Article
Thermodynamics
Pei Zhang, Chuang Sun, Xin-Lin Xia
Summary: The particle filter (PF) and improved algorithm (unscented particle filter (UPF)) are used for predicting transient heat flux (q(t)) on the boundary of participating medium. The study considers the temperature-dependent thermophysical properties and graded index medium. The UPF algorithm, with unscented Kalman filter, outperforms the PF algorithm in terms of stability and accuracy.
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
(2023)
Article
Engineering, Mechanical
J. Ghibaudo, M. Aucejo, O. De Smet
Summary: This paper introduces a novel Bayesian filter for estimating mechanical excitation sources from vibration measurements. The proposed filter unifies most of the state-of-the-art recursive filters developed in the last decade for solving input-state estimation problems. By assuming that the predicted input vector follows a generalized Gaussian distribution, the proposed filter promotes the spatial sparsity of the estimated input vector. Numerical and experimental evaluations show that the proposed filter, called Sparse adaptive Bayesian Filter, outperforms existing filters in terms of input estimation accuracy and avoidance of the drift effect.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Interdisciplinary Applications
Aaron Myers, Alexandre H. Thiery, Kainan Wang, Tan Bui-Thanh
Summary: The SET method generates approximate samples from a Bayesian posterior distribution by solving a sequence of optimal transport problems. It converges weakly to the true posterior as sample size approaches infinity. Compared to standard SMC methods, SET is more robust and requires less computational efforts when exploring complex posterior distributions.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Mathematics, Applied
Felix G. Jones, Gideon Simpson
Summary: In this article, the possibility of using classical filtering methods to solve linear statistical inverse problems is discussed. The authors propose optimizing the regularization parameter in the filters to reduce the mean squared error. Building on previous work, the authors prove that considering the problem in a weaker norm and applying iterate averaging can lead to convergence of 3DVAR in mean square, regardless of the choice of parameter. It is also shown that iterate averaging does not improve the performance of the Kalman filter in this setting.
NUMERICAL ALGORITHMS
(2023)
Article
Engineering, Multidisciplinary
Mo'ath El-Dalahmeh, Maher Al -Greer, Ma'd El-Dalahmeh, Imran Bashir
Summary: This work proposes a physics-informed smooth particle filter (SPF) framework for accurately predicting the remaining useful life (RUL) of lithium-ion batteries. The framework utilizes a single particle model (SPM) to estimate degradation parameters and quantifies degradation mechanisms for more accurate RUL predictions. It is demonstrated to be dependable and robust, even in the presence of noise and dynamic discharging profiles.
Article
Mathematics, Applied
Konrad Simon, Joern Behrens
Summary: A new framework of numerical multiscale methods is introduced for advection-dominated problems in climate sciences, addressing difficulties faced by current methods when lower order terms are dominant. The method involves a semi-Lagrangian based reconstruction of subgrid variability into a multiscale basis by solving local inverse problems, resembling a Eulerian method with multiscale stabilized basis globally. Example runs in one and two dimensions are shown, along with comparisons to standard methods to support the ideas presented. Future extensions to other types of Galerkin methods, higher dimensions and nonlinear problems are discussed.
JOURNAL OF SCIENTIFIC COMPUTING
(2021)
Article
Energy & Fuels
Joonchul Kim, Eunsong Kim, Jung-Hwan Park, Kyoung-Tak Kim, Joung-Hu Park, Taesic Kim, Kyoungmin Min
Summary: This study investigated the impact of data partitioning methods on predicting the remaining useful life (RUL) of batteries. Results showed that the method of adding predicted data from a surrogate model to the training set had the highest accuracy, with an average mean absolute error (MAE) of 47 cycles. In contrast, the slide BOX method, which used only certain cycles before the test set as the training set, had the worst MAE value of 60 cycles. Therefore, this data partitioning method can be implemented to predict the RUL of batteries and aid in the development of next-generation cathode materials with improved performance and stability, as well as achieve reliable predictive maintenance.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Sammy Metref, Emmanuel Cosme, Matthieu Le Lay, Joel Gailhard
Summary: Accurately predicting seasonal streamflow supply is crucial for operating hydroelectric dams and avoiding hydrology-related hazard. However, scarce observation data and oversimplified physics representation may lead to significant forecast errors. This paper aims to improve predictions by assimilating snow observations, which has been rarely studied but has the potential to enhance the forecast accuracy.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Mathematics, Applied
Daniel Zhengyu Huang, Jiaoyang Huang, Sebastian Reich, Andrew M. Stuart
Summary: This article introduces a method for Bayesian inference in large-scale inverse problems. Due to the need for multiple evaluations of an expensive forward model, traditional Markov chain Monte Carlo methods are infeasible. Therefore, a framework based on the Kalman methodology is proposed. The framework approximates the filtering distribution of a mean-field dynamical system to approximate the Bayesian posterior. Ensemble methods and other strategies are used to reduce computational and memory costs, and the effectiveness of the framework is demonstrated through numerical experiments.
