Article
Multidisciplinary Sciences
Kamila Zdybal, Elizabeth Armstrong, James C. Sutherland, Alessandro Parente
Summary: Reduced-order modeling is a method to describe complex systems with high state-space dimensionality using a small number of parameters. This paper presents a quantitative metric for characterizing the quality of manifold topologies. The metric considers non-uniqueness and spatial gradients in physical quantities of interest, and can be used in optimization algorithms to find optimized low-dimensional projections.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics, Applied
Jameson Cahill, Dustin G. Mixon, Hans Parshall
Summary: We introduce an extension to local principal component analysis for learning symmetric manifolds. In particular, we use a spectral method to approximate the Lie algebra corresponding to the symmetry group of the underlying manifold. We derive the sample complexity of our method for various manifolds before applying it to various data sets for improved density estimation.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
(2023)
Article
Mathematics, Applied
T. L. Carroll
Summary: This paper explores the relationship between the manifold dimension and performance of reservoir computers, showing that increasing the coupling between nodes can optimize performance while network sparsity has no impact on performance.
Article
Computer Science, Software Engineering
Kingsley Chinedu Arum, Fidelis Ifeanyi Ugwuowo
Summary: In this article, a new estimator called the robust r-k estimator is developed to handle multicollinearity and outliers in linear regression model. Theoretical analysis proves its superiority and simulation studies as well as real-life application support its efficiency.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Telecommunications
Niharika Thakur, Mamta Juneja
Summary: Early diagnosis of diseases related with retina such as glaucoma is crucial, but outliers in retinal images can affect diagnosis accuracy. Pre-processing retinal images to remove outliers can improve diagnostic accuracy, and a new approach has shown better performance in experiments.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Mathematics, Applied
Giovanni S. Alberti, Angel Arroyo, Matteo Santacesaria
Summary: This paper investigates abstract inverse problems in the context of infinite-dimensional Banach spaces. These problems are often nonlinear and ill-posed, making the inversion process delicate when dealing with limited and noisy measurements. The article assumes that the unknown belongs to a finite-dimensional manifold, which is commonly seen in real-world scenarios where natural objects have low intrinsic dimensions. The authors provide uniqueness and stability results in this general setting, even when only a finite discretization of the measurements is available. They also propose a reconstruction algorithm and prove its global convergence. Furthermore, the authors apply these general results to specific examples, including Calderon's inverse conductivity problem and Gel'fand-Calderon's problem for the Schrodinger equation.
Article
Mechanics
Chao Xia, Mengjia Wang, Yajun Fan, Zhigang Yang, Xuzhi Du
Summary: We propose a novel reduced-order model and examine its applicability to the complex three-dimensional turbulent wake of a generic square-backed bluff body called the Ahmed body. Training datasets are obtained by large eddy simulation. The model reduction method consists of a Visual Geometry Group (VGG)-based hierarchical autoencoder (H-VGG-AE) and a temporal convolutional neural network (TCN). The H-VGG-AE-based TCN is more effective in terms of spatiotemporal predictions.
Article
Neurosciences
Manuel Beiran, Nicolas Meirhaeghe, Hansem Sohn, Mehrdad Jazayeri, Srdjan Ostojic
Summary: Biological brains possess unparalleled adaptability to changing stimuli and environments, and the mechanisms behind this ability are still not fully understood. Previous studies have identified low-dimensional neural activity organization and modulation by contextual inputs as two potential mechanisms. In this study, the researchers combined these two mechanisms and found that confining the dynamics to a low-dimensional subspace allowed parametric control of overall input-output transformation, enabling generalization and adaptation in complex tasks. Reverse-engineering and theoretical analyses demonstrated that this parametric control relied on tonic inputs modulating dynamics along non-linear manifolds while preserving their geometry. Comparisons with data from behaving monkeys confirmed the behavioral and neural signatures of this mechanism.
Article
Physics, Multidisciplinary
Huang Zhi-Wei, Yang Hong-Yu, Zhai Feng, Lu Xiao-Li, Lu Jun-Qiang, Wu Jian
Summary: The research found that when solving the Hamiltonian of a Heisenberg chain model containing more spins, the number of basis vectors required using eigenvector continuation increases with the number of spins in the model increasing.
ACTA PHYSICA SINICA
(2021)
Article
Engineering, Mechanical
Dayang Li, Maosen Cao, Emil Manoach, Minvydas Ragulskis
Summary: Characterizing dynamical systems remains a challenge in real-world applications, with nonlinear time series analysis providing an effective tool, but facing limitations. A novel Constant embedding parameters and Principal component analysis-based PSR (CPPSR) method is proposed for low-dimensional dynamical systems, showing higher accuracy, efficiency, and reliability compared to conventional methods.
