Experimental Data Research Articles

Determination and molecular simulation of ternary solid–liquid phase equilibrium of succinic acid + maleic acid + water from 283.15 K to 333.15 K

This study used the isothermal saturation gravimetric method to examine the solid–liquid phase equilibrium relationships composed of succinic acid + maleic acid + water spanning from 283.15 K to 333.15 K. The experimental data were utilized for the purpose of plotting the equilibrium phase diagrams of ternary systems in the studied temperature range. There were three crystalline regions in each isothermal ternary phase diagram, two saturation boundary curves, and a co-saturation point. To broaden the use of data from binary and ternary phase equilibrium measurements, Wilson and NRTL models were utilized for correlation and expectation, while relative average deviation (RAD) and root mean square deviation (RMSD) were utilized to evaluate the computational exactness. The results show that the theoretically calculated values were found to be in near agreement with the experimental values, and both models were able to obtain more precise theoretical data under different experimental conditions. In addition, molecular simulation was employed to elucidate the interaction between succinic acid, maleic acid, and water. This study provides accurate and reliable data for the separation of succinic acid and maleic acid mixture in water. These findings can be utilized to make sense of the dissolvability conduct of succinic acid and maleic acid in aqueous solution.

Real-Time Rotor Ground Fault Protection Based on the Convolution Integral for Synchronous Motors With Static Excitation

The detection of motor failures is a very active research topic within the literature. In particular, ground faults in wound rotors of synchronous motors have a relatively high failure rate since the rotor winding is exposed concomitantly to electrical, mechanical, and thermal stress cycles. Its detection depends on the grounding type of the electrical power system, and, in its early stages, it may present significant resistance. This manuscript provides a new rotor ground fault protection method based on the convolution integral of the neutral voltage that is measured by a single extra sensor. In addition, it is able to fill out the gaps of the current state-of-the-art, such as: provides a larger detection frontier for incipient faults; has low sensitivity for dynamic variances of the firing angle, allowing its utilization in cyclical applications; and can be implemented in power electronic controller systems with limited bandwidth. In this sense, mathematical modeling, simulations and experimental data are presented to support to the proposed method. Furthermore, it was implemented in the controller of static excitation converters for real-time protection and successfully tested with synchronous motors (2.4MW/3.3kV) of a cold rolling mill plant owned by ArcelorMittal Group.

Network pharmacology analysis of the renal protective effects of bracken (Pteridium aquilinum) using experimental data obtained in an avian (quail) hyperuricemia model.

To investigate the nephroprotective potential of orally administered bracken Pteridium aquilinum extract against renal damage in quails, induced by a high-purine diet, to form a foundation for subsequent clinical studies and applications. A mass spectrometry analysis was conducted on the pteridophyte subjected to steam explosion. Network pharmacological methods were then utilized to pinpoint shared targets and pathways, which suggested that Pteridium aquilinum has a capability to counteract renal injury. A total of 48 specific-pathogen-free (SPF) "Difaku" quails were selected and segregated into six distinct groups. The control group received a standard diet, whereas the other groups were fed a high-purine diet. Beginning on day 14, each group was subjected to designated therapeutic measures. The study continued for 40 days, after which relevant biological markers were assessed. Active compound peaks from the steam-exploded Pteridium aquilinum were isolated. Subsequently, 101 targets and several pathways associated with renoprotective effects were discerned, indicating that the Pteridium aquilinum achieves its nephroprotective function through comprehensive regulatory mechanisms. The high-purine diet successfully induced hyperuricemia in the quails, resulting in renal impairment. Following intervention with varied Pteridium aquilinum dosages, renal protective outcomes were evident, though xanthine oxidase activity remained unaffected. Histological analyses demonstrated a notable decrease in renal lesion dimensions post-intervention. The steam-exploded bracken Pteridium aquilinum may provide nephroprotective benefits against hyperuricemia-induced renal damage in quails through comprehensive regulatory processes. This highlights the Pteridium aquilinum's potential as an innovative nephroprotective therapeutic and dietary solution, presenting a promising avenue for hyperuricemia and renal damage treatment and prevention.

