Estimation Of Volume Fraction Research Articles

Inline estimation of volume fraction in Newtonian emulsions based on ultrasonic velocity profiling viscometry

Abstract We propose a method for estimating volume fraction of emulsions based on ultrasonic velocity profiling viscometry and validate its feasibility through a series of experiments. The experiments involve emulsions composed of silicone oil as continuous phase and tap water with volume fractions ranging from 10vol% to 30vol%. We introduce the viscosity model for this emulsion to establish the mathematical relationship between volume fraction and viscosity. Subsequently, we calibrate this relationship using ultrasonic spinning rheometry (USR). The identified equation is then applied in estimating the inline volume fraction using ultrasonic velocity profiling combined with pressure drop measurement. The key idea is that by measuring the viscosity in a pipeline the volume fraction is then indirectly estimated employing the identified equation. Through the collection and analysis of experimental data, we confirmed that the USR can capture the characteristics of the emulsion viscosity accurately. Moreover, we found that this method demonstrates applicability and accuracy in measuring volume fractions of emulsions. The results indicate that the mean relative error for this estimation is 24.06% at a volume fraction of 10vol%, and 4.78% at a volume fraction of 30vol%.

Light-weight neural network for intra-voxel structure analysis.

We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in diffusion-weighted MRI analysis for brain connectivity and development studies. The network architecture, called the Local Neighborhood Neural Network, is designed to use the spatial correlations of neighboring voxels for an enhanced inference while reducing parameter overhead. Our model exploits these relationships to improve the analysis of complex structures and noisy data environments. We adopt a self-supervised approach to address the lack of ground truth data, generating signals of voxel neighborhoods to integrate the training set. This eliminates the need for manual annotations and facilitates training under realistic conditions. Comparative analyses show that our method outperforms the constrained spherical deconvolution (CSD) method in quantitative and qualitative validations. Using phantom images that mimic in vivo data, our approach improves angular error, volume fraction estimation accuracy, and success rate. Furthermore, a qualitative comparison of the results in actual data shows a better spatial consistency of the proposed method in areas of real brain images. This approach demonstrates enhanced intra-voxel structure analysis capabilities and holds promise for broader application in various imaging scenarios.

Numerical analysis of plastic deformation mechanisms in polycrystalline copper under cyclic loading with different frequencies

The frequency of cyclic loading has a strong effect on the fatigue lifetime of materials. High loading frequencies allow reaching very high cycle fatigue regimes. The effects of cyclic loading with different frequencies are studied using the finite element method within the framework of crystal plasticity. The numerical study represents copper polycrystal that undergoes tension–compression loading with frequencies 80 Hz and 20 kHz respectively. The spatial distributions of stresses, strains, and plastic slip are used for estimation of volume fraction, morphology, and plasticity mechanism of persistent slip bands. The results show that at 80 Hz cyclic loading, plasticity in persistent slip band cells is dominated by the creation and annihilation of dislocations while at 20 kHz cyclic loading, plasticity is driven by the back-and-forth movement of dislocations inside the persistent slip band cells. The numerical study shows a correlation between amplitude and frequency of loading with spatial distribution and morphology of persistent slip bands that can be related to the fatigue lifetime.

The influence of cortical end plate upon ultrasound transit time spectroscopy estimated bone assessment

