Mode Of Operation Research Articles

Methods of active friction control in the presence of lubricant compositions with mesogenic additives

Purpose of research. The aim of the study is to systematize the latest literature data related to the modulation of the friction coefficient by external fields when using lubricant compositions with liquid crystal mesogens and polymer composites.Methods. The article considers the methods of tribological tests using various schemes of friction pairs (cylinder-disk, pin-on-disk). Some commonly used methods of applying coatings to the elements of friction pairs are considered: ion beam-assisted deposition, chemical and physical thermal spraying, molecular layer deposition, photopolymerization, etc. Of the discussed methods of characterizing tribosystems, the following are indicated: dielectric spectroscopy, Raman scattering, polarization optical microscopy, nuclear-physical methods (positron annihilation spectroscopy and Xray diffraction).Results. The article systematizes modern trends in the development of modulated modes of operation of tribological circuits with lubricant compositions containing liquid crystals and polymer composites. The following approaches are used to implement active control of the friction coefficient: 1) modulation of friction by an electric field, 2) modulation of friction by a temperature field, and 3) modulation of friction on optical gratings under light irradiation. These approaches take into account the changed characteristics of the lubricant associated with the use of mesogenic additives, modes of exposure to electromagnetic and thermal fields, electrical characteristics, geometry of the surface of friction pairs, and provide the values of tribological characteristics (friction coefficient and wear) that are achieved as a result of friction modulation.Conclusion. A positive effect of mesogenic additives of liquid crystals and polymers on tribological characteristics with active control of the friction coefficient has been established: a decrease in the friction coefficient is observed, which helps to reduce material wear.

Thermal Fault-Tolerant Asymmetric Dual-Winding Motors in Integrated Electric Braking System for Autonomous Vehicles

A conventional dual-winding (DW) motor has two internal windings consisting of a master part and a slave part, each connected to a different electronic control unit (ECU) to realize a redundant system. However, existing DW motors have a problem related to heat generation in both the healthy mode and the faulty mode of the motor operation. In the healthy mode, unexpected overloads can cause both windings to burn out simultaneously due to equal heat distribution. If the current sensor fails to measure correctly, the motor may exceed the designed current density of 4.7 [Arms/mm2] under air-cooling conditions, further increasing burnout risk. External factors such as excessive load cycles or extreme heat conditions can further exacerbate this issue. In the faulty mode, the motor requires double the current to generate maximum torque, leading to rapid temperature increases and a high risk of overheating. To address these challenges, this paper proposes the design of a thermal fault-tolerant asymmetric dual-winding (ADW) motor, which improves heat management in both healthy and faulty modes for autonomous vehicles. A lumped-parameter thermal network (LPTN) with a piecewise stator-housing model (PSMs) was employed to evaluate the coil temperature during faulty operation. An optimal design approach, incorporating kriging modeling, Design of Experiments (DOE), and a genetic algorithm (GA), was also utilized. The results confirm that the proposed ADW motor design effectively reduces the risk of simultaneous burnout in the healthy mode and overheating in the faulty mode, offering a robust solution for autonomous vehicle applications.

Resource Allocation Approach of Avionics System in SPO Mode Based on Proximal Policy Optimization

Single-Pilot Operations (SPO) mode is set to reshape the decision-making process between human-machine and air-ground operations. However, the limited on-board computing resources impose greater demands on the organization of performance parameters and the optimization of process efficiency in SPO mode. To address this challenge, this paper first investigates the flexible requirements of avionics systems arising from changes in SPO operational scenarios, then analyzes the architecture of Reconfigurable Integrated Modular Avionics (RIMA) and its resource allocation framework in the context of scarcity and configurability. A “mission-function-resource” mapping relationship is established between the reconfiguration service elements of SPO mode and avionics resources. Subsequently, the Proximal Policy Optimization (PPO) algorithm is introduced to simulate the resource allocation process of IMA reconfiguration in SPO mode. The objective optimization process is transformed into a sequential decision-making problem by considering constraints and optimization criteria such as load, latency, and power consumption within the feasible domain of avionics system resources. Finally, the resource allocation scheme for avionics system reconfiguration is determined by controlling the probability of action selection during the interaction between the agent and the environment. The experimental results show that the resource allocation scheme based on the PPO algorithm can effectively reduce power consumption and latency, and the DRL model has strong anti-interference and generalization. This enables avionics resources to respond dynamically to the capabilities required in SPO mode and enhances their ability to support the aircraft mission at all stages.