A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics

Technical education requires regular upgrades in pedagogical methodologies to keep up student’s skill on par with ever demanding job market. This paves the way for creating newer e-learning concepts for classroom to replace or supplement established teaching protocols. In line with this motive, this study deals with the development of an educational software tool to understand the traits of an internal combustion engine. The core of this software tool consists of polynomial regression equations, which in turn was arrived from statistical models using real world experimental data. A MATLAB-based GUI allows the operator to effortlessly interact with the software tool. Upon installation, the software requires the user to define input variables for it to automatically compute data and represent the output data in both visual and tabulated form. The advantage of three-dimensional surface plots for visual representation allows for understating the interactive effect of multiple input parameters on any given output parameter. Overall, average relative error for the model is less than 6%, thus exhibiting a good statistical fit.

Inflammation in Posttraumatic Stress Disorder: Dysregulation or Recalibration?

Despite ample experimental data indicating a role of inflammatory mediators in the behavioral and neurobiological manifestations elicited by exposure to physical and psychologic stressors, causative associations between systemic low-grade inflammation and central nervous system inflammatory processes in posttraumatic stress disorder (PTSD) patients remain largely conceptual. As in other stress-related disorders, pro-inflammatory activity may play an equivocal role in PTSD pathophysiology, one that renders indiscriminate employment of anti-inflammatory agents of questionable relevance. In fact, as several pieces of preclinical and clinical research convergingly suggest, timely and targeted potentiation rather than inhibition of inflammatory responses may actually be beneficial in patients who are characterized by suppressed microglia function in the face of systemic low-grade inflammation. The deleterious impact of chronic stress-associated inflammation on the systemic level may, thus, need to be held in context with the - often not readily apparent - adaptive payoffs of low-grade inflammation at the tissue level.

A generic plug & play diffusion-based denosing module for medical image segmentation

Medical image segmentation faces challenges because of the small sample size of the dataset and the fact that images often have noise and artifacts. In recent years, diffusion models have proven very effective in image generation and have been used widely in computer vision. This paper presents a new feature map denoising module (FMD) based on the diffusion model for feature refinement, which is plug-and-play, allowing flexible integration into popular used segmentation networks for seamless end-to-end training. We evaluate the performance of the FMD module on four models, UNet, UNeXt, TransUNet, and IB-TransUNet, by conducting experiments on four datasets. The experimental data analysis shows that adding the FMD module significantly positively impacts the model performance. Furthermore, especially for small lesion areas and minor organs, adding the FMD module allows users to obtain more accurate segmentation results than the original model.

Detection of Rotating Stall Inception of Axial Compressors Based on Deep Dilated Causal Convolutional Neural Networks

The anomaly detection of outliers and fault diagnosis in the imperfect time-series data is crucial in aero-engine industry. Early detection of the rotating stall is significant for the active stabilization control of the axial compressors, because the rotating stall and surge can cause enormous vibratory stresses in the blades and gives rise to surge to limit the performance of compressors. An aerodynamic instability inception and short-length-scale periodic anomaly prior to stall onset known as stall inception in axial compressors is observed in aero-engine. Based on deep learning theory, this paper conducts the accurate and rapid detection of stall precursors based on deep neural network. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. The LR-WaveNet model can implement fast anomaly detection and stall prediction in long-time term series data. Furthermore, a single LR-WaveNet can be trained to capture and learn the time-domain statistical characteristics of many different stall inception training data with equal fidelity. The trained LR-WaveNet model can rapidly detect the occurrence of anomaly point and predict the probability of rotating stall and surge in axial compressors as an early warning signal. By comparing with the time domain analysis and the wavelet analysis, the calculation results are represented with experimental data to show the effectiveness and feasibility of the stall detection approach based on LR-WaveNet. Especially, the LR-WaveNet can detect the irregular and imperfect stall inception and capture the small fault features when the characteristics of stall inception sigals or data are not very unobvious, irregular and imperfect. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Note to Practitioners–This paper was motivated by the problem of detecting the stall and surge of axial compressor for aeroengine, but it also applies to other small fault diagnosis with imperfect data or time series prediction problem. The stall Inception is very difficult to detect for the reason of imperfect data with small fault or features. These existing detection methods of stall inception can only be applied under certain conditions, because they can only extract the pre-stall features partly. Meanwhile, the excessive number of compressor sensors is demanded in this method, so the application of these traditional methods are limited in practical situations in the stall inception detection. This paper suggests a new approach using Deep Dilated Causal Convolutional Neural Networks. The deep dilated causal convolutional neural network combined with logistic regression(LR) named as LR-WaveNet is applied to the detection and prediction of stall inception in the imperfect time-series data of axial compressors with the rotating stall. In this paper, the new approach is proved to be suitable and feasible to all the types of stall inception including the irregular stall inception with imperfect data. We then show how the deep learning method can be efficiently applied in the imperfect data situation with small fault and time series prediction. The experiments suggest that this approach is feasible. In future research, we will create the novel approach and theory for the detection of stall inception and small fault diagnostics with the imperfect data.

