Terms Of Saturation Research Articles

Research on the strategy of cruise control system for urban traffic jam assistant

This paper proposes a hierarchical cruise control strategy that can adapt to urban traffic scenarios. Firstly, the perception layer defines a target selection strategy by using a novel trapezoidal dangerous area. Then, the upper controller contains speed follow mode and distance follow mode. An improved PI mode with integral separation and saturation terms is developed to achieve the desired speed following control, a safe distance mode via variable time headway is developed to facilitate the safe and smooth distance following control. The lower controller is designed based on feedforward and PI feedback. Finally, the experimental results demonstrate that the strategy can effectively select suitable target vehicles in complex urban scenarios. With the improved safe distance model and the third-order Bessel actuator response delay model, the strategy exhibits adaptability and stability. This study offers valuable suggestions and guidance for designing and applying the cruise control strategy for urban traffic scenarios.

Cluster consensus for nonlinear multi‐agent systems restricted with input saturation by event triggered control

AbstractThis article studies the cluster consensus problem for the nonlinear multi‐agent systems subject to input saturation under the context of event triggered control. Taking ordinary consensus object as a special case, this article considers the problem of cluster consensus. The event triggered mechanism is designed and the Zeno phenomenon is ruled out. The sufficient conditions of control protocol and range estimation of domain of attraction are formulated by the linear matrix inequalities for the constraints of input saturation and nonlinear terms existed in the system dynamics. Finally, the proposed results are validated by one example in the background of spring damping system.

Stochastic Modeling of Human Civilizationʹs Future under Competing AI Influences

This study explores the potential trajectories of human civilization influenced by the development and competition of advanced artificial intelligence (AI) systems. Using a system of stochastic partial differential equations (SPDEs), we model the probability density function representing the state of civilization in a phase space defined by prosperity and knowledge. The model incorporates diffusion, growth, saturation, and drift terms, alongside stochastic noise to reflect the uncertainties and random fluctuations in AI impacts. The simulation results are visualized in a probability density graph, revealing the likelihood of various outcomes ranging from extinction and regression, to prosperity and technological advancement. The analysis highlights the balanced prospects of human future, with significant probabilities in both positive and negative directions. Positive scenarios suggest potential for increased prosperity and knowledge, emphasizing the importance of effective AI management and international cooperation. Conversely, the notable risks of regression and extinction underline the need for strategic interventions to mitigate adverse impacts. Our findings stress the stochastic nature of future developments and the critical role of adaptive and flexible policies in steering human civilization towards favorable outcomes. This study provides a simple yet comprehensive framework for understanding the complex dynamics at play and underscores the importance of proactive strategies in the age of AI

Stabilization of linear KdV equation with boundary time-delay feedback and internal saturation

This paper investigates the question of stabilization of linear Korteweg-de Vries equation with time-delay on the boundary feedback in presence of saturated source term. Under some conditions the well-posedness is proved. The exponential stability result is demonstrated, using an appropriate Lyapunov functional.

Cardiovascular response to body positioning following sub-maximal exercise test among long-COVID individuals

Abstract This study aimed to observe the effect of cardiovascular response to body positioning after cardiopulmonary exercise testing in post covid individuals. Ninety post covid participants (age group 18-45) were assigned into four groups: standing, sitting, supine, and prone position. All participants were assessed cardiovascular response, VO2max baseline, post exercise in standing, sitting, supine and prone by using pulse oximeter, digital sphygmomanometer, polar heart rate and Borg scale. In SpO2, rate of perceived exertion (RPE), diastolic blood pressure, and heart rate, there was no significant difference between the groups at pre-exercise. However, a significant difference was observed at 1 min post-exercise and at 3 min post-exercise. As for systolic blood pressure, there was no significant difference between the groups at pre-exercise and at 1 min post-exercise, but a significant difference was noted at 3 min post-exercise. The findings of this study suggest that, when comparing standing, sitting, supine, and prone positions, the prone position exhibited faster recovery in terms of oxygen saturation and rate of perceived exertion, while the supine position showed quicker recovery in both systolic and diastolic blood pressure. Additionally, individuals with long-COVID symptoms were observed to have a lower VO2max.