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Prediction of train interior noise due to the HVAC system

In recent years, the significance of acoustic comfort in rail vehicles has grown considerably, owing to its direct impact on passenger well-being. As a consequence, interior noise levels are progressively becoming more restrictive. Meeting these requirements is a key challenge for rolling stock manufacturers. The predominant source in stationary condition is the HVAC (Heating, Ventilation, and Air Conditioning) system. To effectively assess and potentially reduce the noise generated by the HVAC system, the availability of tools and methodologies for predicting the propagation of noise through the air ducts and interior noise levels in the passenger compartment during the design phase is crucial. This paper presents a simulation methodology for predicting interior noise levels by evaluating various alternatives within a commercial software. Input data for the simulations encompass the geometry and materials of the supply and return ducts, along with the acoustic power of each outlet measured at the source. This data is associated with the nominal operating mode of the HVAC system. Finally, the paper establishes a correlation between the simulation results and in-situ measurements conducted on the completed train, with the HVAC equipment operating under nominal conditions.

Исследование многоуровневых моделей управления вагонопотоками в адрес припортовых станций с применением теории дифференциальных уравнений

Purpose: To consider the issue of sustainability of multi-level control systems for car flows to port railway stations in the conditions of intellectualization of transportation management processes. To assess the influence of three-level and two-level control systems for the operational work of port roads on the quality of planning the supply of trains to port stations under various modes of operational operation of railways. Determine the conditions for the stability of the car flow control system by constructing and studying the corresponding hard and soft mathematical models described by autonomous systems of ordinary linear differential equations. Methods: In this study, the methods of the theory of stability of solutions of differential equations, as well as the theory of control and dynamical systems are used. A phase space is used in a visual representation, a qualitative and quantitative analysis of the behavior of phase trajectories in it is performed, corresponding to undisturbed and perturbed solutions of systems of differential equations describing the constructed models of control systems. Results: A comparison of the characteristics of multistage car traffic control systems is given. A study of the stability of multilevel models of car traffic management in the context of the intellectualization of management functions is proposed. In the environment of the analytical computing system, the conditions of asymptotic stability in time of a two-level wagon traffic control system are found and studied. Practical importance: The results obtained by computational experiment make it possible to assess the stability of the functioning of port-side transport and technological systems in the context of changes in the organization of production and the intellectualization of transportation management processes. Computer mathematics systems make it possible to implement the heuristic component of research and obtain theoretically sound and visual solutions to problems of optimizing cargo and wagon traffic control modes.

Study on the hydrothermal gradient extraction of hemicellulose by a flow-through reactor

The hydrothermal gradient extraction process based on the hemicellulose constituent units is important for obtaining high quality hemicellulose products. The hydrothermal extraction of sawdust hemicellulose was performed under both non-isothermal and isothermal operations using a flow-through reactor for investigating the extraction patterns. The results show that there were significant differences in the major forms of hemicellulose units at different extraction stages. For glucose, xylose, and galactose units, the selectivity of oligomeric form decreased gradually with increasing temperature, whereas it decreased and then increased under thermostatic operation. The selectivity of the mannose oligomeric form decreased and then increased in both operation modes, reaching a trough at 170 °C (96.81 %) and 60 min (55.21 %), respectively. The molecular weight of extracted hemicelluloses were mainly distributed below 70,000 Da, and gradually decreased with temperature, but increased with time. The results contribute to the quantitative and qualitative understanding of the hemicellulose gradient extraction process.

