Article
Engineering, Environmental
Bing Bai, Fei Dong, Wenqi Peng, Xiaobo Liu
Summary: This paper proposes a novel Bayesian-based method for calibrating water quality model parameters to improve efficiency and accuracy while avoiding local optima and parameter redundancy. By converting the calibration problem into a posterior probability function sampling problem and using the Markov Chain Monte Carlo algorithm, the parameter calibration is achieved. The results show that the method can achieve calibration with less than 10% mean relative error, indicating its effectiveness in water quality modeling.
WATER ENVIRONMENT RESEARCH
(2023)
Article
Engineering, Mechanical
P. L. Green, L. J. Devlin, R. E. Moore, R. J. Jackson, J. Li, S. Maskell
Summary: This paper discusses the optimization of the 'L-kernel' in Sequential Monte Carlo samplers to improve performance, resulting in reduced variance of estimates and fewer resampling requirements.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Review
Green & Sustainable Science & Technology
D. Hou, I. G. Hassan, L. Wang
Summary: Building Energy Model (BEM) calibration is crucial for accuracy, with recent focus on stochastic Bayesian inference calibration. However, confusion remains regarding theory, strengths, limitations, and implementations. Selecting appropriate mathematical models and tools poses a challenge.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Engineering, Mechanical
Adolphus Lye, Alice Cicirello, Edoardo Patelli
Summary: This tutorial paper reviews the use of advanced Monte Carlo sampling methods in Bayesian model updating for engineering applications, introducing different methods and comparing their performance. Three case studies demonstrate the advantages and limitations of these sampling techniques in parameter identification, posterior distribution sampling, and stochastic identification of model parameters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Physics, Multidisciplinary
Christopher G. Albert, Ulrich Callies, Udo von Toussaint
Summary: This study focuses on Bayesian analysis for calibrating process-oriented environmental models, emphasizing the representation of dependencies and distributions among parameters. Model parameters are estimated using Bayesian inference via Markov Chain Monte Carlo sampling, providing a joint probability distribution for parameters. The Bayesian approach allows the incorporation of prior knowledge and highlights the extent to which parameters are constrained by observations.
Article
Engineering, Industrial
Zeyu Wang, Abdollah Shafieezadeh
Summary: This paper presents a new approach to overcome the computational cost problem of Bayesian updating for complex computational models. It decomposes the updating problem into a set of sub-reliability problems with uncertain failure thresholds, enabling precise identification of intermediate failure thresholds and training of surrogate models. The proposed method reduces computational costs significantly while maintaining high accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Agriculture, Dairy & Animal Science
M. A. Stephen, C. R. Burke, N. Steele, J. E. Pryce, S. Meier, P. R. Amer, C. V. C. Phyn, D. J. Garrick
Summary: In this study, the genetic and phenotypic relationships between anogenital distance (AGD) and body stature and fertility traits in dairy cattle were characterized. The results showed that AGD is a moderately heritable trait and is associated with reproductive success in lactating cows.
JOURNAL OF DAIRY SCIENCE
(2023)
Article
Materials Science, Multidisciplinary
Adam P. Generale, Richard B. Hall, Robert A. Brockman, V. Roshan Joseph, George Jefferson, Larry Zawada, Jennifer Pierce, Surya R. Kalidindi
Summary: This study demonstrates the successful application of Bayesian inference for simultaneous estimation of eleven material parameters of a viscous multimode CDM model, providing uncertainty estimates and principled decision making, applicable to mechanical models with high-dimensional parameter sets.
MECHANICS OF MATERIALS
(2022)
Article
Engineering, Environmental
Marco Bacci, Jonas Sukys, Peter Reichert, Simone Ulzega, Carlo Albert
Summary: Due to limited knowledge about complex environmental systems, predicting their behavior under different scenarios or decision alternatives is uncertain. Considering, quantifying, and communicating this uncertainty is important for societal decisions. Stochastic models are often necessary to adequately describe uncertainty, but calibrating these models presents methodological and numerical challenges. To address this, we compare three numerical approaches and find that their performance is comparable for analyzing a stochastic hydrological model with hydrological data, suggesting that generality and practical considerations can guide technique choice for specific applications.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Thermodynamics
Danlin Hou, Liangzhu (Leon) Wang, Ali Katal, Shujie Yan, Liang (Grace) Zhou, Vicky Wang, Mark Vuotari, Ethan Li, Zihan Xie
Summary: Outdoor fresh air ventilation is important in reducing indoor airborne disease transmission, and school classrooms face challenges during the COVID-19 pandemic. This study used Bayesian inference and sensitivity analysis to analyze CO2 readings in four primary schools, identifying outdoor ventilation rate, CO2 generation rate, and occupancy level as key parameters affecting indoor CO2 levels.
BUILDING SIMULATION
(2023)
Review
Statistics & Probability
Christopher Nemeth, Paul Fearnhead
Summary: MCMC algorithms are considered the gold standard technique for Bayesian inference, but the computational cost can be prohibitive for large datasets, leading to the development of scalable Monte Carlo algorithms. One type of these algorithms is SGMCMC, which reduces per-iteration cost by utilizing data subsampling techniques.
