Browse Topic: Pressure
Impacts of laser shock peening (LSP) on the evolution characteristics of microstructure in commercially pure α-phase titanium (α-Ti) are explored by molecular dynamics (MD) simulations of high strain-rate compression. The EAM potential (Zhou potential) is selected for its ability to capture the evolution of microstructures. Considering the LSP-induced peak plasma pressure, the strain rate during the simulated shock compression process is set at 10^9 s-1 to replicate the LSP process. The stress-strain curve of the α-Ti under high strain-rate compression is obtained. The maximum equivalent stress reaches 3.6 GPa, consistent with the theoretically calculated value. The simulation results reveal that mechanical twins (MTs) are activated at a strain of 3%. The number of mechanical twins increases and eventually stabilizes, forming a network structure throughout the grains. In the meantime, numerous partial dislocations are generated adjacent to the grain boundaries. The dislocation density also increases with strain and dislocation reactions occur. Moreover, grain refinement is identified. The grain size is refined from the initial ~ 8 nm to ~ 4 nm in the polycrystalline α-Ti. Twinning, together with dislocation-mediated plasticity, drives the refinement of grain size. Gradients of twin density, dislocation density, and grain size density are induced by LSP on the surface of α-Ti. This study comprehensively investigates how LSP influences the evolution of microstructures by MD simulations. It develops an innovative numerical strategy that offers a foundation for elucidating the underlying mechanisms of LSP.
The stable operation of airborne equipment determines the functionality and performance standards of aircraft. Installing vibration isolation systems on such equipment aims to improve its performance. With the advancement of aircraft capabilities, future evaluations of airborne equipment’s vibration isolation systems will require increasingly real-world experimental assessment. Achieving a ground-based simulation of the complex coupling environment encountered by airborne equipment at high altitudes presents a huge challenge. This paper proposes a method utilizing air springs to simulate differential pressure forces, successfully enabling ground-based testing of “vibration-differential pressure” coupled environments for airborne equipment. The results verify the effectiveness of this approach, and it can be used for this type of environmental testing.
This study carefully designed and successfully developed a mechanical voltage stabilizing control device. The device uses silicone oil as the key component material of the liquid spring and 1Cr13 as the main material of the pressure control unit, enhancing its high-pressure resistance (up to 35 MPa), oxidation resistance, and acid-alkali corrosion resistance. By optimizing the transmission mechanism and simplifying the pressure regulation module, the device achieves a pressure regulation range of 0.1–21 MPa with an accuracy of ±0.01 MPa, significantly broader and more precise than traditional devices. To address manufacturing challenges, advanced CNC machine tools, ceramic cutting tools, and optimized heat treatment processes (e.g., quenching and tempering) were adopted, ensuring component machining accuracy within ±0.02 mm. Field applications in 13 oil wells demonstrated a 15.6% increase in daily oil production (from 25.5 t/d to 29.5 t/d) and a 17.9% increase in daily gas production (from 2800 m^3/d to 3300 m^3/d), with stable casing pressure control at 5.3 MPa. The device has created 1.225 million yuan in economic benefits while eliminating safety hazards, providing critical technical support for efficient and environmentally friendly oil and gas production.
For analysing flow and acoustic induced structural vibration, a fully run time coupled framework combining a hybrid CFD-CAA approach with a modal response simulation was validated and presented at the ISVNH 2022 (SAE Technical Paper 2022-01-0938). In this paper i We apply this CFD–CAA–modal coupling method to a series-representative bonnet geometry and demonstrate its capability to capture flow and aeroacoustically driven vibration with two-way coupling. ii We analyse the modal properties of the bonnet and show that confined air volumes beneath the bonnet can introduce significant fluid loading effects, which are already embedded in experimentally validated FE modal models and must therefore be treated carefully in two-way coupled simulations. iii We validate the fully coupled aeroelastic simulation against wind-tunnel measurements with undisturbed inflow, show close agreement with the measured vibration response and analyse that the dominant excitation is in this case from below the bonnet due to acoustic pressure fluctuations.
Precision agriculture, also known as smart farming, was once reserved for early adopters or large-scale operations, but is now an expectation within the farming industry. Across various regions and farm sizes, smart farming techniques are changing the way crops are planted as well as how they are monitored and harvested. However, farmers today are under increasing pressure to reduce labor, decrease chemical inputs, conserve water and operate in tighter windows. Couple this with factors such as narrow seasonal windows, productivity demands and safety considerations, and the need for smarter decisions becomes imperative. Going one step further, global food demands and environmental pressures are further increasing demand for precise, accurate and intelligent farming solutions.
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart impeding communication among evacuees and rescue personnel potentially compromising safety. Existing aircraft EVAC systems lack integrated wireless communication relying on visual or voice signals that are unreliable in chaotic conditions. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters such as slide air pressure and the floating weight of stranded passengers through embedded sensors. It proposes the adoption of Long Range (LoRa) modulation technology for wireless communication chosen for its low-power, long-range performance and license-free operation in emergency evacuation scenarios. In addition, the usage of this proposed technology can be further extended to locate the aircraft when other existing locating mechanisms fail.
