More Like this

1. Local bilinear computation of Jacobi sets

   https://doi.org/10.1007/s00371-022-02557-4  

   Klötzl, Daniel; Krake, Tim; Zhou, Youjia; Hotz, Ingrid; Wang, Bei; Weiskopf, Daniel (June 2022, The Visual Computer)

   Abstract We propose a novel method for the computation of Jacobi sets in 2D domains. The Jacobi set is a topological descriptor based on Morse theory that captures gradient alignments among multiple scalar fields, which is useful for multi-field visualization. Previous Jacobi set computations use piecewise linear approximations on triangulations that result in discretization artifacts like zig-zag patterns. In this paper, we utilize a local bilinear method to obtain a more precise approximation of Jacobi sets by preserving the topology and improving the geometry. Consequently, zig-zag patterns on edges are avoided, resulting in a smoother Jacobi set representation. Our experiments show a better convergence with increasing resolution compared to the piecewise linear method. We utilize this advantage with an efficient local subdivision scheme. Finally, our approach is evaluated qualitatively and quantitatively in comparison with previous methods for different mesh resolutions and across a number of synthetic and real-world examples. 

   more » « less
2. Spatial-Terminal Iterative Learning Control for Registration Error Elimination in High-Precision Roll-to-Roll Printing Systems

   https://doi.org/10.1115/MSEC2023-106259  

   Wang, Zifeng; Jin, Xiaoning (June 2023, American Society of Mechanical Engineers)

   Abstract Roll-to-roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based electronic products, as opposed to the sheet-to-sheet approach suited for low-volume work. However, one of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at micrometer level), for multi-layer printed electronics. The alignment of the printed patterns in different layers, known as registration, is critical to product quality. Registration errors are essentially accumulated positional or dimensional deviations caused by un-desired variations in web tensions and web speeds. Conventional registration control methods rely on model-based feedback controllers, such as PID control, to regulate the web tension and the web speed. However, those methods can not guarantee that the registration error always converges to zero due to lagging problems. In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method combined with PID control to enable the registration error to converge to zero iteratively, which achieves unprecedented control in the creation, integration and manipulation of multi-layer microstructures in R2R processes. We simulate the registration error generation and accumulation caused by axis mismatch between roller and motor that commonly exists in R2R systems. We show that the STILC-PID hybrid control method can eliminate the registration error completely after a reasonable number of iterations. We also compare the performances of STILC with a constant-value basis and a cosine-form basis. The results show that the control model with a cosine-form basis provides a faster convergence speed for R2R registration error elimination. 

   more » « less
3. Spatial-Terminal Iterative Learning Control for Registration Error Elimination in High-Precision Roll-to-Roll Printing Systems

   Zifeng Wang, Xiaoning Jin (September 2023, Proceedings of the ASME 2023 18th International Manufacturing Science and Engineering Conference)

   Roll-to-roll (R2R) printing techniques are promising for high-volume continuous production of substrate-based electronic products, as opposed to the sheet-to-sheet approach suited for low-volume work. However, one of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at micrometer level), for multi-layer printed electronics. The alignment of the printed patterns in different layers, known as registration, is critical to product quality. Registration errors are essentially accumulated positional or dimensional deviations caused by un-desired variations in web tensions and web speeds. Conventional registration control methods rely on model-based feedback controllers, such as PID control, to regulate the web tension and the web speed. However, those methods can not guarantee that the registration error always converges to zero due to lagging problems. In this paper, we propose a Spatial-Terminal Iterative Learning Control (STILC) method combined with PID control to enable the registration error to converge to zero iteratively, which achieves unprecedented control in the creation, integration and manipulation of multi-layer microstructures in R2R processes. We simulate the registration error generation and accumulation caused by axis mismatch between roller and motor that commonly exists in R2R systems. We show that the STILC-PID hybrid control method can eliminate the registration error completely after a reasonable number of iterations. We also compare the performances of STILC with a constant-value basis and a cosine-form basis. The results show that the control model with a cosine-form basis provides a faster convergence speed for R2R registration error elimination. 

