Performance Deterioration Research Articles

Robust acceleration schedule design for gas turbine engine using multilayer perceptron network with adaptive sample class weighting

The acceleration schedule is crucial for generating acceleration control references for the gas turbine engine (GTE) and ensuring optimal acceleration performance. However, under harsh operating conditions, GTEs may encounter difficult-to-diagnose pressure sensor faults, which lead to inaccurate control references and decreased acceleration performance. This paper proposes a data-driven robust acceleration schedule (RAS) design method to tackle this issue, including fault data augmentation and adaptive sample class weighting (ASCW). Fault data augmentation generates multiple pressure fault sample classes from a normal acceleration schedule dataset. Due to the redundant pressure sensor configuration, the RAS can reconstruct these classes and generate accurate control references when a single pressure sensor fails. The ASCW employs a multilayer perceptron network to reconstruct the normal and fault sample classes accurately. Proportional integral regulation adjusts their weights during training to ensure balanced reconstruction precision. Simulation cases were conducted to verify the effectiveness of the RAS under actual GTE conditions, including engine-model mismatches, performance deterioration, flight envelope, and measurement uncertainty. The results demonstrate that the RAS ensures superior acceleration performance of GTEs across the full flight envelope in both normal and single pressure sensor fault scenarios. Additionally, the ASCW achieves reconstruction precision of 0.068 %, 0.080 %, 0.080 %, 0.082 %, and 0.077 % for normal and fault sample classes, respectively, prioritizing the precision of the normal sample class and balancing the precision of fault sample classes.

Quasi-static cyclic tests of FRP-confined steel-reinforced HPFRCC circular columns

This study presents a novel composite component comprising of steel-reinforced high-performance fiber reinforced cementitious composites (HPFRCC) circular columns confined by fiber-reinforced polymer (FRP), with the goal of improving the mechanical behavior of conventional reinforced concrete (RC) columns. Quasi-static cyclic loading tests were conducted to investigate various aspects of the column specimens, including failure phenomena, load-displacement response, sectional compression-bending performance, plastic hinge length, and lateral strain distributions in the FRP jacket, etc. A self-designed frictional force measurement device was employed to accurately measure the actual force borne by the specimen. The experiment results revealed that FRP confinement effectively improved the load-bearing capacity and deformation capacity of the specimens and delayed the deterioration of the sectional compression-bending performance. The superior tensile ductility of HPFRCC led to increased yielding displacement and peak load compared to FRP-confined concrete specimen, although this enhancement was limited. Furthermore, the peak load of the FRP-confined HPFRCC specimen increased with axial load, while the sectional compression-bending performance after the peak load remained relatively stable until approaching the collapse prevention (CP) limit state.

An Empirical Study of Factors Affecting Sudden Deteriorations in Performance of Newly Listed Companies in China

An Empirical Study of Factors Affecting Sudden Deteriorations in Performance of Newly Listed Companies in China

Microscopic insights into the aggregation dynamics behavior and tribological properties of graphene

The stable dispersion mechanism of graphene nanoparticles in solid-liquid two-phase lubrication systems still lacks in-depth insights into the dynamic aggregation process at the microscale. Herein, the effects of size difference and defect degree on the agglomeration dynamics behavior and tribological properties of graphene nanosheets were revealed by aggregation, pull-out, and friction molecular dynamics simulations. The results showed that for suspended graphene smaller than 100 nm, the increase in size and defect degree effectively improved the dispersion stability of nanosheets in base oil. Attributed to the rise in size improved solid-liquid chemical affinity and promoted out-of-plane deformation of nanosheets to enhance the steric hindrance effect. The increase in defect degree induced spontaneous wrinkling of nanosheets, leading to enhanced interlayer sliding resistance and thus inhibiting agglomeration behavior. Meanwhile, the rise in nanosheet size and degree of defects sufficiently bear the normal load, effectively avoiding the direct contact of asperities and thus improving the wear and temperature rise at the friction interface. However, friction-induced defective graphene curled to form a “carbon nanotube-like” structure, which reduced the oil film strength and led to deterioration of lubrication performance. The research results will provide certain microscale insights into the dispersion and friction of graphene in base oil.

