Operating Conditions Research Articles

Stability of steady-state solutions of Jeffcott rotor with varying rotational speed

AbstractThe behavior of flexible rotating systems with varying rotational speeds is essential in engineering applications. Analysis methods that consider linear dynamic models and many existing nonlinear analysis approaches assume constant rotational speed. These approaches are unsuited to study the dynamic interaction between driving torque and whirling motion in this class of applications. In this paper, an analysis of the stability and control of a Jeffcott rotor under varying operational conditions is presented. A nonlinear dynamic model of the system is formulated to enable a detailed stability and parametric analysis. A proportional-integral (PI) torque command is employed to achieve a steady-state rotational speed. Assuming constant lateral control effort, system equilibrium points and their stability characteristics as functions of the system’s parameters are analyzed. A control law that minimizes the lateral effort is derived. A feedback proportional lateral control strategy is introduced to enhance the system’s region of stability, particularly in the supercritical speed range. Finally, a simulation study is conducted to validate the analytical findings. Simulation results demonstrate the effectiveness of the proposed approach for defining stable operating conditions and improving system performance.

Predictive modeling of CO2 capture efficiency using piperazine solutions: a comparative study of white-box algorithms

The urgency to mitigate carbon dioxide (CO2) emissions and combat climate change has spurred the development of effective CO2 capture technologies. One of the industry’s most well-known CO2 collection processes is CO2 absorption utilizing amine solvents. However, designing a successful amine scrubbing system in power plants requires precise prediction of CO2 absorption in aqueous amine solutions under various operating circumstances. Using aqueous piperazine (PZ) solutions for chemical absorption is promising due to the favorable reactivity of PZ with CO2. In this study, a comprehensive evaluation of PZ solution performance in CO2 capturing, employing the white-box algorithms, namely, Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH), was performed. Through extensive experimentation and data analysis, several correlations were developed with high and acceptable R2 values, such as 0.933 for GP, 0.949 for GEP, and 0.889 for GMDH, which shows high accuracy and reliability in predicting the CO2 capture efficiency of PZ solutions under varying operating conditions. The results of sensitivity analysis revealed that CO2 partial pressure increased CO2 absorption, while PZ concentration and temperature had negative and decreasing effects. These insights provide essential guidance for optimizing process conditions to enhance the CO2 capture efficiency. Finally, the leverage method was used to assess the reliability of both experimental and predicted data from white-box algorithms. This analysis identified potential outliers and validated the accuracy of the model’s predictions, enhancing the credibility of developed correlations and demonstrating the robustness of the approach.

Intelligent Helicopter Turbine Engine Fault Diagnosis Using Multi-Head Attention

A turbine engine provides power to the helicopter, enabling the helicopter to travel and hover in the air. Since the rotorcraft operates at high altitudes, ensuring safety and maintaining a healthy operational status are crucial at all times. Therefore, a prognostics and health management (PHM) system for the turbine engine must be implemented to predict any anomalies or faults to prevent catastrophic accidents. This research proposes a novel fault diagnosis method for helicopter turbine engines based on operational data acquired from actual aircraft. First, the proposed method predicts engine torque using other operational data while accounting for uncertainty. A Bayesian regression approach is employed to predict the engine torque. The torque margin, defined as the difference between the actual torque and the estimated torque, is then used to diagnose engine faults. Specifically, a multi-head attention mechanism is incorporated to capture interactions between various engine parameters. Additionally, domain adaptation techniques are applied to enhance the model's generalization performance, ensuring robustness across diverse operating conditions. The proposed method is validated using seven different datasets, each acquired from a helicopter engine. Four datasets were used for training, while the remaining three were allocated for testing and validation. The results indicated that the proposed method accurately predicted torque. Furthermore, the fault diagnosis showed promising results, leading to a 3rd-place finish in the 2024 PHM Society Data Challenge in terms of validation score.

