NetWork
Development and Application of a Crushing Specific Work Test Device for Phase Change Aggregate Concrete
Liu Chengbin;Chen Ju;Zhang Ruiqiang;Yang Ziming;[Objective] To address the limitation that existing chiseling-specific test equipment can only operate at room temperature and thus cannot support quantitative studies of phase-change-aggregate concrete breakability under complex thermal conditions， this study presents a newly developed test apparatus specifically designed to assess the specific crushing work of phase-change aggregate concrete. [Methods] The apparatus is composed of three critical elements: the specimen area， the crushing device area， and the support structure. To achieve accurate temperature control of the specimen， a cast-aluminum electric heating plate is utilized in conjunction with a highly efficient thermal insulation system. This setup ensures precise maintenance of the desired temperature throughout the testing process. The apparatus features a unique synchronous belt lifting-drop hammer impact system that enables uniform crushing operations in multiple directions. This innovative system works in tandem with an adaptable rotatable central shaft structure， which further enhances the device's ability to conduct crushing actions effectively. The synchronous belt lifting-drop hammer impact system guarantees consistent and controlled crushing actions， while the rotatable central shaft structure allows for flexible positioning of the crushing device to optimize its performance. [Results] To verify the comprehensive performance of the device， a series of performance verification tests were conducted. Phase-change aggregate concrete specimens with different mix proportions were selected for the specific energy of fragmentation test under different temperature conditions， including room temperature， 40℃， 80℃ ，120℃， 160℃ and 200℃. Three groups of parallel specimens were set for each temperature gradient to ensure the representativeness of the test data. The test results show that the testing device operates stably， is convenient to operate， and can accurately measure the specific energy of fragmentation data of phase-change aggregate concrete at different temperatures. The test error is controlled within 5%， indicating good reliability and repeatability. As the test temperature increases， the specific energy of fragmentation of the phase-change aggregate concrete shows a significant downward trend， and the crushability of the material gradually improves. When the temperature is below 120℃， the decrease of the specific energy of fragmentation is relatively gentle， with a reduction range of 15% - 28%. When the temperature is above 120℃， the decline rate of the specific energy of fragmentation accelerates significantly， and the crushability of the material is significantly enhanced. At 200℃， the specific energy of fragmentation decreases by 57% compared to that at room temperature， indicating that the mechanical properties of the phase-change aggregate concrete are significantly weakened in high-temperature environments， showing excellent dismantlability. This pattern is consistent with the phase-change characteristics of phase-change aggregates at high temperatures and the change pattern of the internal structure of the concrete. [Conclusions]The testing apparatus can effectively simulate a variety of complex thermal conditions and accurately perform quantitative measurements and data analyses of the specific crushing energy of phase-change aggregate concrete specimens under different temperature environments， substantially enhancing the stability and accuracy of the experimental results. It not only addresses the technical gap in quantifying the fracture characteristics of phase-change aggregate concrete under coupled thermal effects—thereby establishing a critical technical foundation for systematic investigations， mechanism analyses， and performance evaluations of the material’s crushability—but also provides a feasible approach and an important engineering reference for optimizing mechanical tests， developing dedicated testing equipment， and standardizing test methods for various specialty concretes such as low-temperature and phase-change-modified materials.
