Conversion of a piston-cylinder dimensional dataset to the effective area of a mechanical pressure generator.

Recent developments in diameter metrology at NIST have improved the dimensional characterization of piston-cylinder assemblies (PCAs) to unprecedented precision. For the newest generation of PCAs, the standard uncertainty in the measurement of the outer diameter is 12 nm, while uncertainty in the measurement of the inner diameter is 14 nm. With a high-accuracy dimensional dataset in hand, the task of determining the pressure generated by a specific PCA is reduced to converting the diameter (and straightness and roundness) to an effective area (and distortion coefficient). The details on how this was performed for the artifact PCA2062 are described. PCA2062 was dimensioned in 2017 and 2020; the area repeated within 0.2 × 10 - 6 ⋅ A 0 . The calculation produced estimates of fall rate and rotation decay that agreed with experimental observations within 12 %. The fall rate is proportional to the square of the gap width; therefore, the agreement between calculation and measurement validates the dimensional estimate of the gap width within (36 ± 42) nm, where the 42 nm standard uncertainty is governed by the present state of flow theory. The piston gage model is buttressed by three comparison tests against a laser barometer, which support a view that PCA2062 is linear and reproducible within 0.2 µPa Pa-1. Finally, an estimate of uncertainty in the effective area of a dimensioned artifact is provided: as expected, the diameter measurement is the main culprit, but there are open questions regarding the flow model that preclude an accurate evaluation of the distortion coefficient. For the 530 kPa operating range of PCA2062, distortion is not a significant problem, but the effect would be dominant in assemblies operating at 1 MPa and above.

The effect of piston diameter in primary stapes surgery on surgical success.

PURPOSE: To evaluate the effect of piston diameter in patients undergoing primary stapes surgery on audiometric results and postoperative complications. METHODS: A retrospective single-center cohort study was performed. Adult patients who underwent primary stapes surgery between January 2013 and April 2022 and received a 0.4-mm-diameter piston or a 0.6-mm-diameter piston were included. The primary and secondary outcomes were pre- and postoperative pure-tone audiometry, pre- and postoperative speech audiometry, postoperative complications, intraoperative anatomical difficulties, and the need for revision stapes surgery. The pure-tone audiometry included air conduction, bone conduction, and air-bone gap averaged over 0.5, 1, 2 and 3 kHz. RESULTS: In total, 280 otosclerosis patients who underwent 321 primary stapes surgeries were included. The audiometric outcomes were significantly better in the 0.6 mm group compared to the 0.4 mm group in terms of gain in air conduction (median = 24 and 20 dB, respectively), postoperative air-bone gap (median = 7.5 and 9.4 dB, respectively), gain in air-bone gap (median = 20.0 and 18.1 dB, respectively), air-bone gap closure to 10 dB or less (75% and 59%, respectively) and 100% speech reception (median = 75 and 80 dB, respectively). We found no statistically significant difference in postoperative dizziness, postoperative complications and the need for revision stapes surgery between the 0.4 and 0.6 mm group. The incidence of anatomical difficulties was higher in the 0.4 mm group. CONCLUSION: The use of a 0.6-mm-diameter piston during stapes surgery seems to provide better audiometric results compared to a 0.4-mm-diameter piston, and should be the preferred piston size in otosclerosis surgery. We found no statistically significant difference in postoperative complications between the 0.4- and 0.6-mm-diameter piston. Based on the results, we recommend always using a 0.6-mm-diameter piston during primary stapes surgery unless anatomical difficulties do not allow it.

The Piston Slap Force Reconstruction of Diesel Engine Using WOA-VMD and Deconvolution.

In a diesel engine, piston slap commonly occurs concurrently with fuel combustion and serves as the main source of excitation. Although combustion pressure can be measured using sensors, determining the slap force is difficult without conducting tests. In this study, we propose a method to identify the slap force of the piston to solve this difficult problem. The traditional VMD algorithm easily receives noise interference, which affects the value of parameter combination [k, α] and thus affects the extraction accuracy of the algorithm. First, we obtain the transfer function between the incentive and vibration response through percussion tests. Secondly, a variational modal decomposition method based on whale algorithm optimization is used to separate the slap response from the surface acceleration of the block. Finally, we calculated the slap force using the deconvolution method. Deconvolution is a typical inverse problem of mathematics, often prone to ill-conditioning, and the singular value decomposition and regularization method is used to overcome this flaw and improve accuracy. The proposed method provides an important means to evaluate the angular distribution of the slap force, identify the shock positions on the piston liner, and determine the peak value of the waveform which helps us analyze the vibration characteristics of the piston and optimize the structural design of the engine.