Validating a Calibrated Model of a Groundwater Pump-And-Treat System Using Robust Multiple Regression

Validation of groundwater models is relatively challenging due to the need to reserve scarce water level data for calibration targets. In addition, traditional statistical validation metrics are unintuitive for non-technical audiences and do not directly identify model behaviors that require further refinement. We developed a novel model validation method that analyzes rate change events at pump-and-treat wells and statistically compares the water level responses at nearby monitoring wells between the data and model. The method takes advantage of events that occur alongside ambient pumping, unlike parameter estimation techniques that require independent drawdown or recovery events. The ability of the model to match well connections that are evident (or not evident) in the observations is characterized statistically, leading to four decision scenarios: model matches the observed connection (1) or lack thereof (2), model exhibits a connection that is not observed (3), or model over- or understates the observed connection (4). The method is applied to an FEHM-based groundwater flow and transport model that is shown to match 84.5% of the well connections analyzed. The method provides novel perspectives on the influence of calibration targets on the flow field and suggests that although the overall effect of drawdown targets was to improve the model, the choice of target well pairs creates flow pathways that may be inconsequential during normal operational conditions. The model adequately matches the flow over short spatial scales (<800 m) and over-represents the flow over greater distances (300–1200 m), suggesting the need for “null” drawdown targets in subsequent rounds of calibration.

A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.

Equivalence Ratio-Driven Flame Response of an Industrial Premixed Burner: Experiments and Modeling

Abstract This paper presents an analysis of the unsteady heat release rate response of premixed flames to equivalence ratio perturbations for an industrial premixed swirl-based burner. During this investigation, perfectly and technically premixed flames were acoustically forced via fuel/air mixture flow and air flow modulations, respectively, at the same operating conditions. From the resulting flame transfer functions (FTFs), measured using the multimicrophone method, the equivalence ratio driven FTF was isolated and extracted by removing the velocity driven component, i.e., the measured FTF from the perfectly premixed flame, from the technically premixed FTF with two novel extraction techniques. The results are compared with FTFs obtained directly in a previous experimental campaign where the fuel flow was acoustically forced, the resulting equivalence ratio fluctuations measured via an infrared absorption technique, and the heat release rate response to the forcing was quantified using chemiluminescence measurements. The results from both measurement approaches agreed well highlighting the validity of the techniques. Further, to understand the governing features of the equivalence ratio driven FTF, a physics-based analytical model following the G-equation approach was developed. The contributions from flame surface area, flame speed, and heat of reaction oscillations were modeled to describe the heat release rate dynamics. A limited number of physical parameters in the analytical model were anchored on one test condition, optimized and restricted to values, which were all physically reasonable, and were subsequently used for model predictions at other operating conditions. The FTF model predictions compared well with experimental data across a range of different operating conditions. Finally, the relative contributions from flame surface area, flame speed, and heat of reaction oscillations on the features of the FTFs were identified and explored.

Optimizing Sodium Ion Adsorption Through Robust d–d Orbital Modulation for Efficient Capacitive Deionization

AbstractUnraveling the fundamental mechanisms of sodium ion adsorption behavior is crucial for guiding the design of electrode materials and enhancing the performance of capacitive deionization systems. Herein, the optimization of sodium ion adsorption is systematically investigated through the robust d–d orbital interactions within zinc‐doped iron carbide, facilitated by a novel liquid nitrogen quenching treatment. Liquid nitrogen quenching treatment can enhance the coordination number, strengthen d–d orbital interactions, promote electron transfer, and shift the d‐band center of Fe closer to the Fermi level, thereby enhancing sodium ions adsorption energy. Consequently, the obtained electrode material achieves a superior gravimetric adsorption capacity of 121.1 mg g−1 and attractive cyclic durability. The adsorption capacity is highly competitive compared to the vast majority of related research works in the field of capacitive deionization. Furthermore, sodium ion adsorption/desorption mechanisms are substantiated through ex situ techniques, revealing dynamic atomic and electronic structure evolutions under operational conditions. This work demonstrates that optimizing sodium ion adsorption via robust d–d orbital modulation enabled by liquid nitrogen quenching treatment is an effective approach for developing efficient capacitive deionization electrode materials.

The HVAC system engineering method for a new family of unified cabins of combine harvesters

