Experimental Measurements Research Articles

Experimental and numerical study of lean combustion of propane in divergent porous media burners

Porous inert media burners for industrial applications demand the utilization of flame front stabilization methods, among which the convective or cross-section variation technique stands out. In this work, combustion of lean premixed propane-air mixtures in cylindrical and divergent alumina packed-bed porous inert media burners was numerically and experimentally conducted. Three burners of 0°, 15° and 30° of divergence were used to study the variation of equivalence ratio and inlet velocity. A bidimensional transient mathematical model was developed and solved numerically, whose results show good agreement with experimental measurements. The maximum temperature along the central axis is location dependent, with upstream augmentation of up to 86 %, meaning an increase of ∼ 815 K in the case of the 30° burner. Combustion front propagation velocity of upstream regime flames show acceleration in both divergent burners due to heat transfer and increment of thermal power in relation to cross-sectional areas when reaching the inlet. In comparison to cylindrical burner, a concave shape of the combustion front and a stretch of the maximum temperature zone was obtained, due to the diffusive nature of the velocity field. Stabilized flames on both divergent burners were achieved in the equivalence ratio range from 0.4 to 0.6. Residence time of product gases is greatly prolonged with burner wall angle and the decrease in overall temperature towards the outlet.

Plasma potential and ion energy characteristics in BP-HiPIMS discharge with double layer

Abstract As an emerging ion acceleration plasma source, the bipolar-pulse high power impulse magnetron sputtering (BP-HiPIMS) discharge has been widely studied by academia and industry due to its ability to adjust the ion kinetic energy. Formation of the double layer (DL) potential structure during the BP-HiPIMS positive pulse is vital for accelerating ions, but its structural characteristics are still unclear. In this work, to understand the DL characteristics affected by various discharge parameters, the evolution of plasma potential V p and ion energy in BP-HiPIMS discharge with copper target has been investigated systematically using an emissive probe and mass spectrometer together. Spatial plasma potential measurements show that the DL is established in front of the target during the positive pulse, whose boundary potential drop U DL to accelerate ions can be increased to ∼60 V at a lower operating gas pressure (p= 0.6 Pa) and a higher applied positive pulse voltage (U + = 200 V). The ignition onset time of DL after applying the positive pulse can be shortened to ∼25 μs by decreasing the gas pressure and increasing the positive pulse voltage or negative pulse duration. After DL ignition, a group of high-energy copper ions with energy higher than the surrounding plasma potential can be recognized in the ion energy distribution function curves in the downstream plasma. This result illustrates that the copper ions can be ionized in the high-potential plasma region and be accelerated by the DL boundary potential drop. In addition, a global current balance model of the DL in BP-HiPIMS is developed, which suggests that the U DL can be well adjusted by increasing the positive pulse voltage U + especially for U + > 200 V as verified by the experimental potential measurements. All results suggest that the copper particles play an important role in the formation of DL and the DL plays an important role in accelerating copper ions.

Investigating the role of fibre-matrix interfacial degradation on the ageing process of carbon fibre-reinforced polymer under hydrothermal conditions

The aqueous environment can deteriorate the fibre-matrix interface of carbon fibre-reinforced polymer (CFRP), significantly impairing the non-fibre-dominated mechanical properties. Thus, this study aimed to quantitatively analyse the impact of interfacial degradation on the hydrothermal ageing mechanism and process of the CFRP. Firstly, entire water absorption process of the CFRP under hydrothermal conditions was divided into three stages according to experimental measurement of its water content. Based on this division of stages, a novel water diffusion model was established for the hydrothermally aged CFRP. To measure the mechanical degradation, tensile tests were conducted on unaged, aged, and redried neat epoxy and transversely positioned unidirectional (UD) CFRP specimens. It was found that the transverse tensile strength degradation of UD CFRP was irreversible due to the permanent interfacial debonding between the fibres and matrix, in contrast to the reversible ageing of the epoxy matrix. To further quantify the fibre-matrix interfacial ageing, physically-based models were established for the degraded interfacial strength of CFRP subjected to hydrothermal conditions. After the characterization of the modelling coefficients, the physically-based models can be employed to predict interfacial strength inside the aged CFRP for various ageing durations. The prediction error was only 4.57% for the transverse tensile strength of degraded UD CFRP with various ageing durations from the representative volume element (RVE) simulation with its interfacial strength provided by the physically-based models, validating the effectiveness of the proposed physically-based models for degraded fibre-matrix interface under various ageing conditions.