The SOE Premium and Government Support in China's Credit Market

ABSTRACTStudying China's credit market using a structural default model that integrates credit risk, liquidity, and bailout, we document improved price discovery and a deepening divide between state‐owned enterprises (SOEs) and non‐SOEs. Amidst liquidity deterioration, the presence of government bailout helps alleviate the heightened liquidity‐driven default, making SOE bonds more valuable and widening the SOE premium. Meanwhile, the increased importance of government support makes SOEs more sensitive to bailout, while the heightened default risk increases non‐SOEs' sensitivity to credit quality. Examining the real impact, we find severe performance deteriorations of non‐SOEs relative to SOEs, reversing the long‐standing trend of non‐SOEs outperforming SOEs.

Suppressing Formation of Buried Defects during Annealing Enables Bright Perovskite Light-Emitting Diodes.

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a promising hole-transporting material for perovskite light-emitting diodes (PeLEDs). However, intrinsic luminance quenching at the PEDOT:PSS/perovskite interface causes deterioration of performance. Here, we develop a facile and effective strategy to passivate the interface defects via the modification of PEDOT:PSS by l-norvaline. As a pre-buried additive, l-norvaline not only reacts with PEDOT:PSS, but also forms the coordination and hydrogen bond with perovskite. We demonstrated that the generation of buried defects at the PEDOT:PSS/perovskite interface originates from the crystallization process of the perovskite film during annealing by in-situ photoluminescence measurements. The surface of l-norvaline-modified PEDOT:PSS can passivate the interfacial defects and inhibit exciton quenching. As a result, the PeLED shows a good device performance with a luminance of 80089 cd m-2 at 509 nm and an external quantum efficiency of 13.04%.

Wages: The Worst Transistor Aging Analysis for Large-scale Analog Integrated Circuits via Domain Generalization

Transistor aging leads to the deterioration of analog circuit performance over time. The worst aging degradation is used to evaluate the circuit reliability. It is extremely expensive to obtain it since several circuit stimuli need to be simulated. The worst degradation collection cost reduction brings an inaccurate training dataset when a machine learning (ML) model is used to fast perform the estimation. Motivated by the fact that there are many similar subcircuits in large-scale analog circuits, in this article we propose Wages to train an ML model on an inaccurate dataset for the worst aging degradation estimation via a domain generalization technique. A sampling-based method on the feature space of the transistor and its neighborhood subcircuit is developed to replace inaccurate labels. A consistent estimation for the worst degradation is enforced to update model parameters. Label updating and model updating are performed alternately to train an ML model on the inaccurate dataset. Experimental results on the very advanced 5 nm technology node show that our Wages can significantly reduce the label collection cost with a negligible estimation error for the worst aging degradations compared to the traditional methods.

Optimizing Energy Storage Performance in Polymer Dielectrics through Dual Strategies: Constructing “Peaked” Barrieras and Enhancing Carrier Scattering

AbstractDielectric capacitors play a pivotal role in the advancement of electric power systems and emerging energy technologies. However, the deterioration of dielectric performance in energy storage materials at elevated temperatures represents a significant challenge. In this study, organic electron‐scattering agents into polyetherimide (PEI) are introduced, creating a “peaked barrier” to impede charge carrier transport. By doping PEI with an ultralow volume fraction (0.8%) of the organic molecule filler 4‐(dimethylamino)phenylboronic acid (4‐NB), the electron‐repelling nature of 4‐NB is leveraged in order to regulate charge carrier injection and transport in a synergistic manner. Consequently, the discharged energy density of the PEI composite material increases to 7.93 J cm−3 (720 kV mm−1) at 30 °C. At 150 °C, the discharged energy density increases to 5.21 J cm−3 (580 kV mm−1). In both cases, the charge and discharge efficiencies are maintained at 90%. It is noteworthy that the prepared PEI composite material also exhibits excellent charge dissipation characteristics, maintaining stable charge and discharge efficiency even after 50 000 charge–discharge cycles. In summary, the study's design concept systematically optimizes the processes of charge carrier injection, transport, and dissipation. This approach offers a novel perspective for the development of dielectrics that are suitable for long‐term energy storage.