SCAE-Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples.

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time-frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.

Investigation into the Co-Phase Detection Methodology for Segmented Plane Mirrors Utilizing Grazing Incidence Interferometry.

Segmented plane mirrors constitute a crucial component in the self-aligned detection process for large-aperture space optical imaging systems. Surface shape errors inherent in segmented plane mirrors primarily manifest as tilt errors and piston errors between sub-mirrors. While the detection and adjustment techniques for tilt errors are well-established, addressing piston errors poses a more formidable challenge. This study introduces a novel approach to achieve long-range, high-precision, and efficient co-phase detection of segmented plane mirrors by proposing a segmented plane mirror shape detection method based on grazing incidence interferometry. This method serves to broaden the detection range of piston errors, mitigate the issue of the 2π ambiguity resulting from piston errors in co-phase detection, and extend the detection capabilities of the interferometer. By manipulating the incident angle of the interferometer, both rough and precise adjustments of the segmented plane mirrors can be effectively executed.

The structural analysis of the periplasmic domain of Sinorhizobium meliloti chemoreceptor McpZ reveals a novel fold and suggests a complex mechanism of transmembrane signaling.

Chemotaxis is a fundamental process whereby bacteria seek out nutrient sources and avoid harmful chemicals. For the symbiotic soil bacterium Sinorhizobium meliloti, the chemotaxis system also plays an essential role in the interaction with its legume host. The chemotactic signaling cascade is initiated through interactions of an attractant or repellent compound with chemoreceptors or methyl-accepting chemotaxis proteins (MCPs). S. meliloti possesses eight chemoreceptors to mediate chemotaxis. Six of these receptors are transmembrane proteins with periplasmic ligand-binding domains (LBDs). The specific functions of McpW and McpZ are still unknown. Here, we report the crystal structure of the periplasmic domain of McpZ (McpZPD) at 2.7 Å resolution. McpZPD assumes a novel fold consisting of three concatenated four-helix bundle modules. Through phylogenetic analyses, we discovered that this helical tri-modular domain fold arose within the Rhizobiaceae family and is still evolving rapidly. The structure, offering a rare view of a ligand-free dimeric MCP-LBD, reveals a novel dimerization interface. Molecular dynamics calculations suggest ligand binding will induce conformational changes that result in large horizontal helix movements within the membrane-proximal domains of the McpZPD dimer that are accompanied by a 5 Å vertical shift of the terminal helix toward the inner cell membrane. These results suggest a mechanism of transmembrane signaling for this family of MCPs that entails both piston-type and scissoring movements. The predicted movements terminate in a conformation that closely mirrors those observed in related ligand-bound MCP-LBDs.

Higher Apparent Gas Transfer Velocities for CO2 Compared to CH4 in Small Lakes.

Large greenhouse gas emissions occur via the release of carbon dioxide (CO2) and methane (CH4) from the surface layer of lakes. Such emissions are modeled from the air-water gas concentration gradient and the gas transfer velocity (k). The links between k and the physical properties of the gas and water have led to the development of methods to convert k between gases through Schmidt number normalization. However, recent observations have found that such normalization of apparent k estimates from field measurements can yield different results for CH4 and CO2. We estimated k for CO2 and CH4 from measurements of concentration gradients and fluxes in four contrasting lakes and found consistently higher (on an average 1.7 times) normalized apparent k values for CO2 than CH4. From these results, we infer that several gas-specific factors, including chemical and biological processes within the water surface microlayer, can influence apparent k estimates. We highlight the importance of accurately measuring relevant air-water gas concentration gradients and considering gas-specific processes when estimating k.

A Comparison of Solidification Structures and Submicroscale Cellular Segregation in Rapidly Solidified Stainless Steels Produced via Two-Piston Splat Quenching and Laser Powder Bed Fusion.

