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High-performance, multi-component epoxy resin simulation for predicting thermo-mechanical property evolution during curing
No full textHigh-performance epoxy systems are extensively used in structural polymer‒matrix composites for aerospace vehicles. The evolution of the thermomechanical properties of these epoxies significantly impacts the evolution of process-induced residual stresses. The corresponding process parameters need to be optimized via multiscale process modeling to minimize the residual stresses and maximize the composite strength and durability. In this study, the thermomechanical properties of a multicomponent epoxy system are predicted via molecular dynamics (MD) simulation as a function of the degree of cure to provide critical property evolution data for process modeling. In addition, the experimentally validated results of this study provide critical insight into MD modeling protocols. Among these insights, harmonic- and Morse-bond-based force fields predict similar mechanical properties. However, simulations with the Morse-bond potential fail at intermediate strain values because of cross-term energy dominance. Additionally, crosslinking simulations should be conducted at the corresponding processing temperature, because the simulation temperature impacts shrinkage evolution significantly. Multiple analysis methods are utilized to process MD heating/cooling data for glass transition temperature prediction, and the results indicate that neither method has a significant advantage. These results are important for effective and comprehensive process modeling within the ICME (Integrated Computational Materials Engineering) and Materials Genome Initiative frameworks
Shake-Table Test of a Seismically Resilient 10-Story Mass Timber Building with Supplemental Uplift Friction Dampers
No full textPost-tensioned rocking mass timber walls (PT-RMTWs) have been established as a seismically resilient seismic force-resisting system for mass timber buildings with high potential. The literature is rich in experimental studies that show that mass timber walls exhibit rocking wall behavior but require supplemental damping systems to dissipate energy during an earthquake. In this experimental study, the dynamic seismic response of a recently introduced low-damage uplift friction damper (UFD) for PT-RMTWs is validated. Experimental tests were conducted as a separate payload project using the Natural Hazards Engineering Research Infrastructure (NHERI) TallWood large-scale 10-story mass timber building shake-table test specimen. Tests were conducted at the NHERI Large High Performance Outdoor Shake Table at the University of California, San Diego. This paper reports the results of this experimental test program with the UFDs along with some limited comparisons to numerical results. First, the global response of the test building is presented. Second, local and global behavior of the UFDs installed on the test building is highlighted. Third, a comparison of building response with and without the UFDs of the test building is presented. Lastly, some limited comparisons are made with the experimental results with those predicted obtained from a nonlinear response history analysis of a numerical model of the test building. The reported experimental results show that the UFDs enhanced the seismic response of the test building quantified by increasing the lateral strength, reducing the peak interstory drifts, and adding energy dissipation
Minimax Approximation of the l1-ball by Ellipsoids
No full textIt is shown that, among all the ellipsoids in Rn centered at the origin that are of the same volume as the unit Euclidean ball B2, the ball B2 provides the best approximation of the l1-ball B1
CaLMPhosKAN: Prediction of general phosphorylation sites in proteins via fusion of codon aware embeddings with amino acid aware embeddings and wavelet-based Kolmogorov-Arnold network
Get PDFMOTIVATION: The mapping from codon to amino acid is surjective due to codon degeneracy, suggesting that codon space might harbor higher information content. Embeddings from the codon language model have recently demonstrated success in various protein downstream tasks. However, predictive models for residue-level tasks such as phosphorylation sites, arguably the most studied Post-Translational Modification (PTM), and PTM sites prediction in general, have predominantly relied on representations in amino acid space. RESULTS: We introduce a novel approach for predicting phosphorylation sites by utilizing codon-level information through embeddings from the codon adaptation language model (CaLM), trained on protein-coding DNA sequences. Protein sequences are first reverse-translated into reliable coding sequences by mapping UniProt sequences to their corresponding NCBI reference sequences and extracting the exact coding sequences from their GenBank format using a dynamic programming-based global pairwise alignment. The resulting coding sequences are encoded using the CaLM encoder to generate codon-aware embeddings, which are subsequently integrated with amino acid-aware embeddings obtained from a protein language model, through an early fusion strategy. Next, a window-level representation of the site of interest, retaining the full sequence context, is constructed from the fused embeddings. A ConvBiGRU network extracts feature maps that capture spatiotemporal correlations between proximal residues within the window. This is followed by a prediction head based on a Kolmogorov-Arnold network (KAN) using the derivative of gaussian wavelet transform to generate the inference for the site. The overall model, dubbed CaLMPhosKAN, performs better than the existing approaches across multiple datasets. AVAILABILITY AND IMPLEMENTATION: CaLMPhosKAN is publicly available at https://github.com/KCLabMTU/CaLMPhosKAN
New avenues for community solar adoption research: A qualitative comparative analysis of ten U.S. states
No full textCommunity solar presents an opportunity for every household and business to harness the advantages of solar energy. In the community solar adoption literature, qualitative and case-specific investigations have suggested potential adoption factors, such as related policies, solar technical potential, and socioeconomic measures. However, limited effort has been devoted to evaluating the complex dynamics of these causal conditions, let alone the development of empirical evidence. This paper fills these gaps using fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine the dynamics of U.S. community solar adoption factors. The application of fsQCA, grounded in set-theoretic principles and truth-table analysis, enables a comprehensive evaluation of the various configurations of conditions that contribute to community solar adoption among the top ten adopting states. The results show that, in the most active states, adoption paths vary in government liberalism. For conservative states, the primary factors were a non-wealthy GDP per capita and strong solar potential. On the other hand, liberal states commonly had strong solar potential and high electricity prices as their common factors for adopting community solar
