Supply Pressure Research Articles

Повышение эффективности экспорта сельскохозяйственной продукции в условиях санкционного давления

The article considers modern trends in the development of the export potential of agricultural products in the conditions of the modern concept of production and sale of products in this commodity area. The purpose of the study is to identify current trends in improving the efficiency of exports of agricultural products in the face of increased competition in the global food markets. The relevance of this study lies in the fact that increasing the indicators of exports of agricultural products will minimize the supply pressure on the domestic market of agricultural products. The article discusses issues related not only to the expansion of the export component in the activities of various forms of production of agricultural products, but also possible options for joint participation in the process of optimizing the export of food products. As part of the study, various concepts of international trade were analyzed and studied, their detailed analysis was carried out and positive aspects were identified that can be used to improve the efficiency of exports of products of the agro-industrial sector of the economy.

Pediatric septic shock estimation using deep learning and electronic medical records.

Diagnosing pediatric septic shock is difficult due to the complex and often impractical traditional criteria, such as systemic inflammatory response syndrome (SIRS), which result in delays and higher risks. This study aims to develop a deep learning-based model using SIRS data for early diagnosis in pediatric septic shock cases. The study analyzed data from pediatric patients (<18 years old) admitted to a tertiary hospital from January 2010 to July 2023. Vital signs, lab tests, and clinical information were collected. Septic shock cases were identified using SIRS criteria and inotrope use. A deep learning model was trained and evaluated using the area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). Variable contributions were analyzed using the Shapley additive explanation value. The analysis, involving 9,616,115 measurements, identified 34,696 septic shock cases (0.4%). Oxygen supply was crucial for 41.5% of the control group and 20.8% of the septic shock group. The final model showed strong performance, with an AUROC of 0.927 and AUPRC of 0.879. Key influencers were age, oxygen supply, sex, and partial pressure of carbon dioxide, while body temperature had minimal impact on estimation. The proposed deep learning model simplifies early septic shock diagnosis in pediatric patients, reducing the diagnostic workload. Its high accuracy allows timely treatment, but external validation through prospective studies is needed.

Carbon dioxide separation from natural gas using a supersonic nozzle

Carbon Dioxide (CO2) is often released in the process of natural gases and is one of greenhouse gases that are being treated as the most troublesome environmental issues. One of the promising ways to economically remove CO2 in natural gas processes is to use the technology of supersonic separation that makes use of non-equilibrium condensation in supersonic swirling flows in convergent-divergent nozzle using wet outlet. In the present study, the mixture of Methane (CH4) and CO2 was considered as natural gas. Two-dimensional convergent–divergent nozzle was employed to produce supersonic swirling flow with non-equilibrium condensation. The Peng–Robinson real gas model was used for the mixture gas. A nucleation equation and a droplet growth equation were incorporated into the governing equations of the compressible Navier–Stokes with the k-ω turbulence closure. The predicted results were verified and validated with existing experimental data. The convergent–divergent nozzle was varied to investigate its effect on the non-equilibrium condensation of CO2 in the mixture flow. The Technique for Order of Preference by Similarity to Ideal Solution method was applied to achieve the optimum case with amounts of wetness (the mass fraction of liquid CO2 to the summation of the mass fraction of liquid and vapor CO2 at the outlet of the nozzle) and kinetic energy. Three locations of wet outlets for the optimum case were analyzed. The results show that an increase in the divergent angle of the nozzle, swirling intensity, and inlet supply pressure results in more nucleation of CO2. However, the enhancement of mole fractions of CO2 decreases the nucleation rate and wetness. The exit wetness from wet outlets was increased with increasing distance from the throat.