Probabilistic locked mode predictor in the presence of a resistive wall and finite island saturation in tokamaks

We present a framework for estimating the probability of locking to an error field in a rotating tokamak plasma. This leverages machine learning methods trained on data from a mode-locking model, including an error field, resistive magnetohydrodynamics modeling of the plasma, a resistive wall, and an external vacuum region, leading to a fifth-order ordinary differential equation (ODE) system. It is an extension of the model without a resistive wall introduced by Akçay et al. [Phys. Plasmas 28, 082106 (2021)]. Tearing mode saturation by a finite island width is also modeled. We vary three pairs of control parameters in our studies: the momentum source plus either the error field, the tearing stability index, or the island saturation term. The order parameters are the time-asymptotic values of the five ODE variables. Normalization of them reduces the system to 2D and facilitates the classification into locked (L) or unlocked (U) states, as illustrated by Akçay et al., [Phys. Plasmas 28, 082106 (2021)]. This classification splits the control space into three regions: L̂, with only L states; Û, with only U states; and a hysteresis (hysteretic) region Ĥ, with both L and U states. In regions L̂ and Û, the cubic equation of torque balance yields one real root. Region Ĥ has three roots, allowing bifurcations between the L and U states. The classification of the ODE solutions into L/U is used to estimate the locking probability, conditional on the pair of the control parameters, using a neural network. We also explore estimating the locking probability for a sparse dataset, using a transfer learning method based on a dense model dataset.

Image Transform and Hybrid Optimization Based Feature Reduction for Leaf Diseases Classification

Abstract: In recent years, there has been amazing progress in technology innovation across many sectors, including medical care, homeland security, the arts, and even agriculture. The ability to run one's business well is highly regarded in every sector. Because to recent advancements in technology, such as smart phones and the Internet of Things, this is now a distinct possibility. In this research, we offer a smart management system for horticulture that makes use of machine learning methods. This system is based on the smart management scheme that was previously described. As a component of horticulture management, many academics have advocated for the use of machine learning to analyse plant growth based on the features of the leaf. We used the grayscale image, which employed numerous features for classification while simultaneously normalising the pixel values. As a consequence of this, the processing of damaged leaves in their early stages takes more time and results in less accurate classification. As a result, for the purpose of this work, we do a colour space conversion in order to investigate the image in terms of hue, saturation, and value. Following that, it extracted features from the Value channel by making advantage of the histogram's attributes. After that, the best characteristics were chosen by a process that included both particle swarm and cuckoo search optimisation. Following this, a regression neural network was trained to classify data based on the features that had been selected in the previous step. In order to evaluate how effective the proposed strategy is, classification performance criteria such as accuracy, sensitivity, and specificity were utilised. The suggested approach was tested in a simulated Windows 10 environment using a real-time dataset and the MATLAB software. It was compared to other methods that are already in use. While evaluating performance, comparisons to the current state of the art are conducted with regard to accuracy, sensitivity, and specificity

Fault-Tolerant Fuzzy-Resilient Control for Fractional-Order Stochastic Underactuated System With Unmodeled Dynamics and Actuator Saturation.

This article is considered on underactuated fractional-order stochastic systems (FOSSs) with actuator saturation and incrementally conic nonlinear terms, whose fractional-order α ∈ (0,1) . First, to bring FO dynamic signals, solving the unmodeled dynamics, in the meantime, the saturated nonlinear term of the control input is taken into account. At the time, to cope with the stability issue of FOSS under such situation, the fault tolerant resilient controller based on underactuated condition is designed. Then, according to the method of the Lyapunov and It∧ o differential formulation to design proper multiple Lyapunov-Krasovskii (L-K) functions, such that, a novel sufficient condition of the robustly asymptotically stability of fuzzy FOSS under underactuated conditions is rigorously proved in terms of linear matrix inequality (LMI). Furthermore, in order to research the mean square stability of the above-mentioned system, so the solution of FOSS is obtained to achieve this purpose. By applying the above method, which is proposed in this work that the controlled system can be obtained with faster response and higher control accuracy. At last, to display the superiority of the above-mentioned scheme is effective, tethered satellite system and numerical results are presented.

