Search Results (47)

Background: Glioblastoma (GBM) resists current therapies due to its rapid proliferation, diffuse invasion, and heterogeneous cell populations. We previously showed that a single cold atmospheric plasma discharge tube (DT) reduces GBM viability via broad-spectrum electromagnetic (EM) emissions. Here, we tested whether two DTs arranged in a helmet configuration could generate overlapping EM fields to amplify the anti-tumor effects without thermal injury. Methods: The physical outputs of the single- and dual-DT setups were characterized by infrared thermography, broadband EM field probes, and oscilloscope analysis. Human U87-MG cells were exposed under the single or dual configurations. The viability was quantified with WST-8 assays mapped across 96-well plates; the intracellular reactive oxygen species (ROS), membrane integrity, apoptosis, and mitochondrial potential were assessed by multiparametric flow cytometry. Our additivity models compared the predicted versus observed dual-DT cytotoxicity. Results: The dual-DT operation produced constructive EM interference, elevating electric and magnetic field amplitudes over a broader area than either tube alone, while temperatures remained <39 °C. The single-DT exposure lowered the cell viability by ~40%; the dual-DT treatment reduced the viability by ~60%, exceeding the additive predictions. The regions of greatest cytotoxicity co-localized with the zones of highest EM field overlap. The dual-DT exposure doubled the intracellular ROS compared with single-DT and Annexin V positivity, confirming oxidative stress-driven cell death. The out-of-phase operation of the discharge tubes enabled the localized control of the treatment regions, which can guide future treatment planning. Conclusions: Two synchronously operated plasma discharge tubes synergistically enhanced GBM cell killing through non-thermal mechanisms that coupled intensified overlapping EM fields with elevated oxidative stress. This positions modular multi-DT arrays as a potential non-invasive adjunct or alternative to existing electric-field-based therapies for glioblastoma. Full article

This study proposes a Tube-based Robust Model Predictive Control (Tube-RMPC) strategy for autonomous vehicle control to address model parameter uncertainties and variations in road–tire adhesion coefficients in complex road scenarios. More specifically, the proposed approach improves the representation of vehicle dynamic behavior by introducing a unified vehicle–tire modeling framework. To facilitate computational tractability and algorithmic implementation, the model is systematically linearized and discretized. Furthermore, the Tube-based Robust Model Predictive Control strategy is developed to improve adaptability to uncertainty in the road surface adhesion coefficient. The Tube-based Robust Model Predictive controller ensures robustness by establishing a robust invariant tube around the nominal trajectory, effectively mitigating road surface variations and enhancing stability. Finally, a co-simulation platform integrating CarSim and Simulink is employed to validate the proposed method’s effectiveness. The experimental results demonstrate that Tube-RMPC improves the path-tracking performance, reducing the maximum tracking error by up to 9.17% on an S-curve and 2.25% in a double lane change, while significantly lowering RMSE and enhancing yaw stability compared to MPC and PID. Full article

Ignition delay time (IDT) is a critical parameter for evaluating the autoignition characteristics of aviation fuels. However, its accurate prediction remains challenging due to the complex coupling of temperature, pressure, and compositional factors, resulting in a high-dimensional and nonlinear problem. To address this challenge for the complex aviation kerosene RP-3, this study proposes a multi-stage hybrid optimization framework based on a five-input, one-output BP neural network. The framework—referred to as CGD-ABC-BP—integrates randomized initialization, conjugate gradient descent (CGD), the artificial bee colony (ABC) algorithm, and L2 regularization to enhance convergence stability and model robustness. The dataset includes 700 experimental and simulated samples, covering a wide range of thermodynamic conditions: 624–1700 K, 0.5–20 bar, and equivalence ratios φ = 0.5 − 2.0. To improve training efficiency, the temperature feature was linearized using a 1000/T transformation. Based on 30 independent resampling trials, the CGD-ABC-BP model with a three-hidden-layer structure of [21 17 19] achieved strong performance on internal test data: R² = 0.994 ± 0.001, MAE = 0.04 ± 0.015, MAPE = 1.4 ± 0.05%, and RMSE = 0.07 ± 0.01. These results consistently outperformed the baseline model that lacked ABC optimization. On an entirely independent external test set comprising 70 low-pressure shock tube samples, the model still exhibited strong generalization capability, achieving R² = 0.976 and MAPE = 2.18%, thereby confirming its robustness across datasets with different sources. Furthermore, permutation importance and local gradient sensitivity analysis reveal that the model can reliably identify and rank key controlling factors—such as temperature, diluent fraction, and oxidizer mole fraction—across low-temperature, NTC, and high-temperature regimes. The observed trends align well with established findings in the chemical kinetics literature. In conclusion, the proposed CGD-ABC-BP framework offers a highly accurate and interpretable data-driven approach for modeling IDT in complex aviation fuels, and it shows promising potential for practical engineering deployment. Full article