Hydrogen storage in multilayer Ti3C2Tx MXene
Get PDFIn this work, we systematically investigated the hydrogen storage properties of multilayer Ti3C2Tx MXene using density functional theory (DFT) coupled with the quantum-thermodynamic model to include the thermodynamic effect on hydrogen adsorption and storage behavior over a wide range of applied pressure and temperature. In addition to the surface-adsorbed hydrogen, we show that the interlayer spacing is a plausible storage route that could contribute to an additional hydrogen storage capacity. Due to hydrogen-bond bounded multilayer Ti3C2Tx, the insertion, diffusion, and adsorption of hydrogen molecules into the interlayer spacing of the multilayer structure require sufficient external pressure to overcome the energy penalty to separate the multilayer structure. Using DFT calculations, we presented a novel model in attempt to unveil the mechanism of the nanopump effect, and providing new insights into its underlying process from a theoretical perspective. The calculated upper bound of theoretical gravimetric storage capacity using DFT calculation for multilayer Ti3C2Tx is ∼3.8 wt% H2. While at 77 and 300 K with external pressure of 25 MPa, the predicted gravimetric capacity employing the DFT and quantum-thermodynamic model are found to be ∼2.1 and 0.67 wt% H2, respectively, and experimentally, these H2-stored multilayer Ti3C2Tx structures can be verified based on our simulated XRD analysis. Based on this work, we believe that our current simulation model can provide a reasonable and realistic prediction of hydrogen storage capacity and a systematic study of hydrogen storage mechanisms in other two-dimensional (2D) layered materials, besides multilayer Ti3C2Tx
LIGHT ABSORBING AEROSOL INTERACTIONS WITH WARM CLOUDS
No full textAtmospheric particles such as biomass-burning aerosols, including black carbon - are critical in altering the Earth\u27s energy balance through interactions with light and clouds. Biomass-burning aerosols also contain organic carbon, a diverse mix of organic compounds. As these aerosols age, they evolve by interacting with other atmospheric species, including cloud water, leading to changes in particle morphology and the development of coatings that impact their lifetime and ability to interact with clouds and light.
Some biomass-burning aerosols serve as cloud condensation nuclei. When cloud water droplets form, the properties of the nucleating particles can change. Many clouds evaporate before precipitating, releasing these altered aerosols back into the atmosphere. These modified particles can then interact again with clouds, further modifying their properties. To study these interactions, we developed a method using surrogate black carbon and organic carbon materials to produce mixtures of black carbon, organic carbon, and coated black carbon particles. We then analyzed their interactions with clouds in laboratory experiments using Michigan Tech’s Pi cloud chamber.
Our research provides insights into how coatings, mixing states, and morphologies of biomass-burning aerosols differ between particles that interact with clouds and those that do not. We identified species enriched in cloud-forming residuals, explored factors influencing aerosol evolution, and characterized changes in black carbon coatings due to cloud interactions. In the future, these findings should help reduce uncertainties in climate models
KNOWLEDGE INTEGRATED DEEP LEARNING FOR ENHANCED FAULT DIAGNOSIS AND PROGNOSIS FOR ROTATING MACHINERY AND ITS APPLICATIONS ON MARINE SYSTEMS
Get PDFThis dissertation develops a knowledge-informed deep learning framework for robust fault diagnosis and prognosis across various engineered systems, including bearings, lithium-ion batteries, and mooring systems in wave energy converters (WECs). A deep transfer learning (DTL) method is first developed to improve bearing fault diagnosis by fusing data from multiple sources and extracting key features using convolutional neural networks (CNNs). Building upon this, a knowledge-informed deep network (KIDN) framework is proposed, integrating physics-based features with deep learning to enhance diagnostic accuracy and generalizability. A constrained Gaussian process (CGP) model is further employed to guide adaptive network design and reduce model tuning efforts.
For lithium-ion battery prognostics, a knowledge-constrained machine learning (KcML) framework is developed to predict remaining useful life (RUL). Prior knowledge of battery degradation is used to constrain the learning process, improving accuracy under diverse operational conditions.
The dissertation also extends the knowledge-informed strategy to fault diagnosis of WEC mooring systems. By combining autoregressive (AR) modeling and CNNs, the framework detects faults such as corrosion and biofouling and classifies fault severity based on dynamic responses like surge, heave, and pitch motions.
Overall, this research advances fault diagnosis and prognostics through the integration of domain knowledge and data-driven learning, leading to more accurate, generalizable, and efficient models for critical mechanical and energy systems
Heat stress triggers enhanced nuclear localization of HYL1 to regulate miRNA biogenesis and thermotolerance in plants
No full textPlants have evolved a complex regulatory network to cope with heat stress (HS), which includes microRNAs (miRNAs). However, the roles of the entire miRNA biogenesis machinery in HS responses remain unclear. Here, we show that HS induces the majority of miRNAs primarily through the enhanced nuclear localization of HYPONASTIC LEAVES 1 (HYL1), rather than by upregulating MIR gene transcription in Arabidopsis (Arabidopsis thaliana). Disruption of miRNA biogenesis increases plant susceptibility to HS. We also demonstrate that HYL1 phosphorylation modulates its nuclear localization during HS, which is critical for miRNA induction and thermotolerance. MAP KINASE3 (MPK3) phosphorylates and stabilizes the phosphatase C-TERMINAL DOMAIN PHOSPHATASE-LIKE 1 (CPL1), while CPL1 inhibits MPK3 activity, creating a feedback loop that regulates HYL1 phosphorylation. Disruption of MPK3 function results in increased nuclear HYL1 levels and miRNA production, conferring enhanced HS tolerance to mpk3 mutants. These findings reveal a mechanism by which plants enhance miRNA biogenesis during HS, offering insights into the regulatory networks that govern plant thermotolerance