BACKGROUND: Designing a combine harvester cabin climate system is a complex and multi-stage process that includes solving a set of tasks. To solve them, a design engineer must have knowledge and skills in various fields, starting with thermal engineering calculations and ending with experimental research methods and computer modeling, which requires a large amount of intellectual and time resources. Therefore, the task of creating a unified method of engineering of combine harvester cabin HVAC system is highly relevant. AIM: Development of the HVAC system for a unified cabin of a grain harvester and a forage harvester. The designed HVAC system is purposed to create a comfortable microclimate in the cabin of the combine for 2 people in summer and winter operating conditions. METHODS: The methodology of calculation and selection of the combine harvester cabin HVAC system, which includes the development of engineering calculation methods and mathematical modeling of thermodynamic and ventilation processes in the cabin, is considered. RESULTS: The main parameters were determined: heat intakes and heat losses for the grain harvester and forage harvester cabins, which amounted to 2.8 and 2.2 kW for the grain harvester, 2.9 and 2.35 kW for the forage harvester; the required air flow rate supplied to the cabin to ensure a comfortable temperature — 740 m3/h; the percentage of air recirculation regarding the conditions of absence of fogging and creation of excessive pressure in the cabin — 75%; the cooling and heating capacity of the HVAC system, taking into account the operating conditions of the combine, are 7.8 and 6.3 kW respectively. The selection of the main equipment of the HVAC system for a unified cabin for a new family of unified cabins of combines — the BUHLER 1000 MFWD evaporator-heater and the Valeo TM16 compressor are chosen. CONCLUSION: The HVAC system designed in accordance with the presented methodology is capable of ensuring a comfortable cabin air temperature in the range of 22–24 °C at various operating modes of the combine harvester in different regions. In addition, the HVAC system parameters eliminate fogging of the cabin windows and the penetration of dusty air inside due to the created excessive pressure.

Analysis and Optimization of Output Low-Pass Filter for Current-Source Single-Phase Grid-Connected PV Inverters

In this study, the design of output low-pass capacitive–inductive (CL) filters is analyzed and optimized for current-source single-phase grid-connected photovoltaic (PV) inverters. Four different CL filter configurations with varying damping resistor placements are examined, evaluating performance concerning the output current’s total harmonic distortion (THD), the power factor (PF), and power losses. High-frequency harmonics are effectively attenuated by a second-order CL filter with the damping resistor placed parallel to the filter inductor. In addition, this filter type achieves the best performance by minimizing power loss. A systematic design methodology using filter normalization techniques allows to determine the optimum filter parameters based on the specified cut-off frequency (500 Hz), power loss (5% of rated power), and target THD (<5%). The analysis, simulations, and experiments show that under various operating conditions, this approach meets the grid connection standards (current THD < 5%, power factor between 0.8 leading and 0.95 lagging) while improving efficiency.

Transport of a Mixture of Sand and Water Through a Pump Characterized by Dual Inlets and a Double-Layered Impeller

A centrifugal pump incorporating two inlets and a double-layered impeller is proposed for transporting a mixture of sand and water. The double-layered impeller (primary impeller) encircles a secondary impeller. To reveal the operating and flow characteristics of such a pump, numerical work is conducted with a validated numerical method. The effects of the feed rate of sand and the rotational speed of the impeller are investigated. The results show that the pump efficiency is not monotonically related to the solid volume fraction. At a feed rate of sand of 2.10 m3/min and a rotational speed of 950 rpm, the lowest pump efficiency is reached. In the volute chamber, vortices of various sizes are evidenced. With increasing rotational speed, the overall solid volume fraction in the pump decreases. Meanwhile, when the solid volume fraction attains 0.28, sand particles tend to accumulate near the outer rim of the volute chamber. The axial force acting on the primary impeller increases with the rotational speed. Under different operating conditions, the radial forces point unanimously toward the third and fourth quadrants.

Influence of Dopants on Pt/Al2O3-Based Monolithic Catalysts for Autothermal Oxidative Coupling of Methane

Autothermal oxidative coupling of methane (OCM) is a highly attractive approach for methane utilization. If platinum-based catalysts are operated in short-contact-time reactors with high space velocities, high methane conversion can be achieved. Using a 1 wt.% Pt/Al2O3 catalyst as a benchmark, the present study elucidates how different dopants, namely Ni, Sn, and V2O5, affect the OCM reaction. Kinetic catalyst tests reveal that acetylene (C2H2) is the predominant C2 product, irrespective of the catalyst formulation or operation conditions. Furthermore, the use of bimetallic catalysts allows either for the maintenance or even the improvement of the C2 selectivity during OCM, which is attributed to synergistic effects that occur when expensive Pt is partially replaced by cheaper dopants. In particular, the 1 wt.% Pt/Al2O3 reference catalyst yielded a maximum C2 selectivity of 8.2%, whereas the best-performing doped sample 0.25 wt.% Pt 0.75 wt.% V2O5/Al2O3 yielded a total C2 selectivity of 11.3%.