Proper orthogonal decomposition analysis of flow characteristics of aerostatic bearings based on Large Eddy Simulation

The driving mechanism of the vibration of aerostatic bearings is still unclear. Although there are several interpretations, none of them is conclusive. The difficulty lies in the complicated information concealed beneath the turbulence flow. To this end, the proper orthogonal decomposition (POD) method was systematically implemented in the aerostatic bearing flow field analysis in the present study, and some new findings have been proposed. First, the accuracy of our large eddy simulation (LES) has been validated through quantitative comparisons with the experimental references in terms of pressure distribution. Then, the mode decomposition has been conducted at a circumferential symmetry plane and bottom surface and the influence of supply pressure has been uncovered. It turns out that the vortices within the recess dominate the flowfield when the supply energy Ps=3atm, which could be recognized as the driving mechanisms of vibrations. The convection and shearing process in the vicinity of the recess inlet becomes intense and corresponding vortices become predominant in the cases of Ps=4atm and 5atm. Eventually, the influences of film thickness have also been discussed when Ps=4atm. Different from the case of h = 10 μm, the low-frequency vortices near the recess outlet could be detected when the film thickness is increased to 20 µm. The control of corresponding flow structures might be helpful for the vibration suppression.

Framework for analyzing the relationship between supply, demand, and flow of recreational services in urban park green spaces

Regular recreational activities in urban park green spaces are beneficial to the physical and mental health of residents. The supply capacity of, the residents’ demand for, and the actual flow of recreational services must be distinguished and evaluated to promote the fair and reasonable layout of urban park green spaces. However, the unit dimensions of the supply, demand, and flow indexes of recreational services are often inconsistent or only in simple semiquantitative forms, which cannot be directly compared and analyzed. Thus, this study constructed a research framework of “potential supply/demand–pressure/accessibility–actual supply/demand” for recreational services, and the framework was tested using the central urban area of Xuchang as the experimental area. Results show the following: (1) A spatial mismatch exists in the supply–demand relationship of recreational services in the study area; this relationship is reflected in the semicircular layer structure from the central and southern areas to the periphery, changing from shortage of supply to oversupply. (2) The actual supply of recreational services is highly correlated with the number of people served, which can explain 50.38% of the change in the actual supply. (3) The potential demand for recreational services is highly correlated with the actual demand. It can explain 78.96% of the change in the actual demand. (4) Accessibility is a non-critical factor affecting the travel rate of residential quarters, while supply pressure is a key factor affecting the utilization rate of park green space. The framework can effectively identify important or low-utilization service-providing areas, unmet service-demanding areas, and important service-connecting areas. It is suitable for the study of the relationship between the supply, demand, and flow of user movement-related services.

Financial liberalization or state capitalism? The developmental state and the special purpose bond market in South Korea

ABSTRACT South Korea's state-guaranteed bond market was nearly half the size of its sovereign bond market in 2022, and in the early 2000s, it was nearly twice the size of the latter. What explains the overwhelming prominence of this fixed-income market? This paper finds that the prominence of this market is a path-dependent consequence of developmental legacies and the Asian Financial Crisis. State-guaranteed bonds allowed the state to circumvent conditionalities that limited the state's ability to access domestic savings for policy objectives and helped shield the newly established sovereign bond market from premature supply pressure. This finding demonstrates that the state not only supports or creates markets but also uses markets to maintain its influence against external pressure. It also explains how the South Korean state was able to maintain its developmental policies after market liberalisation by tracking the post-crisis flow of domestic savings.

Multimodal soft valve enables physical responsiveness for preemptive resilience of soft robots.

Resilience is crucial for the self-preservation of biological systems: Humans recover from wounds thanks to an immune system that autonomously enacts a multistage response to promote healing. Similar passive mechanisms can enable pneumatic soft robots to overcome common faults such as bursts originating from punctures or overpressurization. Recent technological advancements, ranging from fault-tolerant controllers for robot reconfigurability to self-healing materials, have paved the way for robot resilience. However, these techniques require powerful processors and large datasets or external hardware. How to extend the operational life span of damaged soft robots with minimal computational and physical resources remains unclear. In this study, we demonstrated a multimodal pneumatic soft valve capable of passive resilient reactions, triggered by faults, to prevent or isolate damage in soft robots. In its forward operation mode, the valve, requiring a single supply pressure, isolated punctured soft inflatable elements from the rest of the soft robot in as fast as 21 milliseconds. In its reverse operation mode, the valve can passively protect robots against overpressurization caused by external disturbances, avoiding plastic deformations and bursts. Furthermore, the two modes combined enabled the creation of an endogenously controlled valve capable of autonomous burst isolation. We demonstrated the passive and quick response and the possibility of monolithic integration of the soft valve in grippers and crawling robots. The approach proposed in this study provides a distributed small-footprint alternative to controller-based resilience and is expected to help soft robots achieve uninterrupted long-lasting operation.