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

B-146 Unlocking the potential of large-cohort proteomics studies with the Orbitrap Astral mass platform

Abstract Background Large-cohort proteomics analysis using mass spectrometry is a powerful approach to discover and validate new biomarkers. In combination with clinical data and computational analysis, large-cohort proteomics study brings opportunities in improving early diagnosis, refining patient stratification, and predicting/monitoring treatment response. Yet, to achieve meaningful biological insights in large-cohort studies, robust, reproducible, and comprehensive proteome profiling in a high-throughput manner still remains challenging. Here, we use a high-resolution accurate mass (HRAM) Oribtrap Astral platform to enable high-quality and robust protein quantification across thousands of LC-MS/MS analyses. With a throughput of 100 samples/day, we can reproducibly profile ∼9000 proteins from human cell line and ∼800 proteins from undepleted plasma across multiple instruments and more than 10 consecutive days in a 24/7 operation mode. Methods Experiments were performed on multiple Orbitrap Astral mass spectrometers with and without FAIMS Pro interface. Chromatographic separations were performed using a trap/elute method on Vanquish Neo system at a 11-minute gradient and a flow rate of 1ul/min, resulting in a throughput of 100 samples/day. Undepleted plasma digest was analyzed in a 24/7 mode for > 1000 injections on each LC-MS/MS setup. In addition, HeLa digest as quality control (QC) was analyzed every 12hours. The Oribtrap Astral platform was operated in data-independent acquisition (DIA) mode using a full scan with m/z 380-980, and DIA MS/MS scans with an isolation window of 2Th. Resulting DIA raw files were automatically transferred and processed using Chimerys in a beta version of Proteome Discoverer software. Results Multiple LC-MS/MS systems were operated in DIA mode either with or without FAIMS Pro device in a 24/7 operation mode at a throughput of 100SPD. Undepleted plasma digest was analyzed with >1000 injections on each LC-MS/MS setup. To monitor the baseline performance, HeLa digest served as QC were analyzed periodically every 12 hours with 3 technical replicates each time. To effectively manage and analyze these thousands of data files generated, we developed an automated data transfer and analysis pipeline. Automatically, the resulting DIA raw data files were immediately transferred to a server, then processed by state-of-the-art intelligent search algorithm Chimerys. Benefiting from the ultra-high scan speed of up to 200Hz on the Orbitrap Astral mass spectrometer, a much narrower isolation window width of 2Th was applied in the DIA method, comparing to 10-20Th on the classic DIA method. As a result, ∼9000 proteins from HeLa digest and ∼800 proteins from undepleted plasma digest were identified within only a 11-minutes gradient, respectively. More than 80% of the plasma proteins were reproducibly identified and quantified from all the runs on each LC-MS/MS setup, indicating a great reproducibility from run-to-run longitudinally. Stable and robust peptide quantitation was observed by extracting peptides with high, medium, and low abundant across the runs. Importantly, QC showed no performance degradation throughout the entire study, indicating high robustness of the entire LC-MS/MS setup. Conclusions Orbitrap Astral mass platform can comprehensively analyze the proteome of >1000s of sample robustly and reproducibly in a high-throughput manner, addressing the needs in large-cohort studies.

Reinforcement learning in cold atom experiments

Cold atom traps are at the heart of many quantum applications in science and technology. The preparation and control of atomic clouds involves complex optimization processes, that could be supported and accelerated by machine learning. In this work, we introduce reinforcement learning to cold atom experiments and demonstrate a flexible and adaptive approach to control a magneto-optical trap. Instead of following a set of predetermined rules to accomplish a specific task, the objectives are defined by a reward function. This approach not only optimizes the cooling of atoms just as an experimentalist would do, but also enables new operational modes such as the preparation of pre-defined numbers of atoms in a cloud. The machine control is trained to be robust against external perturbations and able to react to situations not seen during the training. Finally, we show that the time consuming training can be performed in-silico using a generic simulation and demonstrate successful transfer to the real world experiment.