Fusion-based additive manufacturing techniques leverage rapid solidification (RS) conditions to create parts with complex geometries, unique microscale/nanoscale morphological features, and elemental segregation. Three custom composition stainless steel alloys with varying chromium equivalence to nickel equivalence ratio (Creq/Nieq) between 1.53 and 1.95 were processed using laser powder bed fusion (LPBF) and/or two-piston splat quenching (SQ) to produce solidification rates estimated between 0.4 and 0.8 m/s. Both scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were utilized to collect high-resolution images, electron backscatter diffraction (EBSD) phase identification, and measure cellular segregation. Similar features were observed in both LPBF and SQ samples including phase and microstructure, nanoscale oxide particles, cell size, and segregation behavior. However, dislocation pileup was observed along the cell boundaries only in the LPBF austenite solidified microstructure. Targeted adjustment of the SQ feedstock Cr and Ni concentrations, within the ASTM A240 specification for 316L resulted in no observable impact on the cell size, oxide particle size, or magnitude of segregation. Also, the amount of Ni segregation in the ferrite solidified microstructures did not significantly differ, regardless of Cr/Nieq or processing technique. SQ is demonstrated as capable of simulating RS rates and microstructures similar to LPBF for use as an alternative screening tool for new RS alloy compositions.

Echolocating bats rapidly adjust their mouth gape to control spatial acquisition when scanning a target.

BACKGROUND: As well known to any photographer, controlling the "field of view" offers an extremely powerful mechanism by which to adjust target acquisition. Only a few natural sensory systems can actively control their field of view (e.g., dolphins, whales, and bats). Bats are known for their active sensing abilities and modify their echolocation signals by actively controlling their spectral and temporal characteristics. Less is known about bats' ability to actively modify their bio-sonar field of view. RESULTS: We show that Pipistrellus kuhlii bats rapidly narrow their sensory field of view (i.e., their bio-sonar beam) when scanning a target. On-target vertical sonar beams were twofold narrower than off-target beams. Continuous measurements of the mouth gape of free-flying bats revealed that they control their bio-sonar beam by a ~3.6 mm widening of their mouth gape: namely, bats open their mouth to narrow the beam and vice versa. CONCLUSIONS: Bats actively and rapidly control their echolocation vertical beam width by modifying their mouth gape. We hypothesize that narrowing their vertical beam narrows the zone of ensonification when estimating the elevation of a target. In other words, bats open their mouth to improve sensory localization.

Piston Error Measurement for Segmented Telescopes Based on a Hybrid Artificial Neural Network.

To address the difficulty and complexity of detecting piston errors for segmented telescopes, this paper proposes a new piston error measurement method based on a hybrid artificial neural network. First, we use the Resnet network to learn the mapping relationship between the focal plane degradation image and signs of the piston error. Then, based on the established theoretical relationship between the modulation transfer function and the piston error, a BP neural network is used to learn the mapping relationship between the MTF and the absolute value of the piston error. After the training of the hybrid network is completed, a wide-range and high-precision detection of the piston error of the sub-mirrors can be achieved using the combined output of the two networks, where only a focal plane image of the point source with broadband illumination is used as the input. The detection range can reach the entire coherent length of the input broadband light, and the detection accuracy can reach 10 nm. The method proposed in this paper has the advantages of high detection accuracy, a wide detection range, low hardware cost, a small network scale, and low training difficulty.

Influence of Rotational Speed on Isothermal Piston Compression System.

An isothermal piston is a device that can achieve near-isothermal compression by enhancing the heat transfer area with a porous media. However, flow resistance between the porous media and the liquid is introduced, which cannot be neglected at a high operational speed. Thus, the influence of rotational speed on the isothermal piston compression system is analyzed in this study. A flow resistance mathematical model is established based on the face-centered cubic structure hypothesis. The energy conservation rate and efficiency of the isothermal piston are defined. The effect of rotational speed on resistance is discussed, and a comprehensive energy conservation performance assessment of the isothermal piston is analyzed. The results show that the increasing rate of the resistance work increases significantly proportional to the rotational speed, and the proportion of resistance work in the total work increases gradually and sharply. The total work including compression and resistance cannot be larger than the compression work under adiabatic conditions. The maximum rotational speed is 650 rpm.