Jet fuel from direct coal liquefaction (DCL) is an important alternative kerosene and represents a high-performance fuel for specific applications in civil applications. The study on its chemical positions and combustion properties is critical for the development of surrogate models and related combustion reaction mechanisms, which is valuable for promoting its usage in aeroengines. However, research on DCL-derived jet fuel is rather scarce. Herein, this work reports a systematic study on a DCL-derived jet fuel and its blends with traditional RP-3 jet fuel in two different ratios. Specifically, major physicochemical properties related to the aviation fuel airworthiness certification process are measured. Advanced two-dimensional gas chromatography (GC × GC) analysis is used to analyze the detailed chemical compositions on the DCL derived jet fuel and its blend with RP-3, which is then employed for surrogate model development. Moreover, ignition delay times (IDTs) are measured by using a heated shock-tube (ST) facility for the blended fuels over a wide range of conditions. Combustion reaction mechanisms based on the surrogate models are developed to predict the experimental measured IDTs. Finally, sensitivity analysis and rate-of-production analysis are carried out to identify the key chemical kinetics controlling the ignition characteristics. This work extends the understanding of the physicochemical properties and ignition characteristics of alternative jet fuels and should be valuable for the practical usage of DCL derived jet fuels. Full article

The behavior of long-span concrete-filled steel tube (CFST) arch bridges during the pouring stage is complex. The coupling effect of the time-varying hydration heat and the evolution of the elastic modulus is crucial for the linear control of the structure. Most of the existing models focus on static self-weight analysis but generally ignore the above-mentioned dynamic heat–force interaction, resulting in significant prediction deviations. In response to this limitation, this paper proposes an analysis method for the injection stage considering the time-varying heat of hydration and elastic modulus of concrete inside the pipe. Firstly, based on the composite index model of the hydration heat and through the reduction of the participating materials, the heat source function of the hydration heat of the arch rib was obtained, and its accuracy was verified by using two test components. Secondly, the equivalent application method of the hydration heat temperature field of the bar system model was proposed. Combined with the modified time-varying model of the elastic modulus at the initial age, the analysis method for the pouring stage of concrete-filled steel tube arch bridges was established. Finally, the accuracy of the proposed method was verified by analysis and calculation combined with engineering examples and comparison with the measured results. The results show that the time-varying heat of hydration and the time-varying elastic modulus during the concrete pouring stage inside the pipe can lead to residual deflection after the arch rib is poured. The calculated value of the example reaches 154 mm, while the influence of the lateral displacement is relatively small and recoverable. The proposed method improves the calculation accuracy by 44.19% compared with the traditional method, which is of great significance for the actual engineering construction control. Full article