Prognostic significance of preoperative exercise tolerance in patients with early-stage lung cancer

ObjectiveTo investigate the influence of simple preoperative exercise tests as prognostic factors for early-stage lung cancer. MethodsThis single-institution retrospective study included consecutive patients who underwent pulmonary resection for stage 0 to I lung cancer between April 2017 and December 2019. Before surgery, 7 metabolic equivalents of task in the double Master 2-step test were loaded into the exercise echocardiogram. The relationship between prognosis and exercise stress test results in terms of availability, symptoms, and saturation of percutaneous oxygen was investigated. ResultsThis study included 862 patients with pathological stage 0 to I lung cancer. Among the 862 patients, 673 patients (78.1%) who were able to complete 7 metabolic equivalents of task exercise for 3 minutes without assistance were classified into the complete group. The 5-year survival of the complete group was significantly better than that of the incomplete group. Multivariable analysis revealed that age (hazard ratio, 1.06; P = .008), male sex (hazard ratio, 2.23; P = .011), carcinoembryonic antigen level >5 ng/mL (hazard ratio, 2.33; P = .011), and inability to complete 7 metabolic equivalents of task exercise (hazard ratio, 3.90; P < .001) were the prognostic factors. Patients in the older group who had the ability to complete exercise had a better prognosis than those in the younger group without the ability (P = .003). ConclusionsPreoperative exercise ability is a prognostic factor for early-stage lung cancer. Patients who can tolerate an exercise load of 7 metabolic equivalents of task, even if they are aged 70 years or older, have a better prognosis than patients younger than age 70 years without exercise tolerance.

Effect of non-pharmacological interventions on pain in preterm infants in the neonatal intensive care unit: a network meta-analysis of randomized controlled trials

ObjectiveTo evaluate the effectiveness of different non-pharmacological interventions for pain management in preterm infants and provide high-quality clinical evidence.MethodsRandomized controlled trials (RCTs) of various non-pharmacological interventions for pain management in preterm infants were searched from PubMed, Web of Science, Embase, and the Cochrane Library from 2000 to the present (updated March 2023). The primary outcome was pain score reported as standardized mean difference (SMD). The secondary outcomes were oxygen saturation and heart rate reported as the same form.ResultsThirty five RCTs of 2134 preterm infants were included in the meta-analysis, involving 6 interventions: olfactory stimulation, combined oral sucrose and non-nutritive sucking (OS + NNS), facilitated tucking, auditory intervention, tactile relief, and mixed intervention. Based on moderate-quality evidence, OS + NNS (OR: 3.92, 95% CI: 1.72, 6.15, SUCRA score: 0.73), facilitated tucking (OR: 2.51, 95% CI: 1.15, 3.90, SUCRA score: 0.29), auditory intervention (OR: 2.48, 95% CI: 0.91, 4.10, SUCRA score: 0.27), olfactory stimulation (OR: 1.80, 95% CI: 0.51, 3.14, SUCRA score: 0.25), and mixed intervention (OR: 2.26, 95% CI: 0.10, 4.38, SUCRA score: 0.14) were all superior to the control group for pain relief. For oxygen saturation, facilitated tucking (OR: 1.94, 95% CI: 0.66, 3.35, SUCRA score: 0.64) and auditory intervention (OR: 1.04, 95% CI: 0.22, 2.04, SUCRA score: 0.36) were superior to the control. For heart rate, none of the comparisons between the various interventions were statistically significant.ConclusionThis study showed that there are notable variations in the effectiveness of different non-pharmacological interventions in terms of pain scores and oxygen saturation. However, there was no evidence of any improvement in heart rate.

A study on the analysis and understanding of art works based on graph neural networks

Abstract With the development of the Internet, many art works are uploaded for people to enjoy. To make it more convenient for viewers to understand and analyze the content of art works. In this paper, we use the GNN graph neural network analysis model to extract the art style, creation period, color expression and other features of the works through the work feature saliency attention module, and classify them using the similarity metric. At the same time, we put forward the GNNMMVisRe model to explore the works from the visual multimodality. The analysis results concluded that the Impressionist style of art works had its heyday in the 18th-19th centuries, when the number of works reached 11,674. The Baroque classicism style reached its heyday in the mid-18th century with 5,921 pieces. In terms of color use, low saturation and low luminance color palettes were selected, with average values of 81.3% and 83.4%, respectively. Using the GNN model to categorize, analyze and study the art works improves the understanding and appreciation of the works. It is also significant for the further development of art.

Quantifying Geobacter sulfurreducens growth: A mathematical model based on acetate concentration as an oxidizing substrate.

