Experiment Modeling Research Articles

Modeling of High-Pressure and High-Temperature Microemulsion Experiments using HLD-NAC-Based Equation of State

Summary Surfactant flooding is a promising technique that can reduce interfacial tension (IFT) between oil and water to ultralow values, mobilizing previously trapped oil. For reservoirs at moderate to high pressures, understanding and modeling how pressure affects the phase behavior of a surfactant-brine-oil system is important to the design and implementation of an efficient/cost-effective surfactant flooding project. Typically, however, phase behavior experiments and models of that phase behavior are made only at low pressures. The main objective of this paper is to show how to model experimental data in a unified way for a large range of pressure, temperature, and other parameters, using hydrophilic-lipophilic deviation (HLD) and net-average curvature (NAC)-based equation-of-state (EOS). Pressure and temperature scans show that pressure has a significant effect on the surfactant microemulsion phase behavior, shifting it from an optimal three-phase system at low pressure to a nonoptimal two-phase system at high pressure. Further, multiple scans at different water/oil ratios (WORs) show a shift in the optimum indicating that phase behavior partitioning of the various components is changing with oil saturation. We obtained good fits of all experimental data including all two- and three-phase regions using a single tuned HLD-NAC EOS for a wide range of simultaneous variations in pressure, temperature, salinity, and overall composition. Such a simultaneous match and prediction by a single set of model parameters has never been done before. We also demonstrate the type of data needed for an accurate EOS. When input into a numerical simulator, the tuned EOS improves the predictions of the resulting phase behavior (size and shape of the two-phase lobes and three-phase regions) and IFTs with changing pressure, temperature, salinity, WORs, and surfactant/alcohol concentrations.

Drying Process of Cement Mortar Composites Reinforced with Cellulosic Fibres: Experiment and Mathematical Modelling

Drying Process of Cement Mortar Composites Reinforced with Cellulosic Fibres: Experiment and Mathematical Modelling

Algal biomass-derived nano-activated carbon for the rapid removal of tetracycline by adsorption: Experimentation and adaptive neuro-fuzzy inference system modeling

Tetracycline (TC) is one of the antibiotics, which is detected at high titre in aquatic systems, inducing microbial resistance. This study highlights the adsorptive removal of TC by activated carbon (AC) using low temperature carbonization obtained from Ulva prolifera macroalgal biomass – an abundantly available algae in southern Indian beaches. The AC had rough, irregular, and porous structure with large specific surface area of 197.53 m2/g. The adsorption data was modelled using adaptive neuro-fuzzy inference system that fitted the data satisfactorily. Pseudo-second-order kinetics well-suited TC adsorption confirming to chemisorption. Both Langmuir and Freundlich isotherms suited-well to the data with monolayer adsorption capacity of 54.04 mg/g, which emphasized the suitability of using the algal biomass-derived AC for TC removal over other adsorbents. Therefore, the present investigation highlights the use of a new low-cost nanoadsorbent developed from algal biomass along with the tremendous potential of ANFIS in predicting the adsorption process.

Gumbel mixture modelling for multiple failure data

The Gumbel mixture model is a popular tool for modelling extreme events with mixed formation mechanisms. The lack of a shape parameter in the Gumbel distribution makes it somewhat less practical for mixture modelling compared to the Weibull distribution, which is widely used in structural reliability and safety applications. Since the Gumbel distribution has two forms (i.e., left- and right-skewed), we describe it as a three-parameter distribution. The additional parameter ξ∈{−1,1} is used to control the skewness of the Gumbel distribution. The maximum likelihood parameter estimation procedure for the proposed Gumbel mixture model is derived. Based on the simulation study, we confirmed that 1) the estimation procedure can successfully estimate the parameters of the mixture model, and 2) the proposed three-parameter Gumbel distribution has advantages over the state of the art in mixture modelling using different parametric families. An illustrative real-world example is also considered. Climbing rope failure was observed as a function of two random variables, and two competing failure modes were found. The experiment and subsequent mixture modelling provided further insight into the degradation of climbing ropes. Finally, the proposal is implemented in the freely available R package rebmix.

