Article
Ecology
Valeria Di Biagio, Stefano Salon, Laura Feudale, Gianpiero Cossarini
Summary: This study investigates the subsurface oxygen maximum (SOM) in the Mediterranean Sea, showing different characteristics between the western and eastern Mediterranean in summer. The model-derived concentrations and depths are in agreement with estimations from the literature and display mesoscale variability patterns.
Article
Agricultural Engineering
Zhengyang Wu, Xiushan Wang, Dawei Liu, Fangping Xie, Looh George Ashwehmbom, Zhengzhong Zhang, Qijun Tang
Summary: By calibrating DEM parameters and calculating the mapping relationship, the method developed can effectively predict the depth of sweep cultivation and the stress-strain behavior of the soil.
BIOSYSTEMS ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Zahra Faeli, Brina M. Montoya, Mohammed A. Gabr
Summary: Microbial Induced Calcium Carbonate Precipitation (MICP) in the subsurface can be used in various engineered applications such as geotechnical ground improvement. A reactive transport model was developed to investigate the effects of controlling factors on MICP. Fifteen key parameters were assessed, with microbial activity, microbial attachment process, and treatment frequency showing significant influence on calcium carbonate precipitation content and distribution.
COMPUTERS AND GEOTECHNICS
(2023)
Article
Energy & Fuels
P. J. Johnson, P. H. Stauffer, J. Omagbon, C. R. Moore
Summary: Numerical models of geothermal systems commonly only capture the top of the reservoir, simplifying the deeper areas to a boundary condition. However, this approach does not produce the correct model behavior compared to a model that includes the entire convecting domain with a heat flux only. Various incorrect types of model behavior can arise from the choice of boundary condition, which can lead to parameter compensation and incorrect physics.
Article
Computer Science, Interdisciplinary Applications
Ahmad Jan, Ethan T. Coon, Scott L. Painter
Summary: The study extends a multiscale model from reach scale to river network scales for representing the effects of small-scale hyporheic-zone biogeochemical processes. It uses advection-dispersion-reaction equations for the channel network and advection-reaction subgrid models for the hyporheic zone. The implementation is verified against numerical solutions on a single reach and shows the capability to model denitrification of farm runoff in a subbasin as a demonstration of general-purpose reactive transport.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Engineering, Marine
Malte Mittendorf, Ulrik D. Nielsen, Harry B. Bingham, Shukui Liu
Summary: This paper examines a semi-empirical framework for estimating added resistance in arbitrary wave heading, considering uncertainty quantification. The calibration of the formula's parameter vector is conducted using particle swarm optimization and a database of model test results. The study evaluates the effect of objective functions on prediction accuracy and obtains different parameter combinations for blunt and slender ships. The irreducible statistical uncertainty is accounted for using quantile regression. The proposed approach is validated against model test data and other prediction methods, showing satisfactory performance and reliability.
Article
Engineering, Civil
Idhayachandhiran Ilampooranan, Jerald L. Schnoor, Nandita B. Basu
Summary: This study demonstrates how incorporating crop yield information can improve the robustness and accuracy of hydrologic models. By comparing crop yield measurements with modeled results in an agricultural watershed, structural deficiencies of the model were identified and improvements were made to enhance predictive capabilities.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Jonas Allgeier, Olaf A. A. Cirpka
Summary: Modern physics-based subsurface-flow models often require many parameters and computationally costly simulations. To expedite the calibration process, we propose using surrogate models based on Gaussian Process Regression (GPR), which allows estimation of the posterior parameter distribution using Markov-Chain Monte Carlo (MCMC) simulations. We compared the GPR-based approach to a Neural Posterior Estimation (NPE) scheme and found that the GPR-based MCMC approach reproduced the data better.
WATER RESOURCES RESEARCH
(2023)
Article
Environmental Sciences
Wenguang Shi, Quanrong Wang, Musa Salihu Danlami
Summary: This study presents a novel analytical model of solute transport in aquifer-aquitard systems by incorporating new sets of models that describe mixing processes. The model considers various components of solute transport and first-order chemical reaction. The reliability of the model is evaluated by comparing the finite-difference solution with experimental data, showing good agreement. The new model outperforms previous models in interpreting experimental data.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Geosciences, Multidisciplinary
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frederic Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Alvaro Pardo-Alvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, Tanguy Le Borgne
Summary: This paper discusses the interest and potential for monitoring and characterizing spatial and temporal variability in hydrogeological processes, and proposes a classification of processes and applications at different scales based on high-resolution space-time imaging. The authors call for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Engineering, Industrial
Haoyuan Shen, Yizhong Ma, Chenglong Lin, Jian Zhou, Lijun Liu
Summary: This paper proposes a hierarchical Bayesian support vector regression (HBSVR) model for dynamic high-dimensional reliability modeling, which combines the step-size adaptive accelerated Markov Chain Monte Carlo (SAA-MCMC) method with Sequential Minimal Optimization (SMO) for parameter calibration and dynamic update. The HBSVR model is further improved by applying an active learning algorithm to continuously improve model accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Chemistry, Analytical
Sreekanth Kana, Juhi Gurnani, Vishal Ramanathan, Sri Harsha Turlapati, Mohammad Zaidi Ariffin, Domenico Campolo
Summary: Accurate kinematic modelling is essential for the safe and reliable execution of robotic applications. This study proposes a fast-recalibration approach to extract calibrated intrinsic parameters from a factory-calibrated controller, and minimize the kinematic mismatch between the ideal and factory-calibrated robot models.
Article
Engineering, Manufacturing
Heping Chen, Hongtai Cheng
Summary: This paper proposes a robotic assembly process optimization method based on Gaussian Process Regression, which improves assembly performance using an enhanced Bayesian optimization algorithm, and verifies its effectiveness and efficiency in two industrial assembly processes.
JOURNAL OF MANUFACTURING PROCESSES
(2021)
Article
Electrochemistry
Kevin M. Tenny, Richard D. Braatz, Yet-Ming Chiang, Fikile R. Brushett
Summary: Redox flow batteries are a promising energy storage technology facing technical and economic challenges. Current electrodes are not specifically designed for flow batteries, leading to limitations in performance. A neural network model was developed to predict electrode parameters and optimize cell power density, providing a promising approach for future electrode design strategies.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2021)
Article
Engineering, Chemical
Rasool Alizadeh, Javad Mohebbi Najm Abad, Abolhasan Ameri, Mohammad Reza Mohebbi, Amirfarhang Mehdizadeh, Dan Zhao, Nader Karimi
Summary: This paper presents a novel approach based on machine learning techniques for predicting transport behaviors of complex multiphysics systems, resulting in significant reductions in computational time and cost. The study focuses on a hybrid nanofluid flow over a blunt object in porous media, including mixed convection, entropy generation, local thermal non-equilibrium, and nonlinear thermal radiation. The SVR model is used to approximate various functions, while the PSO meta-heuristic algorithm is applied for proposing correlations, allowing for quantitative evaluation of variables' contributions.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2021)