118 research outputs found
Nonlinear dynamic modelling and performance of deployable telescopic tubular mast (TTM)
No full textThe aim of this work is to present the longitude and transverse vibrations of a deployable TTM, which is attached on a spacecraft system, considering the effect of rigid-flexible coupling phenomenon. The proposed model is derived based on the principle of virtual work and discretized by assumed mode method. To introduce the nonlinear effect, the von Kármán strain is adopted. Additionally, locking and restart behaviors are taken into account in the modelling procedure of the deploying process. Finally, the dynamic phenomena of the longitude and transverse displacements are analyzed at different deploying velocitie
Dynamic Modelling and Performance Analysis of Deployable Telescopic Tubular Mast
No full textThis work presents the longitudinal and transverse coupling vibrations of a deployable Telescopic Tubular Mast (TTM), a multi-stepped structure integrated into a spacecraft system, while considering the rigid-flexible coupling phenomenon. The model is derived using the principle of virtual work and discretized via the variable separation method. The von Kármán strain is employed to incorporate geometric nonlinear effects. Semi-analytical results for the shape functions and natural frequencies of the quasi-static multi-stepped boom are obtained using the extended transfer matrix method (ETMM). These natural frequencies are validated against results from Nastran, confirming the ETMM's accuracy. In addition, the model accounts for the continuously changing natural frequencies and shape functions during the deployment phase. Finally, the dynamic phenomena of the longitudinal and transverse displacements are analyzed at various deploying states, including locking and restart behaviors. The influence of the structural damping on the vibration evolution is also contained in the numerical analysis
Numerical and Experimental Study of the Wetting Characteristics of Water Droplets on Solid Substrates
No full textIn this study, the author first reviewed the background and the mechanism of a highly efficient cooling method—i.e., the thin film evaporative cooling, in which the heat removal performance is highly dependent on the wetting characteristics of the working fluid. Then, the author studied the wetting behavior of water on different solid substrate both numerically and experimentally. By minimizing the free energy, Surface Evolver was used to explore the profile of the static liquid meniscus and the corresponding contact angle of water droplets on plain solid substrate and pillar substrate with sharp edge. Besides, goniometer experiments were performed to study the contact angle of a sessile water droplet on silicon, copper and aluminum substrates with graphene oxide (GO) and reduced graphene oxide (RGO) nanocoatings of different thicknesses. In addition, the author prepared a detailed list of components to be ordered for performing Micro-PIV experiments. An extensive literature study have been done to support the feasibility of the author’s work
L008 CROP-seq Dataset
No full textPlease cite the following work if you find the L008 dataset useful in your research.
https://arxiv.org/abs/2210.00116
@article{wu2022predicting,
title={Predicting cellular responses with variational causal inference and refined relational information},
author={Wu, Yulun and Barton, Robert A and Wang, Zichen and Ioannidis, Vassilis N and De Donno, Carlo and Price, Layne C and Voloch, Luis F and Karypis, George},
journal={International Conference on Learning Representations},
year={2023}
Cellular Response Prediction
No full textPredicting Cellular Responses with Variational Causal Inference and Refined Relational Information (ICLR 2023)
https://github.com/yulun-rayn/graphVCI
@article{wu2022predicting,
title={Predicting cellular responses with variational causal inference and refined relational information},
author={Wu, Yulun and Barton, Robert A and Wang, Zichen and Ioannidis, Vassilis N and De Donno, Carlo and Price, Layne C and Voloch, Luis F and Karypis, George},
journal={International Conference on Learning Representations},
year={2023}
L008 CROP-seq Dataset
No full textPlease cite the following work if you find the L008 dataset useful in your research.
https://arxiv.org/abs/2210.00116
@article{wu2022predicting,
title={Predicting cellular responses with variational causal inference and refined relational information},
author={Wu, Yulun and Barton, Robert A and Wang, Zichen and Ioannidis, Vassilis N and De Donno, Carlo and Price, Layne C and Voloch, Luis F and Karypis, George},
journal={International Conference on Learning Representations},
year={2023}
Cellular Response Prediction
No full textPredicting Cellular Responses with Variational Causal Inference and Refined Relational Information (ICLR 2023)
https://github.com/yulun-rayn/graphVCI
@article{wu2022predicting,
title={Predicting cellular responses with variational causal inference and refined relational information},
author={Wu, Yulun and Barton, Robert A and Wang, Zichen and Ioannidis, Vassilis N and De Donno, Carlo and Price, Layne C and Voloch, Luis F and Karypis, George},
journal={International Conference on Learning Representations},
year={2023}
L008 CROP-seq Dataset
No full textPlease cite the following work if you find the L008 dataset useful in your research.