Comparative numerical study of the energy performance of two different configurations of a hybrid photovoltaic/thermal air collector

Solar energy is a renewable alternative to fossil fuels. When solar energy is converted into electricity by photovoltaic (PV) solar modules, the heat generated increases the temperature of the PV solar cells and, as a result, reduces their electrical efficiency. Several techniques are used to cool PV solar cells in order to reduce their operating temperature. One technique is the use of hybrid photovoltaic/thermal (PVT) solar collectors, which simultaneously produce electricity and useful heat. In the airflow channel, the heat and mass transfer equations and those derived from the energy balance on the solid layers of the collector were discretized using the finite difference method with an implicit alternating directions (ADI) scheme. All the algebraic equations obtained after discretization were solved using the Thomas algorithm. The aim is to make a numerical comparison of the energy performance of the Verre-PV-Tedlar and Verre-PV-Verre flat-plate air PVT hybrid solar collectors in the climatic conditions of Lomé, Togo. Under the same operating conditions, the average electrical efficiencies of the Verre-PV-Verre and Verre-PV-Tedlar flat-plate air PVT hybrid collectors are 15.34% and 13.85% respectively. There is a 1.49% improvement in electrical efficiency for a Verre-PV-Verre air-source PVT hybrid collector compared with a Verre-PV-Tedlar PVT hybrid collector. The Verre-PV-Verre flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.564 kWh and a thermal energy gain of 0.839 kWh, while the Verre-PV-Tedlar flat plate PVT air hybrid collector has an average daily electrical energy gain of 0.548 kWh and a thermal energy gain of 1.403 kWh.

Evaluating the Influence of Thin Film of Water on the Frequency Dependences of Transmission Line S-Parameters at Positive and Negative Temperatures

Relevance. Provision of reliable and uninterrupted radio communication is critically important under changing climatic conditions of its operation. The combined effect of temperature and humidity can lead to changes in the electrical characteristics of transceiving devices and thereby disrupt the communication channel. In difficult climatic conditions of operation, due to constant temperature changes on the surface of the printed circuit boards, which are part of them, condensation can form, affecting the performance of the entire device. In this regard, the electrical characteristics change, which must be taken into account when designing critical REE. When designing transmission lines on printed circuit boards, it is reasonable to evaluate climatic impacts on it in a wide frequency range, which requires the development of new models and methods that allow taking into account these impacts.Goal of the work: to evaluate the influence of the temperature of a thin film of water on the surface of a microstrip transmission line on its frequency dependences of S-parameters. Finite element methods and laboratory experiment were used.Results. A methodology to account for the effects of ambient temperature and humidity on the electrical characteristics of a microstrip transmission line (MTL) is presented, which allows evaluating the variation of the S-parameters of the line over wide ranges of frequencies, air temperature and humidity, as well as the chemical composition of the environment. The S-parameters of water in a container placed inside a coaxial chamber are measured over the frequency and temperature ranges of 10 MHz to 12 GHz and minus 50 to 100℃, respectively. Using the presented model, the frequency dependences of the electrical conductivity of water at different temperatures are calculated. It is shown that at positive temperature, the electrical conductivity can reach 6.5 Sm/m and at negative temperature it can reach 1.3 Sm/m. Using the developed methodology, the influence of different water electrical conductivity on the S-parameters of MTLs is evaluated. The influence of water and ice layer thickness on the S-parameters of MTLs was shown. It is found that models describing the electrical conductivity of water have an excellent influence on the electrical parameters of the transmission line. Novelty. A method of accounting for the influence of ambient temperature and humidity on the S-parameters of the transmission line is presented, which is characterized by the use of a model of water conductivity based on insertion losses calculated from the measured S-parameters of a coaxial chamber with water in the container when its temperature is changed. Practical significance: a model and a methodology for taking into account the impact of temperature and humidity of the environment on the MTL characteristics are presented, allowing estimating the S-parameters of the line in a wide range of frequencies, air temperature and humidity, as well as the chemical composition of the environment.