NONLINEAR DYNAMICS
(2021)
Article
Political Science
Ted Enamorado, Gabriel Lopez-Moctezuma, Marc Ratkovic
Summary: This study introduces a method for scaling and comparing datasets from different sources, showing superior performance in capturing shared and unique latent factors. Applied to U.S. Senate data, the method successfully recovers ideological dimensions and specific subspaces related to words and votes, providing valuable insights into the political landscape.
POLITICAL ANALYSIS
(2021)
Article
Engineering, Mechanical
Shancheng Cao, Ning Guo, Chao Xu
Summary: Damage localization in plate-type structures using full-field vibration measurements has gained increased attention. A general strategy involves accurately extracting and integrating damage features from different modes for robust localization. However, measurement noise and lack of baseline data may degrade the accuracy of feature extraction, leading to conflicting evidence for damage localization. An enhanced robust principal component analysis and data fusion approach are proposed to address these challenges, resulting in a robust method for detecting multiple damage zones.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Multidisciplinary
Xiaobo Jiang, Jie Gao, Zhongming Yang
Summary: Principal component analysis (PCA) is a widely used statistical method for data dimension reduction. However, traditional PCA is sensitive to outliers and the deviation of PCA based on Minimum Covariance Determinant (MCD) estimation increases significantly with higher data dimensions. In this paper, a high-dimensional robust PCA method based on the Rocke estimator is proposed, and simulation studies and real data analysis show that it outperforms existing methods in terms of finite sample performance.
JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING
(2023)
Article
Thermodynamics
Zbigniew Stepien
Summary: This paper provides an overview of the reasons that might lead to low-speed pre-ignition and discusses the influence of engine operation, lubricating engine oil, and fuel properties on the occurrence of this problem. It emphasizes the importance of solving the low-speed pre-ignition problem for the further development of downsizing and improving engine efficiency. The paper also introduces a logical division and systematization of the identified factors influencing the LSPI phenomenon.
INTERNATIONAL JOURNAL OF ENGINE RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Kun Zhou, Qianqian Ge, Cuncun Wei, Yafeng Li, Haiyan Ni, Jie Zou, Jiawen Jian
Summary: This study proposes a method to address the training difficulty and high computational cost caused by high-resolution images in car plate character recognition. By mapping features to a low-dimensional space, the model achieves 99.69% accuracy and reduces algorithm runtime.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Thermodynamics
Kevin Verleysen, Alessandro Parente, Francesco Contino
Summary: Ammonia serves as a crucial energy vector for storing and releasing excess renewable energy. However, the current synthesis process lacks flexibility, requiring large hydrogen storage tanks. To reduce tank capacity, optimizing the dynamic power-to-ammonia process under renewable uncertainty is necessary.
PROCEEDINGS OF THE COMBUSTION INSTITUTE
(2023)
Article
Thermodynamics
Kamila Zdybal, James C. Sutherland, Alessandro Parente
Summary: Reduced-order models (ROMs) for turbulent combustion aim to describe complex reacting flows with a small number of effective parameters. This study proposes a quantitative manifold-informed method for selecting a subset of state variables to improve the quality of low-dimensional data representations. The authors demonstrate that a mixture of major and minor species can be beneficial in reducing non-uniqueness and spatial gradients in the dependent variable space.
PROCEEDINGS OF THE COMBUSTION INSTITUTE
(2023)
Article
Thermodynamics
Mohammad Rafi Malik, Ruslan Khamedov, Francisco E. Hernandez Perez, Axel Coussement, Alessandro Parente, Hong G. Im
Summary: The development of reduced-order combustion models has been challenging in numerical combustion research. Principal Components Analysis (PCA) has shown potential in reducing the dimensionality of reactive systems. The present work applies the Manifold Generated by Local PCA (MG-L-PCA) approach in direct numerical simulation (DNS) of turbulent flames, resulting in accurate and efficient results.
PROCEEDINGS OF THE COMBUSTION INSTITUTE
(2023)
Article
Thermodynamics
R. Amaduzzi, A. Bertolino, A. Ozden, R. Malpica Galassi, A. Parente
Summary: This work focuses on quantifying the predictive uncertainty in RANS simulation of a non-premixed lifted flame by considering the uncertainty in the model parameters of the scalar dissipation rate transport equation. Polynomial Chaos Expansions are used as surrogate models to analyze the uncertainty propagation and global sensitivity of these parameters on the quantities of interest (QoIs). The study demonstrates the effectiveness of this approach in providing predictions with estimates of uncertainty and identifies the significant role of certain parameters in affecting the flame temperature predictions.
