Multiple Operations Research Articles

Modeling Operations in System-Level Real-Time Control for Urban Flooding Reduction and Water Quality Improvement—An Open-Source Benchmarked Case

Advancements in smart sensing and control technologies enable urban drainage engineers to retrofit stormwater storage facilities with real-time control devices for mitigating stormwater in-site overflow, downstream flooding, and overloaded total suspended solids (TSS) in drainage pipes. While the smart technology can improve the performance of the static drainage systems, coordinatively controlling multiple valve and gate operations poses a significant challenge, especially at a large-scale watershed. Using a benchmark stormwater model located at Ann Arbor, Michigan, USA, we assessed the impact of different real-time control strategies (local individual downstream control and system-level multiple control) on balancing flooding mitigation at downstream outlets and TSS reduction at upstream storage units, such as detention ponds. We examined changes in peak water depth, outflow, and TSS as indicators to assess changes in water quantity and quality. The results indicate that system-level control can reduce peak water depth by up to 7.3%, reduce flood duration by up to 34%, and remove up to 67% of total suspended solids compared with a baseline uncontrolled system, with the outflow from upstream detention ponds being the most important hydraulic indicator for control strategy rule set-up. We find that system-level control does not always outperform the individual downstream controls, particularly in alleviating flooding duration at some downstream outlets. With urban growth and a changing climate, this research provides a foundation for quantifying the benefits of real-time control methods as an adaptive stormwater management solution that addresses both water quantity and quality challenges.

Optimal control design for power system stabilizer using a novel crow search algorithm dynamic approach

This paper proposes the first application of a novel improved metaheuristic optimization technique, dynamic crow search algorithm (DCSA) to find the optimal design of the power system stabilizer (PSS). Using the novel DCSA adaptive approach provides a global search capability as well as fastness and effectiveness to get the PSS best parameters under the search space constraints and local optimums. Moreover, PSS parameters has a crucial effect on power system stability that is a major nowadays engineering concern, where its best parameters could make a difference by damping small-signal stability oscillations effectively and in the smallest amount of time if they are obtained through a robust algorithm. As a result, PSS will prevent large types of instabilities which could reach up to some generation stations shedding. The novel proposed algorithm is for first time used and tested under different power system instabilities scenarios by using single-machine infinity bus model over multiple operating conditions to damp out perturbation signals effectively by means of getting optimal PSS design parameters, where Integral absolute error (IAE) performance index is used as an objective function to be minimized. Additionally, results obtained by the proposed approach are compared with those obtained by other methods. It is observed that the proposed method has better convergence characteristics and robustness compared to the original version and other comparison methods. It is revealed that the proposed adaptive method is able to improve the power system stability dynamics and damp out perturbation oscillations successfully.

On the hypercomplex numbers and normed division algebras in all dimensions: A unified multiplication.

Mathematics is the foundational discipline for all sciences, engineering, and technology, and the pursuit of normed division algebras in all finite dimensions represents a paramount mathematical objective. In the quest for a real three-dimensional, normed, associative division algebra, Hamilton discovered quaternions, constituting a non-commutative division algebra of quadruples. Subsequent investigations revealed the existence of only four division algebras over reals, each with dimensions 1, 2, 4, and 8. This study transcends such limitations by introducing generalized hypercomplex numbers extending across all dimensions, serving as extensions of traditional complex numbers. The space formed by these numbers constitutes a non-distributive normed division algebra extendable to all finite dimensions. The derivation of these extensions involves the definitions of two new π-periodic functions and a unified multiplication operation, designated as spherical multiplication, that is fully compatible with the existing multiplication structures. Importantly, these new hypercomplex numbers and their associated algebras are compatible with the existing real and complex number systems, ensuring continuity across dimensionalities. Most importantly, like the addition operation, the proposed multiplication in all dimensions forms an Abelian group while simultaneously preserving the norm. In summary, this study presents a comprehensive generalization of complex numbers and the Euler identity in higher dimensions, shedding light on the geometric properties of vectors within these extended spaces. Finally, we elucidate the practical applications of the proposed methodology as a viable alternative for expressing a quantum state through the multiplication of specified quantum states, thereby offering a potential complement to the established superposition paradigm. Additionally, we explore its utility in point cloud image processing.

