Review
Engineering, Electrical & Electronic
D. D. L. Chung
Summary: This article is the first review of the emerging field of capacitance-based self-sensing in structural materials. Capacitance-based self-sensing is both scientifically rich and widely applicable. It can be used for materials ranging from insulators to conductors, while resistance-based self-sensing is only applicable to conductors. The attributes sensed include stress/strain, temperature, and damage.
SENSORS AND ACTUATORS A-PHYSICAL
(2023)
Article
Nanoscience & Nanotechnology
Vladimir Bordo, Thomas Ebel
Summary: The theory of the effective dielectric function of a nanocomposite dielectric in a capacitor is developed based on first principles. The nanosized inclusions in the dielectric are modeled as point dipoles using the Maxwell-Garnett approach, and the electromagnetic field of the induced dipoles reflected from the electrodes is accounted for using the dyadic Green's function. The developed theory replaces the Maxwell-Garnett approximation for nanocomposites in the subwavelength regime, which is of significance in electrical engineering.
Article
Physics, Applied
Rabeya Binta Alam, Md Hasive Ahmad, S. F. U. Farhad, Muhammad Rakibul Islam
Summary: This study evaluates the dielectric properties of gelatin/single-walled carbon nanotube nanocomposites and elucidates the mechanism behind the improved performance. The addition of carbon nanotubes reduces the relaxation time and increases the conductivity and capacitive element of the nanocomposites.
JOURNAL OF APPLIED PHYSICS
(2022)
Article
Materials Science, Ceramics
Muhammad Aadil, Muhammad Farooq Warsi, Philips O. Agboola, Mohamed F. Aly Aboud, Imran Shakir
Summary: A novel Co3O4/r-GO nanocomposite electrode material with higher specific capacitance, good rate capability, and excellent cycle performance has been prepared for electrochemical applications. The electrode shows a high specific capacitance of 865 Fg(-1) @ 1 Ag-1 and exceptional cycling stability with 93.2% retention after 5000 cycles, making it a promising candidate as an anode material for practical applications in the electrochemical capacitor.
CERAMICS INTERNATIONAL
(2021)
Article
Engineering, Electrical & Electronic
D. D. L. Chung, Xiang Xi
Summary: This paper introduces the concept, principle, sensing method, and related materials science of piezopermittivity, explaining the effectiveness and advantages of different materials in capacitance sensing.
SENSORS AND ACTUATORS A-PHYSICAL
(2021)
Article
Engineering, Electrical & Electronic
Mohammad Nankali, Norouz Mohammad Nouri, Nima Geran Malek, Morteza Amjadi
Summary: This study presents a temperature-dependent 3D percolation model for predicting the sensory response of stretchable strain sensors in complex environmental conditions. Experimental results validate the model's effectiveness for assessing the dynamic response characteristics of strain sensors.
SENSORS AND ACTUATORS A-PHYSICAL
(2021)
Article
Engineering, Multidisciplinary
Robert C. Youngquist, Jedediah M. Storey, Mark A. Nurge, Christopher J. Biagi
Summary: The sensitivity matrix is derived in electrical capacitance tomography without assumptions on the magnitude of the relative permittivity and higher order terms, providing a means to improve and extend the applicability of the algorithm to a wider range of dielectric materials.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Hai Zhu, Jiangtao Sun, Jun Long, Wenbin Tian, Shijie Sun, Lijun Xu
Summary: A deep learning-based method is proposed in this study to derive the permittivity values of multiple different dielectrics by adding a multi-level fusion layer. A hybrid training strategy is implemented to refine low-quality input images and reduce time consumption effectively. Simulation and experimental results confirm the effectiveness of the proposed method compared to conventional reconstruction methods and other deep learning-based methods.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
R. Abirami, R. Kabilan, P. Nagaraju, V. Hariharan, S. Thennarasu
Summary: Mn3O4/MWCNT nanocomposites were synthesized for supercapacitor applications via ultrasonic method, exhibiting improved electrochemical performance compared to pristine Mn3O4 nanoparticles. The composite electrode of Mn3O4/MWCNT demonstrated strong cyclic performance and stable capacitance retention after 5000 cycles, confirming its suitability as a lasting electrode material for supercapacitor applications.
JOURNAL OF ELECTRONIC MATERIALS
(2021)
Article
Engineering, Electrical & Electronic
Garrett C. Thomas, D. D. L. Chung
Summary: This paper presents the first report on the dielectric behavior of electrically conductive metal-particle thick films, which impedes electrical conduction. The experiment reveals that the silver thick film has a high relative permittivity but low DC resistivity, and the inter-particle interfaces in the thick film promote carrier-atom interaction.
JOURNAL OF ELECTRONIC MATERIALS
(2022)
Article
Materials Science, Multidisciplinary
Yexiong Huang, Jiabing Yu, Ku Shu, Xianping Chen
Summary: This study develops a conductive hydrogel with robust mechanical properties, long-term stability, and good conductivity for highly sensitive wearable strain sensors. Carbon nanotubes are introduced to enhance the mechanical performance of the hydrogel. Sodium chloride is introduced to greatly improve its conductivity. This work provides a strategy to balance the different properties of a hydrogel for practical applications in next-generation wearable electronics.