PROCEEDINGS OF THE COMBUSTION INSTITUTE
(2023)
Article
Engineering, Aerospace
Ali C. Ispir, Kamila Zdybal, Bayindir H. Saracoglu, Thierry Magin, Alessandro Parente, Axel Coussement
Summary: Dual-mode ramjet/scramjet engines are commonly preferred air-breathing propulsion systems for hypersonic aircraft. Modeling the fuel-air mixing process is a main challenge in their design to optimize engine performance. Machine learning models, such as artificial neural networks, can be used for multi-objective optimization of design variables. Experimental data can be used to build regression models that predict mixing conditions in reduced-order modeling studies for estimating engine performance.
Article
Energy & Fuels
Alberto Procacci, Marianna Cafiero, Saurabh Sharma, Muhammad Mustafa Kamal, Axel Coussement, Alessandro Parente
Summary: The objective of this work is to build a Digital Twin of a semi-industrial furnace using Gaussian Process Regression coupled with dimensionality reduction. The Digital Twin integrates temperature, chemiluminescence intensity, and species concentration at the outlet. Experimental measurements include flame temperature distribution, chemiluminescence measurements of OH* and CH*, and species concentration in the exhaust gases. The GPR-based Digital Twin approach is successfully applied on numerical datasets and demonstrated to work on heterogeneous datasets from experimental measurements.
Article
Computer Science, Interdisciplinary Applications
Marco Bellegoni, Leo Cotteleer, Sampath Kumar Raghunathan Srikumar, Gabriele Mosca, Alessandro Gambale, Leonardo Tognotti, Chiara Galletti, Alessandro Parente
Summary: This paper proposes a framework based on the Shear Stress Transport (SST) k-! turbulence model for simulating the Atmospheric Boundary Layer (ABL) in environmental studies. The model is implemented in the open-source OpenFOAM code and shows satisfactory performance.
ENVIRONMENTAL MODELLING & SOFTWARE
(2023)
Article
Chemistry, Physical
Saurabh Sharma, Matteo Savarese, Axel Coussement, Alessandro Parente
Summary: This article investigates the combustion characteristics of dimethyl ether and its mixtures with methane/hydrogen under flameless conditions. The results show that pure dimethyl ether combustion minimizes NO formation, with levels below 10 ppm and no CO or unburned hydrocarbons. The addition of methane reduces NO levels, reaching zero at 50% methane, but increases CO production at higher methane levels. Adding hydrogen forms a more intense reaction zone with a visible flame and higher peak temperatures, while NO emissions increase with higher hydrogen concentrations.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2023)
Article
Mechanics
Ilya Simanovskii, Alexander Nepomnyashchy, Antonio Viviani, Patrick Queeckers, Alessandro Parente
Summary: In this study, the influence of two-dimensional spatial inhomogeneity of temperature on the dynamics and instabilities of a droplet floating on a heated liquid substrate was investigated numerically. The results showed that spatial temperature inhomogeneity led to liquid redistribution towards the region of lower temperature, accompanied by a change in droplet shape. Heating from below caused rupture of the substrate layer due to its monotonic instability, and both symmetric and asymmetric droplets were observed under the action of spatial temperature inhomogeneity.
Article
Thermodynamics
Kevin Verleysen, Diederik Coppitters, Alessandro Parente, Francesco Contino
Summary: Regions with abundant renewable energy can establish remote renewable hubs for energy transport to population-dense areas. Ammonia provides a flexible energy carrier for this purpose. A robust design optimization was performed to compare local and remote ammonia production and transport, showing the cost-effectiveness and robustness of the latter approach. However, local production provides higher efficiency and less sensitivity to uncertainties.
APPLICATIONS IN ENERGY AND COMBUSTION SCIENCE
(2023)
Article
Thermodynamics
Kamila Zdybal, Giuseppe D'Alessio, Antonio Attili, Axel Coussement, James C. Sutherland, Alessandro Parente
Summary: In many reacting flow systems, the thermo-chemical state-space evolves close to a low-dimensional manifold (LDM), which can be obtained using dimensionality reduction methods like principal component analysis (PCA). In this paper, the authors demonstrate that local PCA can detect physically meaningful parameterization of the thermo-chemical state-space, even for complex datasets such as turbulent non-premixed flames. The results highlight the potential of enhancing data-driven techniques like local PCA by incorporating prior knowledge of the system.