Article
Engineering, Electrical & Electronic
Chunxia Yang, Jian Zhang, Mei Song Tong
Summary: The article proposes an inversion method combining a customized PSO algorithm and FFT to solve electromagnetic inverse scattering problems. By utilizing constraints and global search features, the method achieves better results, although the computational burden is a major limitation of applying stochastic algorithms.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2021)
Article
Computer Science, Interdisciplinary Applications
M. Leer, M. W. A. Pettit, J. T. Lipkowicz, P. Domingo, L. Vervisch, A. M. Kempf
Summary: This paper presents a simulation method based on the Eulerian-Lagrangian decomposition principle for turbulent flow with high Schmidt or Prandtl numbers. The method separates the scalar quantity field into low-frequency and high-frequency components using low-pass filtering, and transports them using Eulerian and Lagrangian descriptions respectively, improving simulation efficiency and accuracy.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Mathematics
Gennadii Alekseev, Alexey Lobanov
Summary: The article studies inverse problems for a 3D model of electrostatics in the development of technologies for designing electric cloaking and shielding devices. The devices are assumed to consist of concentric spherical layers filled with homogeneous anisotropic or isotropic media. A mathematical technique based on inverse problems for the electrostatic model is developed, solving finite-dimensional extremum problems using global minimization methods. The inverse problems are replaced by control problems, where the permittivities of separate layers act as controls. A numerical algorithm based on the particle swarm optimization method is proposed. The developed algorithm shows simplicity of technical implementation and the highest performance in the class of devices considered.
Article
Computer Science, Information Systems
Syed Musanif Shah, Shafiullah Khan, Ghulam Saddiq, Naveed Abbas, Muhammad Wasim, Amjad Rehman, Sarah Alotaibi, Saeed Ali Bahaj, Tanzila Saba
Summary: Particle swarm optimization (PSO) is an intelligent searching technique for solving complicated design optimization problems. The traditional PSO algorithm is flexible and efficient, but it often gets trapped in local minima when dealing with complex and inverse objective functions. To overcome this limitation, we propose a modified PSO algorithm that introduces crossover and mutation vectors, as well as a novel strategy for maintaining diversity and alignment in the optimization process. Our approach outperforms other methods, as demonstrated by performance evaluations and trajectory curves.
Article
Engineering, Electrical & Electronic
Chun Xia Yang, Jian Zhang, Mei Song Tong
Summary: This article introduces a hybrid inversion approach based on the QPSO method to solve electromagnetic inverse problems, aiming to evaluate the effectiveness in reconstructing 2-D dielectric scatterers and expand the contribution of excellent particles by introducing a weighted mean best position. The hybrid approach, HQPSO, combines the advantages of linear reconstruction algorithms and stochastic optimization algorithms to ensure accuracy and improve computational efficiency in large-scale optimization problems.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2021)
Article
Engineering, Aerospace
Hyung Jun Park, Seokgoo Kim, Junyoung Lee, Nam Ho Kim, Joo-Ho Choi
Summary: This study proposes a system-level prognostics approach for the reaction wheel motor, which is widely used for advanced attitude control of satellites. By considering the motor as a system composed of multiple components, the approach estimates the health degradation and predicts failures based on motor operation data obtained during accelerated-life tests.
ADVANCES IN SPACE RESEARCH
(2023)