JOURNAL OF MATERIALS CHEMISTRY C
(2022)
Review
Engineering, Electrical & Electronic
Aluisio do Nascimento Wrasse, Eduardo Nunes dos Santos, Marco Jose da Silva, Hao Wu, Chao Tan
Summary: This article reviews the different capacitive sensors applied to multiphase flow measurement and imaging. It presents and discusses the operating principles, sensor geometries, and capacitive measuring circuits, along with the advantages and disadvantages focusing on flow applications and possible flow parameters yielded from sensors. Some new trends in capacitive flow sensors are also presented.
IEEE SENSORS JOURNAL
(2022)
Article
Materials Science, Multidisciplinary
Le N. M. Dinh, Bich Ngoc Tran, Vipul Agarwal, Per B. Zetterlund
Summary: This study explores the effects of different preparation methods on polymer nanocomposite materials, and finds that the stability and physical properties of the nanocomposite films are greatly influenced by the preparation method and compositional details.
ACS APPLIED POLYMER MATERIALS
(2022)
Article
Engineering, Environmental
Leema Rose Viannie, N. R. Banapurmath, Manzoore Elahi M. Soudagar, Anilkumar Nandi, Nazia Hossain, Ashwini Shellikeri, Vinita Kaulgud, Ma Mujtaba, Sher Afghan Khan, Mohammad Asif
Summary: In this study, highly flexible polymer nanocomposite sheets were successfully developed using MWCNT in a PDMS matrix via solution processing technique, showing superior electrical conductivity and stability. An optimal filler concentration of 5.58 wt% exhibited high stiffness, tensile strength, and elongation, making it suitable for fabricating flexible strain sensors.
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Wenbin Tian, Peng Suo, Dong Liu, Shijie Sun, Jiangtao Sun, Lijun Xu
Summary: The study proposes a reconstruction framework for electrical capacitance tomography (ECT) based on sparse representation of phase boundaries, which significantly improves image quality and accurately estimates the real-permittivity values of inclusions. Simulations and phantom experiments demonstrate the method's excellent performance in terms of noise reduction and robustness against model parameter choices.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Materials Science, Multidisciplinary
Han-Joo Lee, Julie A. Mancini, Ishan Joshipura, Christopher M. Spadaccini, Kenneth J. Loh
Summary: Waveguides designed by shape optimization are more suitable for additive manufacturing, and can control the propagation of ultrasonic waves and be used for signal transmission or selective heating.
ADVANCED ENGINEERING MATERIALS
(2022)
Article
Computer Science, Interdisciplinary Applications
Travis B. Fillmore, Zihan Wu, Manuel A. Vega, Zhen Hu, Michael D. Todd
Summary: This research proposes an iterative global-local method for quickly simulating cracking on large steel structures and compares it with other methods. The results show that the proposed method has the fastest solution time and comparatively accurate results.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Engineering, Multidisciplinary
Hua-Ping Wan, Zi-Nan Zhang, Yaozhi Luo, Wei-Xin Ren, Michael D. Todd
Summary: This study proposes a multi-fidelity Gaussian process modeling (GPM) method for uncertainty quantification (UQ), which combines high-fidelity (HF) samples with computationally-cheaper low-fidelity (LF) ones to achieve a trade-off between computational cost and accuracy. A generalized co-Gaussian process model (GC-GPM) is developed to mix LF and HF samples, and an adaptive sampling strategy is introduced to reduce the number of training samples. The results show that the proposed adaptive GC-GPM method outperforms traditional GC-GPM in terms of computational accuracy and efficiency.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Yang Han, Xin Feng, Michael D. Todd
Summary: This study proposes a novel method to quantitatively identify the leakage location and negative pressure wave (NPW) velocity in the pipeline based on a fluid dynamics model. Experimental results demonstrate that the method can accurately identify the location and severity of leakage without prior knowledge of NPW velocity, providing a promising technique for pipeline integrity management.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Industrial
Mayank Chadha, Mukesh K. Ramancha, Manuel A. Vega, Joel P. Conte, Michael D. Todd
Summary: This paper proposes an approach to select a maintenance strategy based on the decision maker's behavioral risk profile. The approach considers the posterior distribution of the damage state and the decision maker's evaluation and behavior. The effectiveness of the approach is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Mathematics, Interdisciplinary Applications
David A. A. Najera-Flores, Michael D. D. Todd
Summary: Data-driven machine learning models are effective for complex structures modeling without physical models. However, they tend to perform poorly outside the original training domain. We propose a neural network framework based on Hamiltonian mechanics to enforce a physics-informed structure to the model.