Methodology to Characterize Row Manifolds for High Power Direct to Chip Liquid Cooling Data Centers

Abstract Demand is growing for the dense and high-performing IT computing capacity to support artificial intelligence, deep learning, machine learning, autonomous cars, the Internet of things, etc. This led to an unprecedented growth in transistor density for high-end CPUs and GPUs, creating thermal design power (TDP) of even more than 700 watts for some of the NVIDIA existing GPUs. Cooling these high TDP chips with air cooling comes with a cost of the higher form factor of servers and noise produced by server fans close to the permissible limit. Direct-to-chip cold plate-based liquid cooling is highly efficient and becoming more reliable as the advancement in technology is taking place. Several components are used in the liquid-cooled data centers for the deployment of cold plate based direct to chip liquid cooling like cooling loops, rack manifolds, CDUs, row manifolds, quick disconnects, flow control valves, etc. Row manifolds used in liquid cooling are used to distribute secondary coolant to the rack manifolds. Characterizing these row manifolds to understand the pressure drops and flow distribution for better data center design and energy efficiency is important. In this paper, the methodology is developed to characterize the row manifolds. Water-based coolant Propylene glycol 25% was used as the coolant for the experiments and experiments were conducted at 21 °C coolant supply temperature. Two, six-port row manifolds' P-Q curves were generated, and the value of supply pressure and the flow rate were measured at each port. The results obtained from the experiments were validated by a technique called Flow Network Modeling (FNM). FNM technique uses the overall flow and thermal characteristics to represent the behavior of individual components.

The Distributed Parameter Model of an Electro-Pneumatic System Actuated by Pneumatic Artificial Muscles with PWM-Based Position Control

Today, the analysis and synthesis methods of electro-pneumatic systems with position control actuated by pneumatic artificial muscles (PAMs) are quite well known. In these methods, pneumatic artificial muscle is considered as an object with lumped parameters. However, the PAM is an object with distributed parameters, where the pressure, density, and mass flow rate of gas are varied along the bladder length. Thus, in the case of certain design parameters of the pneumatic artificial muscle and certain frequencies of the supply pressure, resonant gas oscillations affected by the wave processes in the bladder may occur. Thereby, in the PWM-driven PAM-actuated system, certain operation frequencies of the control pneumatic valve can cause oscillations of gas in the bladder and in the connected pipeline. These processes could lead to vibrations of the executive device. To solve this practical problem, a distributed parameter model of the PAM that takes into account the pressure fluctuations in the bladder and in the pipeline was elaborated. Also, in this work, a new method in which the wave processes are described by ordinary differential equations instead of partial differential equations was proposed.

A Steady-Pressure Control Method for Emulsion Pump Station Based on Online Updating of Optimal Flow Rate

In order to solve the problem of unstable fluid supply pressure and serious impact caused by the complicated and changeable working condition of a fully mechanized mining face in coal mines and the sluggish response of the fluid supply system to the fluid demand for the hydraulic support, a control method based on online updating generalized regression neural network (GRNN) was proposed. Firstly, the simulated hydraulic support test platform and co-simulation model were built. Secondly, The optimal flow dataset of steady-pressure fluid supply under different working conditions is calculated by simulation. Furthermore, the GRNN prediction model was established by using dataset and online updating learning technology to predict the optimal fluid supply flow according to environmental parameters. Finally, the optimal flow control method of online updating GRNN was established, and numerical research and experimental verification were also carried out in different working conditions. The results indicated that the proposed control method could track the working conditions of the working face in real time and adjusted the fluid supply flow of the emulsion pump station adaptively, which effectively alleviated the pressure fluctuation and pressure shock, and the system pressure was more stable, meeting the demand of steady-pressure fluid supply on the working face.