Confinement strength prediction of corroded rectangular concrete columns using BP neural networks and support vector regression

The application of machine learning in predicting the mechanical performance of reinforced concrete columns subjected to reinforcement corrosion was investigated in this study. Traditional models, often relying on empirical formulas or simplified assumptions, are known to struggle in capturing all variables and relationships in complex problems, leading to inadequate prediction accuracy. Through theoretical analysis, the confinement effect of stirrups on core concrete and its performance deterioration caused by reinforcement corrosion were determined. To improve the prediction accuracy and generalization ability, the error Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models were employed. Parameter effects in the BPNN model were interpreted using the SHapley Additive exPlanations (SHAP) and compared with traditional parameter sensitivity analyses. The investigation indicates that the machine learning models have a high degree of fit and low error levels, particularly within the training set. After analyzing using the SHAP approach, it is found that the corrosion ratio of the stirrup and longitudinal reinforcement have a significant effect on the confinement strength of the concrete, whereas the effects of the stirrup configuration and size effect are negligible, which is different from conventional parametric sensitivity analysis. As to the stability analysis of model predictions, the SVR model shows lower volatility and higher prediction stability compared with the BPNN model, despite the latter occasionally providing superior generalization ability. This research underscores the significant advantages offered by machine learning models in addressing longstanding challenges associated with strength prediction, and thus presents new perspectives for future study.

Investigation of an Industrially Scalable Production of Sulfur‐Polyacrylonitrile Based Cathodes

AbstractSulfur‐polyacrylonitrile (SPAN) is a sulfur‐based active material for next‐generation lithium‐sulfur battery cathodes. Due to the covalent bonding between sulfur chains and the polymeric backbone, the shuttle effect degrading classical sulfur‐based cathodes can be suppressed while also achieving a high active material content in the cathode. In this paper, we investigate the processability of an industrially scalable SPAN active material with 38 wt.‐% of sulfur in a water‐based and scalable process route. The potential of the SPAN material for industrial adoption and the impact of the process route on the cell performance are discussed. We show that when processed correctly, the SPAN material delivers exceptional cycling stability and good C‐rate performance with ether‐based electrolytes. However, the performance of the SPAN cathode is influenced by the mixing characteristic. Using higher mixing intensities during the slurry preparation leads to deterioration of the electrochemical performance. This can be attributed to a decreasing carbon black percolation with increasing tip speed in combination with the kinetic limitation of sulfur cathodes during Li2S2 and Li2S oxidation.

Electro-Thermo-Magnetic Effect-Induced Large Thermoelectric Performance of Calcium Cobaltite Nanocomposites.

As attractive thermoelectric oxides, Ca3Co4O9-based materials have been intensively studied for their applications in recent years. However, their thermoelectric performance is enormously limited due to the contradiction of electrical resistivity and thermal conductivity. Herein, BaFe12O19 nanospheres were introduced into the Ca3Co4O9 matrix. The metallic Ag, ferrites, and matrix phase survived together, and a high density of nanoscale BaFe12O19 precipitation was observed. The reduction of work function could lead to band bending and form an interface potential due to the electro-thermo-magnetic effect contributing to the hole migration. As a result, a huge ZT value of 0.51 for the 8 wt % BaFe12O19/Ca3Co4O9 nanocomposites was obtained at 1073 K, accompanied by a low electrical resistivity of 6.7 mΩ·cm and a high Seebeck coefficient of 217.5 μV/K. In addition, a significant reduction of thermal conductivity (1.11 W/(m·K)) occurred, which was due to the nanoscale ferromagnetic phase effectively scattering the mid- and short-wavelength heat-carrying phonons. The synergistic enhancement of thermoelectric performance confirmed that the electro-thermo-magnetic effect is an effective way to solve the challenging problem of performance deterioration in oxide thermoelectric materials.