Stochastic Merging of Soil Hydraulic Properties for Vadose Zone Hydrological Modeling

Soil hydraulic properties (SHPs) are commonly determined in soil samples with replicas. Whether these replicas are taken at a same location to represent a specific point or at several locations to represent a larger area, the results should be merged into a final data set to be used in modeling. For this data set to be representative, standard errors and a correlation matrix must be considered in the merging process. We present a method to perform this merging and give an example using stochastic realizations of van Genuchten-Mualem (VGM) parameters generated by Cholesky decomposition to merge the SHP and associated statistics into a merged parameter set. To do so, we used VGM parameters obtained at sample scale in three replicas from a Brazilian savanna soil through inverse modeling of laboratory evaporation experiments. The effectiveness and representativeness of the proposed methodology were evaluated by observing the frequency distribution of different levels of output, comparing individual and merged sample properties. The outputs include VGM parameters, retention and conductivity characteristics, and water balance components stochastically predicted by a hydrological model. The performed stochastic merging correctly represented the variability of the combined replicas, especially with respect to hydrological model outputs of soil water balance components. Using the mean hydraulic property parameter values to deterministically predict water balance components may yield values that are substantially different from the mean values of stochastic realizations. This suggests that the deterministic prediction using mean parameter values in vadose zone hydrological modeling may result in unrepresentative outputs.

Field trials of nitrogen injection enhanced gas drainage in hard-to-drain coal seam by using underground in-seam (UIS) boreholes

Three stages of nitrogen (N2) injection enhanced gas drainage through underground in-seam boreholes were carried out in a coal mine located in the southern Sydney basin. Due to the high CO2 content and low permeability, hard-to-drain coals were encountered during the excavation process in this coal mine. Two parallel in-seam boreholes (the length of 36 m, the diameter of 96 mm and the borehole space of 5 m) were drilled on the rib of the gateroad. A total of ten-cylinder packs of nitrogen (approximately 1500 m3) were injected into the coal seam. Different nitrogen injection pressures (150 KPa, 250 KPa, 350 KPa and 450 KPa) and injection methods (continuous injection and cyclic injection) were employed. Gas flow rates and gas compositions from the production borehole were recorded during and after the nitrogen injection process. It was observed that gas breakthrough was tightly related to the nitrogen injection pressure. The higher injection pressure was, the shorter breakthrough time was. Specifically, no gas breakthrough was obtained when the injection pressure was 150 KPa. The minimum of breakthrough time was 35 min when 450 KPa’s injection pressure was employed. Strong post-injection effect was monitored after each stage of injection and it was affected by the volume of the injected nitrogen. Under the current injection conditions, the performances of different injection methods were like each other, which was out of our expectation. After the injections, the results of cores showed that less than 5 % of the total injected nitrogen was remained in the coal seam. The coal seam gas content dropped 0.99–1.65 m3/t after these three stages of injection. All these findings provide implications and guidance not only for laboratory experiment and numerical modelling, but also for the field application of this new technology.

Modeling and Active Learning for Experiments with Quantitative-Sequence Factors

A new type of experiment that aims to determine the optimal quantities of a sequence of factors is eliciting considerable attention in medical science, bioengineering, and many other disciplines. Such studies require the simultaneous optimization of both quantities and sequence orders of several components which are called quantitative-sequence (QS) factors. Given the large and semi-discrete solution spaces in such experiments, efficiently identifying optimal or near-optimal solutions by using a small number of experimental trials is a nontrivial task. To address this challenge, we propose a novel active learning approach, called QS-learning, to enable effective modeling and efficient optimization for experiments with QS factors. QS-learning consists of three parts: a novel mapping-based additive Gaussian process (MaGP) model, an efficient global optimization scheme (QS-EGO), and a new class of optimal designs (QS-design). The theoretical properties of the proposed method are investigated, and optimization techniques using analytical gradients are developed. The performance of the proposed method is demonstrated via a real drug experiment on lymphoma treatment and several simulation studies. Supplementary materials for this article are available online.