   more » « less
4. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning

   https://doi.org/10.1088/1748-9326/acbbe0  

   ElGhawi, Reda; Kraft, Basil; Reimers, Christian; Reichstein, Markus; Körner, Marco; Gentine, Pierre; Winkler, Alexander J (March 2023, Environmental Research Letters)

   Abstract The process of evapotranspiration transfers liquid water from vegetation and soil surfaces to the atmosphere, the so-called latent heat flux ( Q LE ), and modulates the Earth’s energy, water, and carbon cycle. Vegetation controls Q LE by regulating leaf stomata opening (surface resistance r s in the Big Leaf approach) and by altering surface roughness (aerodynamic resistance r a ). Estimating r s and r a across different vegetation types is a key challenge in predicting Q LE . We propose a hybrid approach that combines mechanistic modeling and machine learning for modeling Q LE . The hybrid model combines a feed-forward neural network which estimates the resistances from observations as intermediate variables and a mechanistic model in an end-to-end setting. In the hybrid modeling setup, we make use of the Penman–Monteith equation in conjunction with multi-year flux measurements across different forest and grassland sites from the FLUXNET database. This hybrid model setup is successful in predicting Q LE , however, this approach leads to equifinal solutions in terms of estimated physical parameters. We follow two different strategies to constrain the hybrid model and therefore control for the equifinality that arises when the two resistances are estimated simultaneously. One strategy is to impose an a priori constraint on r a based on mechanistic assumptions (theory-driven strategy), while the other strategy makes use of more observational data and adds a constraint in predicting r a through multi-task learning of both latent and sensible heat flux ( Q H ; data-driven strategy) together. Our results show that all hybrid models predict the target variables with a high degree of success, with R 2 = 0.82–0.89 for grasslands and R 2 = 0.70–0.80 for forest sites at the mean diurnal scale. The predicted r s and r a show strong physical consistency across the two regularized hybrid models, but are physically implausible in the under-constrained hybrid model. The hybrid models are robust in reproducing consistent results for energy fluxes and resistances across different scales (diurnal, seasonal, and interannual), reflecting their ability to learn the physical dependence of the target variables on the meteorological inputs. As a next step, we propose to test these heavily observation-informed parameterizations derived through hybrid modeling as a substitute for ad hoc formulations in Earth system models. 

   more » « less
5. A preliminary investigation of moisture effect on nanofiber z-threaded CFRP prepreg and laminate

   Uddin, Md N.; Taylor, William W.; Islam, Mohammad R.; Hsiao, Kuang-Ting (May 2022, SAMPE 2022 Conference Proceedings, Charlotte, NA, May 23-26, 2022)

   Moisture is a known issue for carbon fiber reinforced polymer (CFRP) manufacturing. During the process, in which a CFRP prepreg is carefully thawed, cut, stacked, and cured into a laminate, any bad moisture control can cause voids, affect the curing, and degrade the laminate. Recent studies of carbon nanofiber z-threaded CFRP (i.e., ZT-CFRP) prepreg and its laminates showed significant multifunctional improvements in the mechanical strengths, toughness, thermal conductivity, and electrical conductivity. The carbon nanofibers zig-zag thread among the carbon fibers in the through-thickness direction (i.e., z-direction) and mechanically interlock the fiber system together to form an effective 3D-fiber-network reinforced laminate. This paper presents a preliminary experimental study on the ZT-CFRP prepreg when facing the moisture exposure during the prepreg handling and lamination process. Both the ZT-CFRP and traditional CFRP prepregs, subjected to different humidity conditions, will be cut, and cured into laminate samples. The samples will be tested for their interlaminar shear strengths (ILSS) and hardness. Microscope pictures of the samples' fracture patterns will be compared for explaining the combined impact of the moistures and the carbon nanofiber z-threading strategy on the laminates' interlaminar shear strength and curing state. 

   more » « less