Preparation of Supercapacitor Carbon Electrode Materials by Low-Temperature Carbonization of High-Nitrogen-Doped Raw Materials from Food Waste.

To address the problem of the low nitrogen (N) content of carbon materials prepared through the direct carbonization of food waste, soybean meal and egg whites with high N contents were selected to carry out carbonization experiments on food waste. At 220 °C, the effects of hydrothermal carbonization and microwave carbonization on the properties of supercapacitor electrode materials were investigated. The results show that food waste doped with soybean meal and egg whites could achieve good N doping. At a current density of 1 A·g-1, the specific capacitance of the doped carbon prepared by hydrothermal doping is as high as 220.00 F·g-1, which is much greater than that of the raw material prepared through the hydrothermal carbonization of food waste alone, indicating that the hydrothermal carbonization reactions of soybean meal, egg white, and food waste promote the electrochemical properties of the prepared carbon materials well. However, when a variety of raw materials are mixed for pyrolysis carbonization, different raw materials cannot be fully mixed in the pyrolysis process, and under the etching action of potassium hydroxide, severe local etching and local nonetching occur, resulting in a severe increase in the pore size distribution and deterioration of the electrochemical performance of the prepared carbon materials. At a current density of 1 A·g-1, the specific capacitance of these prepared carbon materials is 157.70 F·g-1, whereas it is only 62.00 F·g-1 at a high current density of 20 A·g-1. Therefore, this study suggests that the hydrothermal carbonization process is superior to the microwave pyrolysis carbonization process for preparing supercapacitor electrode materials with multiple samples doped with each other.

The braking performance and failure mechanism of copper-based brake pads during repeated emergency braking at 400 km/h

Copper-based brake pads have played a pivotal role in the braking systems of commercial high-speed trains travelling at speeds ranging from 200 to 350 km/h. In this study, the braking performance and friction and wear mechanism of copper-based brake pads at 400 km/h were further evaluated using a full-scale dynamometer. The results show that under repeated emergency braking conditions at 400 km/h, the mean friction coefficient can be maintained at about 0.3 and remains stable within six braking cycles. Subsequently, a fade phenomenon in the mean friction coefficient emerges accompanied by a significant increase in braking distance, while the mean wear loss is relatively high throughout the entire testing process. Microstructural characteristics of the friction film and matrix indicate that strong oxidation plays an important role in the failure process of the brake pads. The friction film primarily composed of oxides exhibits numerous defects, which are susceptible to peeling off during braking, thereby compromising wear resistance. Additionally, as the braking cycles accumulate, oxidation gradually invades and weakens the matrix, leading to violent migration of the copper-rich friction film across the friction interfaces and resulting in the deterioration of friction performance. This work provides valuable insights into the failure behavior of copper-based brake pads at higher braking speeds and can provide potential direction for further improving their performance.

Impact of Fast-Charging Rates on Electrode Degradation in NMC/Gr Pouch Cells

With the rapid expansion of the electric vehicles market, fast charging lithium-ion batteries has become increasingly imperative and inevitable. However, as fast-charging technologies entering electric vehicle market, persistent challenges related to safety issue and cell degradation, such as lithium dendrite growth, thermal runaway etc. necessitate meticulous consideration. In this study, we investigated fast-charging rate effects on electrode degradation in 1.6 Ah pouch cells using LiNi0.5Mn 0.3Co 0.2O2 cathode and graphite anode provided by Nanoramic Laboratories. Our investigation involves a comparative analysis of the aging behavior of NMC/Gr cells subjected to various charging rates — 0.5C, 2C, 4C, and 6C. Notably, we observed that pouch cells charged at rates of 0.5C and 2C could persist 80% capacity retention over 1000 cycles while cells using 4C and 6C can survive no more than 400 cycles with 80% retention. Through a series of post-mortem characterizations, including electrochemical impedance spectroscopy (EIS), Raman, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS), we scrutinized the harvested NMC and graphite electrodes. As expected, more dendrite formation was observed on graphite anodes with 4C and 6C fast charging rates. The deterioration of cell performance primarily resulted from the degradation of the graphite anode. This degradation could be attributed to distinct dendrite morphologies at the graphite anode under varying charging rates (see Fig. 1). In addition to dendrite formation on graphite electrodes, we observed more severe pulverization of NMC secondary particles under higher charging rates despite their lower cycle numbers. This investigation provides a comprehensive understanding of how NMC and graphite degrade under different charging rates, offering valuable insights into the practical impacts of charging rates on cell lifespan and highlighting potential challenges that need to be addressed.AcknowledgementThis material is based upon work supported by the U. S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, award number DE-EE0009111. Argonne National Laboratory is operated for DOE Office of Science by UChicago Argonne, LLC under contract number DE-AC02-06CH11357 Figure 1