Active frequency response (AFR) plays a crucial role in addressing the challenge of insufficient frequency regulation caused by the spatiotemporal distribution of power grid frequency. However, power fluctuations in renewable energy sources impact the frequency regulation performance of renewable energy units participating in AFR, and there is a lack of systematic assessment of their frequency regulation capabilities. This paper proposes a process-optimized AFR method for renewable energy based on distributed model predictive control (DMPC) using tube and robust control barrier functions (RCBF). The method integrates tube MPC for renewable energy units in fault regions and constrains control parameters in normal regions using RCBF, forming an enhanced DMPC-based coordination process for interconnected systems. This optimization ensures that both conventional and renewable energy units can effectively perform AFR under fluctuating renewable energy conditions. Furthermore, within the AFR online decision-making process, the optimal deloading rate for renewable energy is determined to maintain sufficient power reserves and frequency regulation capabilities. Finally, simulations of an interconnected system with a high proportion of renewable energy validate the effectiveness of this process-driven approach in enhancing the AFR capabilities of renewable energy sources. Full article

The abnormal swab pressure resulting from packer unsetting poses a great threat to the collapse resistance of production casings in deep high-pressure and high-temperature (HPHT) oil wells. This paper proposes an analytical model to predict the transient swab pressure in the A-annulus after packer unsetting based on a U-type tube and an iterative method. The model can further evaluate the collapse failure risk of the production casing in the whole wellbore. An example study and sensitivity analysis were carried out to reveal the variation characteristics of the transient swab pressure in the A-annulus and the failure risk of the production casing after packer unsetting. Furthermore, some preventative measures are proposed. The largest swab pressure occurs at the initial time of packer unsetting, which will lead to sudden collapse failure of the deeper production casing. A smaller width of the annular clearance between the packer rubber and production casing and a larger initial liquid level depth in the A-annulus can reduce the swab pressure in the A-annulus after packer unsetting and collapse failure risk of the production casing. In the example, when the width of the annular clearance decreased from 2.97 to 2 mm, the maximum swab pressure decreased from 88.71 to 27.4 MPa, a decrease of 69.1%. When the initial liquid level depth in the A-annulus increased from 700 to 900 m, the maximum swab pressure decreased from 122 to 57.05 MPa, a decrease of 53.2%. When the width of annular clearance was 2.97 mm, the collapse resistance safety factors for the production casing were less than 1.1 and may suffer from collapse failure for well depth between 3610 m and 6100 m. When the initial liquid level depth in the A-annulus was 700 m, the production casing will suffer from collapse failure for well depth between 2869 m and 6100 m. When the width of the annular clearance was less than 2.5 mm and the initial liquid level depth in the A-annulus was larger than 900 m, the collapse resistance safety factors for the production casing were all greater than 1.1 and the whole production casing was safe. To lower the collapse failure risk of the production casing because of packer unsetting, a packer rubber with a reasonable larger outer diameter and good deformation recovery ability is recommended, and the initial liquid level depth in the A-annulus should be controlled reasonably. The research results are of great significance for preventing the collapse failure of production casings during packer unsetting. Full article

In order to investigate the mechanism of ground settlement induced by shield tunnels better, this study proposes a novel simplified physical model testing method. In this physical model, double layer tubes with different materials are used to model the tunnel boring machine (TBM) and tunnel, respectively. When the outer tube in the experimental box is removed, the gap between the two different tubes can be utilized to reflect the ground settlement caused by TMB construction. Meanwhile, 3D image monitoring technology is introduced to collect ground settlement data for research on the mechanism of ground settlement induced by TBM construction. In order to validate the proposed testing method, firstly, the pilot experiment is performed; then, the obtained settlement curve obeys the Gaussian distribution, and the obtained settlement process is similar to that of the practical situation. Furthermore, based on the proposed testing method, an orthogonal experiment is designed to investigate the influences of the ground loss ratio, burial depth, and stratum condition on the ground settlement during the construction process. The results indicate that the ground loss ratio caused by the gap during construction excavation has a more significant impact than the tunnel burial depth and ground conditions. The findings in this study provide a quantitative guide for settlement monitoring during TBM construction, demonstrating that the ground loss ratio has the most significant impact on settlement (up to 28.7% deviation), while the effects of burial depth and stratum conditions are relatively minor (4.4% and 4.2% deviation, respectively). This method offers a practical and efficient approach for predicting and controlling ground settlement in TBM construction, which is of great importance in its practical application. Full article