Implementation of Energy-saving Technologies in House construction

Objective. Over the past decades, heat pumps have gained great popularity due to their advantages in energy efficiency and environmental friendliness, becoming one of the most attractive alternatives to replace traditional heating systems running on fossil fuels. Heat pump efficiency is a decisive factor that should be taken into account when selecting and optimizing a heat pump system. The aim of the study was to evaluate the efficiency of air source heat pumps of the air-to-water system in the climatic conditions typical of the regions of the Central Federal District. Method. To evaluate the efficiency of air source heat pumps, a mathematical model is used that takes into account the multifactorial nature of the process under study. Result. To adequately compare the obtained research results with the passport performance indicators, the transformation coefficient (COP) was used, showing the ratio of the energy received to the expended work. As a result, the method of heating rooms with an air source heat pump with a combined operation mode was recognized as more preferable. Conclusion. Currently, the potential of heat pumps in the field of energy saving and CO2 emission reduction remains largely untapped, which sets the vector for further research.

PID controller design for manipulating electrorheological valves with mesh electrodes using in 2D matrix display

Abstract The electrorheological (ER) valve with parallel electrodes structure has an inherent characteristic. The electrodes gap is the same as its flow channel gap, which causes a strong coupling relationship between the applied voltages and the flow rates of the ER valve. If flow rates of ER valves in the condition of turn on operational mode were increased, their applied voltages were increased necessarily for keeping their shutdown operational mode, which brings a bunch of problems of product costs and dimensions, especially when multiple ER valves with different operational modes work together, such as application of ER valves matrix used in 2D matrix displays. The mesh electrodes structure can decouple the relationship between the electrodes gap and the flow channel gap of parallel electrodes structure. However, due to the complexity of mesh electrodes structure, a mathematic model is difficult to be established theoretically. And, a mathematic model of ER valve with mesh electrodes plays a vital role in designing its controller. By analyzing the composition elements of an ER valve with mesh electrodes, a semi experimental model was established using the parametric identification toolbox of MATLAB. The consistency between the output of recognition model and the output of validation data reaches 93.46%. Subsequently the PID controller for ER valves with mesh electrodes was designed to meet the operational demands of a 2D matrix display using Simulink. The constrain of simulation was that the pressures of flow channel inlets of ER valves, the dynamic responses of ER valves with mesh electrodes kept stable and within its operational range. The results will be helpful for applications of ER valves with mesh electrodes.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Quasi Z source direct matrix converter based K- to three-phase wind energy conversion system using maximum constant boost current control modulation technique

The conversion system for wind energy has no restriction on the number of output phases. K numbers of output phases can be converted into standard grid-connected three-phase supply using a quasi-Z source direct matrix converter (QZSDMC). The obtained input supply may be five phases from the wind energy conversion system with continuously variable frequency as well as variable terminal voltages. Using the proposed QZSDMC converter with maximum constant boost current control modulation technique constant three-phase voltages as well as frequency can be maintained at the grid side. Additionally, the energy conversion system has high output voltage gain. The proposed converter as well as the modulation technique produces the maximum voltage transfer ratio of 1.05. The output voltage gain reached nearly 1.99 compared with the traditional power converters. The voltage THD and current THD are maintained within the acceptable limit of 5.3 and 3.6 respectively. The power factor have been maintained nearly unity throughout the all mode of operations. To stabilize the grid frequencies and grid voltages additional control blocks are used. This research article presents simulation results for the proposed QZSDMC converter with a maximum constant boost current control modulation technique. Also, the proposed scheme has been validated with suitable experimental results.