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

Proton exchange membrane fuel cells (PEMFCs) are attracting attention for their green, energy-saving, and high-efficiency advantages, becoming one of the future development trends of renewable energy utilization. However, there are still deficiencies in the gas supply system control strategy that plays a crucial role in PEMFCs, which limits the rapid development and application of PEMFCs. This paper provides a comprehensive and in-depth review of the PEMFC air delivery system (ADS) and hydrogen delivery system (HDS) operations. For the ADS, the advantages and disadvantages of the oxygen excess ratio (OER), oxygen pressure, and their decoupling control strategies are systematically described by the following three aspects: single control, hybrid control, and intelligent algorithm control. Additionally, the optimization strategies of the flow field or flow channel for oxygen supply speeds and distribution uniformity are compared and analyzed. For the HDS, a systematic review of hydrogen recirculation control strategies, purge strategies, and hydrogen flow control strategies is conducted. These strategies contribute a lot to improving hydrogen utilization rates. Furthermore, hydrogen supply pressure is summarized from the aspects of hybrid control and intelligent algorithm control. It is hoped to provide guidance or a reference for research on the HDS as well as the ADS control strategy and optimization strategy.

Experimental and numerical evaluation of the dust suppression efficiency of an innovative vortex pneumatic fog screen dust control device

To effectively control the dust generated by coal mining operations, a new type of cyclonic pneumatic mist curtain dust control device was developed. Using CFD software, numerical simulations were conducted on the internal airflow velocity field, the exit velocity of the cyclonic pneumatic mist curtain, and the mist droplet particle field of the curtain. Experiments were carried out to measure the spray coverage, droplet size, and the dust control performance of the model device. The results indicate that when the water pump supply pressure is 8 MPa, the fan supply wind speed is 12 m/s, and the nozzle installation angle is 75 degrees, the cyclonic pneumatic mist curtain dust control device model operates under optimal conditions. The effective coverage of the cyclonic mist curtain is 380 × 3300 mm, fully suppressing dust generation on one side of the curtain. An optimal dust removal distance of about 90 cm was determined. After installing the cyclonic pneumatic mist curtain dust control device, the average dust reduction efficiency for respirable dust reached 91.07%, and the overall dust reduction efficiency achieved 93.34%.

Highly Efficient Production of Cellulosic Ethanol from Poplar Using an Optimal C6/C5 Co-Fermentation Strain of Saccharomyces cerevisiae.

Cellulosic ethanol is the key technology to alleviate the pressure of energy supply and climate change. However, the ethanol production process, which is close to industrial production and has a high saccharification rate and ethanol yield, still needs to be developed. This study demonstrates the effective conversion of poplar wood waste into fuel-grade ethanol. By employing a two-step pretreatment using sodium chlorite (SC)-dilute sulfuric acid (DSA), the raw material achieved a sugar conversion rate exceeding 85% of the theoretical value. Under optimized conditions, brewing yeast co-utilizing C6/C5 enabled a yield of 35 g/L ethanol from 10% solid loading delignified poplar hydrolysate. We increased the solid loading to enhance the final ethanol concentration and optimized both the hydrolysis and fermentation stages. With 20% solid loading delignified poplar hydrolysate, the final ethanol concentration reached 60 g/L, a 71.4% increase from the 10% solid loading. Our work incorporates the pretreatment, enzymatic hydrolysis, and fermentation stages to establish a simple, crude poplar waste fuel ethanol process, expanding the range of feedstocks for second-generation fuel ethanol production.