Study on multi-scale coupled simulation of natural circulation system with 3×3 petal-shaped fuel bundle channel under rolling condition

Petal-shaped fuel rod encompasses a broad application prospect in sea-based nuclear power plants. A multi-scale coupled simulation is conducted based on natural circulation system with 3×3 petal-shaped fuel bundle channel. A multi-scale coupled model considering rolling motion is developed. Its reliability is verified by existing experimental data. The effect of rolling motion on the characteristics of natural circulation system is thoroughly discussed. The results indicate that if the start-up direction of rolling motion is the same as the direction of flow rate, rolling motion will lead to a sharp decrease in flow rate at the initial moment. By increasing rolling amplitude or decreasing rolling period will cause a significant increase in fluctuation amplitude of flow rate. Compared with the results under static condition, the fluid is subjected to the Coriolis force in the non-inertial coordinate system. This research can provide support for the joint optimization of natural circulation system.

In English

The aim of presented work was to synthesize geopolymers based on the metakaolin and to determine their adsorption capability in the process of cesium and strontium ions removal from the aqueous solutions. New approaches were proposed for obtaining the two samples of geopolymers in techologically suitable forms. Morphology of materials was investigated by means of X-ray fluorescence analysis (XRF), low-temperature nitrogen adsorption/desorption and scanning electron microscopic studies (SEM). As it follow from the data of XRF analysis, SiO2 and Al2O3 oxides found to be the major components in all samples investigated (~ 54–84 wt. %). As was determined by SEM studies, geopolymers consisted from nanosized particles, amorphous geopolymers binder and unreacted kaolin. It has been found that all samples involve the mesopores with approx. 1–40 nm radii. The greatest specific surface area calculated by the Brunauer-Emmet-Teller (BET) method had the sample obtained in the forms of spherical rods (SBET = 88 m2/g) that about 10 times greater than for initial kaolin taken for synthesis. The ion exchange capacities of materials in the process of Cu2+, Cs+ and Sr2+ removal from water solution were determined and it was found that these properties depends on the method of materials obtaining. Data showed that the geopolymers were more effective for removal the desired ions than initial kaolin. The greatest adsorption capacity towards cesium ions was received on the samples obtained in forms of pyramids and was reached 1.75 mmol/g. Experimental data were fitted into the Langmuir models and the main Langmuir constants were calculated. When analysing the data of investigation with comparing the literature data it was noted that geopolymers obtained can be used in adsorption technology for purification of water from radionuclides as technologically suitable sorbents.