Investigation of dual plasmonic material integrated wrench-shaped PCF sensor with broadband resonance for cancer cell & chemical detection

In this work, a unique Photonic Crystal Fiber based on the Localized Surface Plasmon Resonance is designed for cancer cell and chemical detection. A novel combination of plasmonic materials namely Aluminum doped Zinc Oxide (AZO) and a Silver (Ag)-TiO2 bilayer is deposited along two horizontal slits to achieve resonance at two separate wavelengths within a particular refractive index. The sensor achieves resonance at different wavelengths along x and y polarizations, broadening analyte detection for a specific RI. Two modes of operation are introduced: mode-1 is optimized to achieve the highest Amplitude Sensitivity (AS), and mode-2 to maximize the Wavelength Sensitivity (WS). The mode-1 is examined for a range of RI from 1.35 to 1.42, while the other is 1.35–1.41. The sensor can showcase an AS of 4641.51 RIU−1 for Ag at RI 1.36, which is the highest value of AS found in literature when Ag is used as plasmonic material, while AZO achieves a high AS of 3204.51 RIU−1 at 1.41 RI. A highest WS of 10,320 and a Double Peak Shift Sensitivity (DPSS) of 8089.5 nm/RIU at 1.41 RI is obtained using the mode-2 configuration with a wavelength resolution of 9.69 × 10−6 RIU and a maximum FOM of 568.22 RIU−1. The sensor is examined to investigate skin, cervical, and blood cancer by analyzing the Basal, HeLa, and Jurkat cells, which yielded superior outcomes when detected using AS among relevant works. In addition, the detection of various industrial chemicals like acetone, hexane, isopropanol, and ethanol is also manifested in this study.

Numerical and Optimization Studies on Reactivity Controlled Compression Ignition Engine with Hydrogen and Split Injections

Abstract Effective abatement of harmful tail-pipe emissions from fossil fuel run engines is achieved through low-temperature combustion strategies; Reactivity Controlled Compression Ignition mode of operation has been successful among such concepts. The present work deals with numerical work performed using ANSYS Forte software with n-heptane as a high-reactivity fuel and hydrogen in different proportions as a low-reactivity fuel, respectively. With total energy fixed, the amount of hydrogen is varied from 0-80% fuel injection is regulated accordingly. Pertinent engine in-cylinder parameters with patterns are extracted, emphasizing the combustion phenomena of RCCI operation with the lowest possible emissions targeted, with the combined effects of hydrogen induction, Start of Injection, and split injections. The contours of fuel vapour and emission parameters are obtained to relate the performance with emissions. It is noted that with a split injection strategy at 50/50 and 75/25 split strategy and 45-50% energy share from hydrogen, the NOx, Soot reductions and thermal efficiency penalty are in the range of about 5.5%, 24% and 7.5%, respectively, also, with 30% EGR, about 95% NOx reduction but with higher soot values. A 75/25 split and advanced injection timing of 25obTDC resulted in the RCCI mode of operation with reduced soot emissions, and the use of EGR has resulted in high levels of soot and poor fuel efficiency. Among the models of machine learning tested, Random Forest regressor emerged as the most suitable, with higher R2 values, indicating better predictive capability.

Electrolyte-supported solid oxide electrochemical cells for versatile operations

Electrolyte-supported solid oxide electrochemical cells (SOCs) offer advantages in terms of easier fabrication and enhanced mechanical properties, but achieving high performance and multifunctionality remains challenging. In this study, we develop high-performance and versatile electrolyte-supported SOCs using La0.9Sr0.1Ga0.8Mg0.2O3-δ (LSGM) electrolyte materials through microstructural engineering of Sr2Fe1.5Mo0.5O6-δ (SFM)-based fuel electrodes. We evaluate the cells for power generation, hydrogen production, and carbon dioxide reduction, showcasing the diverse potential of various fuel electrode configurations. Among these, the impregnated SFM@LSGM electrode achieves the best overall performance, with a maximum power density of 739 mW cm−2 in fuel-cell mode, a current density of −0.81 A cm−2 at 1.3 V during steam electrolysis, and an unprecedented current density of −1.92 A cm−2 at 1.5 V in CO₂ electrolysis. Furthermore, it exhibits satisfactory stability across all operational modes. These findings provide valuable insights into the design of versatile electrolyte-supported SOCs and open new possibilities for their flexible applications in future low-carbon energy systems.

