Large Grid Sizes Research Articles

Optimising Marine Basic Spatial Units (MBSU) for Ocean Accounting using empirical data from Saleh Bay, Indonesia

Ocean Accounts, aligned with the UN System of Environmental Economic Accounting – Environmental Accounting (SEEA EA), bring together economic, social and environmental information in a coherent and standardised manner. Ecosystem extent is a structure to understand environmental assets and uses a basic spatial unit to facilitate the classification and measurement of ecosystems by type. This study tested the impact of grid size and method of designation per grid cell for Marine Basic Spatial Units (MBSU), using Saleh Bay, Indonesia as a case study. The extent of mangrove, seagrass and coral reefs were previously delineated in 2021 for ocean accounting activities. This study tested grids with two different cell sizes (10 x 10 m2 and 25 x 25 m2) and two different methods of designation, namely: (i) dominance (extent-based) and (ii) hierarchy (criteria-based) methods. The results indicated that a larger grid size is related to higher error in estimating both total area per ecosystem and spatial configuration within the study area. The dominance method produced more accurate results than the hierarchy method, although, when considering computational trade-offs, a larger grid size and the hierarchy method observed a much lower computational cost. These results demonstrate the need to carefully consider grid size and method when designating basic spatial units for accounting activities, as they impact linked accounting tables and, in turn, have implications when providing information for management and policy.

Learned 1D Passive Scalar Advection to Accelerate Chemical Transport Modeling: A Case Study with GEOS-FP Horizontal Wind Fields

Abstract We developed and applied a machine-learned discretization for one-dimensional (1D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accurate, three-stencil numerical solver but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger time step—and trained our neural-network-based scheme to match the subsampled integration data. In this way, we created an operator that has low resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1D scheme—when combined with a splitting technique—can be used to predict 2D advection and is in some cases more stable and accurate than the low-resolution reference solver in 2D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution. Significance Statement Chemical transport modeling (CTM) is an essential tool for studying air pollution. CTM simulations take a long computing time. Modeling pollutant transport (advection) is the second most computationally intensive part of the model. Decreasing the resolution not only reduces the advection computing time but also decreases accuracy. We employed machine learning to reduce the resolution of advection while keeping the accuracy. We verified the robustness of our solver with several generalization testing scenarios. In our 2D simulation, our solver showed up to 100 times faster simulation with fair accuracy. Integrating our approach to existing CTMs will allow broadened participation in the study of air pollution and related solutions.

Evaluation of the InterDosi code in estimating S-values with the digimouse voxelized mouse phantom

This study delves into the realm of internal dosimetry in small animals, focusing on the crucial role of S-values (absorbed dose rate per unit activity). The primary objective is to enhance our understanding of ionizing radiation distribution and optimize treatment strategies involving multidisciplinary approach which integrates dosimetry data, computational modeling, clinical expertise, and patient-specific factors to deliver safe, effective, and personalized cancer treatments, paving the way for more effective and tailored radiotherapeutic interventions and diagnostic cancer research. The research specifically targets the assessment of radiation doses absorbed in target organs and adjacent tissues during targeted radionuclide therapy with iodine-131 and yttrium-90. Executed on the ERSN (Radiations and Nuclear Systems Laboratory) computing grid, the study employed optimized calculations using 20 CPU (Central Processing Unit) cores for each of the 103 SAF (Specific Absorbed Fractions) grid sizes of photons and electrons. The InterDosi code, a user-friendly tool, streamlined S-value estimation within the Digimouse phantom. Nine source organs and the entire murine anatomy were comprehensively evaluated. The G4EMLiverMorePhysics package ensured high-confidence dosimetric coefficient simulations. S-values revealed insights into absorbed dose influences, including organ mass and inter-organ distance. Comparative analyses were conducted with reference data, which involved a simplified S-value calculation. The results aligned well with the reference data, especially with similar SAF grid sizes for electrons and photons. Accuracy improved with larger grid sizes. This study confirms InterDosi's effectiveness, highlighting its adaptability and precision in estimating S-values for various radionuclides, making it a valuable tool for evaluating radiation doses in murine anatomy.