Prediction of interface morphology formed by the oblique gas jet impinging on a liquid surface

This study presents a theoretical analysis of the oblique gas jet impingement process on a liquid surface, elucidating the evolution of the flow field distribution and the influence of jet parameters on cavity shape dynamics. By integrating surface tension effects, the existing Blanks and Chandrasekhara model was refined to develop an advanced predictive model for cavity morphology. The theoretical framework was substantiated through numerical simulations and corroborated with experimental measurements of cavity dimensions, captured using state-of-the-art machine vision technology. The findings reveal a consistent trend in cavity dimension variations: an increase in the cavity surface width with the elevation of the impinging angle from the vertical and an escalation in gas jet velocity. Conversely, a reduction in the impinging angle coupled with an increase in jet velocity leads to a deeper cavity. To enhance the predictive accuracy, the model underwent iterative optimization, incorporating experimental data and accounting for jet parameters. The refined model demonstrated achieved a maximum error of 0.135 mm and a minimum error of 0.03 mm, providing reliable forecasts of cavity depth, which is pivotal for applications in fluid dynamics and related engineering fields.

A Comparison of Standard SEI Growth Models in the Context of Battery Formation

Growth of the Solid Electrolyte Interphase (SEI) layer on negative electrode particles during the formation cycle is one of the most complex and least understood steps of lithium-ion battery manufacturing. This initial SEI formation significantly impacts battery performance, lifetime, and degradation. Zero-dimensional models, which reduce the complexity of SEI’s morphology, material, and structure, are commonly used to study long-term SEI growth rates and capacity fade. These models are derived based on limiting mechanisms. We aim to compare the most common SEI growth models, focusing on the first few cycles at low C-rates representing formation protocols. Using consistent parameters across models, we seek to understand if they can capture the dynamics of SEI formation. We conducted qualitative comparisons with experimental measurements of Coulombic efficiency in 2032-type coin cells at low C-rate. Our analysis shows that the models predict SEI growth in the first cycle to be higher than in subsequent cycles. However, the difference between cycles in these models is insufficient to explain the experimental results, which indicate a capacity fade during the first cycle that is two orders of magnitude higher than in later cycles. This suggests new models are needed to accurately describe the physics of the formation cycle.

Prediction of Pressure Fluctuation of Double-Suction Centrifugal Pump in Different Cavitation Conditions based on EMD-GRU

Abstract The double-suction centrifugal pumps play a crucial role in various applications such as in irrigation-drainage projects, water diversion projects, and urban water supply and drainage projects. The pressure fluctuation is one of the crucial parameters for assessing the operational stability of pump. The pressure fluctuation exhibits distinct characteristics in various cavitation conditions. A pressure fluctuation prediction method based on Empirical Mode Decomposition (EMD) and Gated Recurrent Unit (GRU) is proposed in this paper. The model enables accurate forecasting of pressure fluctuation signals when pump operates at different cavitation conditions. The data were obtained from experimental measurements of a double-suction centrifugal pump. The original signal was decomposed into multiple sub-signals with varying energy levels using the EMD method. The neural network of GRU was employed for training and data prediction on individual sub-signals. The predicted results of each sub-signal were combined to form the final pressure fluctuation signals. The method based on EMD-GRU proves effective in predicting pressure fluctuation in three different cavitation conditions. The mean absolute error (MAE) in time-domain characteristics is below 6%, and the prediction error for the main frequency is below 1%. The prediction of low-frequency characteristics still requires improvement.

Experimental evaluation of green nanofluids in heat exchanger made oF PDMS

Conventional methods for synthesizing metallic nanoparticles face challenges such as instability and environmental concerns. Therefore, new, simpler, and more eco-friendly methods are being explored. In this context, the study reports a green synthesis process to produce magnetic iron oxide nanoparticles using an aqueous extract of the alga Chlorella vulgaris. This process leverages natural resources to create a sustainable nanofluid known as green nanofluid. To evaluate the characteristics of this nanofluid, experimental measurements of wettability, viscosity, thermal conductivity, and qualitative stability analysis were conducted. An experimental setup consisting of a heat exchanger made of polydimethylsiloxane (PDMS) was used to assess the thermal performance and the results were compared to theoretical equations and numerical simulation. Additionally, thermographic imaging of temperature gradients as the fluids passed over the heated surface of the serpentine channel were made. The main findings confirmed that the nanofluid was more stable than that obtained by traditional methods and had a more uniform temperature distribution over the heat exchanger. The higher concentration exhibited superior thermal performance compared to DI-Water. Moreover, the green nanofluid was used at a weight concentration of 0.1 wt%, provided thermal performance results of nearly 4.5% superior to those estimated by the numerical model and 6.4% higher than those experimentally obtained with the base fluid, respectively. Finally, the results obtained for the nanofluid also showed an average increase of around 5% in the viscosity of the base fluid, with a more significant sedimentation at a concentration of 0.1 wt%.

Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

Transverse squeeze flow of thermoplastic composite tape during in-situ consolidation via automated fiber placement

Transverse squeezing of thermoplastic composite tapes during automated fiber placement is a challenge in controlling gaps/overlaps of adjacent bands. A theoretical model may provide insights on direct effect of process parameters on deformation of tape. The developed models in this work evaluate non-Newtonian squeeze flow of molten tape using power-law viscosity under three different no slip, perfect slip, and imperfect slip boundary conditions at interface during in-situ consolidation, aiming to predict the final width of tape with minimal computational costs. The results predicted by models are verified using finite element analysis with close agreement. Subsequently, no slip and perfect slip assumptions underestimated and overestimated the experimental measurements of consolidated widths, respectively. However, the squeeze model with imperfect slip condition may effectively capture the trends in the experimental data. This model includes the effect of intimate contact development on the friction parameter during squeezing, utilizing a new non-Newtonian trapezoidal asperity model.

A reduced-order hybrid model for photobioreactor performance and biomass prediction

This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.

New experimental measurements of the Collision Induced Absorptions of H2-H2 and H2-He in the 3600-5500 cm−1 spectral range from 120 to 500 K

New experimental measurements of the Collision Induced Absorptions of H2-H2 and H2-He in the 3600-5500 cm−1 spectral range from 120 to 500 K

A high-performance all-day vertical thermoelectric generator based on a double-sided reflective structure

Harvesting environmental energy sources is crucial for achieving renewable energy generation and net-zero emissions. However, ensuring uninterrupted electricity generation from environmental energy sources remains challenging. In this study, we present a simple, compact, and expandable all-day vertical passive thermoelectric generator (V-TEG) with a double-sided reflective structure that simultaneously harnesses solar and space cold energy. Outdoor experimental measurements, combined with finite element simulation analysis, revealed that the optimal angles of the solar reflector relative to the TEG range from 30° to 50°. Additionally, the TEG placed in the north–south orientation was found to enhance its daytime performance. The use of low-cost black paint combined with the solar reflector achieved equivalent spectral selectivity, effectively minimizing the infrared heat loss of the hot end of the TEG. During a 48-h outdoor test, the V-TEG achieved an average daytime power of 112.00 mW/m2 and a peak of 363.42 mW/m2. Comparative experiments demonstrated that the V-TEG outperformed horizontally placed TEG during the daytime. Furthermore, scaling the system by connecting three TEGs in series significantly increased the daily power output. This compact V- TEG system offers a promising solution for long-term power generation in low-power sensors and off-grid communities, supporting renewable energy and carbon neutrality goals.

Graph neural network-based multiscale thermal modeling for heterogeneous materials with complex structures

This study presents a novel GNN-based model for multiscale thermal characterization of heterogeneous materials with complex microstructures. It incorporates relationships between composition, structure, and thermal transport across multiple scales, leveraging the structure of GNNs. Results demonstrate enhanced performance of the GNN-based model compared with existing methods. Specifically, the GNN model improved thermal conductivity prediction with a mean absolute error of 0.18 W/mK (15 % improvement over the Bayesian neural network-based model) and provided a 30x speedup in computation time. The model successfully connected atomic-scale interactions to macroscale properties, achieving less than 5 % error in predicting effective thermal conductivity from one scale to another. Furthermore, it demonstrated the ability to capture nonlinear thermal transport phenomena with 92 % accuracy. The model also improved the prediction of the transient thermal responses by 20 %, accurately capturing the time-varying behaviour of materials. This work also proved the model’s ability to handle new material systems with transfer learning, achieving 88 % accuracy. Finally, Bayesian neural networks integrated into the model provided uncertainty quantification, with 95 % of experimental measurements falling within the predicted bounds. This work demonstrated the ability of the framework to handle complex microstructures, nonlinear phenomena, and transient responses while maintaining a computationally efficient model that can enable real-time applications in design and optimization needed in modern materials innovation. It represents a powerful tool for data-driven multiscale mechanics that could accelerate materials research and development for the aerospace, electronics cooling, and additive manufacturing industries.