https://arxiv.org/abs/2210.00116
@article{wu2022predicting,
title={Predicting cellular responses with variational causal inference and refined relational information},
author={Wu, Yulun and Barton, Robert A and Wang, Zichen and Ioannidis, Vassilis N and De Donno, Carlo and Price, Layne C and Voloch, Luis F and Karypis, George},
journal={International Conference on Learning Representations},
year={2023}
Design of Efficient Catalysts and Mechanism Study for Photothermochemical Dry Reforming of Methane
No full textThe world's growing demand for energy has led to over-reliance on fossil fuels and increased emissions of greenhouse gases, which contributed to severe global warming. Dry reforming of methane (DRM) is a valuable technique that holds great promise in mitigating two main greenhouse gases (GHGs), CO2 and CH4 in the atmosphere and producing syngas (CO and H2), a valuable industrial feedstock to produce liquid fuels through Fischer-Tropsch processes. However, considerable thermal energy input from burning fossil fuels will be needed due to the endothermic characteristic of DRM, which leads to the reemission of GHGs. Instead, solar energy is a more sustainable and promising energy source to drive DRM while novel catalyst design can efficiently promote the synergy of thermal catalytic and photocatalytic activities. The long-term stable and efficient catalytic activities and fundamental mechanisms study remain a challenge for solar-driven DRM to be commercially applied. The major goals in this research are to design novel catalysts to maximize the DRM activities and to investigate photochemistry and reaction mechanisms. Firstly, the origin of photocatalytic effects from solar irradiation were systematically probed on a photoactive CeO2 supported Pt catalyst (Pt/CeO2) with Pt/ZrO2 as a photo-inactive control. It was found that the contributions of photocatalysis were mainly from lights less than 435 nm in wavelength, and photo-irradiation regenerated surface oxygen vacancies, thus boosting CO2 activation and promoting formate and carbonate intermediates conversion to final products. Based on the understanding of the photocatalytic effect on CeO2, CeO2 was then incorporated in ZrO2-supported Ni NPs (Ni-CeO2/ZrO2) and propelled PTC-DRM performance. Ni-CeO2/ZrO2 accumulated less coke and exhibited elevated and stable PTC-DRM activities. Furthermore, a Ce-substituted LaNiO3 perovskite catalyst was synthesized, and it was found light irradiation induced photocatalytic activities on La0.9Ce0.1NiO3 and enhanced CO2 adsorption and formation of active lanthanum oxycarbonates intermediates, making the CO2 and CH4 conversion among the top-performing literature. Additionally, a metal-organic framework (MOF) confined bimetallic Ni-Cu nanoparticles (9Ni1Cu/MOF) was developed and investigated. 9Ni1Cu/MOF offered full nanoconfinement of Ni-Cu NPs by the MOF-derived tetragonal-ZrO2/C (t-ZrO2/C) nanostructure, which provided large surface area, featured strong metal-support interaction, and hindered the detrimental filamentous carbon deposition and metal aggregation. As a result, the 9Ni1Cu/MOF catalyst delivered high and stable DRM activities, with an average of CO2 and CH4 conversions and H2/CO molar ratio of 76.5%, 76.7%, and 1.07 over the 100-h DRM reaction, which are among the top performing catalysts for DRM process. It is envisioned that this work about fundamental mechanism investigation and material innovation will lead to future large-scale commercial applications of DRM technology and bring scientific advancement into other catalytic and energy-saving processes
Physics-Informed Neural Networks for Advanced Thermal Management in Electronics and Battery Systems: A Review of Recent Developments and Future Prospects
No full textThe growing complexities, power densities, and cooling demands of modern electronic systems and batteries—such as three-dimensional integrated circuit chip packaging, printed circuit board assemblies, and electronics enclosures—have pushed the urgency for efficient and dynamic thermal management strategies. Traditional numerical methods like computational fluid dynamics (CFD) and the finite element method (FEM) are computationally impractical for large-scale or real-time thermal analysis, especially when dealing with complex geometries, temperature-dependent material properties, and rapidly changing boundary conditions. These approaches typically require extensive meshing and repeated simulations for each new scenario, making them inefficient for design exploration or optimization tasks. Physics-informed neural networks (PINNs) emerge as a powerful alternative approach that incorporates physical principles such as mass and energy conservation equations into deep learning models. This approach delivers rapid and adaptable resolutions to the partial differential equations that govern heat transfer and fluid dynamics. This review examines the basic principle of PINN and its role in thermal management for electronics and batteries, from the small unit scale to the system scale. We highlight recent advancements in PINNs, particularly their superior performance compared to traditional CFD methods. For example, studies have shown that PINNs can be up to 300,000 times faster than conventional CFD solvers, with temperature prediction differences of less than 0.1 K in chip thermal models. Beyond speed, we explore the potential of PINNs in enabling efficient design space exploration and predicting outcomes for previously unseen scenarios. However, challenges such as training convergence in fine-grained or large-scale applications remain. Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R2 of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. Finally, we propose future research directions that emphasize the integration of PINNs with advanced hardware and hybrid modeling techniques to advance thermal management solutions for next-generation electronics and battery systems
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