Identification of an algorithm for the analysis and study of urban road network trajectories

The object of this study is a clustering algorithm using various technologies. This paper compares clustering algorithms that are more commonly used to analyze urban road network trajectories, the growth curve model, with the elbow method and the x-means algorithm. Experiments were conducted with various volumes of big data to determine calculation time, accuracy, and ways to increase calculation time. These results can be used to manage traffic jams in congested areas and city streets. Considering the widespread use of clustering algorithms for solving various problems, this study proposes to introduce GCM, SPGK methods for monitoring and analyzing the state of congestion on city roads. The work was carried out in the following steps: research and selection of methods based on efficiency and time, implementation of parallel computing technologies to improve computation speed, demonstration of the selected method based on collected data from a real city with visualization of the results. The growth curve model algorithm has been proven to be almost 5 times more effective than the elbow method and the x-means algorithm. The time allocated for data processing has been calculated. An increase in the volume of processed data showed an almost stable execution time t = 3 s for the GCM algorithm for data with a volume of up to almost 2,000 units. The effectiveness of SPGK-means was shown for different values of the number of points. Models of the Chengdu transport network obtained using a clustering algorithm with maximum grid density of neighborhoods are presented. There are some deviations between the grid and the road network due to the large grid size. This error is explained by an error of up to one between the points and the real grid. The results obtained clearly show how optimization of congested roads can be influenced. They provide information to obtain data on available routes, which allows you to analyze the road network individually and as a whole

Modelling flame-to-fuel heat transfer by deep learning and fire images

In numerical fire simulations, the calculation of thermal feedback from the flame to the solid and liquid fuel surface plays a critical role as it connects the fundamental gas-phase flame burning and condensed-phase fuel gasification. However, it is a computationally intensive task in CFD fire modelling methods because of the requirement of a high-resolution grid for calculating the interface heat transfer. This paper proposed a real-time prediction of the flame-to-fuel heat transfer by using simulated flame images and a computer-vision deep learning method. Different methanol pool fires were selected to produce the image database for training the model. As the pool diameters increase from 20 to 40 cm, the dominant flame-to-fuel heat transfer shifts from convection to radiation. Results show that the proposed AI algorithm trained by flame images can predict both the convective and radiative heat flux distributions on the condensed fuel surface with a relative error below 20%, based on the input of real-time flame morphology that can be captured by a larger grid size. Regardless of growing or decaying fires or puffing flames induced by buoyancy, this method can further predict the non-uniform distribution of heat transfer coefficient on the interface rather than using empirical correlations. This work demonstrates the use of AI and computer vision in accelerating numerical fire simulation, which helps simulate complex fire behaviours with simpler models and smaller computational costs. Highlights A total framework between AI model and fire simulation software is designed to further enhance the reliability of AI-based fire simulations. A standard pool fire simulation database is built using numerical model recommend by the IAFSS Computation Group. A deep learning model is developed to predict both the convective and radiative heat flux distributions on the condensed fuel surface using numerical images database. The demonstration showcases the application of AI and computer vision to accelerate numerical fire simulation.

Influences of regional site condition and grid size on seismic damage assessment of water distribution systems

In this study, the influences of regional site condition and grid size on the seismic damage assessment of water distribution systems (WDSs) are quantitatively examined. A water distribution system, termed M-WDS, is employed as the demonstrated system. With an assumed strike-slip linear fault in the vicinity of M-WDS, a number of spatially correlated peak ground velocity fields are realized for scenario earthquakes for each combination of grid size and regional site-condition parameter. Seismic damage analyses are then conducted for both scenario-based and probabilistic-based earthquake cases. Comparative results under scenario earthquakes indicate that increasing grid size notably increases the uncertainty of the seismic damage estimates of M-WDS, and such grid-size influence is more pronounced for more heterogeneous regional site conditions. It is also found that using a large grid size results in overestimated risk-based assessment for the probabilistic damage estimates of M-WDS, and the annual rates of exceeding large numbers of damaged pipelines are higher as the regional site conditions become more homogeneous. Additional analyses are conducted to investigate such influences on the seismic damage assessment of large-city scale WDSs. The ratio of the grid area to the whole WDS area being 0.02 is generally recommended for choosing an appropriate grid size for WDSs with different scales. A further reduced grid size is recommended for study areas exhibiting heterogeneous regional site conditions.