Study the impact of spacer at thermal degradation process of MLI-based insulation in fire condition

To reduce CO2 emissions, energy carriers such as hydrogen are considered to be a solution. Consumption of hydrogen as a fuel meets several limitations such as its low volumetric energy density in gas phase. To tackle this problem, storage as well as transportation in liquified phase is recommended. To be able to handle this component in liquid phase, an efficient thermal insulation e.g., MLI insulation is required. Different studies have been addressed the vulnerability of such insulation against high thermal loads e.g., in an accident engaging fire. Some of research works have highlighted the importance of considering the MLI thermal degradation focusing on its reflective layer. However, limited number of studies addressed the thermal degradation of spacer material and its effect on the overall heat flux.In this study, through systematic experimental measurements, the effect of thermal loads on glass fleece, glass paper as well as polyester spacers are investigated. The results are reported in various temperature and heat flux profiles. Interpreting the temperature profiles revealed that, as the number of spacers in the medium increases, the peak temperature detectable by the temperature sensor on the measurement plate decreases. Each individual spacer contributes to mitigating the radiative energy received by the measurement plate. Stacks of 20–50 spacers (this is the number of layers in commercial MLI systems applied for liquid hydrogen applications) can potentially reduce the thermal radiation by 1–2 orders of magnitude.An empirical correlation to predict a heat flux attenuation factor is proposed, which is useful for further numerical and analytical studies in the temperature range from ambient to 300 °C.

Analysis and numerical simulation of different flowmeters based on an open channel in an irrigation area

ABSTRACT This study establishes an ultrasonic open channel flow measurement test and numerical simulation model and systematically compares the performance and reliability of the ultrasonic method and the traditional flowmeter in flow measurement in the irrigation area by observing and analysing the test data and combining it with the numerical simulation. In the indoor ultrasonic flowmeter flow measurement experiments in the trapezoidal nullah section, the flow rate measurement was carried out by using a high-precision variable slope flume and a variety of flow measurement tools. A numerical study was carried out using the VOF method to calculate the flow in the open channel flume under two-phase flow conditions. Combining experimental measurements and numerical simulations, this study aims to quantify the magnitude of flow measurement errors and water surface fluctuations in open channel flumes and to address the discrepancies between different flow measurement methods in open channels in irrigation areas.

A bulk-surface mechanobiochemical modelling approach for single cell migration in two-space dimensions

In this work, we present a mechanobiochemical model for two-dimensional cell migration which couples mechanical properties of the cell cytosol with biochemical processes taking place near or on the cell plasma membrane. The modelling approach is based on a recently developed mathematical formalism of evolving bulk-surface partial differential equations of reaction–diffusion type. We solve these equations using finite element methods within a moving-mesh framework derived from the weak formulation of the evolving bulk-surface PDEs. In the present work, the cell cytosol interior (bulk) dynamics are coupled to the cell membrane (surface) dynamics through non-homogeneous Dirichlet boundary conditions. The modelling approach exhibits both directed cell migration in response to chemical cues as well as spontaneous migration in the absence of such cues. As a by-product, the approach shows fundamental characteristics associated with single cell migration such as: (i) cytosolic and membrane polarisation, (ii) actin dependent protrusions, and (iii) continuous shape deformation of the cell during migration.Cell migration is an ubiquitous process in life that is mainly triggered by the dynamics of the actin cytoskeleton and therefore is driven by both mechanical and biochemical processes. It is a multistep process essential for mammalian organisms and is closely linked to a vast diversity of processes; from embryonic development to cancer invasion. Experimental, theoretical and computational studies have been key to elucidate the mechanisms underlying cell migration. On one hand, rapid advances in experimental techniques allow for detailed experimental measurements of cell migration pathways, while, on the other, computational approaches allow for the modelling, analysis and understanding of such observations. The bulk-surface mechanobiochemical modelling approach presented in this work, set premises to study single cell migration through complex non-isotropic environments in two- and three-space dimensions.

Diffusion and mobility measurements for propane gas with a nanodosimetric detector

For the operation of nanodosimetric detectors with propane gas, it is essential to know the gas properties at room temperature, such as drift velocity, ion mobility, longitudinal and transversal diffusion coefficients. For propane gas there is only limited experimental data available for the ion mobility and transverse diffusion coefficients. In this work, the drift velocities and ion mobilities for propane were obtained over the reduced electric field range E/N from 24 to 212 Td (1 Td = 10−17 Vcm2) based on arrival time measurements. The reduced ion mobility K0 for propane was determined to be (0.71±0.09) cm2/Vs. The longitudinal diffusion coefficients were determined from the widths of the experimental arrival time spectra and found to be NDL = (3.7 ± 0.5)⋅1019 1/ms in the low field limit below 30 Td.The results presented in this work extend the range of available experimental data for propane, as well as introduce first experimental measurements of the longitudinal diffusion coefficient of propane.