Quantitative ultrasound (QUS) is considered a reliable tool for routine assessment of bone. However, none of the QUS parameters has a direct measure of bone quantity or quality. The concept of ultrasound transit time spectroscopy (UTTS) has recently offered the possibility of effective estimates of bone volume fraction (BVF) as an indicator of bone density. This study aimed to investigate the influence of cortical end plate thickness around the cancellous bone on the measurement accuracy of UTTS solid volume fraction (UTTS-SVF). Two categories of cortical discs were designed and three-dimensional printed; category (I) is planer discs of constant different thickness and category (II) is variable-thickness step-wedge discs. In this experimental and simulated study, the discs were placed coaxially with the 3D-printed cylindrical iliac crest cancellous bone sample. A through-transmission 1 MHz ultrasound pulses were recorded, from which the deconvoluted TTS were derived. Then UTTS-SVF was determined and compared with the measured SVF via microcomputed tomography (μCT-SVF) showing a high agreement with an average of 94% ± 3.7% and 95% ± 4% for the planar cortical discs and 95.3% ± 5.5% and 96.5% ± 3.7% for variable-thickness step-wedge cortical discs for experiment and simulation respectively. The variations between the derived UTTS_SVF and the standard μCT-SVF are assumed to be due to the existence of phase interference within the cortical discs. Hence, the cortical end plate creates a negative influence on the derived UTTS-SVF.

MHD Casson ternary hybrid nanofluid flow through a vertical porous plate with thermal radiation: A finite difference approach

AbstractThis entire study aims to investigate the impacts of linear thermal radiation and MHD Casson ternary hybrid nanofluid flows over a vertical porous plate for comparison of nanofluid, hybrid, and tri‐hybrid nanofluids with Newtonian and non‐Newtonian fluids. We used a ternary hybrid nanofluid, Blood contains three types of oxides and metals: spherical ferric oxide (Fe3O4), platelet‐shaped zinc (Zn), and cylindrically‐shaped gold (Au) nanoparticles. The coupled nonlinear dual partial differential equations (PDEs) are turned into PDEs using nondimensional quantities. The Finite Difference Method (FDM) and the Perturbation Method are then used to solve the PDEs. The impacts of different parameters on temperature, velocity, Nusselt number, and Skin friction profiles have been discussed. The increase in viscosity occurs because an increase in Gr also causes an increase in the velocity field for nanofluid, hybrid, and tri‐hybrid nanofluids. A tri‐hybrid nanofluids performs better among the three, such as nanofluid, hybrids and tri‐hybrid nanofluids. As the volume fractions (Fe3O4) increase, the temperature increase for both Newtonian and non‐Newtonian fluids. The increase in temperature is due to the thermal conductivity of nanoparticles, which is enhanced by growth estimates of the nanoparticle volume fraction. The high temperature of the fluid is observed for large estimates of nanoparticle volume fraction. An increase gold (Au) also increases the temperature for shapes (cylinder, platelet, and spherical). A spherical shape performs better among the three, such as cylinder, platelet, and spherical. In this model, biomedical applications such as antiviral and therapeutic, treatment of the COVID‐19 virus, cancer treatment, and anticancer medication delivery systems.

Novel contouring method for optimizing MRI flow quantification in patients with aortic valve disease.

Optimizing MRI aortic flow quantification is crucial for accurate assessment of valvular disease severity. In this study, we sought to evaluate the accuracy of a novel method of contouring systolic aortic forward flow in comparison to standard contouring methods at various aortic levels. The study included a cohort of patients with native aortic valve (AoV) disease and a small control group referred to cardiac MRI over a 1-year period. Inclusion criteria included aortic flow quantification at aortic valve and one additional level, and no or trace mitral regurgitation (MR) documented both by the MRI AND an echocardiogram done within a year. In addition to flow quantification with standard contouring (SC), a novel Selective Systolic Contouring (SSC) method was performed at aortic valve level, contouring the area demarcated by the AoV leaflets in systole. The bias in each technique's estimate of aortic forward flow was calculated as the mean difference between aortic forward flow and left ventricular stroke volume (LV SV). 98 patients (mean age 56, 71% male) were included: 33 with tricuspid and 65 with congenitally abnormal (bicuspid or unicuspid) AoV. All methods tended to underestimate aortic forward flow, but the bias was smallest with the SSC method (p < 0.001). Therefore, SSC yielded the lowest estimates of mitral regurgitant volume (4.8 ml) and regurgitant fraction (3.9%) (p < 0.05). SSC at AoV level better approximates LV SV in our cohort, and may provide more accurate quantitative assessment of both aortic and mitral valve function.