APPLICATIONS IN ENERGY AND COMBUSTION SCIENCE
(2023)
Review
Engineering, Aerospace
Soledad Le Clainche, Esteban Ferrer, Sam Gibson, Elisabeth Cross, Alessandro Parente, Ricardo Vinuesa
Summary: This review focuses on the impact of new developments in machine learning on the multi-disciplinary field of aerospace engineering. It discusses the state of the art and the advantages and challenges of ML methods in various aerospace disciplines, as well as future opportunities. The article highlights the improvement of aircraft performance through ML and predicts its significant impact in the near future.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)
Article
Thermodynamics
Rodolfo S. M. Freitas, Arthur Pequin, Riccardo M. Galassi, Antonio Attili, Alessandro Parente
Summary: The accuracy of combustion predictions in Large Eddy Simulations (LES) can be affected by deficiencies in traditional/simplified closure models, especially for nonconventional fuels and combustion regimes. This study combines machine learning and sparsity-promoting techniques to improve the predictive capabilities of the Partially Stirred Reactor (PaSR) model and its associated cell reacting fraction sub-model. The obtained models are parsimonious and demonstrate the ability of machine learning approaches to improve turbulence-chemistry reactor-based combustion models.
COMBUSTION AND FLAME
(2023)
Article
Mechanics
Eva Munoz, Himanshu Dave, Giuseppe D'Alessio, Gianluca Bontempi, Alessandro Parente, Soledad Le Clainche
Summary: In this study, the flow fields generated by synthetic jets were simulated and four dimensionality reduction techniques were compared. The results showed that VQPCA has advantages in developing accurate ROMs, while HODMD is useful for understanding the dynamics of synthetic jets.
Article
Computer Science, Software Engineering
Kamila Zdybal, Elizabeth Armstrong, Alessandro Parente, James C. Sutherland
Summary: We present an update to our open-source Python package, PCAfold, which assists researchers in generating, analyzing, and improving low-dimensional data manifolds. The new version, PCAfold 2.0, introduces innovative tools and algorithms for evaluating and optimizing low-dimensional manifolds. These include a method for generating a map of local feature sizes to identify problematic regions, a novel cost function for characterizing manifold topology, and two feature selection algorithms based on principal component analysis. Additionally, we propose a dimensionality reduction strategy that considers the quantity of interest (QoI) and an implementation of partition of unity networks (POUnets) for efficient reconstruction of QoIs from low-dimensional manifolds.
Article
Thermodynamics
Yifan Yang, Haodong Zhang, Linye Li, Mingming Gu, Xi Xia, Fei Qi
Summary: This paper investigates the formation of a blue whirl by controlling tangential and radial airflows. By using a unique fire whirl apparatus, the blue whirl can be formed directly upon ignition without going through the transient phase. The study also discovers new flame regimes and explores the mechanism behind the formation and transition of the blue whirl.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Xiaobin Qi, Songyan Gao, Zhiping Zhu, Qinggang Lyu, Haixia Zhang
Summary: This study experimentally investigated the propagation characteristics of reverse combustion under oxygen-limited and enriched conditions. The contribution of volatiles gas-phase oxidation and char surface oxidation to reverse combustion was evaluated. The results showed that oxygen enrichment expanded the operating range of oxygen flow rate for reverse combustion and enhanced the low-temperature oxidation of the solid fuel. The findings provide a better understanding of the driving mechanism of reverse combustion and have important implications for efficient thermal conversion of solid fuels.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Bingjie Chen, Peng Liu, Bingzhi Liu, Zhandong Wang, Xiang Gao, William L. Roberts
Summary: In this study, the low temperature oxidation of 1,2,4-trimethylbenzene was investigated using experiments and numerical simulations. The results showed the presence of toxic oxygenated aromatic compounds and proposed potential formation pathways. The numerical simulations accurately predicted the mole fractions of most compounds, but some compounds were missing.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Meng Sui, Zhiheng Zhu, Fashe Li, Hua Wang
Summary: The effect of adding ferrocene as a combustion catalyst to Jatropha biodiesel on its pyrolysis and combustion performance is investigated. The results show that adding ferrocene reduces activation energy and harmful emissions while improving combustion efficiency.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Manaf Sheyyab, Mohammed Abdulrahman, Subharaj Hossain, Patrick T. Lynch, Eric K. Mayhew, Kenneth Brezinsky
Summary: Fuel surrogates, simplified representations of complex fuels, accurately model speciation results and reaction kinetics, reproduce the ignition quality and chemical functional group compositions of their parent fuels.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Yan Wang, Shumeng Xie, Hannes Bottler, Yiqing Wang, Xinyi Chen, Arne Scholtissek, Christian Hasse, Zheng Chen