Synergistic Role of Dual-Metal Sites (Ag-Ni) in Hexagonal Porous g-C3N4 Nanostructures for Enhanced Photocatalytic CO2 Reduction

Harnessing solar energy to convert CO2 into hydrocarbon fuels presents a viable strategy for mitigating CO2 emissions. For effective photocatalytic CO2 reduction (PCR), it is crucial to optimize both photoinduced and chemical reactions synergistically. In this research, hexagonal porous g-C₃N₄ (CN) nanostructures with Ag-Ni dual metal site loadings were synthesized using a hydrothermal method followed by calcination, significantly enhancing PCR efficiency. The optimal results demonstrated significant production rates of 77.65 μmol g-1 for CO and 17.89 μmol g-1 for CH4, showcasing exceptional photocatalytic performance. This enhanced performance is attributed to several factors: the high porosity of the g-C₃N₄, the synergistic effects at the Ag-Ni dual metal sites, and the increased surface area. Detailed experimental measurements, coupled with comprehensive density functional theory (DFT) calculations, have elucidated the mechanisms underlying the significant improvements in the photocatalytic activity of the developed catalyst. This study not only demonstrates an effective approach for converting CO₂ into valuable hydrocarbon fuels but also significantly advances our understanding of complex photocatalytic systems, providing insights that could guide future developments in this field.

Understanding of additively manufactured material cyclic behavior at the grain scale by neutron diffraction and crystal plasticity modelling

Additive manufacturing (AM) offers distinct advantages in terms of complexity, reduced material waste, and shorter production times. However, the microstructures of AM alloys differ significantly from conventionally manufactured ones, and their impact on grain behavior remains uncertain. This study employs neutron diffraction, coupled with Crystal Plasticity Finite Element (CPFE) modeling, to investigate microstructural effects on cyclic behavior in 316L LPBF alloys. Neutron diffraction provides high-resolution strain and stress data at the polycrystalline level during loading, offering valuable insights into grain behavior. The CPFE model is initially validated at the grain scale using neutron diffraction, demonstrating its ability to predict local mechanical behavior in additively manufactured materials. This methodology holds promise for broader applications in various AM alloys, particularly when macroscopic identification of mechanical behavior is insufficient. Furthermore, the comparison between neutron diffraction and CPFE predictions allows to identify the influence of dendrites and dislocation substructures on the mechanical behavior at the grain scale. Notably, the micromechanical anisotropy resulting from dislocation organization along dendritic structures with specific orientations is highlighted. This study shows that despite the observed anisotropy, experimental measurement of intragranular strain distribution are correctly captured with a quantification of the deviation observed on some specific orientations due to the above cited microstructure sub-structures.

Experimental investigation and numerical simulation of swaging process of tubes for oil country tubular goods

AbstractPremium integrated seamless pipes made from high strength low alloy steels are used to produce oil country tubular goods. Operational requirements of these pipes in petroleum industry lead to an adequate tube end connection with better mechanical strength and sealing properties. Therefore a cold swaging process or calibration of pipe end is necessary for the mechanical threading process of the tube. Applications of typical calibration mandrels result in heavy wear due to higher swaging loads and the optimal necessary geometry of end tube could not be achieved. In this study, a new mandrel geometry with a finished surface was developed to modify the pipe end profile and reduce the wear tool. The investigated pipe made from P110- pipe steel was plastically preformed using a cold swaging process. Experimental measurements of the pipe end geometry after swaging process resulted an increase of the inner tube diameter of approximately 4.9% and an outer tube diameter of 3.7% respectively. Furthermore, a rather constant tube wall thickness was also observed. An Abaqus plug-in was developed to characterize the swaging process of pipe ends. The influence of friction between tool and pipe surfaces on the final geometry of pipe ends and residual stresses in pipe ends due to preforming were numerically predicted. The new designed swaging process resulted a better final pipe end geometry and low tool wear during the preforming of pipe end. The presented plug-in in this work could be used for tool optimization and characterization of swaging process of pipes in oil and gas industries.