Automated and Efficient Test-Generation for Grid-Based Multiagent Systems

Automatic generation of random test inputs is an approach that can alleviate the challenges of manual test case design. However, random test cases may be ineffective in fault detection and increase testing cost, especially in systems where test execution is resource- and time-consuming. To remedy this, the domain knowledge of test engineers can be exploited to select potentially effective test cases. To this end, test selection constraints suggested by domain experts can be utilized either for filtering randomly generated test inputs or for direct generation of inputs using constraint solvers. In this paper, we propose a domain specific language (DSL) for formalizing locality-based test selection constraints of autonomous agents and discuss the impact of test selection filters, specified in our DSL, on randomly generated test cases. We study and compare the performance of filtering and constraint solving approaches in generating selective test cases for different test scenario parameters and discuss the role of these parameters in test generation performance. Through our study, we provide criteria for suitability of the random data filtering approach versus the constraint solving one under the varying size and complexity of our testing problem. We formulate the corresponding research questions and answer them by designing and conducting experiments using QuickCheck for random test data generation with filtering and Z3 for constraint solving. Our observations and statistical analysis indicate that applying filters can significantly improve test efficiency of randomly generated test cases. Furthermore, we observe that test scenario parameters affect the performance of the filtering and constraint solving approaches differently. In particular, our results indicate that the two approaches have complementary strengths: random generation and filtering works best for large agent numbers and long paths, while its performance degrades in the larger grid sizes and more strict constraints. On the contrary, constraint solving has a robust performance for large grid sizes and strict constraints, while its performance degrades with more agents and long paths.

Procedural Terrain Generation by Sampling a 2D Monochrom Perlin Noise Map in Unity

This article presents a study on procedural terrain generation using Perlin noise in the Unity game engine, building upon prior research and established techniques [1,2]. The objective of the this study is to create realistic and visually appealing landscapes by leveraging algorithmic generation [3]. By utilizing a two dimensional monochrome Perlin noise map, the algorithm generates terrains with organic features such as hills, valleys, and mountains [4]. The research employs the Unity game engine, which offers a wide range of tools and technologies for implementing procedural generation algorithms [5].
 The performance analysis conducted during this study yielded important insights. The execution time test revealed that the algorithm's execution time increased significantly with larger grid sizes, emphasizing the need to consider the scale and resolution of the terrains [6]. This finding highlights the trade-off between generating detailed terrains and the time required for the algorithm to complete its computations. Additionally, the memory usage test demonstrated a quadratic relationship between memory usage and grid size, indicating the necessity of efficient memory management [7] for generating higher-resolution terrains. Optimizing memory usage is crucial to avoid excessive consumption and ensure efficient resource utilization.
 These findings from the performance analysis provide valuable guidance for optimizing and refining the algorithm discussed in this article. Techniques such as algorithmic refinements, parallelization [8,6], and memory optimization strategies can be employed to reduce execution times and minimize memory overhead.
 By understanding the algorithm's performance characteristics and leveraging the insights gained from the analysis, developers can make informed decisions to generate visually compelling and diverse landscapes within the Unity game engine.

Spatiotemporal Companion Pattern (STCP) Mining of Ships Based on Trajectory Features

Spatiotemporal companion pattern (STCP) mining is one of the means to identify and detect group behavioral activities. To detect the spatiotemporal traveling pattern of ships from massive spatiotemporal trajectory data and to understand the movement law of group ships, this article proposes a feature-driven approach for STCP mining that consists of (1) generating the grid index via the rasterizing of geospace and characterizing trajectory points via the spatiotemporal trajectory grid sequences (STTGSs) of ships; (2) designing filtering rules with the constraints of range, time and distance to construct a candidate set for ship STCP mining; and (3) measuring the STTGS similarity of the associated ships and setting the confidence threshold to realize spatiotemporal companion mining. The effectiveness of the proposed method is practically validated on a real trajectory dataset which is collected from the Taiwan Strait waters. The experimental results are as follows: 825 pairs of associated ships and 225 pairs of accompanying ships are mined when the grid size is 0.05° and the confidence is 0.5. Larger grid sizes can increase the inclusiveness of the associated ship trajectory similarity measurement, which can result in an increase in confidence of pattern. A large number of pseudo-accompaniment ships are extracted to the result set, resulting in a more dispersed distribution of pattern confidence. By verifying the proposed method, accompanying behavioral activities such as ship cooperative operation, companion navigation method, and so on, can be detected. These results can provide a reference for the research of ship group behavior identification and have an important application value for water transportation management.