Wind Resource Assessment in Building Environment: Benchmarking of Numerical Approaches and Validation with Wind Tunnel Data

In the framework of the wind energy network for distributed generation in urban environments for most South American countries, wind resource assessment studies have been carried out in activities to establish a suitable methodology to assess the wind potential in urban environments. Some methodologies are already published in research studies, and the wind tunnel is the most accurate solution to obtain insight into the wind resource when measurements are unavailable, which is the most frequent case. Nevertheless, besides its validity, one cannot disregard the high cost of producing a scaled urban model and access to a wind tunnel. In this sense, this paper compares results from a wind tunnel experiment and different numerical modeling approaches. Two commercial models (WindSim and Wasp Engineering) and one open-source CFD code (OpenFOAM) are used. The results from the modeling simulation concluded that CFD models could achieve lower deviations for the mean wind speed and turbulence intensity when compared with non-CFD models. With such results, CFD modeling is a promising tool for reliably evaluating wind potential in urban environments.

Optical response of media and structures exhibiting spatial dispersion.

We present a general method for redefining the permittivity and permeability tensors of a medium or structure exhibiting spatial dispersion (SD). The method effectively separates the electric and magnetic contributions that are intertwined in the traditional description of the SD-dependent permittivity tensor. The redefined material tensors are the ones to be used in the common methods for calculating the optical response of layered structures, thus enabling modeling of experiments in the presence of SD.

Investigation on flow resistance reduction and EOR mechanisms by activated silica nanofluids: Merging microfluidic experimental and CFD modeling approaches

Activated silica nanofluids (ASN) flooding has been proven to be an effective method to enhance oil recovery. However, due to the variety of nanoparticles and surfactants used for ASN synthesis, the main mechanisms of flow resistance reduction and oil recovery enhancement by ASN are still unclear, and most studies are based on physical experiments, which are too cumbersome and inefficient. In this study, the ASN flooding experiment and CFD modeling are combined based on microscopic visualization experiments. Firstly, three kinds of ASN with different hydrodynamic diameters were synthesized by BS-12 and nano-silica sol, and the basic properties were tested to obtain the modeling parameters. Surface flow experiments were also carried out. Then, a microscopic model based on the real pore-throat size was developed, and combined with image processing technology, the quantitative study and EOR mechanism analysis of the ASN flooding process was carried out. Finally, CFD modeling was carried out based on microscopic visualization experiments to predict the recovery improvement after improving ASN performance. The results show that at the optimal concentration of 1%, the ASN with a smaller hydrodynamic diameter performs better in wettability alteration and reducing interfacial tension and viscosity ratio. ASN can significantly reduce the flow resistance coefficient by 30.36% to 95.43%. The reduction of micro-resistances is the important EOR mechanism of ASN. The results of the microscopic visualization experiment and CFD simulation are compared, and errors ranged from 3.9% to 5.22% for recovery factors in various injection scenarios. The prediction results by CFD simulation show that the viscosity reduction ability has the most significant effect on the recovery factor. The best performance parameters of ASN under the highest recovery factor are also predicted. Simulation results guide the selection of surfactants and nanoparticles in the subsequent ASN synthesis process, which has the advantages of high efficiency and low cost.

Mixing Enthalpies of Liquid Ag-Mg-Pb Alloys: Experiment vs. Thermodynamic Modeling.