Stricter environmental legislation is driving ever-more-demanding performance targets for gasoline particulate filters (GPFs). This study constructs a multi-scale filtration model based on fractal characteristics, taking into account particle size distribution and particle deposition, to investigate the influence of the microstructure of porous media on GPF performance and analyze the impact of structural parameters on capture efficiency and pressure drop. The results show that: (1) Increasing the wall thickness can improve the capture efficiency and pressure drop, and a thicker wall has a stronger inertial interception capacity for larger particles. (2) A reduction in porosity markedly alters both filtration efficacy and flow pressure drop. For particles in the intermediate size range (0.1-0.5 μm), the capture efficiency of a low-porosity structure is more sensitive to the diffusion deposition of small particles, while the inertial collision efficiency of large particles is higher. (3) Shrinking the pore size markedly enhances capture efficiency while simultaneously increasing pressure drop; the finer pore network markedly improves the retention of sub-micron particles, but the passage restriction of large particles is more obvious.
This paper presents a study of gunshot acoustic signal detectability in the near field of propeller noise, with a focus on the isolation of external gunshot signatures masked by propeller-induced noise. Controlled measurements were conducted in a Recirculation Delayed Anechoic Chamber (RDAC), where acoustic data were collected across varying rotor speeds, source locations, and propagation distances. Propeller noise characteristics were verified using UCD-QuietFly. The recorded signals were analyzed for the acoustic pressure, sound pressure level, and overall sound pressure level directivity to quantify masking effects. Results show that RPM is the dominant factor governing signal detectability. At 3000 RPM, the gunshot signal remains clearly identifiable within the low frequency range of 200–2000 Hz. At 4000 RPM, the signal becomes partially masked, while at 5000 RPM, propeller noise fully dominates and the gunshot signal becomes undetectable. Detectability is further reduced with increasing propagation distance. In-plane microphone locations provide improved detectability. A machine learning-based spectral separation framework was developed to suppress propeller noise and enhance the visibility of impulsive gunshot signatures in multichannel spectrograms. Experimental results show that learning-based denoising is effective at lower RPMs where the signal-to-noise ratio remains favorable, but performance degrades as broadband masking intensifies at higher rotor speeds.
Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density and mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia decomposition technology resolves this problem through on-demand hydrogen real-time production. Among existing ammonia decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for onboard hydrogen production given its high processing flow rate，decent hydrogen conversion rate, and transient response capability. Prevailing research predominantly relies on experimental approaches, with insufficient understanding of the effects of specific electrical field parameters and inlet pressure on system performance. This study established a quasi-one-dimensional numerical model for GAP-assisted ammonia decomposition. A comprehensive analysis was conducted to examine the influence of key electric field parameters, such as reduced electric field strength (REFS) and electron density (De), on ammonia conversion rate and energy efficiency. Furthermore, the study explored the synergistic effects of inlet pressure and electric field parameters on system performance under constant mass flow rate conditions. The results indicate that increasing REFS and De significantly substantially elevates ammonia conversion rate, but energy efficiency decreases as these parameters increase. Keeping a constant NH3 inlet mass flow rate, the gas velocity decreases when the inlet pressure increases and then extends the residence time. Consequently, the ammonia conversion rate significantly improves while the energy efficiency slightly decreases. By increasing inlet pressure and simultaneously reducing REFS or De, system energy efficiency can be effectively enhanced without altering ammonia conversion rates. This study demonstrates the synergistic regulation mechanism of electric field parameters and inlet pressure on hydrogen production performance, providing optimization strategies for GAP reactor design.
Ammonia is emerging as a promising energy vector for decarbonising the maritime sector. However, its low flame speed can lead to incomplete combustion, reduced engine efficiency, and increased emissions of unburned ammonia (NH3). Blending hydrogen with ammonia helps to address these issues, but the fundamental combustion characteristics of such mixtures remain insufficiently understood. This study examines the combustion dynamics of an NH3–H2 blend containing 30% hydrogen at 3 bar initial pressure. Experiments were performed in a 1.2 L optically accessible constant-volume combustion chamber fitted with a wall-mounted surface spark plug. High-speed shadowgraph imaging with 6,000 fps captured the flame evolution throughout the combustion process. The pressure and temperature values were monitored using piezoresistive pressure transducers and K-type thermocouples. Combustion times and flame extensions were extracted via post-processing of flame images using custom MATLAB algorithms. The combustion process was examined from the initial start to a diameter of 60mm. Complementary CFD simulations were carried out in CONVERGE using the C3MechV3.5 chemical mechanism. To match the experimental conditions, the numerical studies were conducted at an ambient pressure of 0.3 MPa and an equivalence ratio of 1.0. The model predicted flame propagation times accurately, achieving an average relative error of 2.95% and an R2 value of 0.991. A third-order polynomial correlation was derived to predict instantaneous flame diameter as a function of time, enabling interpolation for intermediate combustion stages for both simulation and experimental results. Error analysis indicated that the model achieved its best performance for medium-sized flames (30–45 mm) but exhibited larger discrepancies at the smallest and largest diameters. Nevertheless, within the 20–60 mm range, deviations remained between −9.5% and +3.4%.
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