Transmembrane signaling and cytoplasmic signal conversion by dimeric transmembrane helix 2 and a linker domain of the DcuS sensor kinase.

Transmembrane (TM) signaling is a key process of membrane-bound sensor kinases. The C4-dicarboxylate (fumarate) responsive sensor kinase DcuS of Escherichia coli is anchored by TM helices TM1 and TM2 in the membrane. Signal transmission across the membrane relies on the piston-type movement of the periplasmic part of TM2. To define the role of TM2 in TM signaling, we use oxidative Cys cross-linking to demonstrate that TM2 extends over the full distance of the membrane and forms a stable TM homodimer in both the inactive and fumarate-activated state of DcuS. An S186xxxGxxxG194 motif is required for the stability and function of the TM2 homodimer. The TM2 helix further extends on the periplasmic side into the α6-helix of the sensory PASP domain and on the cytoplasmic side into the α1-helix of PASC. PASC has to transmit the signal to the C-terminal kinase domain. A helical linker on the cytoplasmic side connecting TM2 with PASC contains an LxxxLxxxL sequence. The dimeric state of the linker was relieved during fumarate activation of DcuS, indicating structural rearrangements in the linker. Thus, DcuS contains a long α-helical structure reaching from the sensory PASP (α6) domain across the membrane to α1(PASC). Taken together, the results suggest piston-type TM signaling by the TM2 homodimer from PASP across the full TM region, whereas the fumarate-destabilized linker dimer converts the signal on the cytoplasmic side for PASC and kinase regulation.

Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34.

In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.

A Study on the Application of Machine and Deep Learning Using the Impact Response Test to Detect Defects on the Piston Rod and Steering Rack of Automobiles.

The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, sampling, and simple tensile tests; however, if there is a problem or abnormality, it is difficult to identify the type and location of the defect. Usually, these defects are likely to cause surface cracks during processing, which in turn accelerate the deterioration of the shock absorber and steering, causing serious problems in automobiles. As a result, the purpose of this study was to present, among non-destructive methods, a shock response test method and an analysis method that can efficiently and accurately determine the defects of the piston rod and steering rack. A test method and excitation frequency range that can measure major changes according to the location and degree of defects were proposed. A defect discrimination model was constructed using machine and deep learning through feature derivation in the time and frequency domains for the collected data. The analysis revealed that it was possible to effectively distinguish the characteristics according to the location as well as the presence or absence of defects in the frequency domain rather than the time domain. The results indicate that it will be possible to quickly and accurately check the presence or absence of defects in the shock absorber and steering in the automobile manufacturing process line in the future. It is expected that this will play an important role as a key factor in building a smart factory.

Robust Instant Angle Speed Measurement for Internal Combustion Engines-A Novel Sensing Suite and Methodology.

This paper presents the development and implementation of a novel robust sensing and measurement system that achieves fine granularity and permits new insights into operation of rotational machinery. Instant angle speed measurements offer a wealth of useful information for complex machines in which the motion is the result of multidimensional, internal, and external interactions. The implementation of the proposed system was conducted on an internal combustion engine. The internal combustion engine crankshaft's angular velocity is the result of the integration of all variables of motor and resisting forces. The crankshaft angular velocity variation also reflects the interaction between the internal thermodynamic cycle of the engine and the plant it powers. To minimise the number of variables, we used for our experiments an aero piston engine for small air-vehicles-a well-made and reliable powerplant-connected to a propeller. This paper presents the need for a better sensing and measurement system. Then, we show the development of the system, the measurement protocol and process, recording and analysis of the data, and results of some experiments. We then demonstrate the possibilities this sensing suite can achieve-a deeper insight into the operation of the machine-by performing high-quality analyses of engine cycles, well beyond capabilities in the state of the art. This system can be generalised for other rotational machines and equipment.

Power-Optimal Control of a Stirling Engine's Frictional Piston Motion.