Entwicklung eines automatischen Open-Source-Werkzeugwechslers

Abstract Open source machine tools (OSMT) have the potential to democratize manufacturing by lowering access barriers, enabling new modes of value creation. However, there is still a lack of an open source automatic tool changer (ATC), due to its inherent complexity in terms of mechanical design and control. An ATC system is crucial for automating manufacturing processes that require multiple operations with different tools for a complete production step. This paper describes the design of a fully open source ATC concept, detailing its mechanical design and control schematic.

A Cascaded ReRAM-based Crossbar Architecture for Transformer Neural Network Acceleration

Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide spectrum of artificial neural networks. However, there remain significant challenges to apply existing ReRAM-based PIM accelerators to the most popular Transformer neural networks. Since Transformers involve a series of matrix-matrix multiplication (MatMul) operations with data dependencies, they should write intermediate results of MatMuls to ReRAM crossbar arrays for further processing. Conventional ReRAM-based PIM accelerators often suffer from high latency of ReRAM writes and intra-layer pipeline stalls. In this paper, we propose ReCAT, a ReRAM-based PIM accelerator designed particularly for Transformers. ReCAT exploits transimpedance amplifiers (TIAs) to cascade a pair of crossbar arrays for MatMul operations involved in the self-attention mechanism. The intermediate result of a MatMul generated by one crossbar array can be directly mapped to another crossbar array, avoiding costly analog-to-digital conversions. In this way, ReCAT allows MVM operations to overlap with the corresponding data mapping, hiding the high latency of ReRAM writes. Furthermore, we propose an analog-to-digital converter (ADC) virtualization scheme to dynamically share scarce ADCs among a group of crossbar arrays, and thus significantly improve the utilization of ADCs to eliminate the performance bottleneck of MVM operations. Experimental results show that ReCAT achieves 207.3 ×, 2.11 ×, and 3.06 × performance improvement on average compared with other Transformer acceleration solutions—GPUs, ReBert, and ReTransformer, respectively.

Tolerance to asynchrony in algorithms for multiplication and modulo

In this article, we study some parallel processing algorithms for multiplication and modulo operations. We demonstrate that the state transitions that are formed under these algorithms satisfy lattice-linearity, where these algorithms induce a lattice among the global states. Lattice-linearity implies that these algorithms can be implemented in asynchronous environments, where the nodes are allowed to read old information from each other. It means that these algorithms are guaranteed to converge correctly without any synchronization overhead. These algorithms also exhibit snap-stabilizing properties, i.e., starting from an arbitrary state, the sequence of state transitions made by the system strictly follows its specification.

Effect of Crop Establishment Method, Residue and Nutrient Management on Productivity of Rice-Wheat Cropping System at Rupandehi, Bhairahawa, Nepal

The puddling operation and multiple tillage operations done in traditional rice-wheat system have negative impact on soil resulting into low productivity of rice - wheat system. A field experiment was conducted to find the alternate practices for enhancing the productivity of rice-wheat system at National Wheat Research Program, Bhairahawa during 2018/19. Three crop establishment methods: surface seeded wheat (SSW) followed by (fb) unpuddled transplanted rice (U-TPR), zero tilled wheat (ZTW) fb zero tilled direct seeded rice (ZT-DSR) and conventionally tilled wheat (CTW) fb puddled transplanted rice (PTR) were tested in two levels of residue management: residue removed (R0) and residue retention (R50). Three levels of nutrient management: recommended dose of NPK (F100), 25% higher dose of NPK (F125) and farmer’s practice (FP) were assigned in wheat. Strip-split plot design with 3 replications was used in wheat while strip-plot design with 9 replications was used in rice. Crop establishment methods (CSM) were assigned in vertical strips; residue management in horizontal blocks and nutrient management were assigned in subplots. The number of tillers at maximum tillering stage, maximum leaf area index, effective tiller per square meter and number of grains per spike of wheat were significantly affected by CSM, where ZTW had shown better results (413.7 tillers, 2.12 LAI, 224.1 effective tiller per square meter and 34.27 grains per spike) than SSW and CTW but the CSM had no significant effect on grain yield of both rice and wheat crop. R0 had produced significantly higher straw yield than R50 of both rice and wheat, whereas, R50 had produced significantly higher harvest index than R0 in rice. The application of 25% more nutrients than the recommended dose resulted in the significantly better growth, yield attributes, and yield of wheat. The ZTW fb ZT-DSR had produced significantly higher system yield than SSW fb U-TPR where, CTW fb PTR was statistically at par with both systems. The recommended dose of nutrients (F100) and 25% higher than F100 (F125) had produced significantly higher system yield than farmer’s practice (FP) dose. Research results revealed that ZTW fb ZT-DSR system can be suggested instead of traditional CTW fb PTR system without significant yield differences.