A drop calorimetric method was used to measure liquid Ag–Mg–Pb alloys. The partial and integral mixing enthalpies of the investigated alloys were determined at a temperature of 1116 K. The experiments were performed for four separate series starting from binary alloys with a constant xMg/xPb ratio of 1/3, 1, 3 ((Mg0.25Pb0.75)1−xAgx, (Mg0.50Pb0.50)1−xAgx, (Mg0.75Pb0.25)1−xAgx) and xAg/xMg ratio of 1/3 (Ag0.25Mg0.75)1−xPbx. Next, the ternary interaction parameters were determined using the Muggianu model, the thermodynamic properties of binary systems in the form of the Redlich-Kister equations and the values of the mixing enthalpy changes, which were determined in this study. The partial mixing enthalpies of Ag, Mg, and Pb were calculated based on the binary and elaborated ternary interaction parameters for the same intersections in which the measurements were conducted. It was found that the ternary Ag-Mg-Pb liquid solutions are characterized by negative deviations from the ideal solutions, with a maximal value slightly lower than –13 kJ/mol for alloys with the ratio (Mg0.75Pb0.25) and xAg = 0.4166.

Size effects on process performance and product quality in progressive microforming of shafted gears revealed by experiment and numerical modeling

As one of the indispensable actuating components in micro-systems, the shafted microgear is in great production demand. Microforming is a manufacturing process to produce microgears to meet the needs. Due to the small geometrical size, there are uncertain process performance and product quality issues in this production process. In this study, the shafted microgears were fabricated in two different scaling factors with four grain sizes using a progressively extrusion-blanking method. To explore the unknown of the process, grain-based modeling was proposed and employed to simulate the entire forming process. The results show that when the grains are large, the anisotropy of single grains has an obvious size effect on the forming behavior and process performance; and the produced geometries and surface quality are worsened; and the deformation load is decreased. Five deformation zones were identified in the microstructures with different hardness and distributions of stress and strain. The simulation by using the proposed model successfully predicted the formation of zones and revealed the inhomogeneous deformation in the forming process. The undesirable geometries of microgears including material unfilling, burr and inclination were observed on the shaft and teeth of gear, and the inclination size is increased obviously with grain size. To avoid the formation of inclination and material unfilling, the punch was redesigned, and a die insert was added to constraint the bottom surface of the gear teeth. The new products had then the better forming quality.

3D bioprinted microparticles: Optimizing loading efficiency using advanced DoE technique and machine learning modeling

Current microparticle (MP) development still strongly relies on the laborious trial-and-error approach. Herein, we developed a systemic method to evaluate the significance of MP formulation factors and predict drug loading efficiency (DLE) using design of experiment (DoE) and machine learning modeling. A first-in-class 3D printing concept was initially employed to fabricate polymeric MPs by a 3D printer. Sprayed Multi Adsorbed-droplet Reposing Technology (SMART) was developed to combine extrusion-based printing with emulsion evaporation technique to fabricate a small molecule drug i.e., 6-thioguanine (6-TG) loaded poly (lactide-co-glycolide) (PLGA) MPs. Compared to conventional emulsion evaporation method, SMART employs the shear force exerted by the printing nozzle rather than the sonication energy to generate smaller emulsion droplets in a single step. Furthermore, the applied shear force in the 3D printing process reported herein is controllable since the emulsion is extruded through the nozzle under preset printing conditions. The formulated MPs exhibited spherical structure with size distribution ∼ 1-3μ m in diameter and reached ∼ 100 % drug release at 10 h. Also, the papain-like protease (PLpro) inhibition efficacy of 6-TG in formulated MPs was maintained even after the printing process under different printing conditions. Furthermore, the formulation factor importance was assessed by DoE statistical analysis and further validated by machine learning modeling. Among the four process parameters (drug amount, printing speed, printing pressure, and nozzle size), drug amount was the most influential formulation factor. Moreover, it is interesting that nearly all the machine learning models, especially decision tree (DT), demonstrated superior performance in predicting DLE compared to DoE regression models. Overall, incorporating DoE and machine learning modeling shows great promises in the prediction and optimization of MP formulations factors by means of a novel SMART technology. Moreover, this systemic approach helps streamline the development of MP with programmable pharmaceutical attributes, representing a new paradigm for digital pharmaceutical science.