Improved energy utilization in heavy-duty automatic transmission: Advanced modeling and multi-objective optimization

In the current research of electro-hydraulic actuators for heavy-duty automatic transmissions, achieving accurate output is proven to be insufficient to meet growing demand. Aiming at this deficiency, this paper proposes a dynamic model, which combines detailed loss analysis and multi-objective optimization to further improve energy efficiency. Firstly, considering the key resistance factors, including viscous damping coefficient, spring constant and supply pressure, a dynamic model is established. Subsequently, the overall response of the model is verified based on the existing experimental data. In addition, the accuracy and stability time are used as evaluation indexes to analyze the influence of resistance on the function pattern. Finally, a multi-objective optimization method is used to balance the relationship between energy utilization efficiency, accuracy and shift quality. The slight changes in mechanical energy and stable time lead to a significant decrease in jerk by 22.11 %, which makes the shifting process smoother and effectively improves the shifting performance.

Numerical and experimental investigation on two-phase flow and oil capture efficiency of under-race lubrication for high-speed bearings

The increasing rotational speed and temperature levels in modern aeroengines impose higher demands on the lubrication and cooling design of the roller bearings supporting the main shaft. To design a compact and efficient under-race lubrication system, a comprehensive understanding of the characteristics of oil–air two-phase flow and the variations in oil capture efficiency is necessary. This study experimentally investigated the oil capture efficiency and employed numerical simulations to elucidate the mechanisms of oil–air two-phase flow. The results reveal a favorable consistency between the numerical simulations and experimental findings, both in terms of the oil capture efficiency and the oil–air distribution. The decreasing trend of oil capture efficiency gradually stabilizes when the jet distance exceeds 10 mm. To ensure stable and effective testing results for oil capture efficiency, the jet distance should be maintained within the range of 10–14 mm during experiments. Under different operating conditions, there exists an optimal jet angle that maximizes the oil capture efficiency. The corresponding optimal jet angle becomes smaller with lower oil supply pressure and higher rotational speed of the radial oil scoop. Within a momentum flux ratio range of 5–870, an innovative predictive correlation for the optimal jet angle is derived, with a maximum relative error between predicted and experimental values being 4.0%. The proposed correlation provides theoretical support and methodological basis for refining the design of radial under-race lubrication in aeroengines.

Research on temperature control of proton exchange membrane electrolysis cell based on MO‐TD3

AbstractTo solve the problem of temperature control in proton exchange membrane electrolytic cell (PEMEC), this paper presents a temperature control method based on multi‐experience pool probability playback and Ornstein‐Uhlenbeck noise‐twin delay depth deterministic strategy gradient. Firstly, considering the influence of water supply, anode and cathode pressure, and natural heat dissipation on temperature, a refined thermal model of PEMEC is established and transformed into a Markov model under the framework of deep reinforcement learning (DRL). Then, to solve the training instability and poor control effect of DRL caused by inertia delay of the PEMEC temperature control system, multi‐empirical pool probability playback and Ornstein‐Uhlenbeck random process noise techniques are introduced on the basis of the traditional DRL method. Finally, the simulation and hardware‐in‐the‐loop experience results show that the proposed method outperforms other advanced methods.

Influence of starvation and cavitation on hybrid journal bearing under limited supply pressure

A new starvation model is established to explore the effect of starvation on cavitation and their couple influence on the performance of hybrid journal bearing. The starvation region is located in the recessed circumference and symmetry with the axial center. The novel starvation model aims to solve the issues of flow non-conservation by the axial starvation model and avoid calculation limitation by the JFO cavitation condition under severe starvation. The cavitation areas obtained by the approach, JFO model, and Reynolds model are compared to indicate the influence of starvation on cavitation. The static and dynamic performances are investigated by the three methods and the relative pressure distribution is given to analyze the effect of starvation and cavitation on the hybrid journal bearing. The result indicates that starvation has a significant impact on the cavitation region and has a slight effect on the bearing’s performance.