FEATURES OF THE USE OF PARALLEL CALCULATIONS IN THE PACKAGE OF APPLICATION PROGRAMS «MIRELA+»

The article examines approaches to the optimization of structural calculations, which require a comprehensive analysis of deformation processes under the action of operational loads. The finite element method is usually used to solve such problems. Modeling of structures taking into account stress concentrators requires the use of large-sized calculation grids. In real operating conditions, most structural elements are under the influence of mechanical and thermal loads. The package of application programs «MIRELA+» is intended for solving problems of thermoelasticity of structures made of weakly compressible elastomers and composites with an elastomeric matrix. One of the stages of solving problems of thermoelasticity is the formation of stiffness matrices taking into account weak compressibility and thermal conductivity of finite elements. When forming the stiffness and thermal conductivity matrices, it is necessary to perform integration over the area of ​​the finite element. For elements of the same type, calculations are used according to the same procedure, and the number of such calculations determines the time to solve the problem. In the traditional approach, these calculations are performed sequentially. In the case of large grid sizes, the number and time of calculations increase, which requires optimization of calculations using parallel calculation algorithms. Using the OpenMP library, a parallel algorithm for forming the matrix of solving equations was built. The calculation of terms according to the quadrature scheme is included in the parallel area of ​​the algorithm. Filling of the stiffness matrix of the finite element takes place in the sequential region. The execution time of parallel calculations is determined by the slowest subtask. Using the moment scheme of finite elements, displacement and deformation are approximated by the same polynomials, which simplifies the calculation of integrals. Parallel programming algorithms for constructing solving equations of the “MIRELA+” program package have been developed. Solutions for calculation grids of different sizes have been obtained. The effect of parallelization on the calculation time was studied.

Response of Fruit Body Assemblage Color Lightness to Macroclimate and Vegetation Cover

Understanding how species relate mechanistically to their environment via traits is a central goal in ecology. Many macroecological rules were found for macroorganisms, however, whether they can explain microorganismal macroecological patterns still requires investigation. Further, whether macroecological rules are also applicable in microclimates is largely unexplored. Here we use fruit body-forming fungi to understand both aspects better. A recent study showed first evidence for the thermal-melanism hypothesis (Bogert’s rule) in fruit body-forming fungi and relied on a continental spatial scale with large grid size. At large spatial extent and grid sizes, other factors like dispersal limitation or local microclimatic variability might influence observed patterns besides the rule of interest. Therefore, we test fungal assemblage fruit body color lightness along a local elevational gradient (mean annual temperature gradient of 7°C) while considering the vegetation cover as a proxy for local variability in microclimate. Using multivariate linear modeling, we found that fungal fruiting assemblages are significantly darker at lower mean annual temperatures supporting the thermal-melanism hypothesis. Further, we found a non-significant trend of assemblage color lightness with vegetation cover. Our results support Bogert’s rule for microorganisms with macroclimate, which was also found for macroorganisms.

An Impact of the Small and Large Grid Sizes on Differential Equations

The finite difference technique is oldest numerical method to solve differential equations. Like many differential equations, Helmholtz differential equation which is used to describe many physical phenomena, has long been solved using finite difference method. can be described by Helmholtz Differential equations. The solution of the Helmholtz type differential equations is very important. The information that it belongs together because it tells one coherent story just knowing a little bit about finite differences through to how to solve differential equations an especial technique is used, how to implement finite difference method and the tool which is used as generic enough that will immediately be given a whole new differential equation. The analysis of small to moderate sized presented with the help of a few examples. The improved finite difference method is presented with examples, the method is simple, clear, and short the MatLab code is available, the improved finite difference method is suitable and easy to implement, manually as well as computationally.

