIBRIA
(2021)
Review
Chemistry, Physical
Humphrey Adun et al.
Summary: This paper focuses on the specific heat capacity of hybrid nanofluids and presents the synthesis, characteristics, and stability of these fluids. The study shows that volume concentration and temperature are crucial factors influencing the specific heat capacity of hybrid nanofluids.
JOURNAL OF MOLECULAR LIQUIDS
(2021)
Review
Energy & Fuels
Okorie E. Agwu et al.
Summary: Drilling mud is a mixture used for cleaning drilled wells, and a robust mud rheological model is needed to improve the drilling process. This study synthesizes existing research on oil well drilling mud rheological properties, critiquing current models and suggesting future research directions. While various models have been developed, there is limited critical evaluation and literature review of these models, making this study novel and contributing to the understanding of mud rheology.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Review
Thermodynamics
Mohammad Hossein Ahmadi et al.
Summary: Research shows that using intelligent models can effectively model heat pipes and accurately estimate their thermal behavior. The accuracy and applicability of the models depend on various factors, such as input variables, algorithms, and model structures. In the future, data-driven approaches should be promoted in heat pipe modeling and optimization methods should be applied to enhance the accuracy of the models.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2021)
Review
Engineering, Environmental
Augustine Uhunoma Osarogiagbon et al.
Summary: The study investigates the current status of using supervised machine learning in hazardous drilling events, identifying artificial neural network as the most popular algorithm among researchers, with deep learning, random forest and support vector machine gaining momentum in recent use. A critical review of literature on hazardous events and supervised machine learning algorithms reveals insights into their applications, successes, limitations, and impact of input parameters. The review emphasizes the importance of publicly accessible large databases for developing machine learning algorithms to enhance drilling activities.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Review
Engineering, Electrical & Electronic
Sangam Srikanth et al.
Summary: The droplet based microfluidics (DBMF) combined with Machine Learning (ML) enables efficient micro-reactions, reliable detection and screening in various fields, and analysis and applications of large droplet datasets.
SENSORS AND ACTUATORS A-PHYSICAL
(2021)
Article
Green & Sustainable Science & Technology
Basma Souayeh et al.
Summary: The circular tube with novel corrugated spring tape inserts shows potential for enhancing heat transfer performance, significantly impacting heat duty and pumping power in heat transfer and fluid flow. Through an artificial neural network machine learning model, experimental studies on such geometries can aid in predicting and optimizing heat transfer effects, reducing experimental time and cost.
Review
Chemistry, Multidisciplinary
Ines Goncalves et al.
Summary: Nanofluids are increasingly being considered for various applications, with research focusing on factors such as nanoparticle stability and predictive modeling when it comes to thermal conductivity.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Esther Andres-Perez et al.
Summary: Computational fluid dynamics (CFD) simulations are widely used in aeronautical industries to analyze aerodynamic performance, with surrogate models being considered as a substitute for reducing time and cost. This paper reviews surrogate regression models for aerodynamic coefficient prediction and compares them using three different aeronautical configurations.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Review
Mechanics
Bernhard Vowinckel
Summary: Traditional sediment transport simulations face challenges of different scales, requiring various computational approaches. Proper closure arguments are necessary for simulating processes at different scales. Efficient macroscale models and machine learning strategies are essential for improving sediment transport modeling in the future.
Review
Pharmacology & Pharmacy
Mateus Sa Magalhaes Serafim et al.
Summary: Drug design and discovery of new antivirals using machine learning techniques have shown promising perspectives. Recent innovative studies have highlighted the importance of different ML techniques applied to antivirals, while continuous improvements and combinations of methods have contributed to the advancement in this area. The emergence of new algorithms and enhancements in old approaches have led to successful results, as seen in the case of SARS-CoV-2.
EXPERT OPINION ON DRUG DISCOVERY
(2021)
Article
Multidisciplinary Sciences
Ryley McConkey et al.
Summary: The surge in machine learning augmented turbulence modelling offers a promising approach to address RANS models' limitations. This work introduces the development of an open-source dataset curated for immediate use in machine learning augmented corrective turbulence closure modelling, reducing effort required for training and testing new RANS models.
Article
Mechanics
Hao Xu et al.
Summary: Data-driven methods have recently made great progress in discovering partial differential equations from spatial-temporal data. A new framework combining neural network, genetic algorithm, and stepwise methods is proposed to address challenges including sparse noisy data and incomplete library. The proposed algorithm is able to discover parametric PDEs with spatially or temporally varying coefficients on various equations.
Article
Environmental Sciences
Andrea E. Brookfield et al.
Summary: Advances in understanding and predicting freshwater algal bloom dynamics have highlighted the significant role of groundwater inputs in modulating algal growth, depending on unique local conditions. The distinct chemistry of groundwater can either support or prevent algal blooms, with key mechanisms including the redox state of the subsurface and stability of groundwater discharge. More research is needed to improve predictions of algal blooms by considering changes in land use, water management, and climate impacting groundwater dynamics.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Review
Chemistry, Analytical
Hasan Asy'ari Arief et al.