We developed a mathematical model to simulate dynamics associated with the proliferation of Geobacter and ultimately optimize cellular operation by analyzing the interaction of its components. The model comprises two segments: an initial part comprising a logistic form and a subsequent segment that incorporates acetate oxidation as a saturation term for the microbial nutrient medium. Given that four parameters can be obtained by minimizing the square root of the mean square error between experimental Geobacter growth and the mathematical model, the model underscores the importance of incorporating nonlinear terms. The determined parameter values closely align with experimental data, providing insights into the mechanisms that govern Geobacter proliferation. Furthermore, the model has been transformed into a scaleless equation with only two parameters to simplify the exploration of qualitative properties. This allowed us to conduct stability analysis of the fixed point and construct a co-dimension two bifurcation diagram.

Evaluation of oxidative stress on Zebrafish under different pH and dissolved oxygen levels using MDA as biomarker

Oxidative stress on zebrafish (Danio rerio) has been evaluated for pH (four levels namely pH 4.5-5.5, 5.5-6.5, 7.5-8.5 and 8.5-9.5) and Dissolved Oxygen (DO 20-30%, 40-50% and 60-70%) in terms of oxygen saturation. Temporally, the exposure for pH levels were maintained from as 1h, 2h, 3h and 4h and for DO, it was (4h, 8h, 12h and 16h. The malondialdehyde (MDA) was used as biomarker of oxidative stress. It was observed that out of all tissues (skeletal muscle, liver, gill and brain) analyzed, zebrafish showed highest stress response in skeletal muscle against both environmental ambiances. In case of pH levels, significant increase in MDA level in skeletal muscle was recorded at pH 4.5-5.5, and the MDA level of skeletal muscle at 2h showed significant increase at this level of pH. Whereas, at DO 20-30% saturation level, skeletal muscle tissue significantly responded at this level of DO saturation and showed the maximum MDA level compared to the other tissues. On analysis for the duration of exposure, it was observed that skeletal muscle of zebrafish showed highest levels of MDA at DO 40-50% O2 saturation for 12h. In conclusion, it has become evident that zebrafish may undergo oxidative stress at pH 4.5-5.5 when exposed for 2hours, similarly, for DO it is 20-30% saturation for 12 hour exposure.

Strength characteristics of foamed concrete under coupling effect of constant compressive loading and freeze-thaw cycles

In cold regions, foamed concrete used for the tunnel reserved deformation layer is usually subjected to environmental factors, such as the groundwater erosion, peristaltic pressure of surrounding rock, and freeze-thaw cycles. In this research, experiments considering single-factor (i.e., water immersion, constant compressive loading, and freeze-thaw cycles) and coupled multi-factor effects (the coupling of water saturated - constant loading and constant loading - freeze thaw cycles) were conducted on foamed concretes with different densities to examine the strength variation under different conditions. The results show that under the effect of single-factor environment, the average compressive strength of saturated foamed concrete is about 10% lower than the dry foamed concrete. The compressive strength of foamed concrete significantly decreases at the beginning of constant compressive loading but gradually increases with the loading time. Furthermore, the strength loss of foamed concrete after 30 freeze-thaw cycles can exceed 10%. Under the multi-factor coupled environment, the hydraulic effect accelerates the degradation of foamed concrete, particularly in terms of water saturation - constant loading coupling. Furthermore, for loading - freeze thaw cycles coupling, the compressive loading of 0.3 fc (fc is the unconfined compressive strength at 28 days) can improves the frost resistance of foamed concrete. In addition, the results of orthogonal analysis prove that the density has the most important influence, followed by the freeze-thaw cycle and the slightest effect of constant compressive loading. Additionally, a prediction equation of compressive strength as a function of density, freeze-thaw cycles, and the constant pressure value was established to describe the strength variation of foamed concretes under coupled-loading effects.

Characteristics of different flow patterns for proton exchange membrane water electrolysis with circular geometry

This study comprehensively analyzes five circular-shaped flow field configurations (one-path, two-path, two-channel, two-segment, and four-segment serpentine), using membrane electrode assembly (MEA) area of 38.5 cm2. Here, it aims to identify the optimal pattern in terms of velocity, pressure, liquid saturation, temperature, and current density distribution. Further, a comparison is conducted between the uniformity index, polarization curves, and average membrane temperature curves of various cases. Using a finite element method, a three-dimensional, non-isothermal, two-phase model is developed based on the conservation equations of mass, momentum, electric charge, energy, and species. To confirm modelling reliability, the numerical results have been validated and demonstrated a strong match with the experimental data. Findings indicate that the one-path, followed by the two-path serpentine pattern, yield the most favorable flow distribution and temperature uniformity within the PEMWE. Conversely, the four-segment serpentine flow pattern results in the least desirable outcomes. At a cell voltage of 2.1 V, the uniformity index of liquid saturation, temperature and current density and average membrane temperature for one-path and two-path serpentine patterns exhibit reductions of 70 and 29 %, 95.5 and 95.2 %, 33 and 31 %, and 9.1 and 6 K, respectively, compared to four-segment serpentine design. Moreover, the four-segment, two-segment, and two-path serpentine flow patterns exhibit optimal performance in terms of polarization curves.