The rapid development of AI (artificial intelligence), sensor technology, high-speed Internet, and cloud computing has demonstrated the potential of data-driven approaches in structural health monitoring (SHM) within the field of structural engineering. Algorithms based on machine learning (ML) models are capable of discerning intricate structural behavioral patterns from real-time data gathered by sensors, thereby offering solutions to engineering quandaries in structural mechanics and SHM. This study presents an innovative approach based on AI and a fiber-reinforced polymer (FRP) double-helix sensor system for the prediction of forces acting on steel tube members in offshore wind turbine support systems; this enables structural health monitoring of the support system. The steel tube as the transitional member and the FRP double helix-sensor system were initially modeled in three dimensions using ABAQUS finite element software. Subsequently, the data obtained from the finite element analysis (FEA) were inputted into a fully connected neural network (FCNN) model, with the objective of establishing a nonlinear mapping relationship between the inputs (strain) and the outputs (reaction force). In the FCNN model, the impact of the number of input variables on the model’s predictive performance is examined through cross-comparison of different combinations and positions of the six sets of input variables. And based on an evaluation of engineering costs and the number of strain sensors, a series of potential combinations of variables are identified for further optimization. Furthermore, the potential variable combinations were optimized using a convolutional neural network (CNN) model, resulting in optimal input variable combinations that achieved the accuracy level of more input variable combinations with fewer sensors. This not only improves the prediction performance of the model but also effectively controls the engineering cost. The model performance was evaluated using several metrics, including R², MSE, MAE, and SMAPE. The results demonstrated that the CNN model exhibited notable advantages in terms of fitting accuracy and computational efficiency when confronted with a limited data set. To provide further support for practical applications, an interactive graphical user interface (GUI)-based sensor-coupled mechanical prediction system for steel tubes was developed. This system enables engineers to predict the member forces of steel tubes in real time, thereby enhancing the efficiency and accuracy of SHM for offshore wind turbine support systems. Full article

Liquid carbon dioxide (L-CO₂) phase transition blasting technology, known for its high efficiency, environmental friendliness, and controllable energy output, has been widely applied in mine safety fields such as coal roadway pressure relief and coal seam permeability enhancement. However, the synergistic control mechanism between L-CO₂ blasting loads and in situ stress conditions on coal seam fracturing and permeability enhancement remains unclear. This study systematically investigates the key process parameters of L-CO₂ phase transition blasting in deep coal seams using response surface methodology and numerical simulation. First, three commonly used L-CO₂ blasting tubes with the overpressure of 150 MPa, 210 MPa, and 270 MPa were selected, and the corresponding material parameters and state equations were established. A dynamic mechanical constitutive model for a typical low-permeability, high-gas coal seam was then developed. A numerical model of L-CO₂ phase transition blasting, considering fluid–solid coupling effects, was then constructed. Multiple experiments were designed based on response surface methodology to evaluate the effects of blasting pressure, in situ stress, and stress difference on L-CO₂ fracturing performance. The results indicate that the overpressures of the three simulated blasting loads were 156 MPa, 215 MPa, and 279 MPa, respectively, and the load model closely matches the actual phase blasting load. L-CO₂ blasting creates a plastic deformation zone and a pulverized zone around the borehole within 500 μs to 800 μs after detonation, with a tensile fracture zone appearing at 2000 μs. By analyzing radial and tangential stresses at different distances from the explosion center, the mechanical mechanisms of fracture formation in different blast zones were revealed. Under the in situ stress conditions of this study, the number of primary fractures generated by the explosion ranged from 0 to 12, the size of the pulverized zone varied from 1170 cm² to 2875 cm², and the total fracture length ranged from 44.4 cm to 1730.2 cm. In cases of unequal stress, the stresses display axial symmetry, and the differential stress drives the fractures to expand along the direction of the maximum principal stress. This caused the aspect ratio of the external ellipse of the explosion fracture zone to range between 1.00 and 1.72. The study establishes and validates a response model for the effects of blasting load, in situ stress, and stress difference on fracturing performance. A single-factor analysis reveals that the blasting load positively impacts fracture generation, while in situ stress and differential stress have negative effects. The three-factor interaction model shows that as the in situ stress and stress difference increase, their inhibitory effects become stronger, while the enhancement effect of the blasting load continues to grow. This research provides a theoretical basis for blasting design and fracture propagation prediction using L-CO₂ phase transition blasting in the coal seam under varying in situ stress conditions, offering valuable data support for optimizing the process of L-CO₂ phase transition fracturing technology. Full article

