Thermomechanical fatigue life prediction for a marine diesel engine piston considering ring dynamics

A newly designed marine diesel engine piston was modeled using a precise finite element analysis (FEA). The high cycle fatigue (HCF) safety factor prediction procedure designed in this study incorporated lubrication, thermal, and structure analysis. The piston ring dynamics calculation determined the predicted thickness of lubrication oil film. The film thickness influenced the calculated magnitude of the heat transfer coefficient (HTC) used in the thermal loads analysis. Moreover, the gas pressure of ring lands and ring grooves used in mechanical analysis is predicted based on the piston ring dynamics model

Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting

The gradually increased penetration of photovoltaic (PV) power into electric power systems brings an urgent requirement for accurate and stable PV power forecasting methods. The existing forecasting methods are built to explore the function between weather data and power generation, which ignore the uncertainty of historical PV power. To manage the uncertainty in the forecasting process, a novel ensemble method, named the evidential extreme learning machine (EELM) algorithm, for deterministic and probabilistic PV power forecasting based on the extreme learning machine (ELM) and evidential regression, is proposed in this paper. The proposed EELM algorithm builds ELM models for each neighbor in the k-nearest neighbors initially, and subsequently integrates multiple models through an evidential discounting and combination process. The results can be accessed through forecasting outcomes from corresponding models of nearest neighbors and the mass function determined by the distance between the predicted point and neighbors. The proposed EELM algorithm is verified with the real data series of a rooftop PV plant in Macau. The deterministic forecasting results demonstrate that the proposed EELM algorithm exhibits 15.45% lower nRMSE than ELM. In addition, the forecasting prediction intervals obtain better performance in PICP and CWC than normal distribution

Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting

The gradually increased penetration of photovoltaic (PV) power into electric power systems brings an urgent requirement for accurate and stable PV power forecasting methods. The existing forecasting methods are built to explore the function between weather data and power generation, which ignore the uncertainty of historical PV power. To manage the uncertainty in the forecasting process, a novel ensemble method, named the evidential extreme learning machine (EELM) algorithm, for deterministic and probabilistic PV power forecasting based on the extreme learning machine (ELM) and evidential regression, is proposed in this paper. The proposed EELM algorithm builds ELM models for each neighbor in the k-nearest neighbors initially, and subsequently integrates multiple models through an evidential discounting and combination process. The results can be accessed through forecasting outcomes from corresponding models of nearest neighbors and the mass function determined by the distance between the predicted point and neighbors. The proposed EELM algorithm is verified with the real data series of a rooftop PV plant in Macau. The deterministic forecasting results demonstrate that the proposed EELM algorithm exhibits 15.45% lower nRMSE than ELM. In addition, the forecasting prediction intervals obtain better performance in PICP and CWC than normal distribution

Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty

To realize the best performances of the distributed energy system (DES), many uncertainties including demands, solar radiation, natural gas, and electricity prices must be addressed properly in the planning process. This study aims to study the optimal sizing and performances of a hybrid combined cooling, heating, and power (CCHP) system under uncertainty in consideration of the operation parameters, including the lowest electric load ratio (LELR) and the electric cooling ratio (ECR). In addition, the ability of the system to adapt to uncertainty is analyzed. The above works are implemented separately under three operation strategies with multi-objectives in energy and cost saving, as well as CO2 reducing. Results show that the system with optimized operation parameters performs better in both the deterministic and uncertain conditions. When the ECRs in the summer and in mid-season as well as the LELR are set at 50.00%, 50.00%, and 20.00% respectively, the system operating in the strategy of following the electric load has the best ability to adapt to uncertainty. In addition, among all the uncertainties, the single uncertain natural gas price and the single uncertain heating demand have the smallest and largest effects on the optimal design respectively

Analysis of the Tribological and Dynamic Performance of the Self-Adapting Water-Lubricated Stern Bearing

According to the design requirements of load equalization and vibration reduction for the stern bearing, a water-lubricated stern bearing with self-adaptation capacity is proposed. The bearing is mainly composed of three parts: the bearing bush, the elastic element, and the damping alloy. The elastic element is used to realize static and dynamic load sharing of the stern bearing, reduce the edge effect of the stern bearing, and make the contact pressure evenly distributed in the axial direction, thereby improving the service life of the bearing and reducing the frictional excitation of the bearing. Damping alloy is used to attenuate the shaft vibration transmission from the bearing to the foundation to optimize the vibration transmission characteristics. The revised lubrication models for such bearings are put forward. By analyzing the vibration characteristics of the stern bearing, the results show that the vibration transmission characteristics of the thruster excited to the bearing node are optimized, and the vibration response at the first-order fixed frequency is significantly reduced. A moderate increase in the support stiffness of the foundation can significantly reduce the vibration response of the bearing

Analysis of the Tribological and Dynamic Performance of the Self-Adapting Water-Lubricated Stern Bearing

