Experimental Data Research Articles

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.

Adaptation to Simulated Hypergravity in a Virtual Reality Throwing Task

According to previous research, humans are generally poor at adapting to earth-discrepant gravity, especially in Virtual Reality (VR), which cannot simulate the effects of gravity on the physical body. Most of the previous VR research on gravity adaptation has used perceptual or interception tasks, although adaptation to these tasks seems to be especially challenging compared to tasks with a more pronounced motor component. This article describes the results of two between-subjects studies ( n = 60 and n = 42) that investigated adaptation to increased gravity simulated by an interactive VR experience. The experimental procedure was identical in both studies: In the adaptation phase, one group was trained to throw a ball at a target using Valve Index motion controllers in gravity that was simulated at five times of earth’s gravity (hypergravity group), whereas another group threw at a longer-distance target under normal gravity (normal gravity group) so both groups had to exert the same amount of force when throwing (approximated manually in Study 1 and mathematically in Study 2). Then, in the measurement phase, both groups repeatedly threw a virtual ball at targets in normal gravity. In this phase, the trajectory of the ball was hidden at the moment of release so that the participants had to rely on their internal model of gravity to hit the targets rather than on visual feedback. Target distances were placed within the same range for both groups in the measurement phase. According to our preregistered hypotheses, we predicted that the hypergravity group would display worse overall throwing accuracy and would specifically overshoot the target more often than the normal gravity group. Our experimental data supported both hypotheses in both studies. The findings indicate that training an interactive task in higher simulated gravity led participants in both studies to update their internal gravity models, and therefore, some adaptation to higher gravity did indeed occur. However, our exploratory analysis also indicates that the participants in the hypergravity group began to gradually regain their throwing accuracy throughout the course of the measurement phase.

Moiré Pattern Controlled Phonon Polarizer Based on Twisted Graphene.

Twisted van der Waals materials featuring Moiré patterns present new design possibilities and demonstrate unconventional behaviors in electrical, optical, spintronic, and superconducting properties. However, experimental exploration of thermal transport across Moiré patterns has not been as extensive, despite its critical role in nanoelectronics, thermal management, and energy technologies. Here, thefirst experimental study is conducted on thermal transport across twisted graphene, demonstrating a phonon polarizer concept from the rotational misalignment between stacked layers. The direct thermal and acoustic measurements,structural characterizations, and atomistic modeling, reveal a modulation up to 631% in thermal conductance with various Moiré angles, while maintaining a high acoustic transmission. By comparing experiments with density functional theory and molecular dynamics simulations, mode-dependent phonon transmissions are quantified based on the angle alignment of graphene band structures and attributed to the coupling among flexural phonon modes. The agreement confirms the dominant tuning mechanisms in adjusting phonon transmission from high-frequency thermal modes while having negligible effects on low-frequency acoustic modes near Brillouin zone center. This study offers crucial insights into the fundamental thermal transport in Moiré structures, opening avenues for the invention of quantum thermal devices and new design methodologies based on manipulations of vibrational band structures and phonon spectra.

Rotary Heat Recovery Wheel (RHRW) system with an embedded PCM: Proof of concept

This work presents the thermal performance of a novel Rotary Heat Recovery Wheel (RHRW) system incorporating a Phase Change Material (PCM) embedded in the solid structure of the matrix of a rotating recuperator. The main aim of this device is to increase the thermal efficiency of the RHRW in two ways: by storing large amounts of thermal energy in latent form, and by permitting approach temperatures (around PCM melting point) that are closer to the desired setpoint temperatures for heating/cooling purposesA numerical model of the RHRW system with an embedded PCM was developed, comparing its performance against an identical RHRW without the PCM. The numerical model was validated with experimental data from the literature. A sensitivity analysis was carried out, varying the content of PCM embedded and the rotating speed of the wheel. Under the conditions tested in this work, for intermediate rotating speeds (between 0.1 and 0.7rpm) the PCM increases the efficiency of the system. For n=0.3rpm the efficiency augments from 0.44 to 0.87 with an addition of 15% in mass of PCM. There is also a certain rotating speed, nmax, for which the efficiency is maximum as the phase change of the PCM limits the temperature change. A relationship is proposed between the rotating speed and the mass fraction of PCM to reach the maximum efficiency.