Traditional methods of weed control during field management often result in herbicide waste. Precision herbicide application is crucial in agricultural production. This study presents a monodisperse droplet-generation system designed for precision herbicide application, capable of generating monodisperse droplets induced by an electric field. Droplet-generation experiments were conducted to investigate the effects of capillary tube outlet shape, liquid flow rate, and capillary tube size on the generation of charged droplets. A droplet diameter prediction model was established based on the system parameters. Experimental results indicated that as the applied voltage increased, the droplet diameter decreased, and the droplet-generation patterns transitioned sequentially from dripping, micro-dripping, to unstable dripping modes. In a weak electric field, capillaries with beveled outlets produced smaller droplets with more stable diameter distributions compared to those with blunt outlets. In a strong electric field, the smallest droplet diameter from blunt capillaries was 138.2 μm, whereas from beveled capillaries it was 198.7 μm. Within the design parameter range, droplet diameter was basically positively correlated with liquid flow rate and capillary tube size. By controlling the applied voltage, liquid flow rate, and capillary tube size, stable droplet generation could be achieved within a diameter range of 198.7–2520.8 μm, and the coefficient of variation of droplet diameter under the same working conditions was generally less than 6%. The monodisperse droplet-generation system developed in this study can effectively reduce herbicide usage and improve application efficiency. Full article

A new method of machining ultrasonic cavitation abrasive flow based on phase control technology was proposed for improving the machining efficiency of the inner wall of TC4 (Ti-6Al-4V) titanium alloy tubes. According to ultrasonic phase control theory and Hertzian contact theory, a model of ultrasonic abrasive material removal rate under phase control technology was established. Using COMSOL Multiphysics 6.1 software, the phase control deflection effect, acoustic field distribution, and the size of the phase control cavitation domain on the inner wall surface were examined at different transducer frequencies and transducer spacings. The results show that the inner wall polishing has the most excellent effect at a transducer frequency of 21 kHz and spacing of 100 mm. In addition, the phased deflection limit was explored under the optimal parameters, and predictive analyses were performed for voltage control under uniform inner wall polishing. Finally, the effect of processing time on polishing was experimented with, and the results showed that the polishing efficiency was highest from 0 to 30 min and stabilized after 60 min. In addition, the change in surface roughness and material removal of the workpiece were analyzed under the control of the voltage applied, and the experimental results corresponded to the voltage prediction analysis results of the simulation, which proved the viability of phase control abrasive flow polishing for the uniformity of material removal of the inner wall of the tube. Full article