Summary: This paper provides a comprehensive technical review of the data analysis techniques for distributed fibre optic technologies, focusing on characterizing fluid flow in pipes. The study aims to help end-users establish reliable, robust, and accurate solutions that can be deployed in a timely and effective way, paving the way for future developments in the field. The review covers classical methods and data-driven machine learning counterparts, such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Ensemble Kalman Filter (EnKF) algorithms.
Article
Engineering, Petroleum
T. A. Olukoga et al.
Summary: There is a growing interest in implementing machine learning in the oil and gas industry to enhance profitability, but there is a lack of detailed practical guidance for practitioners. This study systematically reviews recent publications to identify challenges faced by oil and gas practitioners and summarizes the use of ML techniques in addressing these challenges. Artificial neural networks, support vector machines, and regression are the most commonly used ML algorithms in drilling applications such as predicting drilling events and properties of drilling fluids.
SPE DRILLING & COMPLETION
(2021)
Article
Physics, Fluids & Plasmas
Karthik Duraisamy
Summary: This paper reviews recent developments in using machine learning to enhance Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) models of turbulent flows, emphasizing the importance of consistent ML augmentation in modeling. It discusses techniques for promoting model-consistent training and choosing the feature space based on physical and mathematical considerations, highlighting the potential of machine learning in turbulence modeling.
PHYSICAL REVIEW FLUIDS
(2021)
Review
Geosciences, Multidisciplinary
Debanjan Chandra et al.
Summary: Imaging and image analysis of shale provide valuable insights into its properties, with different methods offering complementary information. However, choosing the appropriate imaging tools is crucial for visualizing the desired features effectively.
EARTH-SCIENCE REVIEWS
(2021)
Article
Geochemistry & Geophysics
Jian Zhang et al.
Summary: The prediction of lithology/fluid characteristics using deep-learning-based data-driven methods faces the challenge of spatial correlation, which can lead to noisy or unreliable results. To address this, a spatially coupled data-driven approach combining convolutional neural networks and spectral decomposition was proposed, showing improved lateral continuity and geological reliability in 2-D and 3-D datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Multidisciplinary Sciences
Christoph Schran et al.
Summary: The study introduces a machine learning framework for developing and validating models for complex aqueous systems efficiently through a data-driven active learning protocol. The approach is applied to diverse aqueous systems and evaluated with an automated validation protocol for accuracy and precision of force prediction.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Review
Engineering, Aerospace
Jiaqing Kou et al.
Summary: Aerodynamic modeling plays a crucial role in unstable aerodynamics, with traditional methods restricted by theoretical and empirical research. Data-driven methods offer high accuracy, low computational cost, and great potential for flow control and engineering optimization.
PROGRESS IN AEROSPACE SCIENCES
(2021)
Article
Multidisciplinary Sciences
Filippos Sofos et al.
Summary: This study utilizes machine learning techniques to predict slip length at the nanoscale, with non-linear models based on neural networks and decision trees showing better performance. The trained model accurately predicts slip length values, showing that slip length is mainly affected by wall roughness and wettability as channel dimensions increase.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Marine
Fahimeh Hadavimoghaddam et al.
Summary: This study successfully predicted water saturation using new machine learning algorithms, avoiding the issues of traditional methods relying on resistivity log data in specific formations. Super Learner and XGBoost were found to produce the most accurate predictions, with Super Learner considered the best among all models.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Biomedical
Sinisa Bratulic et al.
Summary: Precision oncology aims to tailor clinical decisions to individual patients by accurately characterizing tumors using omics information. Liquid biopsies, as non-invasive alternatives to traditional tissue biopsies, can provide multiple layers of tumor-specific biological information, leading to discovery of new diagnostic markers with the help of statistical and machine learning approaches. This rapidly advancing field of cancer biomarker research faces both successes and challenges.
REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE
(2021)
Review
Chemistry, Multidisciplinary
Spyridon Koutsoukos et al.
Summary: Ionic liquids have diverse properties and applications, with machine learning algorithms playing a key role in predicting these properties. Continuously optimizing training datasets and models can enhance the accuracy and efficiency of predictions.
Review
Chemistry, Multidisciplinary
Kai Guo et al.
Summary: Artificial intelligence, especially machine learning and deep learning algorithms, is increasingly utilized in materials and mechanical engineering for predicting materials properties and designing new materials. Trained ML models offer fast exploration of design spaces, but challenges remain in data collection, preprocessing and model selection. Recent breakthroughs in ML techniques have opened up vast opportunities in overcoming mechanics problems and developing novel materials design strategies.
MATERIALS HORIZONS
(2021)
Review
Chemistry, Physical
Caroline Desgranges et al.
Summary: This review explores the use of machine learning combined with molecular simulation algorithms to accurately predict thermodynamic properties, overcoming computational challenges and accelerating the discovery of free energies.
MOLECULAR SYSTEMS DESIGN & ENGINEERING
(2021)
Review
Mechanics
Steven L. Brunton et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52
(2020)
Review
Energy & Fuels
Timur Bikmukhametov et al.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2020)
Review
Engineering, Mechanical
J. P. Panda
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2020)
Article
Water Resources
Pejman Tahmasebi et al.