Position control of a digital electrohydraulic system with limited sensory data using double deep q-network controller

In this study, we propose a reinforcement learning controller for position control of a digital electrohydraulic system with independent metering valve structure. Our study addresses the inherent challenges of traditional control methods, such as the need for continuous pressure measurement and complex control structures. As a solution, a double deep q-network (DDQN) controller which relies solely on position feedback from D-EHS is suggested. A simulation environment was developed to train the DDQN controller, wherein the artificial neural network structure, training hyperparameters, and reward function were optimized through preliminary studies. The DDQN controller was trained using a sinusoidal position reference signal of a single frequency and evaluated experimentally for its position control performance. Tracking performance was assessed against various position reference signals, including ramp and sinusoidal signal with different frequencies. Robustness against external disturbances was also tested by increasing the nominal supply pressure by 20% and 40%. The results demonstrate that the DDQN controller successfully controls the digital electrohydraulic system and is achieving good tracking performance for the training position reference signal. Furthermore, the controller exhibits satisfactory tracking performance for non-training position reference signals, highlighting its ability to learn the digital electrohydraulic system dynamics with limited training data. Even under varying supply pressure conditions, the DDQN controller maintains an acceptable level of control performance. Our findings emphasize the generalizability and reliability of the DDQN reinforcement learning controller, making it a suitable candidate for application in digital electrohydraulic systems and contributing to the advancement of control theory research.

Quantitative analysis and cancellation of rotating modulation noise in gravity gradient measurement

Rotating accelerometer gravity gradiometer (RAGG) is an important instrument for auxiliary navigation and mineral exploration. The turntable in RAGG modulates the gravity gradient signal to a high frequency to avoid the influence of low-frequency noise. In order to quantitatively analyze and cancel the rotating modulation noise in gravity gradient measurement, the theoretical RAGG output error model with rotating modulation noise is derived and experimentally verified on a high-precision air-bearing turntable. The experimental results indicate that the dominant rotating modulation noise is turntable vibration noise, which results from the supply pressure fluctuations revealed by further analysis. Based on the rotating modulation noise characteristics, a deep neural network (DNN) is trained to cancel the rotating modulation noise from the RAGG output. Positively, the root-mean-square error (RMSE) of the trained DNN output is 1.7 E in sample data. Furthermore, a gravity gradient calibration platform is established to validate the effectiveness of this method by generating a gravity gradient signal with the same frequency as the rotating modulation noise and amplitude of 100 E. After cancelling the noise by the trained DNN, the RMSE of RAGG output reduced from 94 E to 7.7 E, while the RMSE of the result processed by wavelet analysis remains at 87 E. These results demonstrate that the trained DNN successfully cancels 92% of the rotating modulation noise in actual RAGG measurement without affecting the gravity gradient signal.

Multi-objective optimization analysis of air-cooled heat dissipation coupled with thermoelectric cooling of battery pack based on orthogonal design

As the energy supply core of electric vehicles (EVs), the battery's performance is closely related to its temperature. Therefore, the battery thermal management system (BTMS) plays a crucial role in ensuring the vehicle's driving safety and power performance. This paper proposes the air cooling as the primary heat dissipation method, combined with a semiconductor refrigeration sheet (SRS) to improve heat transfer and reduce local high temperature. Firstly, determine the optimum air volume with the U-type air-cooled structure. Additionally, orthogonal analysis is used to investigate the inlet and outlet locations, the front deflector position and length of the shunt chamber, and the rear deflector position on the air-cooling effect. Then, through multi-objective optimization, the optimal air-cooled structure is selected based on the air supply pressure drop. The chosen structure is the same-side dual-exit ventilation with a front deflector located 150 mm from the near-wind end wall and a length of 30 mm, and a rear deflector located 100 mm from the far-wind end wall. Finally, SRS coupled air cooling is used to dissipate the heat. Four SRSs are used to analyze the effect of mounting position and different currents on the battery pack. The findings suggest that the optimal placement for SRSs is parallel to the lower end of the battery module at the far-wind end. When SRSs pass a current of up to 0.4A at 37 ℃, the entire battery module can complete 7200 s discharge with 0.5C. This leads to a decrease in the maximum temperature (Tmax) to 46.09 ℃ and a drop in the temperature uniformity index (δT) to 1.36.