Evaluation of the Use of the Angular Domain and Order Domain in a Bearing Fault Detection Framework using Deep Learning

Bearing failures are very common in the industrial environment, requiring effective fault detection methods, which can be categorized into physics-based, knowledge-based and data-driven types. Data-driven methods are efficient in differentiating healthy conditions from faulty conditions by characterizing machine signals, involving stages of data acquisition, feature extraction, and condition determination. Traditionally, feature extraction and condition determination were manual, but advances in artificial intelligence and machine learning, especially deep learning, have automated this process. Although deep learning automatically learns the best features from the input data, the signal domain can influence the model's performance. Time and frequency domain representations are widely used in fault detection methodologies using vibration signals, while angular and order domains are more common in variable operating conditions, but direct use of these domains with deep learning is still rare in the literature. Considering this, this study evaluates a bearing fault detection methodology using vibration signals in different domains (time, frequency, angular, and order) under various rotational conditions. Three distinct approaches were tested to assess the effectiveness of these representations. The results indicated that the frequency domain representation had the best overall performance and the study concluded that the angular and order domains do not offer significant advantages compared to the frequency domain. Nonetheless, it is recommended to conduct a more in-depth analysis with more diverse datasets, especially those containing early-stage bearing fault signals.

The green footprint of anammox processes under simulated actual operating conditions: Focusing on the nitrous oxide and methane production

The anammox process has attracted increasing attention due to its advantages of low-carbon and energy-saving, nevertheless, greenhouse gas was still generated during its engineering applications process. Hence, it is vital to comprehensively understand the production characteristics and mechanisms of N2O and CH4 in anammox processes by responding to practical conditions including dissolved oxygen, temperature, and salinity. Results showed that N2O production increased by 192 %–358 %, while nitrogen removal efficiency (NRE) increased by 64.2 %–86.8 % with increasing temperature. The increased salinity inhibits 40.60 %–65.33 % N2O production with a decrease NRE of 7.85 %–18.2 %. CH4 production was the highest at 18–27 °C, reaching 3.07 ± 0.11–4.06 ± 0.16 mg·L−1, which were 1.59–2 and 1.29–1.38 times higher than that at 8–17 °C and 28–37 °C, respectively. Denitratisoma, Thauera, and Nitrosomonas were the main functional microbes for greenhouse gas production in anammox consortia. Notably, H2O2-induced intracellular Fenton reaction may be critical for the CH4 production in anammox consortia. This work provides valuable insights into achieving efficient nitrogen removal and minimizing carbon footprint in anammox systems and provides a theoretical basis for implementing the net-zero emission idea in wastewater treatment plants.

Pressure and Temperature Prediction of Oil Pipeline Networks Based on a Mechanism-Data Hybrid Driven Method

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions.

Device optimization of all-perovskite triple-junction solar cells for realistic irradiation conditions

All-perovskite two-terminal tandem solar cells, comprising two or more junctions, offer high power conversion efficiencies (PCEs) that exceed the limits of single-junction photovoltaics. Realizing high-efficiency SCs requires carefully optimizing the photoactive layer, front electrodes, and functional layers. Here, we first aim to determine the optimal device architecture, i.e., the perovskites bandgaps and optimum layer thicknesses, for standard test conditions (STCs). We then optimize the energy yield (EY) under realistic outdoor conditions (ROCs), i.e., the overall electrical energy to be expected in one year at a specific location. In the first step, we reference our simulation with two terminal all-perovskite triple-junction SCs (2T3J-PSCs) to previously experimentally realized PSC with a PCE of 20.1% as a benchmark to derive the underlying diode parameters and use our in-house energy yield code combined with a hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) to find the optimal bandgap combination and perovskite layer thicknesses. The optimized SCs with the optimal bandgap combination offer a PCE of 25.1% with a current density of 10.1 mA/cm2. Furthermore, the effect of the other functional layers, such as transparent conductive oxide (TCO), hole transport layer (HTL), and recombination junctions (RJs), is also investigated to enhance the cell performance further. The SCs with optimized layers exhibit improved parameters: PCE of 27.1%, with a relative improvement of 34.8% in the PCE compared with the fabricated cell, and a high current matching a of 10.8 mA/cm2. The numerical results showed that the reported cell can potentially achieve a PCE of 36% at an eVoc/EG ratio of 0.72. However, the optimal parameters may vary in real-world operating conditions due to variations in temperature, humidity, and light exposure. As a result, the optimal energy yield (EY) parameters may differ. Therefore, the energy yield optimizing process is also carried out by considering ROCs in Phoenix, AZ, USA, to find the best parameters under these conditions. We found that the optimum top layer bandgap under ROCs is lower than under STCs due to a bluer, more shifted spectrum in ROCs. After that, the optimized cell under ROCs is tested in several locations exhibiting different climatic conditions (Seattle, Honolulu, Los Angeles, Miami, and Milwaukee). The numerical results show that the optimized cell under ROCs offers an increase in energy yield in several locations compared to conventional single-junction crystalline Si-SCs (PCE = 23.6%) with a high energy yield of 648.2 kWh/m2 in Phoenix, with an improvement of 39.9% in the EY compared to the Si-SC counterpart. Our results provide design guidelines for fabricating a highly efficient triple-junction perovskite SC in the lab and outdoor applications and improve the efficiency of 2T3J-PSCs beyond the 30% limit.