Sticky Hard-Sphere Model for Characterizing Tumor Microstructure via Quantitative Ultrasound.

The use of the structure function (SF) to model interscatterer contribution to ultrasonic scattering is a major step to improve the capability and accuracy of quantitative ultrasound (QUS) and tissue characterization. However, existing QUS-based SF models rely on the hard-sphere (HS) model, which is limited in its applicability for complex scatterer distributions in real tissue. This article introduces the sticky HS (SHS) model for QUS and tissue characterization, which considers a very short-range attractive potential that accounts for the adhesive nature of biological cells and yields a new parameter called stickiness. Herein, the analytical SF expression is presented for monodisperse scatterer size and validated using simulations of scatterer distributions with varying degrees of grouping and volume fractions (0.16, 0.32, and 0.40) over the frequency range from 15 to 110 MHz. The SHS model is applied to three mammary tumor types with differing spatial distributions of tumor cells. The histology-derived SF is computed by considering the nuclei as the main sources of scattering. The results show that the SHS model provides more accurate scatterer radius and volume fraction estimates than the HS model when fit to histology-derived SF versus frequency curves. Furthermore, the new stickiness parameter provided by SHS is sensitive to the grouping structure in tumor cell distribution. This stickiness parameter, combined with the radius and volume fraction estimated from the SHS model, enables better differentiation between different tumor types than using the radius and volume fraction obtained from the HS model. This study demonstrates the potential of the SHS model to improve the QUS tissue characterization.

Lipid Storage, Lipolysis, and Lipotoxicity in Obesity.

The ratio of free fatty acid (FFA) turnover decreases significantly with the expansion of white adipose tissue. Adipose tissue and dietary saturated fatty acid levels significantly correlate with an increase in fat cell size and number. The G0/G1 switch gene 2 increases lipid content in adipocytes and promotes adipocyte hypertrophy through the restriction of triglyceride (triacylglycerol: TAG) turnover. Hypoxia in obese adipose tissue due to hypertrophic adipocytes results in excess deposition of extracellular matrix (ECM) components. Cluster of differentiation (CD) 44, as the main receptor of the extracellular matrix component regulates cell-cell and cell-matrix interactions including diet-induced insulin resistance. Excess TAGs, sterols, and sterol esters are surrounded by the phospholipid monolayer surface and form lipid droplets (LDs). Once LDs are formed, they grow up because of the excessive amount of intracellular FFA stored and reach a final size. The ratio of FFA turnover/lipolysis decreases significantly with increases in the degree of obesity. Dysfunctional adipose tissue is unable to expand further to store excess dietary lipids, increased fluxes of plasma FFAs lead to ectopic fatty acid deposition and lipotoxicity. Reduced neo-adipogenesis and dysfunctional lipid-overloaded adipocytes are hallmarks of hypertrophic obesity linked to insulin resistance. Obesity-associated adipocyte death exhibits feature of necrosis-like programmed cell death. Adipocyte death is a prerequisite for the transition from hypertrophic to hyperplastic obesity. Increased adipocyte number in obesity has life-long effects on white adipose tissue mass. The positive correlation between the adipose tissue volume and magnetic resonance imaging proton density fat fraction estimation is used for characterization of the obesity phenotype, as well as the risk stratification and selection of appropriate treatment strategies. In obese patients with type 2 diabetes, visceral adipocytes exposed to chronic/intermittent hyperglycemia develop a new microRNAs' (miRNAs') expression pattern. Visceral preadipocytes memorize the effect of hyperglycemia via changes in miRNAs' expression profile and contribute to the progression of diabetic phenotype. Nonsteroidal anti-inflammatory drugs, metformin, and statins can be beneficial in treating the local or systemic consequences of white adipose tissue inflammation. Rapamycin inhibits leptin-induced LD formation. Collectively, in this chapter, the concept of adipose tissue remodeling in response to adipocyte death or adipogenesis, and the complexity of LD interactions with the other cellular organelles are reviewed. Furthermore, clinical perspective of fat cell turnover in obesity is also debated.

Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy.

Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction ( ), which in turn is key for noninvasive estimation of the axonal volume fraction ( ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation of . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics of . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmental and can substantially enhance the comparability between EM- and DWI-based metrics of . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-based . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.

Dynamic Forced Performance of an O-Rings Sealed Squeeze Film Damper Lubricated With a Low Supply Pressure and a Simple Method to Quantify Air Ingestion

Abstract Contemporary squeeze film dampers (SFDs) in air-breathing engines are short in length to limit weight and part count and lubricated with a low feed pressure to reduce oil storage and pumping power. In SFDs, O-rings (ORs) restrict side leakage and increase the viscous damping while a adding a modest centering stiffness. Continuing a long-term project characterizing SFDs for aircraft engines and extending the original work (San Andrés and Rodríguez, 2021, “On the Experimental Dynamic Force Performance of a Squeeze Film Damper Supplied Through a Check Valve and Sealed With O-Rings,” ASME J. Eng. Gas Turb. Power, 143(11), p. 111011) the paper details measurements of the forced performance of an OR sealed damper (OR-SFD) with diameter D = 127 mm, land length L = 0.2 D, and radial clearance c = 0.0022 D. Lubricant ISO VG 2 supplied at 0.69 bar(g) fills an upstream oil plenum and flows into the middle of the land through a single orifice configured with a check valve. Measurements of applied single-frequency dynamic loads, along with the ensuing damper displacements and accelerations serve to identify the parameters of the test structure, ORs, and SFD. The tests encompass centered whirl motions with amplitude r = 0.05–0.45c, and a range of whirl frequencies, ω = 10–130 Hz. Note the squeeze velocity vs = rω reaches 102 mm/s. The ORs force coefficients are nearly invariant with frequency but do depend on the orbit amplitude. The ORs' stiffness (KOR) decreases by 75% as the motion amplitude increases, r → 0.45c, likely due to the large elastic deformations and slow recovery of the ORs material. For small amplitude motions (r/c = 0.05 and 0.10), the ORs damping coefficient (COR) is ∼10% of the overall coefficient for the lubricated system (CL), while for r/c &amp;gt; 0.25, COR ∼ 0.03CL. For small amplitudes of whirl (r ≤ 0.25c), the SFD experimental viscous damping (CSFD) and added mass (MSFD) coefficients, identified over a shorter frequency range (vs&amp;lt;30 mm/s) equal theoretical magnitudes for a fully sealed damper. As the orbit size grows to r = 0.45c MSFD drops nearly by 75% and CSFD decreases by ∼40% due to the onset of both lubricant cavitation and air ingestion occurring for squeeze velocities vs ≥ 24.5 mm/s, as also seen in the recorded dynamic pressures and video recordings of a bubbly mixture leaving the damper. A comprehensive flow model predicts CSFD and MSFD about 8% and 12% larger than the experimentally identified coefficients. A novel approach enables the estimation of the gas volume fraction (GVF) generated in the damper which rapidly increases as vs grows. The simple procedure draws into a deflated balloon the material contents in the squeeze film, weighs the sample, and identifies its volume to produce an estimate of the GVF. The procedure to quantify the GFV will assist in the validation of predictive tools.

Shear influence on colloidal cluster growth: a SANS and USANS study.