COMPUTATIONAL MECHANICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Said Quqa, Luca Landi, Kenneth J. Loh
Summary: Sensing skins and electrical impedance tomography are cost-effective alternative methods for distributed sensing in civil structures, but their performance declines with aging of the sensing film. This paper proposes a novel approach using electrical resistance tomography and deep neural networks for crack identification in nanocomposite paint sprayed on structural components. The method incorporates crack annotations collected during visual inspections and utilizes transfer learning to improve crack identification performance. The results demonstrate that the proposed method outperforms traditional approaches for crack localization in complex damage patterns.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Engineering, Mechanical
Guan-Sen Dong, Hua-Ping Wan, Yaozhi Luo, Michael D. Todd
Summary: Vibration data often contains important information about dynamic characteristics, but the large volume of data from high-frequency vibration poses challenges in transmission and storage. We propose a novel deep learning method using DCGAN for vibration data reconstruction, which directly learns the mapping between compressed and original signals and achieves high computational efficiency and accuracy.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Said Quqa, Sijia Li, Yening Shu, Luca Landi, Kenneth J. Loh
Summary: This paper explores the efficacy of supervised machine learning in solving the inverse electrical impedance tomography problem and reconstructing the conductivity distribution of a piezoresistive sensing film. A deep neural network is used to reconstruct the conductivity distribution within the painted area by using voltage measurements collected at sparse boundary locations. The paper presents a new approach to test the suitability of synthetic datasets built using a finite element model of the sensing film. Promising results are obtained compared to conventional methods.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Manufacturing
Han-Joo Lee, Jorge Canada, Luis Fernando Velasquez-Garcia
Summary: This study presents the design, fabrication, and characterization of the first 3D-printed peristaltic pumps for creating and maintaining dry vacuum in compact systems. The pumps utilize a novel actuator design that requires less force to seal, enabling low vacuum at low actuation speed. The devices are made of PLA and FiberFlex 40D, with the latter demonstrating satisfactory fatigue life and hyperelastic behavior. Experimental characterization shows that the prototypes achieve significantly lower base pressure compared to a state-of-the-art diaphragm vacuum pump. The technology is important for in-situ, low-waste manufacturing in remote areas, including space applications.
ADDITIVE MANUFACTURING
(2023)
Article
Engineering, Multidisciplinary
David A. Najera-Flores, Zhen Hu, Mayank Chadha, Michael D. Todd
Summary: In order to predict the remaining useful life (RUL) of lithium-ion batteries, simplified physical laws and machine learning-based methods can be used to develop a capacity degradation model. While simplified physical models are easy to implement, they may result in large errors in failure prognostics. Data-driven models can provide more accurate degradation forecasting but may require a large amount of training data and may produce predictions inconsistent with physical laws. Existing methods also face challenges in predicting RUL at the early stages of battery life.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Mechanical
Guofeng Qian, Zhen Hu, Michael D. Todd
Summary: This paper proposes a novel adaptive surrogate modeling framework to perform physics-based corrosion reliability analysis of large structures using mesoscale simulations, addressing the computational challenge of coupling macro and mesoscale models. The framework first constructs a global surrogate model from a finite element mechanical model to propagate input uncertainty from the macroscale to local stress responses. Then, a mesoscale surrogate model is constructed from phase-field simulations to predict failure probability by considering uncertainty in both scales. The proposed method is demonstrated to efficiently and accurately generate a failure probability map for large structures and outperforms existing surrogate model-based reliability analysis algorithms.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
David A. Najera-Flores, Guofeng Qian, Zhen Hu, Michael D. Todd
Summary: This paper proposes a physics-constrained machine learning method for surrogate modeling of high-fidelity multi-physics pitting corrosion simulation. The proposed surrogate model uses a convolutional variational autoencoder to reduce the dimension of pitting corrosion shape images and a Bayesian multi-layer perceptron network to model the evolution of corrosion pit morphology over time. A physics constraint is added to ensure the corrosion rate is strictly negative. Compared to a purely data-driven surrogate model, the proposed physics-constrained surrogate model is significantly more accurate.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Jice Zeng, Michael D. Todd, Zhen Hu
Summary: This paper proposes an innovative Recursive Inference method based on Invertible Neural Networks (RINN) for updating multi-level computational models. The method compresses high-dimensional video monitoring data into low-dimensional latent-space data and trains a likelihood-free inference model using synthetic video monitoring data. The paper further introduces a recursive model updating strategy that combines the likelihood-free inference with particle filtering. The efficacy of the proposed RINN framework is demonstrated through a case study on degradation model updating for a miter gate application.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Mechanical
Zihan Wu, Jice Zeng, Zhen Hub, Michael D. Todd
Summary: This paper presents a novel physics-informed UAV inspection planning framework for infrastructure structural health assessment based on model-based diagnostics and prognostics enabled by physics-based probabilistic analysis. It bridges the gap between UAV mission planning and inspection with model-based probabilistic analysis, optimizing the key UAV inspection parameters to achieve a more effective inspection.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