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
(2021)
Article
Computer Science, Artificial Intelligence
Davide Luciani, Alessandro Magrini, Carlo Berzuini, Antonello Gavazzi, Paolo Canova, Tiziano Barbui, Guido Bertolini
Summary: This study effectively combines existing causal medical knowledge with clinical information to predict diagnoses of cardiopulmonary disorders. By refining initial causal assumptions without loss of generality, the Bayesian Network (BN) produced well-calibrated diagnostic predictions for the most common disorders.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Energy & Fuels
Seongyoon Kim, Sanghyun Kim, Yun Young Choi, Jung-Il Choi
Summary: This study proposes a comprehensive framework of Bayesian parameter identification for electrochemical models in lithium-ion batteries. It determines the parameter distributions and estimates the global sensitivity of the parameters. The framework can help to analyze the behaviors of batteries according to specific operating conditions and materials.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Xiaoyan Qiu, Hang Zhang, Yiwei Qiu, Yi Zhou, Tianlei Zang, Buxiang Zhou, Ruomei Qi, Jin Lin, Jiepeng Wang
Summary: Utility-scale hydrogen production via alkaline electrolysis is an effective way to reduce carbon emissions in various industries. The efficiency, flexibility, and safety of the alkaline electrolysis system are influenced by electrochemical, thermal, and mass transfer dynamics. However, the lack of a comprehensive parameter estimation method has hindered the accuracy and adaptability of existing models. To address this, a fast and accurate parameter estimation method based on Bayesian inference and Markov chain Monte Carlo is proposed. Experimental results demonstrate the effectiveness of this method in improving estimation accuracy and providing fault diagnosis and sensitivity analysis for alkaline electrolysis systems.
Article
Computer Science, Interdisciplinary Applications
Weimin Zhou, Umberto Villa, Mark A. Anastasio
Summary: Medical imaging systems are often evaluated and optimized using objective measures of image quality, with the Bayesian Ideal Observer (IO) representing an upper limit for performance evaluation. A new MCMC method called MCMC-GAN, employing a GAN-based stochastic object model (SOM), shows promise in extending the applicability of MCMC methods for IO analyses of medical imaging systems.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Chemistry, Physical
Tianyu Chen, Zhibin Lu, Guangjin Zeng, Yongmin Xie, Jie Xiao, Zhifeng Xu
Summary: The study introduces a high-performance LSGM electrolyte-supported tubular DC-SOFC stack for portable applications, which shows great potential in developing into high-performing, efficient, and environmentally friendly portable power sources for distributed applications.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Wenbin Tong, Yili Chen, Shijie Gong, Shaokun Zhu, Jie Tian, Jiaqian Qin, Wenyong Chen, Shuanghong Chen
Summary: In this study, a three-dimensional porous NiO interface layer with enhanced anode dynamics is fabricated, forming a Schottky contact with the zinc substrate, allowing rapid and uniform zinc plating both inside and below the interface layer. The resulting NiO@Zn exhibits exceptional stability and high capacity retention.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yafeng Bai, Kaidi Li, Liying Wang, Yang Gao, Xuesong Li, Xijia Yang, Wei Lu
Summary: In this study, a flexible zinc ion supercapacitor with gel electrolytes, porous alpha-MnO2@reduced graphene oxide cathode, and activated carbon/carbon cloth anode was developed. The device exhibits excellent electrochemical performance and stability, even at low temperatures, with a high cycle retention rate after 5000 cycles.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Anmol Jnawali, Matt D. R. Kok, Francesco Iacoviello, Daniel J. L. Brett, Paul R. Shearing
Summary: This article presents the results of a systematic study on the electrochemical performance and mechanical changes in two types of commercial batteries with different anode chemistry. The study reveals that the swelling of anode layers in batteries with silicon-based components causes deformations in the jelly roll structure, but the presence of a small percentage of silicon does not significantly impact the cycling performance of the cells within the relevant state-of-health range for electric vehicles (EVs). The research suggests that there is room for improving the cell capacities by increasing the silicon loading in composite anodes to meet the increasing demands on EVs.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yohandys A. Zulueta, My Phuong Pham-Ho, Minh Tho Nguyen
Summary: Advanced atomistic simulations were used to study ion transport in the Na- and K-doped lithium disilicate Li2Si2O5. The results showed that Na and K doping significantly enhanced Li ion diffusion and conduction in the material.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Zongying Han, Hui Dong, Yanru Yang, Hao Yu, Zhibin Yang
Summary: An efficient phase inversion-impregnation approach is developed to fabricate BaO-decorated Ni8 mol% YSZ anode-supported tubular solid oxide fuel cells (SOFCs) with anti-coking properties. BaO nanoislands are successfully introduced inside the Ni-YSZ anode, leading to higher peak power densities and improved stability in methane fuel. Density functional theory calculations suggest that the loading of BaO nanoislands facilitates carbon elimination by capturing and dissociating H2O molecules to generate OH.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Suresh Mamidi, Dan Na, Baeksang Yoon, Henu Sharma, Anil D. Pathak, Kisor Kumar Sahu, Dae Young Lee, Cheul-Ro Lee, Inseok Seo