A study of dimensional effects on the stress characteristics of Yunnan pine shafts considering seismic strain rates

Abstract The mechanical properties of wood are affected by member size and loading rate. Axial compression tests with different seismic strain rates and sizes of specimens were carried out to investigate the static and dynamic dimensional effects of Yunnan pine timber. And obtained load-displacement curves, compressive strength, modulus of elasticity, peak strain and Poisson’s ratio. Weibull’s weakest chain theory, Bazant’s law of dimensional effects and Carpinteri’s law of multiple fractal dimensional effects were used to analyze the dimensional effects on the compressive strength of wood, and to establish a wood strength model that takes strain rate effects and dimensional effects into account. The results showed that the compressive strength, elastic modulus, and Poisson’s ratio of Yunnan pine wood exhibit both size and strain rate effects. Under the coupling effect of strain rate effect and size effect, the size effect of Yunnan pine wood strength decreases with increasing strain rate, while the strain rate effect increases with increasing size. A size effect model for compressive strength of wood considering strain rate effect based on three theories and experimental data can well describe the dynamic size effect of Yunnan pine wood strength.

Experimental results for RVK-500 recombiner tested in conditions typical for pressurized water NPP severe accidents

Severe accidents at NPPs with light water coolant can lead to situations when large quantities of hydrogen are released due to zirconium-steam reaction. To avoid explosion consequences, they started to install hydrogen passive autocatalytic recombiners (PARs) at NPPs. Calculating simulation based on PAR models is used to predict and justify the needed quantity of PARs and the chosen place of their location inside the NPP. To validate PAR computational models, experimental data are need. Within the scope of this work, the integrated tests of the RVK-500 type recombiner, which included 20 experiments, were conducted to obtain large body of data on PAR operation in different modes and conditions that can be realized in case of hypothetical accident at NPP. For this purpose, a facility equipped with a measuring set able to work at high pressures, temperatures, and humidity was constructed. The tests were conducted in accord with thepredevopled PAR testing procedure and program to describe PAR characteristics, such as its start-up, H2 recombination rate, efficiency, and ignition limit. In addition, a small-scale facility was used to study the potential of particles separated from the catalytic elements to induce combustion.

Enhancing cladding mechanical modelling during DBA/LOCA accidents with FRAPTRAN: The TUmech one-dimensional model

The analysis of the cladding mechanical performance is essential for predicting rod failure during the heat-up phase of a loss-of-coolant accident (LOCA). Previous modelling and simulation exercises have shown that there is room for improvement in the accuracy of fuel performance simulations under such conditions. In this work, TUmech, a simplified standalone version of the mechanical model in TRANSURANUS, has been developed and coupled with FRAPTRAN-2.0. After highlighting the most significant differences between both approaches, particularly in their mechanical treatment and material property correlations, simulations have been performed for IFA-650.2 (fresh fuel) and IFA-650.10 (high burnup fuel) of the Halden LOCA test series. The results obtained from both FRAPTRAN versions have been compared with experimental data and benchmarked against TRANSURANUS estimates, focusing on rod inner gas pressure, post-test cladding outer diameter, and burst time. In both scenarios, FRAPTRAN-TUmech has proven to enhance the accuracy in predicting these three variables compared to the FRAPTRAN default version. However, further validation is required before drawing definitive conclusions based on the encouraging results.