This study examines the time evolution of silica/water clusters where the formation of a gel network from unitary silica particles is interrupted by a simple Couette shear field. The aim is to enable the general understanding of this simple system by examining the microscopic basis for the changes in viscosity by providing structural inputs from small-angle scattering for a simple theoretical model. The experimental system is an 8.3 nm particle silica solution (Ludox) where the gelation has been initiated by lowering the pH in a Couette cell providing a constant shear rate of 250 s-1. A unified small-angle neutron scattering (SANS) and ultra-small-angle neutron scattering (USANS) procedure is described to measure the scattered intensity in a wavevector range of 3 × 10-4 ≤ q (nm-1) ≤ 3.1 × 10-1, probing structural changes over a broad range of length scales from the nanometre to the micrometre. Scattering data provide a new means of better understanding the behaviour of colloidal clusters when subjected to an external applied shear over a continuous time sequence after gel initiation; a fit of the time-dependent scattered intensity leads to an estimation of the cluster's effective volume fraction and size as a function of time. A reductionist theoretical basis is described to predict the time-dependent viscosity behaviour of the sheared colloidal suspension gel-initiated cluster growth from the volume fraction of the clusters.

On heat, temperature, and cavity oil volume fraction of an under-race lubricated angular contact ball bearing

Proper thermal management of bearing is extremely important, because high temperature beyond its operational limit can degrade material and lubricant properties, causing an unexpected failure. This research aims to investigate the thermal characteristics of an angular contact ball bearing. First, we have built an in-house program to predict the bearing’s temperature and heat generation. Accurate estimation of oil volume fraction inside the bearing cavity is necessary for accurate prediction of heat generation, thus we improved Parker’s formula to match it with fluid dynamics simulation. The in-house program was then verified with respect to our high-speed, high-load, high-temperature experiment. A series of parametric studies was also conducted to identify the effects of each operation parameters to the bearing’s temperature and heat.

High-resolution reconstruction algorithm for the three-dimensional velocity field produced by atomization of two impinging jets based on deep learning

The velocity fields measured by experiments or determined through simulations are essential in advancing our understanding of the complex atomization process of impinging jets. However, existing methods are expensive and time-consuming. In this study, we apply deep learning to the estimation of the three-dimensional velocity fields produced by the atomization of two impinging jets. Two deep learning models are developed, namely, a liquid volume fraction (LVF) estimation model based on the Swin Transformer architecture and a three-dimensional velocity field estimation model based on four-dimensional convolution (4D-Conv). The dataset for training the models is generated by direct numerical simulations (DNS). To train the LVF model, we utilize two gray images generated by a pinhole camera model, mimicking the acquisition of experimental images. We then introduce a mask generated by binocular vision techniques into the LVF model. The LVF fields estimated with the mask are in better agreement with the reference DNS data. We further utilize the estimated LVF fields to train the 4D-Conv-based model. The mean absolute percentage error compared with the results of a full-flow test is found to be less than 5%. The results indicate that the proposed approach has the potential to accurately reconstruct volume velocity data from two-dimensional images.

The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density

Objective. To document the bias of the simplified free water model of diffusion MRI (dMRI) signal vis-à-vis a specific model which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE). Approach. Both models assume that volume fraction f of the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). The specific and simplified models are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to an in vivo healthy brain dMRI dataset. For both synthetic and in vivo data we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles. Main results. From the simulations we see that the difference between the estimated simplified f and specific f is largest for mid-range ground-truth f, and it increases as SNR increases. The estimation of volume fraction f is sensitive to the choice of model, simplified or specific, but the estimated diffusion parameters are robust to small perturbations in the simulation. Specific f is more accurate and precise than simplified f. In the white matter (WM) regions of the in vivo images, specific f is lower than simplified f. Significance. In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.

Mid-IR Observations of IRAS, AKARI, WISE/NEOWISE, and Subaru for Large Icy Asteroid (704) Interamnia: A New Perspective of Regolith Properties and Water Ice Fraction