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Introduction. Control of the presence of subsurface defects in products from composite materials is necessary for verification of products after release from production and in the process of operation. Aim of the Study. The purpose of the presented work is to estimate the parameters of subsurface defects using local laser thermography, suitable for quality control of both small objects and suspicious areas of large objects with curved surfaces. Materials and Methods. The laboratory setup on which this work was carried out includes a robotic arm, a COX CG640 thermal imager and a 3 W laser. The method was tested on a fiberglass sample with introduced delamination defect simulations located at different depths below the surface. By means of computer modeling rational parameters of thermographic control were selected, providing reliable detection of the defect at a depth of up to 3 mm under the surface of the composite sample. Results. Numerical modeling of surface temperature field induced by moving focused laser beam was carried out using COMSOL software package. It showed that laser beam with 3 W power moving at 5 mm/s provided the thermal contrast sufficient to detect the defects at the depth up to 3 mm. The obtained experimental data are in satisfactory agreement with numerical modeling both qualitatively and quantitatively. Experimental data were used to construct a regression model for determining defect depth based on the maximal thermal contrast and the time interval between heating and the contrast maximum. Discussion and Conclusion. The results obtained in this work allow us to propose a technique for detecting defects in fiberglass plastics and estimating their depth. The coefficient of determination for the obtained regression model was found to be equal to 0.95, and the mean square error of the metric was no more than 0.016 mm2. The use of a robotic arm to scan objects will make it possible to investigate objects with complex curved surfaces.

Numerical Analysis of the Effects of Fuel Injection Duration and Spray Angle on the Combustion Process in a Compression Ignition Engine

The changes in injection strategies for diesel engines have a major impact on the performance and pollutant emission characteristics of diesel engines. If injection strategies like injection duration, injection timing, injection pressure and spray angle are properly adjusted, combustion can be improved. The engine performance will increase and emissions will decrease with the combustion improvement. In this work, the influences of injection duration and spray angle on the combustion characteristics of single cylinder, natural aspirated, electronically controlled injection, compression ignition engine were investigated. In the first stage of the work, experiments were executed on a single cylinder CI engine using a Cussons P8160 DC dynamometer. After the experiments, the piston bowl geometry of the engine was modeled and numerical simulation studies were achieved at 7 different injection durations and 7 different spray angles using Converge CFD software. As a result of this study, it was observed that there is a good match between experimental and simulation data of heat release rate (HRR) and in-cylinder pressure. In-cylinder pressure decreased with longer injection duration. The highest max. in-cylinder pressure was roughly 101.0 bar at 4°CA injection duration and the lowest max. in-cylinder pressure was roughly 82.0 bar at 10°CA injection duration. When the HRR data were analyzed, it was seen that as the injection duration increased, the amount of heat released by combustion decreased. When examining the results of the spray angle analysis, it was concluded that there were not very large differences in-cylinder pressure and HRR data, and there was a difference of 1.4 bar between the highest and lowest max. in-cylinder pressure values. In addition, the highest in-cylinder pressure of approximately 86.7 bar was obtained at a spray angle of 77°. It was observed that the CA50 value was obtained at angles closer to the top dead center by increasing the spray angle and decreasing the injection duration. Moreover, the longest combustion durations were realized at 60° spray angle and 10°CA injection duration.

Leveraging Halogen Interactions for a Supramolecular Nanotube.

We demonstrate the formation of supramolecular nanotubes from molecular triangles in a single crystal by balancing the hydrogen bonds and halogen interactions between individual macrocycles. Thereby, we template the supramolecular nanotube growth by intermolecular interactions encoded directly in the macrocycles instead of those provided by the crystallization solvent. Ultimately, we show that replacing bromines for iodines in the macrocycle is necessary to achieve this supramolecular organization by enhancing the strength of the halogen interactions and concomitant reduction of the detrimental hydrogen bonds. We investigated the nature and the interplay of the individual intermolecular interactions by analysis of the experimental single crystal data and quantum chemical calculations. This work enriches the available toolbox of supramolecular interactions and will aid and abet the development of rationally-designed materials with a long-range 1D tubular organization.