Summary: Li-CO2 batteries, which utilize CO2 and have a high energy density, are hindered in practical applications due to slow kinetics and safety hazards. This study introduces a stable and highly conductive ceramic-based solid electrolyte and a metal-organic framework catalyst to improve the safety and performance of Li-CO2 batteries. The optimized Li-CO2 cell shows outstanding specific capacity and cycle life, and the post-cycling analysis reveals the degradation mechanism of the electrodes. First-principles calculations based on density functional theory are also performed to understand the interactions between the catalyst and the host electrode. This research demonstrates the potential of MOF cathode catalyst for stable operation in Li-CO2 batteries.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Ganghua Xiang, Zhihuan Qiu, Huilong Fei, Zhigang Liu, Shuangfeng Yin, Yuen Wu
Summary: In this study, a CeFeOx-supported Pt single atoms and subnanometric clusters catalyst was developed, which exhibits enhanced catalytic activity and stability for the preferential oxidation of CO in H2-rich stream through synergistic effect.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Dimitrios Chatzogiannakis, Marcus Fehse, Maria Angeles Cabanero, Natalia Romano, Ashley Black, Damien Saurel, M. Rosa Palacin, Montse Casas-Cabanas
Summary: By coupling electrochemical testing to operando synchrotron based X-ray absorption and powder diffraction experiments, blended positive electrodes consisting of LiMn2O4 spinel (LMO) and layered LiNi0.5Mn0.3Co0.2O2 (NMC) were studied to understand their redox mechanism. It was found that blending NMC with LMO can enhance energy density at high rates, with the blend containing 25% LMO showing the best performance. Testing with a special electrochemical setup revealed that the effective current load on each blend component can vary significantly from the nominal rate and also changes with SoC. Operando studies allowed monitoring of the oxidation state evolution and changes in crystal structure, in line with the expected behavior of individual components considering their electrochemical current loads.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Chiara Cementon, Daniel Dewar, Thrinathreddy Ramireddy, Michael Brennan, Alexey M. Glushenkov
Summary: This Perspective discusses the specific power and power density of lithium-ion capacitors, highlighting the fact that their power characteristics are often underestimated. Through analysis, it is found that lithium-ion capacitors can usually achieve power densities superior to electrochemical supercapacitors, making them excellent alternatives to supercapacitors.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Weihao Wang, Hao Yu, Li Ma, Youquan Zhang, Yuejiao Chen, Libao Chen, Guichao Kuang, Liangjun Zhou, Weifeng Wei
Summary: This study achieved an improved electrolyte with excellent low-temperature and high-voltage performance by regulating the Li+ solvation structure and highly concentrating it. The electrolyte exhibited outstanding oxidation potential and high ionic conductivity under low temperature and high voltage conditions, providing a promising approach for the practical application of high-voltage LIBs.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Martin Bures, Dan Gotz, Jiri Charvat, Milos Svoboda, Jaromir Pocedic, Juraj Kosek, Alexandr Zubov, Petr Mazur
Summary: Vanadium redox flow battery is a promising energy storage solution with long-term durability, non-flammability, and high overall efficiency. Researchers have developed a mathematical model to simulate the charge-discharge cycling of the battery, and found that hydraulic connection of electrolyte tanks is the most effective strategy to reduce capacity losses, achieving a 69% reduction.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
M. Rodriguez-Gomez, J. Campo, A. Orera, F. de La Fuente, J. Valenciano, H. Fricke, D. S. Hussey, Y. Chen, D. Yu, K. An, A. Larrea
Summary: In this study, we analysed the operando performance of industrial lead cells using neutron diffraction experiments. The experiments revealed the evolution of different phases in the positive electrode, showed significant inhomogeneity of phase distribution inside the electrode, and estimated the energy efficiency of the cells.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Jiawei Liu, Chenpeng Wang, Yue Yao, Hao Ye, Yinglong Liu, Yingli Liu, Xiaoru Xu, Zhicong Chen, Huazheng Yang, Gang Wu, Libin Lei, Chao Wang, Bo Liang
Summary: The study focuses on utilizing double conductive Ni-pads as anode collectors in micro-tubular solid oxide fuel cells. The simulation results show excellent performance and stability of DCNPs, and also highlight the potential applications in various fields.
JOURNAL OF POWER SOURCES
(2024)
Article
Chemistry, Physical
Yang Wang, Kangjie Zhou, Lang Cui, Jiabing Mei, Shengnan Li, Le Li, Wei Fan, Longsheng Zhang, Tianxi Liu
Summary: This study presents a polyimide sandwiched separator (s-PIF) for improving the cycling stability of Li-metal batteries. The s-PIF separator exhibits superior mechanical property, electrolyte adsorption/retention and ion conductivity, and enables dendrite-free Li plating/stripping process.
JOURNAL OF POWER SOURCES
(2024)