The power output of Stirling engines can be optimized by several means. In this study, the focus is on potential performance improvements that can be achieved by optimizing the piston motion of an alpha-Stirling engine in the presence of dissipative processes, in particular mechanical friction. We use a low-effort endoreversible Stirling engine model, which allows for the incorporation of finite heat and mass transfer as well as the friction caused by the piston motion. Instead of performing a parameterization of the piston motion and optimizing these parameters, we here use an indirect iterative gradient method that is based on Pontryagin's maximum principle. For the varying friction coefficient, the optimization results are compared to both, a harmonic piston motion and optimization results found in a previous study, where a parameterized piston motion had been used. Thus we show how much performance can be improved by using the more sophisticated and numerically more expensive iterative gradient method.

Adsorbed protein film on pump surfaces leads to particle formation during fill-finish manufacturing.

During fill-finish manufacturing, therapeutic proteins may aggregate or form subvisible particles in response to the physical stresses encountered within filling pumps. Understanding and quantitating this risk is important since filling may be the last unit operation before the patient receives their dose. We studied particle formation from lab-scale to manufacturing-scale using sensitive and robust protein formulations. Filling experiments with a ceramic rotary piston pump were integrated with a rinse-stripping method to investigate the relationship between protein adsorption and particle formation. For a sensitive protein, multilayer film formation on the piston surface correlated with high levels of subvisible particles in solution. For a robust protein formulation, adsorption and subvisible particle formation were minimal. These results support an aggregation mechanism that is initiated by adsorption to pump surfaces and propagated by mechanical and/or hydrodynamic disruption of the film. The elemental analysis confirmed that ceramic wear debris remained at trace levels and did not contribute appreciably to protein aggregation.

Piston Error Measurement for Segmented Telescopes with an Artificial Neural Network.

A piston error detection method is proposed based on the broadband intensity distribution on the image plane using a back-propagation (BP) artificial neural network. By setting a mask with a sparse circular clear multi-subaperture configuration in the exit pupil plane of a segmented telescope to fragment the pupil, the relation between the piston error of segments and amplitude of the modulation transfer function (MTF) sidelobes is strictly derived according to the Fourier optics principle. Then the BP artificial neural network is utilized to establish the mapping relation between them, where the amplitudes of the MTF sidelobes directly calculated from theoretical relationship and the introduced piston errors are used as inputs and outputs respectively to train the network. With the well trained-network, the piston errors are measured to a good precision using one in-focused broadband image without defocus division as input, and the capture range achieving the coherence length of the broadband light is available. Adequate simulations demonstrate the effectiveness and accuracy of the proposed method; the results show that the trained network has high measurement accuracy, wide detection range, quite good noise immunity and generalization ability. This method provides a feasible and easily implemented way to measure piston error and can simultaneously detect the multiple piston errors of the entire aperture of the segmented telescope.

Use of the K-Nearest Neighbour Classifier in Wear Condition Classification of a Positive Displacement Pump.

This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.

Acoustic Emission-Based Condition Monitoring and Remaining Useful Life Prediction of Hydraulic Cylinder Rod Seals.

The foremost reason for unscheduled maintenance of hydraulic cylinders in industry is caused by wear of the hydraulic seals. Therefore, condition monitoring and subsequent estimation of remaining useful life (RUL) methods are highly sought after by the maintenance professionals. This study aimed at investigating the use of acoustic emission (AE) sensors to identify the early stages of external leakage initiation in hydraulic cylinders through run to failure studies (RTF) in a test rig. In this study, the impact of sensor location and rod speeds on the AE signal were investigated using both time- and frequency-based features. Furthermore, a frequency domain analysis was conducted to investigate the power spectral density (PSD) of the AE signal. An accelerated leakage initiation process was performed by creating longitudinal scratches on the piston rod. In addition, the effect on the AE signal from pausing the test rig for a prolonged duration during the RTF tests was investigated. From the extracted features of the AE signal, the root mean square (RMS) feature was observed to be a potent condition indicator (CI) to understand the leakage initiation. In this study, the AE signal showed a large drop in the RMS value caused by the pause in the RTF test operations. However, the RMS value at leakage initiation is seen to be a promising CI because it appears to be linearly scalable to operational conditions such as pressure and speed, with good accuracy, for predicting the leakage threshold.