3D modeling of a double-driver ion source considering ion magnetization: an investigation of plasma symmetry modulation methods

A three-dimensional fluid model of a double-driver negative hydrogen ion source for China Fusion Engineering Test Reactor (CFETR) neutral beam injection is developed. In this model, the magnetic filter field is generated by 16 permanent magnets, which are surrounded by a soft iron. In order to accurately describe the transportation of charged species in the presence of strong magnetic field, both the electron magnetization and ion magnetization are taken into account, and the accuracy of the model has been proved by comparison with experimental data. By employing this model, the spatial distributions of the plasma parameters have been investigated, and three methods are proposed to optimize the symmetry at the bottom of the expansion region of a double-driver source. The results indicate that by adjusting the power of Driver I while keeping the power of Driver II constant, the symmetry of the electron density and negative hydrogen ion density could be improved. Furthermore, the inclusion of partition improves the symmetry of the electron temperature and density but has no impact on the regulation of the negative hydrogen ion density distribution. Finally, the application of magnetic shield can not only improve the symmetry of the electron density and negative hydrogen ion density, but also increase their densities at the bottom of the expansion region.

A novel camera calibration method based on known rotations and translations

It is often difficult to align the camera optical center with the rotation center of the motion mechanism during active vision calibration, and this dis-alignment could lead to inaccuracy of the constraint equations and calibration results. To circumvent such issues, this paper proposes a novel method for active vision camera calibration and facilitates its real-world implementation. In this method, the rotational motion axis is assumed not necessarily to pass through the camera’s optical center, and the constraint equations are established by correlating image matched points before and after camera motions. The displacements caused by rotation are incorporated into the constraint equations to improve the accuracy of calibration. Numerical simulation and experiments are conducted. The feasibility of this proposed method is justified by using both synthetic and experimental data, and the results show that this method could estimate camera intrinsic parameters with high accuracy. Furthermore, it is found that small rotation angles can better the calibration accuracy even under the influence of external noises. This study suggests that this novel method has great potential for visual and image applications.

Experimental study on single- to two-phase flow and heat transfer of water in a small tube

Experimental investigation on flow boiling heat transfer in a small tube under high system pressure has been conducted in this study. The test-section is constructed with a 316L stainless steel tube. The inner diameter and heated length of the test-section are 5 mm and 930 mm, respectively. The working fluid is deionized water. The operating conditions of the experiments are as follows, the mass flux from 548 to 978 kg/(m2·s), the wall heat flux from 136 to 238 kW/m2, and the system pressure from 1.0 to 2.9 MPa. The experimental results showed that the inlet subcooling mainly affects the onset of nucleate boiling, but its impact on the heat transfer coefficient at the same vapor quality is almost negligible. The heat transfer coefficient increases with the increase of mass flux and wall heat flux, especially in the region where xe < 0.02. The average heat transfer coefficient is higher under high system pressure owing to the variation of the thermophysical properties. Additionally, the existing flow and heat transfer correlations cannot predict the flow boiling heat transfer coefficient and frictional pressure drop in the experiments accurately enough. Therefore, the new flow and heat transfer correlations are developed based on the experimental data in this paper, and the mean absolute deviation of the flow boiling heat transfer correlation and frictional pressure drop correlation are 13.3% and 12.1%, respectively.

Intersectional Motherhood and Candidate Evaluations in the United States

Abstract In the United States, emphasizing their families and talking about parenthood can be a potent strategy for political candidates as voters use information about a candidate’s family life to make assessments of the candidate’s personal attributes and issue competencies. We nonetheless know little about how a candidate’s race affects these assessments. We thus consider how Black women use and benefit from politicizing motherhood, and we argue that the unique intersectional identities of Black women shaped jointly by their race and gender can give Black women a stronger strategic advantage from highlighting motherhood compared to white women. Using both observational and experimental data, we apply this intersectional framework by examining motherhood messages. We identify the extent to which Black women rely on messages about motherhood and how voters respond. Our results show that Black women are just as likely to use motherhood messages relative to white women, and that Black women receive positive evaluations from voters from a message emphasizing motherhood. White and minority voters are equally likely to positively rate Black women who emphasize motherhood.