Research and practice on the operation and management of the deep Earth laboratory
LI Yasong;KANG Kai;LI Zaiqiang;[Objective] Deep earth laboratories use thick rock formations to shield cosmic rays， providing an experimental environment with an extremely low radiation background for frontier fields such as particle physics， nuclear physics， and life sciences. They are excellent venues for conducting cutting-edge basic scientific research. These laboratories exhibit unique characteristics in operational management， such as extreme environmental conditions， interdisciplinary nature， and complex risks. This paper takes the China Jinping Underground Laboratory as the research object. As a major national science and technology infrastructure， it boasts the deepest rock cover， the largest underground space， and the lowest cosmic ray flux in the world. Located 2400 meters underground， it has a total construction area of 40，000 square meters and a space volume of 300，000 cubic meters. It has attracted research teams from more than ten top scientific research institutions in China， including Tsinghua University， Shanghai Jiao Tong University， Beijing Normal University， and Sichuan University. [Methods] To address the core issues in laboratory operation and management， this research focuses on analyzing the key operational management challenges based on the deep-earth characteristics of the Jinping Laboratory， such as pre-management and settlement guarantee for project access， long-term maintenance of the extremely low-radiation background experimental environment， safety risk management and control during laboratory operation， and the construction and management of the operation support team. This paper proposes a series of efficient and advanced management measures in terms of project access， background environment maintenance， safety risk management and control， and support team construction. These measures include: ① a scientific approval and resource scheduling system with hierarchical coordination， covering project approval， access， exit and equipment installation; ② low-background safeguard measures including radon suppression， dedicated ventilation and radiation monitoring; ③ a safety system tailored for deep underground scenarios， supporting experimental operation， earthquake monitoring and fire prevention; ④ a professional operation management and support team for integrated management and implementation guarantee. [Results] Endowed with the unique advantage of 2400-meter vertical rock coverage， the Jinping Laboratory faces the challenge of reconstructing the management paradigm in the extreme deep-earth environment while creating an "ultra-low background" scientific research environment. By systematically analyzing and addressing prominent issues in core dimensions such as scientific collaboration， background maintenance， safety control， and team building， this paper develops an operational management solution suitable for deep underground scenarios. This solution ensures the safe， efficient， and reliable operation of the laboratory， supports the achievement of more high-level scientific experimental results， and improves the efficiency of laboratory operation and management. [Conclusions] This research breaks through the management thinking of traditional ground-based scientific research facilities. Guided by scientific research needs， based on safety and stability， and supported by technological innovation， it realizes the in-depth integration of scientific management and engineering operation and maintenance. The operational management practice of the Jinping Laboratory will not only lay a solid foundation for its own cutting-edge scientific research but also contribute to the professional and refined operation and management of major science and technology infrastructure， and provide a practical paradigm with Chinese characteristics for the operation and management of deep earth laboratories worldwide.
Development and experimental research of a vacuum infiltration sintering apparatus
WANG De;YANG Puyuan;ZHEN Zhen;XIAO Xiaofeng;LIU Huan;WANG Wenqin;[Objective] Wear-resistant seal coating on the tip of single-crystal turbine blades plays a crucial role in ensuring the airtightness and operational efficiency of aeroengines. With the continuous increase in turbine inlet temperature， the harsh service environment imposes increasingly stringent requirements on the coating， such as superior high-temperature wear resistance， oxidation resistance and adhesion. However， existing preparation technologies face prominent challenges: thermal spraying and laser cladding produce coatings with flat surfaces where abrasive particles are uniformly distributed inside， failing to meet the protruding morphological requirement; electrodeposited yields coatings that suffer from insufficient adhesion and increased brittleness with increasing thickness; brazing， while improving interface bonding， damages the base metal because of the diffusion of melting-point-lowering elements. Additionally， Al?O? ceramic particles， as ideal reinforcing phases， exhibit poor wettability with metal melts， and their particle size and content significantly affect coating quality， yet relevant systematic research is scarce. To address these issues， this study aims to develop a high-performance preparation technology for NiCoCrAlYTa–Al?O? blade-tip wear-resistant coatings， synergistically integrating the high-temperature wear resistance of particles and protective performance of the