ParTransgrid: A scalable parallel preprocessing tool for unstructured‐grid cell‐centered computational fluid dynamics applications

AbstractThe development of a basic scalable preprocessing tool is the key routine to accelerate the entire computational fluid dynamics (CFD) workflow toward the exascale computing era. In this work, a parallel preprocessing tool, called ParTransgrid, is developed to translate the general grid format like CFD General Notation System into an efficient distributed mesh data format for large‐scale parallel computing. Through ParTransgrid, a flexible face‐based parallel unstructured mesh data structure designed in Hierarchical Data Format can be obtained to support various cell‐centered unstructured CFD solvers. The whole parallel preprocessing operations include parallel grid I/O, parallel mesh partition, and parallel mesh migration, which are linked together to resolve the run‐time and memory consumption bottlenecks for increasingly large grid size problems. An inverted index search strategy combined with a multi‐master‐slave communication paradigm is proposed to improve the pairwise face matching efficiency and reduce the communication overhead when constructing the distributed sparse graph in the phase of parallel mesh partition. And we present a simplified owner update rule to fast the procedure of raw partition boundaries migration and the building of shared faces/nodes communication mapping list between new sub‐meshes with an order of magnitude of speed‐up. Experiment results reveal that ParTransgrid can be easily scaled to billion‐level grid CFD applications, the preparation time for parallel computing with hundreds of thousands of cores is reduced to a few minutes.

Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts

As Amazon deforestation rates reach the highest levels observed in the past decade, it is extremely important to direct conservation efforts to regions containing preserved forests with a high risk of deforestation. This requires forecasting deforestation, a complex endeavor due to the interplay of multiple socioeconomic, political, and environmental factors across different spatial and temporal scales. Here we couple high-resolution land-cover maps with Bayesian hierarchical spatial models to identify the main drivers of recent deforestation in the Brazilian Amazon and predict which areas are likely to lose a larger proportion of forest in the next 3 years. Recent deforestation was positively associated with forest edge density, the length of roads and waterways, elevation and terrain slope; and negatively associated with distance to urban areas, roads, and indigenous lands, area designated as protected or indigenous territory, and municipality GDP per capita. From these variables, forest edge density and distance to roads showed the largest effect sizes and highest predictive power. Predictive accuracy was highest for shorter time windows and larger grid sizes. Predicted deforestation was largely concentrated in the North-Eastern portions of the Brazilian Amazon, and amounted to roughly 3, 5, and 6 million hectares for 2020, 2021, and 2022, respectively. About 50% of this predicted deforestation is expected to occur inside protected areas or indigenous lands. Our short-term forecasts can help plan preventive measures to limit deforestation while meeting the specific needs of local areas.

Spatial Coevolution for Generative Adversarial Network Training

Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, as well as in combination, play critical roles. Lipizzaner succumbs less frequently to critical collapses and, as a side benefit, demonstrates improved performance. In addition, we show a GAN-training feature of Lipizzaner: the ability to train simultaneously with different loss functions in the gradient descent parameter learning framework of each GAN at each cell. We use an image generation problem to show that different loss function combinations result in models with better accuracy and more diversity in comparison to other existing evolutionary GAN models. Finally, Lipizzaner with multiple loss function options promotes the best model diversity while requiring a large grid size for adequate accuracy.

Small scale simulation of artificial smoke optical properties

The use of numerical simulations in Fire Safety Engineering provides faster and more affordable ways of research and development, with the drawback that their use is often conditioned by available expertise and thorough validation. In this paper, the results of two small scale numerical simulations of artificially generated smoke are being validated by comparison of the smoke’s light extinction coefficient values and those of the visibility levels in the testing enclosures with the results obtained through experimental testing. The influence of grid size on simulation accuracy and computational time is evaluated by presenting the small gain in accuracy and the important increase in computational time for different sized meshes, showing that at least in numerical simulations dealing with artificial smoke, a larger grid size can produce fairly accurate results. A comparative evaluation of the effect that the positioning of the measuring devices inside the testing enclosure has on the results is also performed, both for the experimental setup and the numerical simulations.