Mean square exponential stabilization analysis of stochastic neural networks with saturated impulsive input

The exponential stabilization of stochastic neural networks in mean square sense with saturated impulsive input is investigated in this paper. Firstly, the saturated term is handled by polyhedral representation method. When the impulsive sequence is determined by average impulsive interval, impulsive density and mode-dependent impulsive density, the sufficient conditions for stability are proposed, respectively. Then, the ellipsoid and the polyhedron are used to estimate the attractive domain, respectively. By transforming the estimation of the attractive domain into a convex optimization problem, a relatively optimum domain of attraction is obtained. Finally, a three-dimensional continuous time Hopfield neural network example is provided to illustrate the effectiveness and rationality of our proposed theoretical results.

Flow Rate Dependency of Steady-State Two-Phase Flows in Pore Networks: Universal, Relative Permeability Scaling Function and System-Characteristic Invariants

The phenomenology of steady-state two-phase flow in porous media is conventionally recorded by the relative permeability diagrams in terms of saturation. Yet, theoretical, numerical and laboratory studies of flow in artificial pore network models and natural porous media have revealed a significant dependency on the flow rates—especially when the flow regime is capillary to capillary/viscous and part of the disconnected non-wetting phase remains mobile. These studies suggest that relative permeability models should incorporate the functional dependence on flow intensities. In the present work, a systematic dependence of the pressure gradient and the relative permeabilities on flow rate intensity is revealed. It is based on extensive simulations of steady-state, fully developed, two-phase flows within a typical 3D model pore network, implementing the DeProF mechanistic–stochastic model algorithm. Simulations were performed across flow conditions spanning 5 orders of magnitude, both in the capillary number, Ca, and the flow rate ratio, r, and for different favorable /unfavorable viscosity ratio fluid systems. The systematic, flow rate dependency of the relative permeabilities can be described analytically by a universal scaling function along the entire domain of the independent variables of the process, Ca and r. This universal scaling comprises a kernel function of the capillary number, Ca, that describes the asymmetric effects of capillarity across the entire flow regime—from capillarity-dominated to mixed capillarity/viscosity- to viscosity-dominated flows. It is shown that the kernel function, as well as the locus of the cross-over relative permeability values, are single-variable functions of the capillary number; they are both identified as viscosity ratio invariants of the system. Both invariants can be correlated with the structure of the pore network, through a function of Ca. Consequently, the correlation is associated with the wettability characteristics of the system. Among the potential applications, the proposed, universal, flow rate dependency scaling laws are the improvement of core analysis and dynamic rock-typing protocols, as well as integration into field-scale simulators or associated machine learning interventions for improved specificity/accuracy.

Seismic interpretation and sequence stratigraphic analysis of the Bahariya Formation in the South Umbaraka oilfields (Western Desert, Egypt): insights into reservoir distribution, architecture, and evaluation

The integration of multiscale datasets, including seismic, well-logs and stratigraphy, is providing a precise picture about reservoir evaluation and spatial distribution. This study focuses on the seismic interpretation and sequence stratigraphic analysis of the Bahariya Formation in the South Umbaraka oilfields (Selkit, Khepri, and Sethos), located in the Western Desert of Egypt. Seismic interpretations reveal various structural closures, including anticlinal domes and faulted anticlines in different fields. The study area is divided into northern and southern blocks separated by a major fault, and structural cross sections provide valuable information on reservoir zones regarding lateral extension, thickness variation, and fault distribution. The Bahariya Formation is subdivided into two depositional sequences: Lower Bahariya sequence (BAH.SQ-1) and Upper Bahariya sequence (BAH.SQ-2) with distinct wireline-log characteristics, seismic reflections, and lithofacies variations. BAH.SQ-1 comprises mixed siliciclastic-dominated units with some carbonate units, while BAH.SQ-2 consists of mixed siliciclastic and carbonate units. Multiple sequence boundaries, depositional surfaces, and systems tracts are identified within BAH.SQ-1 and BAH.SQ-2, indicating changes in sedimentary environments and sea-level fluctuations. The petrophysical evaluation reveals promising reservoir zones in both the Lower and Upper Bahariya sequences. However, the Lower Bahariya sequence exhibits superior reservoir characteristics in terms of net-pay thickness, shale volume, effective porosity, and water saturation. Particularly, the sandstone-dominated LST-1 within the Lower Bahariya Formation is identified as a favorable reservoir zone. Conversely, the Upper Bahariya Formation has limited reservoir potential. This study underscores the significance of sequence stratigraphy and reservoir architecture, emphasizing the role of faulting, folding, and depositional environments in the distribution and quality of reservoir zones. The findings contribute valuable insights into the structural and stratigraphic characteristics of the Bahariya Formation, enhancing understanding of hydrocarbon plays and reservoir distribution in the studied area.