coating while avoiding damage to the single-crystal base metal. [Methods] A visualized high-vacuum infiltration sintering apparatus was developed based on a traditional vacuum tube furnace. Key improvements included equipping a 10× visual window， a high-speed camera (maximum shooting rate of 3980 frames/s)， and a synchronous light source for real-time recording of the entire experimental process， as well as integrating a high-flux diffusion pump， vacuum gauge， and vacuum meter to achieve a high-vacuum environment of 1 × 10?3 Pa with real-time monitoring. Al?O? particles with three sizes (1， 10， and 30 μm) and five weight percentages (0%， 3%， 5%， 8%， and 10%) were selected as reinforcing phases， NiCoCrAlYTa as the coating matrix， and NiCrSi as the infiltration alloy. Electroless Ni–P alloy plating was applied to Al?O? particles to improve their wettability with the metal melt. The coating preparation followed a specific thermal cycle: heating to 420 °C at 10 °C/min for 30 min， subsequent heating to 1200 °C at 10 °C/min for 2 h， cooling to 600°C at a rate not exceeding 5°C/min， and final natural cooling to room temperature. The microstructure and surface morphology of the coatings were characterized using precision image measuring instruments， scanning electron microscopy (SEM) equipped with energy dispersive spectroscopy (EDS)， and 3D profilometers. Friction and wear tests were conducted on a self-designed rig with a normal load of 1.5 N， sliding speed of 1 m/s， and total sliding distance of 1000 m; the wear resistance was evaluated by measuring the weight loss of coatings and mating graphite disks. [Results] The electroless Ni–P plating effectively improved the wettability of Al?O? particles with the metal melt， reducing the contact angle from 93.425° to 80.371°. Particle size had a considerable impact on coating formation: coatings reinforced with 1 and 10 μm Al?O? particles contained numerous pores due to particle agglomeration， while those with 30 μm Al?O? particles did not exhibit pore defects， resulting in dense coatings with protruding Al?O? particles exhibiting an exposure height of 220–240 μm， which met the morphological requirements. Regarding mass percentage， when the Al?O? content was ≤5%， the total coating porosity remained stable at approximately 0.6% with gas pores as the main defects; beyond 5%， particle agglomeration intensified， clogging seepage channels and leading to a sharp increase in porosity (0.56% for the 3% sample and 2.22% for the 10% sample). Friction and wear test results showed that all coatings containing Al?O? particles exhibited considerably lower wear rates than the coating without Al?O?. The sample with 8wt% 30 μm Al?O? particles achieved the lowest wear rate of 0.00228 mg·N?1·m?1， and the mating graphite disks formed effective wear marks with a depth of 60–90 μm， indicating excellent wear resistance and cutting–sealing performance. The main wear mechanism of the coatings was the spallation of protruding Al?O? particles， and the wear rate increased at 10% Al?O? content because of excessive particle agglomeration.[Conclusions] This study successfully developed a visualized high-vacuum infiltration sintering apparatus that enables real-time monitoring of the sintering process. The optimal preparation parameters were determined as follows: 30 μm Al?O? particles with a mass percentage of 5–8%， sintering temperature of 1 200°C， and holding time of 2 h. The NiCoCrAlYTa–Al?O? coating prepared under these parameters exhibits excellent comprehensive performance， including low porosity， high wear resistance， and strong adhesion without damaging the single-crystal base metal. This technology solves the key technical problems of existing preparation methods and provides important theoretical and experimental support for the engineering application of high-performance wear-resistant seal coatings on single-crystal turbine blade tips.
Influent shock forecasting in wastewater treatment plants: A case study of coupling of shock-preserving preprocessing with a SARIMAX model
WU Hanjiang;LU Donghui;SHAN Ning;XIE Min;ZHU Aifen;XIA Xiuyun;LU Lichao;[Objective] Wastewater treatment plants (WWTPs) are essential for urban water environmental protection， yet their influent is frequently subjected to multi-source disturbances such as rainfall-induced infiltration and atypical discharges， triggering short-term quality shocks that impose significant operational risks. These shock events can rapidly alter organic and nutrient loading， causing mismatches in dissolved oxygen， sludge recirculation， and chemical dosing controls， thereby elevating the risk of effluent non-compliance. Accurate forecasting of influent shocks is therefore critical for stable WWTP operation. Data-driven methods have shown promise in influent prediction， but their deployment is often constrained by high data requirements and reliance on external driving information unavailable at most plants. Under the realistic condition of using only in-plant online monitoring data， ARIMA-family models retain unique advantages in interpretability， low computational cost， and suitability for online deployment. However， two bottlenecks hinder their application to shock prediction: conventional preprocessing uniformly removes outliers through smoothing， inadvertently clipping genuine sustained shock peaks and causing systematic underestimation during high-risk intervals; and fixed-parameter models exhibit response lag and error amplification during shock-induced structural breaks. [Methods] This study proposes a shock forecasting framework coupling three optimization strategies with a SARIMAX model， using 4，901 hourly records of COD， NH?