Bioprocess optimization for glycopeptide biosurfactant production by means of Lactobacillus delbrueckii : Design expert laden approach

A bacterial strain, Lactobacillus delbrueckii, was isolated and screened for high potential of biosurfactant (BS) production yield. The current research emphasizes the optimization of process parameters through OFAT (one factor at a time) and RSM (response surface methodology) to achieve optimal BS production. Therefore, xylose and peptone were screened as the best carbon and nitrogen sources respectively by means of OFAT, along with temperature (37°C), inoculum size (5%), and fermentation time (39 h). Furthermore, optimization of xylose (1% w/v), peptone (0.5%w/v), and pH (6.5) through Box–Behnken statistical design was validated by ANOVA (analysis of variance) and found significant (p < .05) with optimal BS yield (7.31 g/L). The solvent-extracted glycopeptide BS displayed cationic nature, with thermal (0–120°C), pH (2–12), and NaCl (2%–12% w/v) stability. The functional attributes of BS like antiadhesive and antimicrobial activity toward Gram-positive and Gram-negative bacteria and emulsifying potency against various vegetable oils make it worthwhile and applicable. Practical applications The recent applications of BS and hazards of pathogenic biofilm make it challenging to produce enhanced yield of BS. In light of this, present work is focused to optimize yield from Lactobacillus delbrueckii for its applications as antibiofilm and bioemulsifier agent in various industries. Furthermore, the glycoproteinaceous BS from microbe in question is itself unique and novel. Additionally, the statistical design of optimization provides experiment modeling, production rate, and significance of opted design which makes the whole process feasible to explore new possibilities for BS production.

Development of a manufacturability predictor for periodic cellular structures in a selective laser melting process via experiment and ANN modelling

ABSTRACT Manufacturability analysis is a critical step before manufacturing to reduce costs and risks. It is used widely in conventional manufacturing (CM) processes. However, to the best of our knowledge, there is no natural method to evaluate the manufacturability of additive manufacturing (AM) processes that have more uncertainty-derived risks and costs than CM processes. A clear definition of the manufacturability of AM processes has not been established, and there is no standard to check whether a component is manufactured successfully by an AM process, particularly for porous complex components. This study introduces the development of a new machine learning-based method to solve the problem mentioned above. It is based on the statistical measurement of experimental samples. The proposed method can be used to perform the manufacturability analysis for periodic cellular structures printed by a selective laser melting (SLM) process. A novel definition of the manufacturability of the SLM-ed periodic cellular structure was proposed. Experimental results indicate that the developed learning model (ANN model) can achieve up to 94% classification accuracy and 96% prediction accuracy, which satisfies the application requirements of the AM industry. Moreover, the developed model can be adapted for the manufacturability analysis of different AM processes.

Flows of ionizing gas in the plasma accelerator channel with longitudinal magnetic field

Model and results of numerical experiments are presented for axisymmetric flows of ionizing gas in the presence of an additional longitudinal magnetic field in the channel of quasi-stationary plasma accelerator considered as perspective electric propulsion plasma engine. A two-dimensional flow model for a three-component medium consisting of atoms, ions, and electrons is based on magnetogasdynamic equations written in terms of the vector potential of magnetic field, taking into account electrical conductivity, thermal conductivity, and radiation transfer. A three-dimensional model of radiative transfer includes main mechanisms of emission and absorption for different parts of the spectrum. Numerical studies of the ionization process and radiation transfer were carried out in an approximation of local thermodynamic equilibrium. The stabilizing effect of flow rotation in the presence of a longitudinal magnetic field and thermal conductivity along the magnetic field on non-stationary unstable flows of ionizing gas, which arise at low discharge currents, is considered.