Comparison of the Sedative Effects of Dexmedetomidine and Remifentanil in Vitrectomy Surgery: A Retrospective Study.

Background Vitrectomy surgery is a painful and lengthy procedure. Therefore, administering sedation to reduce patient agitation provides both surgical comfort and hemodynamic stability. However, various complications can arise during the perioperative period depending on the sedation agent used. In our study, we aimed to evaluate the effects of dexmedetomidine and remifentanil sedation applications on patient hemodynamics and perioperative complications in vitrectomy surgery. Methods Our retrospective study included patients aged 18-70 who underwent vitrectomy surgery between 2021 and 2022 with complete file data and ASA scores of 1-3 after obtaining approval from our hospital's ethics committee. Patients were classified into two groups based on the sedation agent used: Group D for dexmedetomidine and Group R for remifentanil. Demographic data of patients, heart rate, mean arterial pressure, oxygen saturation, and bispectral index values during perioperative monitoring, operation duration, and complications such as perioperative nausea, vomiting, and low saturation were recorded. The data of both groups were statistically evaluated, with p<0.05 values considered statistically significant. Results Because of missing data in 18 out of 58 patient files, these cases were excluded from the study. A total of 40 patients were included in the study, with 20 in Group D and 20 in Group R. The mean age of the patients was 64. Among them, 18 (45%) were male, and 22 (55%) were female. The mean operation duration was 61.8 ± 24.1 minutes in Group D and 56.3 ± 17.2 minutes in Group R. The heart rate in Group D was statistically significantly lower than in Group R, starting from the 20th minute of the perioperative period. There were no significant differences between the groups in terms of mean arterial pressure, oxygen saturation, and bispectral index values. One case of bradycardia occurred in Group D, requiring intervention. Conclusion In vitrectomy surgery, both dexmedetomidine and remifentanil infusions can be used for sedation, but caution is advised regarding bradycardia in Group D. Anticipating potential complications with an experienced anesthesia team is crucial for both patient and surgical comfort.

A Computational Fluid Dynamics Enhanced Oil Recovery Formulation Based on the Volume Average Procedure on the Full Momentum Equation: Effects of Polymer Solution Injection in Real Reservoirs with Anisotropic Permeability

Summary In this work, we propose a new methodology to simulate the process of enhanced oil recovery (EOR) in a 3D domain, considering a non-Newtonian fluid phase. The mass balance for the two phases and the balance of momentum are based on the volume averaging theory, which upscales the information on the microscale to make viable the solution in a real case. The full balance of momentum is used in place of the usually adopted Darcy’s law, and the equations are written for each phase in terms of porosity and fluid saturation. The equations are introduced in a new numerical solver developed for the OpenFOAM toolbox, which is an open-source C++ library created to simulate problems of computational fluid dynamics (CFD). First, the methodology is verified by comparing the results obtained from the volume averaging theory equations with Darcy’s law. Then, Newtonian and non-Newtonian cases for Buckley-Leverett, 2D, and 3D meshes are presented. We also present cases with a 3D mesh in a domain extracted from a real reservoir and properties of real injector fluids. The present approach is able to accommodate anisotropic permeability, heterogeneity, and non-Newtonian effects. We compare fluid saturation over time for the different cases as well as the accumulated volume of oil over time and the flow output of the domain. The simulations performed were able to demonstrate the effectiveness of polymeric solutions, comparing polyacrylamide (PAA) and polymeric surfactant (PS) cases with different concentrations, for better use in the production of available resources in reservoirs. In the five-spot cases with polymeric solutions, lower flow rates are achieved when the concentration is increased and the oil production until breakthrough is up to 45% higher when compared with water injection.