-N， TN， and TP from a WWTP online monitoring system (80/20 chronological split). The first strategy is shock-preserving preprocessing: a duration-based discrimination logic classifies outlier segments persisting ≤2 hours as transient instrumental spikes for local repair， while those exceeding 2 hours are recognized as genuine shocks and entirely preserved. Outlier detection follows the Pauta criterion applied to a 24-point sliding window， and two metrics—shock retention rate and peak preservation ratio—quantify preprocessing fidelity. The second strategy extends ARIMA to SARIMAX with a 24-hour seasonal period for diurnal-cycle modeling to capture daily periodicity driven by urban water-use rhythms. The third strategy is shock-triggered refitting: upon shock detection during the prediction phase， the model automatically refits parameters on recent data to mitigate parameter mismatch caused by structural breaks. Four model configurations are systematically compared: M0 (conventional preprocessing + ARIMA)， M1 (shock-preserving preprocessing + ARIMA)， M2 (conventional preprocessing + SARIMAX with strategies 2～3)， and M3 (all three strategies combined). A three-factor factorial ablation experiment decomposes single-factor， pairwise， and three-way interaction effects to quantify each mechanism’s contribution. [Results] Shock-preserving preprocessing raised the COD shock retention rate from 34.29% to 97.14%， with NH?-N， TN， and TP all increasing from 0% to 100%; peak preservation ratios rose to 1.000 across all indicators. For shock-period prediction， M3 achieved MAE reductions over M0 of 54.8% for COD (from 57.869 to 26.151)， 61.2% for NH?-N (from 11.999 to 4.659)， 51.1% for TN (from 5.330 to 2.608)， and 65.2% for TP (from 0.470 to 0.164). Factorial analysis revealed that shock-preserving preprocessing was the dominant contributor to shock-period improvement， with the largest single-factor MAE reductions for COD (25.430)， NH?-N (5.952)， and TP (0.231). A clear positive synergy was observed between preprocessing and shock-triggered refitting， particularly for COD (interaction effect 8.196) and NH?-N (1.389)， indicating that refitting better tracks structural changes when shock morphology is preserved in the training data. Diurnal-cycle modeling exhibited mostly negative synergy during shock periods but contributed to overall prediction mainly through combined interactions with other strategies. [Conclusions] The proposed method， relying solely on in-plant monitoring data， raises shock retention rates to 97%～100% and reduces shock-period MAE by 54.8%～65.2% compared with the conventional baseline. Shock-preserving preprocessing is the primary contributor to shock-period performance gains， with notable positive synergy with shock-triggered refitting， while diurnal-cycle modeling enhances overall prediction through synergistic interactions.
Analysis of the seepage mechanism of brine-CO2 oil displacement and storage in heterogeneous porous media with carbonate coating
WANG Xiaopu;MA Kefan;WANG Qingxuan;ZHANG Liming;ZHANG Kai;ALFARISI Omar;LI Zhaomin;LI Binfei;Carbonate reservoirs have become strategic targets for reserve expansion in China and the Middle East， driven by the dual goals of reducing carbon emissions and ensuring energy security. However， their significant heterogeneity， complex pore structures， and wettability changes present considerable challenges to the efficiency of CO2-based enhanced oil recovery (EOR). At the pore level， the interaction of capillary forces， viscous forces， and the evolution of multiphase interfaces causes unstable displacement fronts and severely limits sweep efficiency in low-permeability areas. To tackle these issues， this study aims to reveal the pore-scale multiphase seepage mechanisms of brine–CO2 displacement in carbonate-coated heterogeneous porous media. This provides a microscopic foundation for optimizing CO2 flooding parameters and enhancing sweep performance in actual carbonate reservoirs. A heterogeneous pore network was constructed using a microfluidic chip， and calcium carbonate was coated in situ to simulate authentic carbonate reservoir surfaces and wettability. A series of visualization experiments were conducted at a controlled temperature (40 ℃). CO2 foam flooding and brine flooding at different injection rates were compared. A CCD imaging system was used to capture pore-scale evolution of oil， water， and gas phases， and gas saturation and residual oil distributions were quantified through image processing. To improve the accuracy of residual oil characterization， the ResNet152 deep neural network was trained on 2，885 labeled microfluidic sub-images from CO2 flooding， CO2–water alternating flooding， and brine flooding. Using weighted cross-entropy loss， AdamW optimization， and learning rate scheduling， the model achieved high classification accuracy for dispersed， mixed， and heterogeneous residual oil. Results showed that flooding performance was strongly affected by injection rate and pore-structure heterogeneity. At moderate flow rates (0.5–3 μL·min–1)， CO2 foam greatly improved sweep efficiency， nearly eliminating residual oil saturation. Foam viscosity and the Jamin effect effectively suppressed viscous fingering and prevented preferential flow through high-permeability channels， forcing the displacing phase into low-permeability