Summary: This study investigates how flow affects the ignition and transition process of a cool flame. The results show that the ignition energy determines the highest temperature and the strain rate influences the flame propagation and the transition from cool flame to hot flame.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Tanusree Chatterjee, Mengyuan Wang, Goutham Kukkadapu, Chih-Jen Sung, William J. Pitz
Summary: Cycloalkanes, including cyclohexane, are important hydrocarbons in transportation fuels. However, limited oxidation data at low-to-intermediate temperatures and inadequate predictive ability of kinetic models have hindered the understanding and improvement of cyclohexane oxidation. This study provides experimental and modeling results to develop a more accurate kinetic model for cyclohexane oxidation.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Tao Wu, Erik Hagen, Haiyang Wang, Dylan J. Kline, Michael R. Zachariah, Carole Rossi
Summary: It was found that incorporating CuO into Al/I2O5 can significantly reduce the ignition time and enhance the combustion performance. The optimum composition of 80/20 wt% of I2O5/CuO shows a 30 times shorter ignition time and produces a peak pressure and pressurization rate 4 and 26 times greater than traditional Al/I2O5. A series of characterizations helped unravel the cause of improvement and propose a reaction mechanism for this ternary Al/I2O5/CuO system. This study proposes a facile, inexpensive, and efficient way to enhance the combustion performance of Al/I2O5 biocidal nanoenergetic materials.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Mahmoud Gadalla, Shervin Karimkashi, Islam Kabil, Ossi Kaario, Tianfeng Lu, Ville Vuorinen
Summary: In this study, the flame initiation process in dual-fuel spray assisted combustion is explored through scale-resolved simulations, providing numerical evidence on the initiation of premixed flames. It is found that there is a transient mixed-mode combustion phase after ignition, followed by a primarily deflagrative combustion mode. The interactions between turbulence and premixed flame front are characterized in the corrugated regime.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Neeraj Kumar Pradhan, Arindrajit Chowdhury, Debasis Chakraborty, Neeraj Kumbhakarna
Summary: In this study, a modified model for predicting the burn rate of composite solid propellants is proposed. The model has been validated against experimental and theoretical results, and it outperforms existing models in all cases considered. The model is highly robust and provides results quickly, making it highly efficient in terms of time, effort, and computational resources.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Lili Ye, Zhihe Zhang, Fan Wang, Xiaodong Wang, Yiming Lu, Lei Zhang
Summary: This study investigated the pyrolysis mechanism of octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) explosive using ab initio and kinetic modeling simulations. The results showed that N-NO2 bond fission and C-H beta-scission are important channels in the decomposition of HMX.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Andrei N. Lipatnikov, Hsuchew Lee, Peng Dai, Minping Wan, Vladimir A. Sabelnikov
Summary: This study investigates the importance of thermodiffusive and hydrodynamic instabilities of laminar flames in turbulent flows through numerical simulations. The analysis suggests that laminar flame instabilities play a minor role at sufficiently high Karlovitz numbers.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Shijie Xu, Yue Qiu, Leilei Xu, Jianqing Huang, Shen Li, Elna J. K. Nilsson, Zhongshan Li, Weiwei Cai, Marcus Alden, Xue-Song Bai
Summary: Metal powder is a promising carbon-free and recyclable energy carrier. In this study, a computational model for the combustion and phase change of micron-sized iron particles was proposed and validated. The model successfully captures the melting, surface reactions, cooling, and solidification processes. The study also reveals a two-stage solidification phenomenon and identifies a diffusion-controlled mechanism during the melting process. The reaction between iron and CH4/O2/N2 flame products is found to play a significant role in the iron combustion process.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Khalid Aljohani, Abd El-Sabor Mohamed, Haitao Lu, Henry J. Curran, S. Mani Sarathy, Aamir Farooq
Summary: This study investigates the impact of exhaust gas recirculation (EGR) and NOx on the ignition delay time of oxygenated gasoline. A gasoline surrogate model is developed and the experimental data are useful for predicting fuel ignition behavior in internal combustion engines. The results show that EGR inhibits gasoline reactivity, while NOx has a promoting effect at high temperatures. This research is important for understanding the combustion behavior of gasoline in engines.
COMBUSTION AND FLAME
(2024)
Article
Thermodynamics
Chengcheng Ao, Jia Yan, Tong Yan, Lidong Zhang, Pan Wang
Summary: This study investigates the inhibitory effect of ammonia blended with hydrocarbon fuels on soot formation. The results show that there is a chemical interaction between ammonia and polycyclic aromatic hydrocarbons (PAHs), blocking the formation of larger PAHs.
COMBUSTION AND FLAME
(2024)