Simulation of Low Inertia Power Systems Based on Shifted Frequency Analysis

New types of power system transients with lower time constants are emerging due to the replacement of synchronous generation with converter interfaced generation and are challenging the modeling approaches conventionally applied in power system simulation. Quasi-stationary simulations are based on classical phasor models, whereas EMT simulations calculate the instantaneous values of models in the time domain. In addition to these conventional modeling approaches, this paper investigates simulation based on dynamic phasor models, as has been proposed by the Shifted Frequency Analysis. The simulation accuracy of the three modeling approaches was analyzed for characteristic transients from the electromagnetic to the electromechanical phenomena range, including converter control as well as low inertia transients. The analysis was carried out for systems with converter interfaced and synchronous generation whilst considering the simulation step size as a crucial influence parameter. The results show that simulations based on dynamic phasors allow for larger step sizes than simulations that calculate the instantaneous values in the time domain. This can facilitate the simulation of more complex component models and larger grid sizes. In addition, with dynamic phasors, more accurate simulation results were obtained than with classical phasors, in particular—but not exclusively—in a low inertia case. Overall, the presented work demonstrates that dynamic phasors can enable fast and accurate simulations during the transition to low inertia power systems.

Numerical simulations on propane/oxygen detonation in a narrow channel using a detailed chemical mechanism: formation and detailed structure of irregular cells

Numerical simulations of two-dimensional inviscid detonations for a stoichiometric propane/oxygen gas mixture are performed using a detailed chemical reaction model. The UC San Diego model which includes 57 chemical species and 268 elementary reactions is mainly used in the present study. It is shown that a grid size of 3 µm can capture important features such as the unburned gas pocket behind the detonation when compared to larger grid sizes. The effects of channel width show that the detonation propagates with the CJ (Chapman–Jouguet) velocity for all cases and for more than 100 times the channel width of 4.5 mm. Increasing the channel width results in an irregular detonation cell structure. A transverse detonation forms with cross-hatching marks on the maximum pressure history. The irregular detonation cell structure forms because both the reduced activation energy and the stability parameter have a value of approximately 10; however, the maximum thermicity in the detonation is one. The free radicals C3H7 and H2O2 play an important role in the propane oxidation under the high temperature in the detonation. The maximum concentration exists at a temperature of 2000–3000 K. The fifth-order WCNS (weighted compact nonlinear scheme) scheme can resolve the contact surface and complicated flow structure behind the detonation front compared to the second-order MUSCL (Monotonic Upstream-centered Scheme for Conservation Laws).

Development of data-driven filtered drag model for industrial-scale fluidized beds

Simulations of large-scale gas-particle flows using coarse meshes and the filtered two-fluid model approach depend critically on the constitutive model that accounts for the effects of sub-grid scale inhomogeneous structures. In an earlier study (Jiang et al., 2019), we had demonstrated that an artificial neural network (ANN) model for drag correction developed from a small-scale systems did well in both a priori and a posteriori tests. In the present study, we first demonstrate through a cascading analysis that the extrapolation of the ANN model to large grid sizes works satisfactorily, and then performed fine-grid simulations for 20 additional combinations of gas and particle properties straddling the Geldart A-B transition. We identified the Reynolds number as a suitable additional marker to combine the results from all the different cases, and developed a general ANN model for drag correction that can be used for a range of gas and particle characteristics.

Pseudorelative Permeabilities for Simulation of Unstable Viscous Oil Displacement

Summary Relative permeabilities are well understood for light oils involving stable displacement. However, conflicting arguments have been presented in the literature regarding relative permeabilities for viscous oils. Most nonthermal viscous oil displacements are unstable. Depending on the magnitude of mobility ratio, displacement is influenced by varying degrees of viscous instability, often referred to as fingering. For viscous oils (>500 cp), even a polymer flood must be designed at partially stable conditions (mobility ratio > 1) to maintain an economical processing rate [% pore volume (PV) injected (PVI) per year]. Typically, viscous fingering is difficult to model in full-field simulation because of the large grid sizes used. To design and optimize a partially stable polymer or waterflood, it is critical to correctly upscale the laboratory-generated relative-permeability curves for reservoir simulation. In recent years, such models have been published in the Society of Petroleum Engineers literature. Unfortunately, most of these models require multiple fitting parameters (at least three). In this work, we present a simplified technique that requires systematic change in only one parameter to generate upscaled relative permeability curve for a given viscosity ratio. Using fine-grid simulations, we show that the flow at high-viscosity ratio is channelized even in a core perceived to be homogeneous at laboratory scale. This happens because of small-scale heterogeneities that are present in every rock. Upscaling averages these fine variations in heterogeneities, causing the grids to be overswept, thus overpredicting recovery. To compensate for this shortcoming, it is recommended to upscale the relative-permeability curves in the simulation model.