areas. Conversely， at very low injection rates (0.1 μL·min–1)， foam instability caused large dispersed gas bubbles， limiting gas saturation to 24.93%， and hindered oil droplet mobilization， resulting in a high residual oil saturation of 44.01%. Gas saturation displayed a parabolic relationship with flow rate， with the maximum (92.46%) at 1 μL·min–1， where bubble size was smallest， and foam stability was optimal. Deep-learning-based oil classification also showed that brine flooding and CO2–water alternating flooding primarily produced dispersed residual oil， whereas surfactant-assisted CO2 flooding created a mixture of dispersed (49%)， mixed (36%)， and heterogeneous (14%) oil， reflecting foam instability and uneven sweep in highly heterogeneous zones. The model achieved a validation accuracy of 92%， confirming its effectiveness in pore-scale residual oil identification. This study clarifies the mechanisms underlying brine–CO2 displacement in carbonate-coated heterogeneous media. Calcium carbonate coating increases hydrophobicity， delays breakthrough in high-permeability pathways， and significantly enhances sweep in low-permeability zones， reducing residual oil by up to 28%. CO2 foam flooding is highly sensitive to injection rate， with moderate flow rates producing stable foam， high gas saturation， and efficient oil mobilization， whereas very low or high rates reduce displacement stability. By combining microfluidic visualization and deep-learning image analysis， this research offers microscopic insights for optimizing CO2 flooding conditions and provides technical guidance for deploying CO2-based EOR in Middle Eastern carbonate reservoirs. The findings also support international cooperation under the Belt and Road initiative and contribute to global efforts in the low-carbon， efficient development of carbonate oilfields.
Research and development of internal inspection devices for large cylinder-type vessels and steel cylinders of long-tube trailers
SHI Kun;ZHONG Maohua;ZHOU Yunyi;HE Yu;CAI Kangjian;[Objective] With the increasing demand for gaseous energy， the demand for equipment for storing and transporting gaseous substances is also growing. Cylinder-type vessels and tube trailer cylinders are the main equipment for storing and transporting gaseous substances， and both have highly similar structural types and damage patterns. Although these two types of equipment have different safety technical requirements， an external inspection mode is always adopted due to factors such as structural type， traditional inspection mode， and operability. However， using an external inspection mode for detecting high-risk internal surface defects does not yield satisfactory results， as it is prone to missing the detection of defects， with certain drawbacks. If an internal inspection is to be conducted， specific inspection equipment must be used， and a series of problems， such as the contracting and expanding of inspection device components， stable support， and multi-directional driving， must be solved. [Methods] To address the deficiencies existing in external inspection and develop an inspection mode that can replace the external inspection mode， we focus on the characteristics of “small mouth and large belly” of cylinder-type vessels and tube trailer cylinders and develop a dedicated internal inspection device based on the required functions of an internal inspection platform. This device is constructed using a modular combination of a detection sensor module， an internal support module unit， a cylinder mouth support module， a motor module， and a drive rod module. The drive rod comprises multiple short rods connected together， and its length can be continuously increased as the detection progresses. Considering its structural strength， stiffness， and lightweight， the device’s main material is aluminum alloy. The analysis involves technologies such as mechanics， electronics， control， simulation， and testing by adopting a method that combines theoretical calculation， simulation modeling， and experimental analysis. [Results] The internal inspection device is easy to install， and the support frame can be flexibly contracted and expanded to achieve “in and out” capabilities. It can also be equipped with various detection sensors. The device can adopt two detection operation modes: axial stepwise circumferential detection and circumferential stepwise axial detection. Although the device has a long rod structure， the maximum deflection generated by the drive rod is acceptable， and the positioning error caused by the drive rod’s torsional deformation can be automatically compensated through the program. We installed multichannel eddy current detection sensors， conducted experiments using comparison specimens， and performed tests at the equipment site. After repeated experiments and tests， the device was proven to be practical， reliable， and stable， capable of performing automatic detection and meeting the internal inspection and detection requirements of cylinder-type vessels and tube trailer cylinders. [Conclusions] In summary， the internal inspection device provides a new platform for inspecting large-volume cylinder-type vessels and long-drum trailer cylinders， solving the long-standing problem of the inability of internal inspection for these systems. The internal inspection mode not only complements external inspection but can also replace the traditional inspection mode， providing an effective means for inspecting new composite material cylinder-type vessels.