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

Efficient MAC Unit Design for DSP Processors Using Multiplication and Accumulation Operations

Efficient MAC Unit Design for DSP Processors Using Multiplication and Accumulation Operations

Interpretable reduced-order optimization research for improving indoor environmental quality in buildings based on dimensional analysis and surrogate-based optimization

Implementing large-scale optimization designs and identifying optimal design variables in fields such as architectural engineering is crucial for improving indoor environmental quality. However, the high-dimensional nonlinear characteristics complicate the understanding of the optimization process and increase the difficulty of finding optimal solutions. This study proposes an interpretable reduced-order optimization (ROO) method based on dimensional analysis, which integrates numerous variables into a single reduced-order coefficient through a power form. The goal is to develop a reduced-order and approximately one-dimensional nonlinear relationship during the optimization process and to reveal the mechanisms of single-objective and multi-objective optimization, thereby enhancing the interpretability of the optimization process. The reliability of the ROO is validated through single-objective optimization of building thermal bridges and multi-objective optimization of squirrel cage fans. The study shows that the power in the reduced-order coefficient can be used to determine the sensitivity and impact of design variables on optimization targets. The nonlinear relationship between the composite structural parameters of the building thermal bridge and the total heat flux can be reduced to an approximately one-dimensional linear relationship, significantly reducing the total heat flux of the thermal bridge by 10.3 %. The ROO accurately describes the aerodynamic performance of the fan and its high-dimensional nonlinear relationship with eight design variables, eliminating optimization conflicts among multiple operating conditions and significantly enhancing the aerodynamic performance. The ROO method proposed in this study offers a new approach to making the optimization process more interpretable, playing a crucial role in optimization design and revealing optimization mechanisms in engineering applications.

Boolean Computation in Single-Transistor Neuron.

Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single-transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra-compact design. By leveraging the spatiotemporal integration of inputs, bio-realistic spiking-dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft-XOR-based neural network via algorithm-hardware co-design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra-compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super-scalable neural networks for resource-efficient brain-inspired information processing.

Efficient memristor accelerator for transformer self-attention functionality

The adoption of transformer networks has experienced a notable surge in various AI applications. However, the increased computational complexity, stemming primarily from the self-attention mechanism, parallels the manner in which convolution operations constrain the capabilities and speed of convolutional neural networks (CNNs). The self-attention algorithm, specifically the matrix-matrix multiplication (MatMul) operations, demands a substantial amount of memory and computational complexity, thereby restricting the overall performance of the transformer. This paper introduces an efficient hardware accelerator for the transformer network, leveraging memristor-based in-memory computing. The design targets the memory bottleneck associated with MatMul operations in the self-attention process, utilizing approximate analog computation and the highly parallel computations facilitated by the memristor crossbar architecture. Remarkably, this approach resulted in a reduction of approximately 10 times in the number of multiply-accumulate (MAC) operations in transformer networks, while maintaining 95.47% accuracy for the MNIST dataset, as validated by a comprehensive circuit simulator employing NeuroSim 3.0. Simulation outcomes indicate an area utilization of 6895.7 μm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu m^2$$\\end{document}, a latency of 15.52 seconds, an energy consumption of 3 mJ, and a leakage power of 59.55 μW\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu W$$\\end{document}. The methodology outlined in this paper represents a substantial stride towards a hardware-friendly transformer architecture for edge devices, poised to achieve real-time performance.