(704) Interamnia is one of the largest asteroids located in the outer main-belt region, which may contain a large amount of water ice underneath its surface. We observe this asteroid using 8.2 m Subaru telescope at mid-infrared wave bands and utilize a thermophysical model for realistic surface layers to analyze mid-infrared data from Subaru along with those of IRAS, AKARI, and Wide-field Infrared Survey Explorer (WISE)/NEOWISE. We optimize the method to convert the WISE magnitude to thermal infrared flux with temperature-dependent color corrections, which can provide significant references for main-belt asteroids at a large heliocentric distance with low surface temperature. We derive best-fitting thermal parameters of Interamnia—a mean regolith grain size of μm, with a roughness of and rms slope of deg, thereby producing thermal inertia ranging from 9 to 92 Jm−2 s−1/2 K−1 due to seasonal temperature variation. The geometric albedo and effective diameter are evaluated to be and , respectively, being indicative of a bulk density of 1.86 ± 0.63 g cm−3. The low thermal inertia is consistent with typical B/C-type asteroids with D ≥ 100 km. The tiny regolith grain size suggests the presence of a fine regolith on the surface of Interamnia. Moreover, the seasonal and diurnal temperature distribution indicates that thermal features between the southern and northern hemispheres appear to be very different. Finally, we present an estimation of volume fraction of water ice of 9%–66% from the published grain density and porosity of carbonaceous chondrites.

Quantitative estimation of bubble volume fraction of submarine seep plumes by modeling seismic oceanography data

Quantitative estimation of bubble volume fraction of submarine seep plumes by modeling seismic oceanography data

Reference Values of Myocardial T1 Relaxation Times in Healthy Latin American Patients

Theoretical framework. Cardiac magnetic resonance imaging (CMR) is an invaluable tool for diagnosis and risk stratification in a broad spectrum of cardiac diseases. Magnetic resonance imaging techniques are increasingly complemented by mapping sequences that allow quantitative assessment of myocardial tissue with measurement of absolute relaxation times T1, T2, and T2*. The clinical utilities are diverse, among which is the quantification of edema and/or fibrosis through the estimation of the extracellular volume fraction calculated from pre-contrast, post-contrast and hematocrit T1 relaxation times. There are multiple ways to obtain T1 mapping images, among which the modified look-locker inversion recovery technique (MOLLI) stands out. However, different manufacturers have designed various methods of obtaining them with slight variability between the equipment. Aim To determine the reference values of myocardial T1 relaxation times in our population to justify their clinical interpretation in the tissue evaluation of different cardiovascular diseases. Material and method. Type of study: prospective observational and single center. Field intensity/sequence A short CMR protocol was performed in a 1.5 T resonator, using the MOLLI technique, in T1 mapping. Evaluation Global and segmental T1 values were quantified, using the American Heart Association (AHA) model, for the entire left ventricle, in three short-axis slices (basal, mid, and apical). The post processing and analysis of the data with the commercialized software, Cvi42. Results The mean global myocardial T1 value was 1069.41 ms with a SD of 38.73 msec. Among the age groups, the &lt;34-year-old group had the lowest global T1 values (1057 SD 33 msec), and the 35-year-old group -44 years obtained the highest values (1100 SD 50 msec), so no relationship was found between the linear increase in age and the variability of relaxation times. Between both sexes, men had lower global T1 values than women (1049 vs 1086). conclusions Native T1 ranges can serve as a basis for the quantitative characterization of the myocardium in the context of focal or diffuse diseases, in patients with infarcts, storage diseases, and inflammatory diseases. We are getting closer to standardizing the clinical use of these techniques by CMR. In conclusion, this analysis allows us to take another step to establish their use in the Latin American population.

Glassy and compressed nanoemulsions stabilized with sodium dodecyl sulfate in the presence of poly(ethylene glycol)-diacrylate.