Research on SSDPT-based acoustic detection and adaptive thresholding methods for internal leakage of hydropower auxiliary valves
HAN Changlin;MENG Yifei;ZHANG Weijun;LI Yifan;YAN Yanan;[Objective] Early-stage internal leakage in valves used within hydropower-unit auxiliary systems (e.g.， compressed-air subsystems) is typically small， concealed， and strongly affected by operating-condition variability， which makes it difficult to detect using fixed empirical thresholds and difficult to model using supervised learning due to the scarcity of on-site fault samples. This study aims to establish a practical acoustic anomaly-detection route that (i) learns “healthy” valve acoustic signatures mainly from normal data， (ii) remains usable under multi-pressure operating conditions， and (iii) supports robust alarm decision-making through condition-aware and online-adaptive thresholding. [Methods] A controllable experimental platform was constructed by replicating the layout of a hydropower-station auxiliary compressed-air system and integrating a real in-service valve (a DN200 hemispherical valve) as the test object. Valve sounds were recorded using two microphones placed symmetrically around the valve body at approximately 1.0–1.1 m， with synchronized timestamps to ensure consistent multi-channel acquisition. Data were collected under multiple pressure levels from 0.2 to 0.7 MPa. Two representative states were considered: a fully closed valve as the normal condition and a 10% opening to emulate internal leakage. To improve the stability of acoustic inputs， multi-channel waveforms were aligned via cross-correlation， DC offsets were removed， and a band-pass filter was applied to retain diagnostic frequency content; recordings were then resampled to a unified sampling rate and segments with clipping or long saturation were discarded. The processed signals were segmented using a fixed-length sliding window (about one second per segment) with different strides for training and evaluation so that normal data could provide sufficient training diversity while test-time analysis retained high temporal coverage. Log-Mel spectrograms were extracted as compact time–frequency representations using short-time Fourier transform， Mel filter-bank projection， logarithmic compression， and per-channel standardization based only on normal data statistics. On the modeling side， a self-supervised dual-path Transformer (SSDPT) was employed to alternately capture dependencies along time and along frequency， enabling fine-grained characterization of leakage-induced spectral structures and their temporal evolution. Training combined a discriminative identification objective (learning to recognize normal operating signatures across groups/conditions) with a reconstruction objective under random patch masking， which encourages robust representation learning without requiring extensive labeled fault samples. For inference， anomaly scores were computed primarily from the classification-based confidence decay (i.e.， lower confidence in the learned “healthy ID” implies higher abnormality)， and the scoring strategy was analyzed against reconstruction-inclusive alternatives. [Results] The classification-based score provided the most reliable separation between normal and leakage segments. On the complete dataset， the overall area under the ROC curve reached 0.707， while the partial AUC in the low-false-alarm region (false positive rate ≤ 0.10) reached 0.417， indicating meaningful discrimination capability under practical low-false-alarm constraints. Performance， however， was strongly pressure-dependent: medium and high pressures exhibited clearer separability and more stable high-score tails associated with leakage， whereas certain low and mid pressures showed substantial score overlap between normal and leakage segments， limiting the effectiveness of a single global threshold. Pressure-wise analysis highlighted near-complete separability at the highest pressure level and useful separability at some medium pressures， while other lower-pressure settings approached chance-level ordering except for a small subset of strongly abnormal segments detectable under very low false-positive rates. To translate scores into actionable alarms， two complementary thresholding mechanisms were developed. First， pressure-level-specific thresholds were designed to compensate for systematic distribution shifts across pressures and to reduce mismatches caused by mixed-condition score scaling. Second， an online adaptive threshold scheme was formulated to update alarm boundaries during long-term operation by tracking a rolling high quantile of recent scores and calibrating robustness via median absolute deviation， thereby improving stability against gradual background changes and intermittent disturbances. [Conclusions] The study demonstrates that SSDPT-based self-supervised acoustic anomaly detection can serve as a feasible and engineering-oriented approach for internal leakage monitoring in hydropower auxiliary valves when fault labels are limited. Multi-pressure experiments confirm that operating conditions significantly affect score distributions and detection separability， making condition-aware thresholding essential for reliable deployment. The proposed pressure-level and online adaptive threshold strategies improve decision robustness across operating regimes and over time. Remaining challenges are concentrated in low-pressure scenarios where leakage signatures may be weak or masked by background noise; future work can target these regimes through richer sensing configurations and acoustic–vibration multimodal fusion， as well as validation in longer-term field monitoring pipelines.