A new intelligent scheduler to improve reactive OpenFlow communication in SDN-based IoT data streams

The significant adoption of the Internet of Things (IoT) has increased the challenges in providing adequate IoT infrastructures meeting essential requirements, such as dynamicity networks and low latency. In this context, the Software-Defined Networking (SDN) paradigm and the OpenFlow protocol provide new possibilities for IoT networks. Based on the global view of network elements enabled by the Controller, SDN allows the programmability and control of the infrastructure according to the actual demands of applications. The OpenFlow protocol defines the exchange of messages between controllers and switches, enabling communication and network control. OpenFlow implements three operation modes: proactive, reactive, and hybrid. Due to the dynamic characteristic of the IoT data stream, the reactive mode is mainly used and indicated for IoT environments. As the OpenFlow controller installs rules dynamically, there is no need to know the network’s sources, destinations, and paths in advance. Although reactive mode introduces dynamicity, it can generate additional delay due to switch-controller communication. This delay increases the response time of the IoT data stream. We propose an SDN-IoT scheduler based on Deep Neural Networks (DNN) to predict the time between data stream changes from IoT devices and install rules in advance, suppressing the existing delay in reactive mode. The proposal automatically uses previous data from the IoT data stream to calculate the time of the following communication from IoT devices. Our results indicate that predicting IoT data stream changes and installing OpenFlow rules in advance reduced about 51% of communications response time.

A dual-stator threaded linear ultrasonic motor with preload mechanism.

The cylindrical ultrasonic motor, noted for its straightforward design, ease of miniaturization, and high precision, has significant potential in fields such as optics, healthcare, and precision engineering. However, miniaturization presents challenges, including diminished output force and complications in applying preloading stress. To address the reduced output force, this study presents a dual-stator, high-thrust cylindrical linear ultrasonic motor. This innovative motor features two stators, a mover, and a mechanism for applying preloading stress. A key design element is the parallel arrangement of the two stators along a threaded output shaft, separated by elastic components. This setup enables axial preloading via the threads' engagement. Simulations were conducted to refine the stators' parameters, assess their operational modes, and evaluate their in-plane paths. A prototype was subsequently built to test its impedance characteristics and mechanical performance. Test results showed that under a 200Vpp drive, the single-stator motor generated a maximum force of 4.3 N, while the dual-stator configuration produced 6.8 N. After applying preloading stress, the output force of the dual-stator motor increased to 9.4 N.

Post mortem Study of Catalyst Degradations Occurring in High-Temperature Proton Exchange Membrane Fuel Cells Upon Start-Stop Operation

High-temperature proton exchange membrane fuel cells (HT-PEMFCs) could replace fossil fuel-based technologies for applications which cannot involve bulky/heavy cooling systems, such as aeronautics. However, severe materials degradations upon operation prevent performance retention for acceptable lifetimes. While others have already reported degradations in HT-PEMFC, post mortem characterizations of used HT-PEMFC membrane electrode assemblies (MEAs) remain scarce. Herein, HT-PEMFC performance degradation is studied by applying a startup/shutdown protocol to a short-stack operated at 160 °C; one MEA is characterized using complementary physicochemical/electrochemical techniques to identify/understand the degradation mechanisms and their origin. This start/stop operation mode (co-flow gas reactants) leads to substantial degradation inhomogeneity. For the anode, migration, coalescence, and detachment of Pt nanoparticles are witnessed induced by high-surface-area carbon support functionalization and corrosion. The anode electrochemical surface area (ECSA) remains constant at the inlet and increases significantly at the outlet, following inhomogeneous degradation of the cathode catalyst: the Ptz+ ions formed at high potential/oxidizing conditions concentrate towards the outlet, where they redeposit locally or at the anode, after diffusion/migration across the PBI membrane. Hence, the cathode ECSA decreases significantly at the inlet. Furthermore, intense Ni-leaching from the initial PtNi alloy catalyst is reported as a result of O2 mass-transport and phosphoric acid dilution inhomogeneity.