The rheology of concentrated nanoemulsions is critical for their formulation in various applications, such as pharmaceuticals, foods, cosmetics, and templating advanced materials. The rheological properties of nanoemulsions depend on interdroplet interactions, Laplace pressure, dispersed phase volume fraction, and continuous phase properties. The interdroplet forces can be tuned by background electrolytes (i.e., charge screening), surfactant type, the excess surfactant micelle concentration, and depletant molecules such as polymer chains. In the current research, we study the effect of varying the content of poly(ethylene glycol)-diacrylate (PEGDA) on the interfacial tension of the water-oil phase and rheological properties of concentrated nanoemulsions with 50% and 60% volume fractions. Sodium dodecyl sulfate (SDS) is used as the ionic surfactant. The final concentrated nanoemulsions are repulsive according to overall interaction potentials and are in the glass and compressed states based on the effective volume fraction estimation. They contain nearly same SDS concentration on the droplet surface and also in the bulk, but a different amount of PEGDA. The scaled rheological properties of the glassy nanoemulsions show a higher dependency on the PEGDA content and the possible effect of polymer-surfactant complexations compared to those of the compressed ones. This dependency is more pronounced in small strain amplitudes but not in large strains in the non-linear regime. These results provide insights into formulating concentrated nanoemulsions with controlled rheology for expanded application areas.

Application of Wavelet Characteristics and GMDH Neural Networks for Precise Estimation of Oil Product Types and Volume Fractions

Given that one of the most critical operations in the oil and gas industry is to instantly determine the volume and type of product passing through the pipelines, in this research, a detection system for monitoring oil pipelines is proposed. The proposed system works in such a way that the radiation from the dual-energy source which symmetrically emits radiation, was received by the NaI detector after passing through the shield window and test pipeline. In the test pipe, four petroleum products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated in pairs in different volume fractions. A total of 118 simulations were performed, and their signals were categorized. Then, feature extraction operations were started to reduce the volume of data, increase accuracy, increase the learning speed of the neural network, and better interpret the data. Wavelet features were extracted from the recorded signal and used as GMDH neural network input. The signals of each test were divided into details and approximation sections and characteristics with the names STD of A3, D3, D2 and were extracted. This described structure is modelled in the Monte Carlo N Particle code (MCNP). In fact, precise estimation of oil product types and volume fractions were done using a combination of symmetrical source and asymmetrical neural network. Four GMDH neural networks were trained to estimate the volumetric ratio of each product, and the maximum RMSE was 0.63. In addition to this high accuracy, the low implementation and computational cost compared to previous detection methods are among the advantages of present investigation, which increases its application in the oil industry.

Model-based three-material decomposition in dual-energy CT using the volume conservation constraint

Objective. We develop a model-based optimization algorithm for ‘one-step’ dual-energy (DE) CT decomposition of three materials directly from projection measurements. Approach. Since the three-material problem is inherently undetermined, we incorporate the volume conservation principle (VCP) as a pair of equality and nonnegativity constraints into the objective function of the recently reported model-based material decomposition (MBMD). An optimization algorithm (constrained MBMD, CMBMD) is derived that utilizes voxel-wise separability to partition the volume into a VCP-constrained region solved using interior-point iterations, and an unconstrained region (air surrounding the object, where VCP is violated) solved with conventional two-material MBMD. Constrained MBMD (CMBMD) is validated in simulations and experiments in application to bone composition measurements in the presence of metal hardware using DE cone-beam CT (CBCT). A kV-switching protocol with non-coinciding low- and high-energy (LE and HE) projections was assumed. CMBMD with decomposed base materials of cortical bone, fat, and metal (titanium, Ti) is compared to MBMD with (i) fat-bone and (ii) fat-Ti bases. Main results. Three-material CMBMD exhibits a substantial reduction in metal artifacts relative to the two-material MBMD implementations. The accuracies of cortical bone volume fraction estimates are markedly improved using CMBMD, with ∼5–10× lower normalized root mean squared error in simulations with anthropomorphic knee phantoms (depending on the complexity of the metal component) and ∼2–2.5× lower in an experimental test-bench study. Significance. In conclusion, we demonstrated one-step three-material decomposition of DE CT using volume conservation as an optimization constraint. The proposed method might be applicable to DE applications such as bone marrow edema imaging (fat-bone-water decomposition) or multi-contrast imaging, especially on CT/CBCT systems that do not provide coinciding LE and HE ray paths required for conventional projection-domain DE decomposition.