Design of an Aerodynamic Experimental Teaching Platform for Wind Energy Drag Reduction Based on the Magnus Effect
SUN Huawei;ZHAO Xingyu;HAN Yang;ZHAO Dagang;ZHOU Guangli;Under the background of the “New Engineering Education” initiative， traditional experimental teaching in fluid mechanics and aerodynamics has been facing challenges such as insufficient integration with engineering practice， an overemphasis on verification-based experiments， and limited comprehensiveness and innovation. To address these issues， this study designs and develops an aerodynamic experimental teaching platform for wind-assisted drag reduction devices based on the Magnus effect. Centered on rotor wind tunnel experiments， the platform integrates fluid mechanics theory， aerodynamic measurement techniques， and ship drag reduction engineering applications. Through a modular experimental framework， the aerodynamic characteristics and flow interference phenomena of rotating cylinders under varying inflow velocities， spin ratios， and rotor arrangements are systematically investigated， providing intuitive and efficient experimental support for teaching complex aerodynamic mechanisms. In terms of system design， the platform integrates a variable-speed rotating cylinder device， a multi-component force balance， rotational speed and wind speed measurement units， and a data acquisition and processing system. It enables synchronous measurement of lift， drag， and aerodynamic torque with good stability and repeatability. Systematic experiments on single-rotor and dual-rotor configurations were conducted to obtain aerodynamic response characteristics under different parameter combinations. The results demonstrate that the platform effectively reveals the physical mechanism of rotation-induced lift in the Magnus effect， as well as the influence of inter-rotor flow interference on aerodynamic performance， providing reliable experimental evidence for wind-assisted propulsion and ship drag reduction applications. In teaching practice， the experimental platform has been incorporated into fluid mechanics and ship engineering–related courses through a three-level experimental framework comprising fundamental verification， parametric analysis， and engineering extension. This approach guides students progressively from theoretical understanding to engineering application. During the experiments， students systematically acquire skills in wind tunnel testing， rotating system control， multi-component force measurement， data processing， and uncertainty analysis， significantly enhancing their experimental design capability， engineering thinking， and teamwork skills. Moreover， the close integration of experimental content with research problems enables students to gain initial exposure to complex nonlinear flows and engineering optimization issues， stimulating their interest in cutting-edge technologies and innovative research. Overall， the proposed experimental teaching platform achieves an effective integration of “research-driven teaching and teaching-supported research.” It not only enhances the depth， challenge， and engineering orientation of experimental education， but also provides strong support for cultivating innovative and well-rounded engineering talents in ship and ocean engineering and related disciplines. The study indicates that the platform has strong demonstrative significance and broad applicability for experimental curriculum development and engineering education reform under the New Engineering Education framework.