Low complexity and finer granularity quantization scheme for perturbation-based fiber nonlinearity compensation in coherent optical communication systems

In the efficient implementation of perturbation theory-based nonlinearity compensation method for reliable fiber-optic coherent communication systems, different quantization schemes have been proposed to reduce the required computational and implementation complexity. For example, the k-means clustering algorithm focused on reducing the number of perturbation coefficients to alleviate the burden of calculating the perturbation terms. In this paper, from the bit-level architectures of multipliers, we propose a multi-segment mixed-word-length quantization scheme that assigns different word lengths for the quantized perturbative coefficients to save computational resources, where Bayesian optimization is employed to derive the optimal hyperparameters involved in this scheme. The proposed scheme was evaluated by numerical simulations of single-channel and multi-channel optical fiber communication systems with dual-polarization 16-QAM modulation. The comprehensive numerical simulation results show that our proposed scheme demonstrates a complexity reduction of more than 65% without performance degradation compared with the traditional uniform quantization method with fixed-word-length, and was robust to the fiber length and baud-rate. We also carried out an experiment of single-carrier transmission over 18 × 100 km standard single-mode fiber with 20 Gbaud single-polarization 16-QAM signal. Similar to the numerical results, the hardware complexity can be reduced by 70.7% with negligible performance deterioration over the uniform quantization scheme. The proposed scheme shows great potential in real-world applications.

Team personality characteristics and performance in a simulated largeaircraft instrument control task

We investigated the relationship between personality characteristics and performance on a simulated large-aircraft instrument control task. The sample comprised 155 undergraduates in China, who completed the Cattell Sixteen Personality Factor Questionnaire to assess personality characteristics, and were randomly allocated to one of 31 teams of five people. We used multiple regression analysis to establish a predictive model of team performance based on team personality elevation and team personality diversity integration models. The results showed that when the team personality elevation integration was used as the independent variable, an increase in rule consciousness led to improvement in team performance, whereas an increase in vigilance, dominance, and social boldness led to a deterioration in team performance. When team personality diversity integration was used as the independent variable, an increase in warmth and openness to change led to improvement in team performance, and an increase in sensitivity led to deterioration in team performance. The simulation task designed in this study provides a specific tool for research involving flight crews on large aircraft. The findings provide a theoretical reference for optimum allocation of flight crews.

Capillary effect-based selective sealing strategy for increasing piezoelectric MEMS speaker performance

To address the serious acoustic performance deterioration induced by air leakage in the low-frequency range and the asynchronous vibration in electroacoustic transduction structures near the resonant frequency, a novel sealing strategy is proposed that targets one of the most widely reported piezoelectric MEMS speaker designs. This design consists of multiple cantilever beams, in which the air gaps between cantilevers are automatically and selectively filled with liquid polydimethylsiloxane (PDMS) via the capillary effect, followed by curing. In the proof-of-concept demonstration, the sound pressure level (SPL) within the frequency range lower than 100 Hz markedly increased after sealing in an experiment using an IEC ear simulator. Specifically, the SPL is increased by 4.9 dB at 20 Hz for a 40 Vpp driving voltage. Moreover, the deteriorated SPL response near the resonant frequencies of the cantilever beams (18 kHz–19 kHz) caused by their asynchronous vibration induced by the fabrication process nonuniformity also significantly improved, which successfully increased the SPL to approximately 17.5 dB. Moreover, sealed devices feature nearly the same SPL response as the initial counterpart in the frequency band from 100 Hz to 16 kHz and a total harmonic distortion (THD) of 0.728% at 1 kHz for a 40 Vpp driving voltage. Compared with existing sealing methods, the current approach offers easy operation, low damage risk, excellent repeatability/reliability and excellent robustness advantages and provides a promising technical solution for MEMS acoustic devices.