Mechanistic model of critical heat flux in rod bundles based on a high-precision subchannel code

A mechanistic model for predicting critical heat flux (CHF) in rod bundles is developed based on the high-precision subchannel code ATHAS. To account for the non-uniform distribution of quality in subchannels caused by the presence of mixing vane grids (MVGs) and guide tubes (GTs), further subdivision of conventional subchannels and the incorporation of MVG crossflow models are implemented in ATHAS to provide a more detailed local flow field for the CHF model. The CHF mechanism model is developed based on the concepts of bubble crowding and assumption of local phenomenon. An equivalent-tube concept is proposed to convert the turbulent intensity distribution on the tube surface to that on the rod bundle surface. The present model is implemented in ATHAS and evaluated against CHF experimental data of rod bundles under the conditions of interest for pressurized water reactors (PWRs). The predicted results show good agreement with the experimental CHF data, with an average error and root mean square of 0.99% and 4.69%, respectively, for a total of 601 data points. Furthermore, owing to the use of the subchannel subdivision method, the present model does not require any cold wall effect correction and can accurately predict the radial position of CHF.

Experimental study on the use of the ARM Cortex M7 processor for measuring far-field blast waves

AbstractThe ongoing study of blast waves and blast wave mitigation continues to play an essential role in protecting structures and personnel. The methodology, however, for capturing far-field blast waves in large-scale tests has remained largely unchanged for three decades, relying on large arrays of pressure transducers connected by hundreds of meters of cabling and requiring a considerable amount of time to set up. This paper evaluates the use of a modern low-cost microprocessor with high computational power to capture blast waves with sufficient fidelity to provide scientists and engineers with credible data. The system utilizes an ARM Cortex M7 processor as an experimental data acquisition (DAQ) system for measuring far-field blast waves in an open-air blast arena at sampling speeds of up to 1.8 Msps (megasamples per second). The experimental system’s performance was evaluated by comparing it to a traditional commercial system used for measuring blast waves. The comparison showed an average Spearman correlation coefficient r of 0.928 between the two systems, suggesting a low variance between the commercial and experimental DAQ systems. This suggests that, despite its simplicity, the experimental system is an effective and low-cost alternative for accurately measuring blast waves.

Research on the Remaining Life Prediction Method of Rolling Bearings Based on Optimized TPA-LSTM

The whole life cycle degradation data set of rolling bearings has the characteristics of large capacity, diversity, and non-stationarity. As a powerful tool for processing such time series data in deep learning algorithms, LSTM is prone to the loss of important time series information in the process of the life prediction of rolling bearings, which leads to a decline in prediction accuracy. Therefore, a method for predicting the remaining useful life (RUL) of rolling bearings based on the combination of temporal pattern attention mechanism (TPA) and LSTM is proposed. The method firstly combines hierarchical clustering and principal component analysis (PCA) to construct a multi-faceted and multi-scale preferred feature set reflecting the degradation information of rolling bearings, then strengthens the information correlation between hidden layers of the LSTM model through TPA and optimates the parameters of the fusion model of TPA and LSTM by using the gazelle optimization algorithm (GOA). Finally, the model is applied to the experimental data set of rolling bearing degradation. The results show that, compared with the traditional model, this method is more suitable for the remaining life prediction of rolling bearings.

Elasticity of Diametrically Compressed Microfabricated Woodpile Lattices

Modulus–porosity relationships are invaluable to rational material design of porous and structured solids. When struts in a lattice are compressed diametrically, the mechanics is rather complex. Herein, the problem of modulus–porosity in the spirit of scaling arguments and analyses based on simple ansatz followed by variational minimization of the elastic potential energy is addressed. Using scaling arguments, a simple power law where the apparent modulus of elasticity scales quadratically with the volume fraction for diametrically compressed elastic lattices is obtained. The modulus–porosity relationship is found to be consistent with computations and laboratory experiments on additively manufactured woodpile lattices with various cross‐sectional shapes and lattice spacing. It is also shown that the persistence length of diametrically pinched elastic rods is small, so that the effect of compressive strain from neighboring sites can be ignored. The decay behavior is surprisingly accurately captured by the variational approach and is consistent with computations. Finally, the range of validity of the quadratic power law presented here, up to relative density ~80%, is identified. On the apparent modulus–porosity plane, the experimental data aligns well with the power law for modulus–porosity predicted from simple analyses and finite element calculations.