ADVANCES IN WATER RESOURCES
(2020)
Article
Mechanics
Jean Rabault et al.
JOURNAL OF HYDRODYNAMICS
(2020)
Article
Physics, Fluids & Plasmas
Isabel Scherl et al.
PHYSICAL REVIEW FLUIDS
(2020)
Article
Mechanics
Lionel Agostini
Review
Materials Science, Multidisciplinary
Edgar A. Galan et al.
Article
Thermodynamics
Abdolhossein Hemmati-Sarapardeh et al.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2020)
Review
Energy & Fuels
Birol Dindoruk et al.
JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING
(2020)
Article
Cardiac & Cardiovascular Systems
Subhashaan Sreedharan et al.
CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE
(2020)
Review
Mathematics, Interdisciplinary Applications
Michael Frank et al.
Article
Biochemical Research Methods
Junjie Zhong et al.
Article
Computer Science, Interdisciplinary Applications
Frederic Gibou et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Review
Mechanics
Karthik Duraisamy et al.
ANNUAL REVIEW OF FLUID MECHANICS, VOL 51
(2019)
Article
Computer Science, Interdisciplinary Applications
Nicholas Geneva et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Article
Mechanics
Kai Fukami et al.
JOURNAL OF FLUID MECHANICS
(2019)
Review
Pharmacology & Pharmacy
Saifur R. Khan et al.
DRUG DISCOVERY TODAY
(2019)
Article
Instruments & Instrumentation
E. Nazemi et al.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
(2019)
Article
Physics, Fluids & Plasmas
M. P. Brenner et al.
PHYSICAL REVIEW FLUIDS
(2019)
Article
Physics, Fluids & Plasmas
Jared Callaham et al.
PHYSICAL REVIEW FLUIDS
(2019)
Article
Mechanics
Maziar Raissi et al.
JOURNAL OF FLUID MECHANICS
(2019)
Article
Mechanics
R. Maulik et al.
JOURNAL OF FLUID MECHANICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Andrea Beck et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2019)
Review
Engineering, Aerospace
Jian Yu et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING
(2019)
Article
Mechanics
Zhiwen Deng et al.
Review
Radiology, Nuclear Medicine & Medical Imaging
Stewart M. Benton et al.
JOURNAL OF THORACIC IMAGING
(2018)
Article
Computer Science, Artificial Intelligence
Yu-Gang Jiang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2018)
Article
Computer Science, Interdisciplinary Applications
Maziar Raissi et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2018)
Review
Biochemical Research Methods
Jina Ko et al.
Article
Computer Science, Information Systems
Alexandra L'Heureux et al.
Article
Mechanics
J. Nathan Kutz
JOURNAL OF FLUID MECHANICS
(2017)
Article
Engineering, Aerospace
Dimitris Drikakis et al.
AERONAUTICAL JOURNAL
(2016)
Article
Computer Science, Interdisciplinary Applications
Eric J. Parish et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2016)
Article
Computer Science, Interdisciplinary Applications
H. Xiao et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2016)
Article
Mechanics
Julia Ling et al.
JOURNAL OF FLUID MECHANICS
(2016)
Article
Biochemical Research Methods
Michael K. K. Leung et al.
Article
Computer Science, Interdisciplinary Applications
W. N. Edeling et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2014)
Article
Computer Science, Artificial Intelligence
Clement Farabet et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2013)
Article
Nanoscience & Nanotechnology
Nikolaos Asproulis et al.
MICROFLUIDICS AND NANOFLUIDICS
(2013)
Article
Mechanics
Javier Jimenez
Article
Engineering, Electrical & Electronic
Geoffrey Hinton et al.
IEEE SIGNAL PROCESSING MAGAZINE
(2012)
Article
Mechanics
Hiromichi Kobayashi et al.
JOURNAL OF TURBULENCE
(2011)
Article
Engineering, Industrial
Sai Hung Cheung et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2011)
Article
Chemistry, Multidisciplinary
Nikolaos Asproulis et al.
JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE
(2009)
Article
Engineering, Aerospace
David Amsallem et al.
Article
Water Resources
Fi-John Chang et al.
HYDROLOGICAL PROCESSES
(2008)
Article
Computer Science, Interdisciplinary Applications
Ben Thornber et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2007)
Article
Mechanics
A. Moreau et al.
Review
Food Science & Technology
Tomas Norton et al.
TRENDS IN FOOD SCIENCE & TECHNOLOGY
(2006)
Review
Engineering, Aerospace
D Drikakis
PROGRESS IN AEROSPACE SCIENCES
(2003)
Article
Computer Science, Interdisciplinary Applications
F Sarghini et al.
COMPUTERS & FLUIDS
(2003)
Article
Computer Science, Interdisciplinary Applications
M Milano et al.
JOURNAL OF COMPUTATIONAL PHYSICS
(2002)
Article
Mechanics
F Giralt et al.