On the Challenges of Integrating a Rotating Detonation Combustor with an Industrial Gas Turbine and Important Design Considerations for Row-1 Blades

Abstract In an effort to increase the efficiency and performance of gas turbine power cycles, pressure gain combustion (PGC) has gained significant interest. Since Rotating Detonation Combustors (RDC) can provide a quasi-steady mode of operation, research has been triggered to integrate RDC with power-generating gas turbines. However, the presence of subsonic and supersonic flow fields which are generated due to the shock waves that stem from the detonation wave front and the highly non-uniform temperature and velocity profiles may cause a depreciation in the turbine performance. The current study seeks to investigate the challenges of integrating the RDC with nozzle guide vanes (NGV) of an industrial, can-annular gas turbine and attempts to understand the major contributors that impact efficiency and identify the key areas of optimization that need to be considered for maximizing performance. The RDC was integrated with the NGVs through a non-optimized straight duct-type geometry with a diffuser cone. 3-Dimensional Numerical analyses were performed to investigate sources of total pressure loss and to understand the unsteady effects of RDC which contribute towards the deterioration of performance. The entropy generation at different regions of interest was calculated to identify the major irreversibilities in the system. Also, total pressure and temperature distribution along the radial direction at the exit of the transitional duct is presented.

The influence of transcranial direct current stimulation to the trigeminal nerve on attention and arousal.

One mechanism by which transcranial direct current stimulation (tDCS) has been proposed to improve attention is by transcutaneous stimulation of cranial nerves, thereby activating the locus coeruleus (LC). Specifically, placement of the electrodes over the frontal bone and mastoid is thought to facilitate current flow across the face as a path of least resistance. The face is innervated by the trigeminal nerve, and the trigeminal nerve is interconnected with the LC. In this study, we tested whether stimulating the trigeminal nerve impacts indices of LC activity and performance on a sustained attention task. We replicated previous research that shows deterioration in task performance, increases in the rate of task-unrelated thoughts, and reduced pupil responses due to time on task irrespective of tDCS condition (sham, anodal, and cathodal stimulation). Importantly, tDCS did not influence pupil dynamics (pretrial or stimulus-evoked), self-reported attention state, nor task performance in active versus sham stimulation conditions. The findings reported here are consistent with theories about arousal centered on a hypothesized link between LC activity indexed by pupil size, task performance, and self-reported attention state but fail to support hypotheses that tDCS over the trigeminal nerve influences indices of LC function.

Comparative study on the compressive performance of NSC and HSC with preload under 24-hour high temperatures

This study explores the compressive performance of normal strength concrete (NSC) and high strength concrete (HSC) with preload under 24-hours sustained high temperature (SHT). A unique test setup was designed to accomplish the compressive tests of concrete under high temperatures. The effects of temperature, concrete strength and preloading are given attention. Besides, microscopic tests were carried out to reveal the variation mechanism of concrete performance. The results show that the degradation of the compressive strength of concrete under SHT is significantly more severe than under short-term high temperatures. At the same temperature and initial load level, HSC shows greater strength degradation than NSC. Pre-loaded concrete generates considerable creep under SHT, and its effect on the peak strain of concrete cannot be ignored. The peak strain of NSC and HSC is most significantly affected by the initial load level and temperature, respectively. At temperatures not higher than 350 °C, the deterioration of the compressive performance of concrete is mainly attributed to the dehydration decomposition of calcium silicate hydrate and the cracking of interface transition zone. Preloading can inhibit these reactions and improve the compressive performance of concrete under SHT. Further, a general model for the compressive stress-strain relationship of concrete under SHT considering concrete strength and preloading was developed, which can be used for the design and analysis of concrete structures serving at high-temperature environments.