According to the design requirements of load equalization and vibration reduction for the stern bearing, a water-lubricated stern bearing with self-adaptation capacity is proposed. The bearing is mainly composed of three parts: the bearing bush, the elastic element, and the damping alloy. The elastic element is used to realize static and dynamic load sharing of the stern bearing, reduce the edge effect of the stern bearing, and make the contact pressure evenly distributed in the axial direction, thereby improving the service life of the bearing and reducing the frictional excitation of the bearing. Damping alloy is used to attenuate the shaft vibration transmission from the bearing to the foundation to optimize the vibration transmission characteristics. The revised lubrication models for such bearings are put forward. By analyzing the vibration characteristics of the stern bearing, the results show that the vibration transmission characteristics of the thruster excited to the bearing node are optimized, and the vibration response at the first-order fixed frequency is significantly reduced. A moderate increase in the support stiffness of the foundation can significantly reduce the vibration response of the bearing

Fiber-Supported Acid–Base Bifunctional Catalysts for Efficient Nucleophilic Addition in Water

A series of fiber supported acid–base bifunctional catalysts (FABCs) were developed by successive grafting of acrylic acid and 4-vinylpyridine onto the polypropylene fiber (PPF). The FABCs can efficiently catalyze a number of reactions whose key steps involve in nucleophilic addition. The obviously enhanced activities of the bifunctional catalysts compared with that of the individual fiber supported acid or base upon the nitro-aldol and Knoevenagel reactions indicate that the FABCs perform in a cooperative catalyzing model and their activities can be easily tuned by controlling the acid–base ratio. An optimized bifunctional catalyst was successfully applied to the aqueous Gewald, tandem Michael–Henry reaction and one three-component reaction with excellent catalytic performances. In addition, this newly developed bifunctional fiber catalyst also exhibits excellent recyclability and reusability with simple treatment

Crystallization of the NADH-oxidizing domain of the Na+-translocating NADH:ubiquinone oxidoreductase from Vibrio cholerae

The FAD domain of the NqrF subunit from the Na+-translocating NADH dehydrogenase from V. cholerae has been purified and crystallized. A complete data set was recorded at 3.1 Å

Physiological Ovarian Aging Is Associated with Altered Expression of Post-Translational Modifications in Mice

Post-translational modifications (PTMs) have been confirmed to be involved in multiple female reproductive events, but their role in physiological ovarian aging is far from elucidated. In this study, mice aged 3, 12 or 17 months (3M, 12M, 17M) were selected as physiological ovarian aging models. The expression of female reproductive function-related genes, the global profiles of PTMs, and the level of histone modifications and related regulatory enzymes were examined during physiological ovarian aging in the mice by quantitative real-time PCR and western blot, respectively. The results showed that the global protein expression of Kbhb (lysineβ-hydroxybutyryllysine), Khib (lysine 2-hydroxyisobutyryllysine), Kglu (lysineglutaryllysine), Kmal (lysinemalonyllysine), Ksucc (lysinesuccinyllysine), Kcr (lysinecrotonyllysine), Kbu (lysinebutyryllysine), Kpr (lysinepropionyllysine), SUMO1 (SUMO1 modification), ub (ubiquitination), P-Typ (phosphorylation), and 3-nitro-Tyr (nitro-tyrosine) increased significantly as mice aged. Moreover, the modification level of Kme2 (lysinedi-methyllysine) and Kac (lysineacetyllysine) was the highest in the 3M mice and the lowest in 12M mice. In addition, only trimethylation of histone lysine was up-regulated progressively and significantly with increasing age (p < 0.001), H4 ubiquitination was obviously higher in the 12M and 17M mice than 3M (p < 0.001), whereas the modification of Kpr (lysinepropionylation) and O-GlcNA in 17M was significantly decreased compared with the level in 3M mice (p < 0.05, p < 0.01). Furthermore, the expression levels of the TIP60, P300, PRDM9, KMT5B, and KMT5C genes encoding PTM regulators were up-regulated in 17M compared to 3M female mice (p < 0.05). These findings indicate that altered related regulatory enzymes and PTMs are associated with physiological ovarian aging in mice, which is expected to provide useful insights for the delay of ovarian aging and the diagnosis and treatment of female infertility

Polyacrylonitrile Fiber Supported <i>N</i>‑Heterocyclic Carbene Ag(I) As Efficient Catalysts for Three-Component Coupling and Intramolecular 1,3-Dipolar Cycloaddition Reactions under Flow Conditions

A series of recoverable and reusable <i>N</i>-heterocyclic carbene silver complexes supported on polyacrylonitrile fiber were prepared and characterized. Their catalytic performances in three-component coupling reactions of aldehydes, amines, and alkynes (A3 coupling) to synthesize propargylamines were evaluated. The catalysts were also used to prepare fused triazoles in one-pot reactions with excellent yields and diastereoselectivities. In these reactions, the substrates underwent A3 coupling followed by intramolecular 1,3-dipolar cycloaddition. The above reactions were also successfully performed in a continuous-flow process, and sustainable, modular, and efficient syntheses of propargylamines and fused triazoles were achieved