Microscopic mechanism of plastic heterogeneous deformation of columnar-grained polycrystalline ice

Studying ice deformation starting from crystal scale can provide insight into the microscopic mechanisms of ice deformation. In this paper, a crystal plasticity constitutive model of ice considering the dislocation slip mechanism and grain-size dependence was implemented within the finite element framework, and a modeling approach starting from ice single crystals based on the crystal plasticity finite element method (CPFEM) was proposed to study the elastic-viscoplastic deformation of polycrystalline ice samples. Finite element models of ice single crystals and columnar-grained polycrystalline ice characterizing the microstructure were established successively, and the proposed method was validated by comparing the uniaxial compression prediction results of ice single crystals and columnar-grained polycrystalline ice with the existing experimental data. Finally, the microscopic mechanism of plastic heterogeneous deformation of columnar-grained polycrystalline ice under constant strain rate compression conditions was analyzed. The results indicate that the modeling approach from ice single crystals to polycrystalline ice samples based on CPFEM can reasonably describe the mechanical properties of ice deformation. The deformation of columnar-grained polycrystalline ice globally exhibits apparent slip bands around 30° - 60° relative to the loading direction and locally presents intragranular localization bands parallel or perpendicular to the C-axis. This plastic heterogeneous deformation phenomenon is related to the elastic-viscoplastic anisotropy of ice single crystals and the interaction of neighboring grains at grain boundaries (GBs).

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning

Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.

Experimental characterization and non-isothermal simulation of a zero-gap alkaline electrolyser with nickel-iron porous electrode

The availability of self-supported porous electrodes with excellent hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) catalytic activity has been an important cornerstone for the performance of alkaline water electrolysers (AWEs). In addition, the management of heat and two-phase flow during ion/electron transfer in porous electrodes poses significant challenges to the stable operation of AWE electrolysers. In this work, a two-dimensional non-isothermal multiphysical model considering two-phase flow, mass, heat and charge transfer processes is developed based on COMSOL Multiphysics and well validated with reliable experimental data. The model exhibits excellent agreement with actual electrolyser operating voltages between 323–343 K, and the relative error is within acceptable 2.2% even at high current densities up to 1.5 A cm−2. In addition, the electrode kinetic data obtained based on standard three-electrode experiments allow the model to predict the corresponding HER and OER overpotentials of the porous electrodes with great accuracy, as well as to obtain the distribution of temperature and ionic species inside the porous electrodes under various volumetric flow rates and current densities. This work provides a practical and reliable guide for the design of porous electrodes and the performance enhancement of electrolysers from both experimental and simulation perspectives.

Simultaneous extraction of cadmium, nickel, and cobalt ions from acidic aqueous solutions by emulsion liquid membrane using sunflower-kerosene mixture as a diluent; breakage and mass transfer studies

In this study, a simultaneous removal of cadmium, cobalt, and nickel from an aqueous solution through the Green Emulsion Liquid Membrane (GELM) method was carried out. A 7:3 ratio of sunflower oil to kerosene was utilized as the membrane phase stabilized by Span 80 as the emulsifier. D2EHPA as a carrier and HCl as an inner phase were used. Extraction and stripping efficiencies of 97.45% and 98.42% for Cd2+, 96.82% and 97.84% for Co2+, 89.74% and 92.69% for Ni2+, respectively, with the lowest breakage (0.8%) were obtained under the best operating conditions. An extensive investigation of the influence of homogenizer speed, external and internal phase acidity, the HLB value of the surfactant (Tween 80 and Span 80), surfactant and carrier concentrations, volume ratio of the membrane to internal phase, mixer speed, ratio of external to membrane phase emulsification time and extraction time on the stability of membrane, extraction, and stripping efficiencies were carried out. Kinetic analysis of cadmium, cobalt, and nickel extraction was performed using zero, first, and second-order models at optimum conditions. The experimental data fit well with first-order for three metal ions. The coefficient of the total mass transfer (Ft) of cadmium through the GELM technique was 3.28% higher than that of cobalt and 28.61% higher than that of nickel, while the Ft of cobalt is 25.33% higher than that of nickel. Using the recovered membrane, the extraction of the three metal ions for the initial six cycles was extremely efficient under the best conditions.