White Light Generation and Stability Analysis of High-Power Blue LDs with Remote YAG Phosphors

This paper presents a comprehensive analysis of white light generation and the associated aging dynamics using high-power blue laser diodes (LDs) combined with transmissive single crystal remote YAG phosphors. By systematically varying input currents (ranging from 0.6 A to 3 A) and phosphor thicknesses (250 μm and 500 μm), this study elucidates the optical and electrical characteristics of LD-phosphor systems under diverse operating conditions. The results highlight the system’s potential for stable and efficient white light generation, making it suitable for high-power lighting applications. Experimental setups included both single LDs and a 4 × 2 LD array. For the single LD, a peak optical output of 4.16 W was achieved at 3 A, corresponding to an initial luminous flux of approximately 700 Lm and a correlated color temperature (CCT) of 4653 K, with minimal color temperature shift observed during a 60 min aging process. The 4 × 2 LD array demonstrated consistent white light output across varying phosphor thicknesses, with maximum luminous fluxes of 1857 Lm at 1.4 A and 2622 Lm at 1.6 A for phosphor thicknesses of 250 μm and 500 μm, respectively. Importantly, the phosphor exhibited excellent thermal stability throughout the aging process, with the CCT maintained within a range of 4600 K to 5500 K. These findings underscore the reliability and applicability of LD-based white light systems in demanding high-power lighting environments, offering a promising alternative to conventional light sources for automotive, industrial, and specialized lighting applications.

Long‐Term Estimation of SoH Using Cascaded LSTM‐RNN for Lithium Batteries Subjected to Aging and Accelerated Degradation

ABSTRACTAccurate estimation of state of health (SoH) of the battery over long‐term is a critical challenge for the battery management systems in electric vehicles. This is due to the challenges in accurately modeling the accelerated aging and degradation phenomena caused by diverse operating conditions of the battery. This paper presents a cascaded recurrent neural networks (RNN) with long short‐term memory (LSTM) to estimate the internal resistance and SoH, taking account of various abnormal operating conditions of the battery. A datasheet‐based degradation model of the battery is developed using fade equations. The training and validation data set for LSTM‐RNN are generated by subjecting the battery model to various factors that cause accelerated degradation, such as fast charging, varying operating temperatures, overutilization, and cell imbalance. The cascaded LSTM‐RNN is trained to estimate SoH only once after the completion of every charge–discharge cycle. The training error index parameters of the proposed SoH estimator are well within 1%, demonstrating the reliability and robustness of the estimator to diverse operating conditions of the battery.

Operation study of the photovoltaic thermal heat pump integrated energy system under trigeneration condition and harsh conditions

In recent years, the photovoltaic thermal heat pump has gained significant attention as a near zero-carbon energy technology, due to the comprehensive ability to provide heating in winter, cooling in summer, photovoltaic power generation and domestic hot water throughout the year. However, owing to the incomplete research on the system operating characteristics in trigeneration condition and harsh conditions, the production performance and operational stability of the photovoltaic thermal heat pump were not widely recognized. In view of the above problems, the following studies were conducted in this paper: First, the integrated energy system based on the modular photovoltaic thermal heat pump was designed and constructed. Then, the system operation in severe hot and cold season was carried out and the operational stability of the system under harsh operating conditions was demonstrated sufficiently. Afterwards, the operation of the system under the continuous trigeneration operation were analyzed. During the 7-day trigeneration operation, the average hot water output was 12.12 tons and the average photovoltaic power generation was 88.5 kWh in the daytime heating operation. Besides, the average coefficient of cooling performance was 2.32 in the nighttime cooling operation and the effective cooling-supply time of the system was approximately 4 h.