Teaching experimental design of thermogravimetry for the co-pyrolysis of biomass and waste plastics
ZHANG Liqiang;CHEN Kai;CHEN Sulan;YUAN Jinfeng;ZHU Ningmin;LIN Riyi;[Objective] Biomass conversion and utilization technology is an important component of the curriculum in the major of New Energy Science and Engineering. At present， there are relatively few teaching experiments related to bioenergy in universities， and it is therefore necessary to explore and develop relevant experimental teaching activities. To improve teaching quality and enhance students’ practical abilities， an experiment on the co-pyrolysis of biomass and waste plastics was designed， and the corresponding teaching experimental design was developed. [Methods] A thermogravimetric analyzer is an important instrument for studying the relationship between the mass of raw materials and temperature or time. Thermogravimetric analysis is one of the main methods for investigating the co-pyrolysis characteristics and synergistic effects of biomass and waste plastics. It is easy to operate and has a high level of experimental safety. In this study， a thermogravimetric analyzer was employed to design a teaching experiment on the co-pyrolysis of biomass and waste plastics. Waste polypropylene and Nannochloropsis were selected as the experimental raw materials， and the thermal weight loss characteristics of their individual pyrolysis and co-pyrolysis at different mixing ratios were investigated. The characteristic pyrolysis temperatures at different stages were determined. The synergistic effect of co-pyrolysis at different mixing ratios was analyzed by calculating the curve overlap ratio. The influence of different mixing ratios on the pyrolysis kinetic mechanism was discussed， and the reaction models and activation energies under different conditions were clarified. [Results] The results showed that the TG/DTG curves of the mixed samples were not equal to the sum of the TG/DTG curves of the two individual raw materials， although microalgae and plastic dominated different temperature ranges during pyrolysis. When the ratio of plastic to microalgae was 1∶2， 1∶1， and 2∶1， the differences between the experimental curves and the calculated curves were relatively large. At these ratios， the overlap ratio values were smaller， indicating that the synergistic interaction between plastic and microalgae was more significant. As the proportion of microalgae increased， the activation energy of co-pyrolysis showed a trend of first decreasing and then increasing. At the ratio of 1∶1， the synergistic effect was strongest， and the activation energy was lowest. The reaction models at different ratios also varied， including nucleation models， diffusion models， reaction order models， and phase boundary reaction models. These results indicate significant changes in the reaction control steps during the co-pyrolysis process. The ratio of raw materials not only influenced the energy required for pyrolysis but also substantially altered the fundamental mechanism of the pyrolysis reaction. [Conclusions] This study introduces the co-pyrolysis technology of biomass and waste plastics as a cutting-edge research topic into undergraduate experimental teaching and achieves close integration between teaching experiments and course content. Through this experiment， students can independently search the literature to understand the latest research frontiers in biomass energy， learn to operate a thermogravimetric analyzer， and conduct co-pyrolysis thermogravimetric experiments under different conditions. Students can also effectively process experimental data and， by combining experimental results with course knowledge and literature analysis， study the kinetics and synergistic effects of co-pyrolysis. This process can improve students’ ability to analyze complex problems.
A machine learning-based optimization design method for fracturing fluid flow rate targeting equilibrium proppant bank height
LIU Hua-jie;LI Zhao-peng;Sergey Chernyshov;ZHANG Liming;LIN Wen-xiang;DING Fu-quan;Theis Ivan Solling;[Objective] The Middle East is a key region for oil and gas within the Belt and Road Initiative， but it faces challenges because most oil fields lack optimized fracturing designs. To achieve the desired stimulation results， high-displacement fracturing fluid operations are often used， which can lead to significant groundwater contamination from broken gel fluid. Visual plate experiments are commonly employed to optimize fracturing parameters; however， they are time-consuming and require substantial material， which hinders the development of green and intelligent oilfields. Therefore， this study aimed to develop an efficient machine learning model to predict the equilibrium proppant bank height within primary fractures and to use the output to inversely optimize the fracturing fluid displacement rate. [Methods] After a comprehensive review of existing literature， a dataset was established， and data standardization was carried out. By evaluating predictive performance using five-fold cross-validation and a test set， four machine learning algorithms were compared， and the best predictive model was selected. A method leveraging this optimal model to improve both the plate experiment process and the fracturing fluid displacement rate was then proposed. [Results] The random forest model showed the best predictive performance， achieving a coefficient of determination of 0.947， a root mean square error of 3.62， and a mean absolute error of 2.27 on the test set. For the validation set with 21 samples， the absolute error was within 1.50 cm， with a fitted curve slope of 1.07 and an intercept of -0.81. The inversely designed， optimized fracturing fluid displacement rate was significantly lower than the empirical rate， saving at least 106.2 m3 of fracturing fluid per hour while still ensuring effective fracture filling. [Conclusions] This study not only addresses current limitations of plate experiments but also offers guidance for designing fracturing fluid displacement rates in field operations within the Middle East.