Crude oil wax: A review on formation, experimentation, prediction, and remediation techniques

Wax deposition during crude oil production, transportation, and processing has been a headache since the early days of oil utilization. It may lead to low mobility ratios, blockage of production tubing/pipelines as well as fouling of surface and processing facilities, among others. These snags cause massive financial constraints increasing projects' turnover. Decades of meticulous research have been dedicated to this problem that is worth a review. Thus, this paper reviews the mechanisms, experimentation, thermodynamic and kinetic modeling, prediction, and remediation techniques of wax deposition. An overall assessment suggests that available models are more accurate for single than multi-phase flows while the kind of remediation and deployment depend on the environment and severity level. In severe cases, both chemical and mechanical are synergistically deployed. Moreover, future prospective research areas that require attention are proposed. Generally, this review could be a valuable tool for novice researchers as well as a foundation for further research on this topic.

Strain-rate dependent tensile behavior of railway wheel/rail steels with equivalent fatigue damage: Experiment and constitutive modeling

The actual and accurate dynamic constitutive relationship of railway wheel/rail steels is crucial to availably simulate the wheel-rail impact behavior in service, which has increasingly aroused great concern in the field of rail transport but the related studies have been rarely reported. In this study, the tensile mechanical behavior of D1 railway wheel and U71MnG rail steels was investigated at a wide range of strain rates, considering the practical impact loading and initial fatigue damage of the wheel-rail system. An equivalent fatigue damage test method was first established based on the finite element simulations and corresponding maintenance period, and then the dislocation density and void distribution of post-fatigue-tested specimens were detected with X-ray diffractometer (XRD) and X-ray computed tomography (XCT), respectively. The dynamic tensile tests on pre-fatigued wheel/rail steel specimens at the strain rate up to 2200 s−1 were subsequently conducted using a split Hopkinson tension bar (SHTB) apparatus. Finally, a modified Johnson-Cook (JC) model was developed to describe the plastic flow behavior of D1 railway wheel and U71MnG rail steels, where the variable strain rate hardening coefficient and initial equivalent fatigue damage were taken into account. Verifications for experimental data demonstrated the modified JC model can provide a good prediction for the rate-dependent tensile response of the wheel/rail steels with initial fatigue damage.

Strengthening of cost-effective Co-free medium entropy alloys by Al/C alloying

The effects of the elemental Al or C addition on the microstructure and room-temperature mechanical properties of a cost-effective Cr12Fe60Mn16Ni12 medium-entropy alloy were investigated. The yield stress of a medium-entropy alloy substantially increased from 230 MPa to 458 MPa following the addition of small amounts of elemental Al (∼2.0 at.%) or C (∼1.0 at.%). Throughout experimentation and theoretical modeling, we were able to systematically show that the improvement in the yield stress following the Al/C addition was attributed to the combination of various strengthening mechanisms, such as grain boundary strengthening, precipitation strengtheing, and solid-solution strengthening mainly caused by the lattice distortion or lattice strain. On the fundamental level, this work provides insights into the effect of the lattice constant on the stress increase for both substitutional and interstitial solid-solution strengthening.

Effect of rotation in NMR diffusion experiments on micron-sized particles: A generalized theoretical treatment

We have recently developed an analytical framework to interpret the results of pulsed field gradient (PFG) NMR experiments on solution samples of micron-sized amyloid fibrils [Angew. Chem. Int. Ed. 60 (2021) 15445–15451. https://doi.org/10.1002/anie.202102408]. Here we generalize this result by reporting a rigorous theoretical model of such experiments, implemented in a form of efficient computational scheme. In particular, the new treatment fully accounts for the anisotropy of fibrils’ translational diffusion and takes into consideration the finite length of the gradient pulses. The results hold not only for the historic spin-echo sequence, but also for the widely used stimulated echo experiment. We have found that fibrils’ rotation can attenuate the echo by a factor comparable with that of translation. However, contrary to some recent claims, the rotational mechanism cannot lead to an apparent fast-diffusion situation.