Based on the Newton-Euler method, a coupling rigid-body dynamics model of a Trans-Medium Vehicle (TMV) separating from an Unmanned Underwater Vehicle (UUV) has been established. The modeling is based on the “holistic method” and “Kane” ideas respectively, so that most of the equations can be derived without considering the internal forces between the two bodies. The separation propulsion force, which is an internal force, only appears in the relative glide dynamics equation of the TMV along the axis of the separation tube that is installed on the UUV. This greatly reduces the workload of modeling and derivation. The UUV works entirely underwater, while the hydrodynamic shape of the TMV changes continuously during the process of the TMV separating from the UUV. Therefore, accurate hydrodynamic calculations for the UUV and TMV are the basis of numerical resolution for the two rigid bodies’ coupling dynamics model in water. A large number of numerical simulations was conducted using CFD methods to investigate the hydrodynamic performance of the UUV and TMV under various conditions. These simulations aim to establish a hydrodynamic database, and accurate hydrodynamic models were developed through fitting methods and online interpolation. In the process of solving the coupling dynamics of two bodies, the hydrodynamic model is used to calculate the hydrodynamic force experienced by the UUV and TMV. This balances the accuracy and efficiency of a numerical simulation. Finally, numerous simulations and comparative analyses were conducted under various operational conditions and separation parameters. The simulation results indicate that the impact of TMV separation on the motion state of the UUV becomes more prominent with smaller UUV to TMV mass ratios or deeper TMV separation depths. This effect can further influence the stability control of the UUV. The coupling rigid body dynamics analysis method established in this paper provides a fast and effective prediction method for use during the scheme design and separation safety evaluation phases of creating UUV-TMV systems. Full article

This study presents a comprehensive three-dimensional computational fluid dynamics (CFD) analysis of abrasive fluid flow and its erosive effects on the active components of the WUXI YCWJ-380-1520 water jet cutting machine. The research investigates the behavior and impact of abrasive particles within the fluid, determining the erosion rates for particles with diameters of 0.19 mm, 0.285 mm, and 0.38 mm (dimensions resulting from the granulometry of the experimentally established sand), considering various abrasive flow rates. The methodology includes a detailed granulometric analysis of the abrasive material, identifying critical particle sizes and distributions, with a focus on M50 granulation (average particle size of 0.285 mm). Additionally, the study employs the Wadell method to determine the shape factor (Ψ_i = 0.622) of the abrasive particles, which plays a significant role in the erosion process. Experimental determination of the abrasive flow rate is conducted, leading to the development of a second-order parabolic model that accurately predicts flow variations based on the control settings of the AWJ machine. The maximum erosion occurs at the entry surface of the mixing tube’s truncated zone, with a higher intensity as the particle size increases. For the 0.19 mm particles, the erosion rates range from 1.090 × 10⁻⁶ kg/m²·s to 2.022 × 10⁻⁶ kg/m²·s and follow a parabolic distribution. The particles of 0.285 mm show erosion rates ranging from 2.450 × 10⁻⁶ kg/m²·s to 6.119 × 10⁻⁶ kg/m²·s, also fitting the second-order parabolic model. The largest particles (0.38 mm) exhibit erosion rates ranging from 3.646 × 10⁻⁶ kg/m²·s to 7.123 × 10⁻⁶ kg/m²·s, described by a third-order polynomial. The study concludes that larger particle sizes result in higher erosion rates due to their increased mass and kinetic energy. Therefore, the present investigation demonstrates a significant relationship between particle size, abrasive flow rate, and erosion rate, highlighting critical wear points in the machine’s components. The findings contribute to optimizing the design and operational parameters of water jet cutting machines, thereby enhancing their efficiency and lifespan. Full article

In this paper, we present a data-driven model predictive control (DDMPC) framework specifically designed for constrained single-input single-output (SISO) nonlinear systems. Our approach involves customizing a set-theoretic receding horizon controller within a data-driven context. To achieve this, we translate model-based conditions into data series of available input and output signals. This translation process leverages recent advances in data-driven control theory, enabling the controller to operate effectively without relying on explicit system models. The proposed framework incorporates a robust methodology for managing system constraints, ensuring that the control actions remain within predefined bounds. By means of time sequences, the controller learns the underlying system dynamics and adapts to changes in real time, providing enhanced performance and reliability. The integration of set-theoretic methods allows for the systematic handling of uncertainties and disturbances, which are common when the trajectory of a nonlinear system is embedded inside a linear trajectory state tube. To validate the effectiveness of our DDMPC framework, we conduct extensive simulations on a nonlinear DC motor system. The results demonstrate significant improvements in control performance, highlighting the robustness and adaptability of our approach compared to traditional model-based MPC techniques. Full article