Development of Local Culture-Based Image Media to Improve Student Learning Outcomes on the Material of Arithmetic Operations of Multiplication of Natural Numbers

The aim of this research is to develop Image Media learning media to calculate the multiplication of natural numbers for class II students at SDN Tejamari, Baros District. This research uses the type of research and development (R&D) from S. Thiagarajan's development model, which has 4 stages of development, namely defining, designing, developing and disseminating. The media developed is validated by material experts and media experts before conducting initial field trials on students. Data was collected using questionnaires from media experts, material experts, class II teacher questionnaires, student questionnaires, interview observations and documentation. Data resulting from research were analyzed using quantitative and qualitative analysis techniques. Based on the validation results, material experts got a score of 46 with a percentage of 83% with the criteria "Good", validation from media experts with a score of 44 with a percentage of 88% with the criteria "Very Good", and the results of class II teacher validation with a score of 99 with a percentage 94% with "Very Good" criteria. As for the results of the initial field trials, the percentage was 90% - 100% with the criteria "Very Good". Overall, the learning media Image Media is suitable for use by making it easier to calculate multiplication of natural numbers by students who have difficulty calculating multiplication using direct observation. This image media makes it easier for students to learn multiplication. Based on the results of the Pre-Test before using image media and the Post-Test using image media, the average score was 0.898 with high criteria. And obtained an N-Gain percentage score of 89.833 in the effective category. So, the learning media Image Media has been effectively used for students in improving learning outcomes for the arithmetic operation of multiplying natural numbers.

Disentangling new physics in K→πνν¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ K\ o \\pi \ u \\overline{\ u} $$\\end{document} and B→KK∗νν¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ B\ o K\\left({K}^{\\ast}\\right)\ u \\overline{\ u} $$\\end{document} observables

We investigate the possibility of disentangling different new physics contributions to the rare meson decays and through kinematic distributions in the missing energy . We employ dimension-6 operators within the Low-Energy Effective Field Theory (LEFT), identifying the invisible part of the final state as either active or sterile neutrinos. Special emphasis is given to lepton-number violating (LNV) operators with scalar and tensor currents. We show analytically that contributions from vector, scalar, and tensor quark currents can be uniquely determined from experimental data of kinematic distributions. In addition, we present new correlations of branching ratios for K and B-decays involving scalar and tensor currents. As there could a priori also be new invisible particles in the final states, we include dark-sector operators giving rise to two dark scalars, fermions, or vectors in the final state. In this context, we present new calculations of the inclusive decay rate for dark operators. We show that careful measurements of kinematic distributions make it theoretically possible to disentangle the contribution from LEFT operators from most of the dark-sector operators, even when multiple operators are contributing. We revisit sum rules for vector currents in LEFT and show that the latter are also satisfied in some new dark-physics scenarios that could mimic LEFT. Finally, we point out that an excess in rare meson decays consistent with a LNV hypothesis would point towards highly flavor non-democratic physics in the UV, and could put high-scale leptogenesis under tension.

RSD-based high-performance radix-4 Montgomery Modular Multiplication for Elliptic Curve Cryptography

This paper proposes a high-performance radix-4 Montgomery Modular Multiplication (MMM) algorithm and its corresponding hardware architecture for Elliptic Curve Cryptography (ECC), in which the quotient and the partial product accumulation are computed in parallel in each iteration. Additionally, in this MMM, the Redundant Signed Digit (RSD) representation and the Signed Digit Adder (SDA) are used to eliminate the long carry chain and achieve parallel computation, as well as remove pre-computation and integrate modular reduction operations. Our MMM algorithm is implemented in 256-bit and 1024-bit versions on Xilinx Virtex-6 and Virtex-7 FPGAs, respectively. It consumes only 1.55k/10.18k Look-Up Tables (LUTs), takes 133/517 clock cycles, and runs at maximum frequencies of 558.8/641.7 MHz. According to the comparison in terms of Area Time Product (ATP), our design can achieve the ATP of 0.369 over the 256-bit NIST prime domain, which is approximately half of that of the state-of-the-art works. The Scalar Point Multiplication (SPM) scheme using this MMM algorithm consumes 14.19k LUTs and completes a single Scalar Point Multiplication (SPM) operation in 0.217 ms, and it also has a lower ATP than most other SPM algorithms currently in existence.

Optimizing an express delivery mode based on high-speed railway and crowd-couriers

The rapid development of e-commerce has led to various challenges in the express delivery industry. To provide efficient and economical delivery services, this study introduces a joint transportation system for parcel delivery utilizing high-speed railway (HSR) and crowd-couriers. In this system, long-haul transportation between cities is conducted by HSR, which enables multiple transshipments in different operation lines. The pre-haul parcel pickup and end-haul delivery services within the city are performed by crowd-couriers, thus achieving door-to-door transportation. A mathematical model is developed to jointly optimize the freight allocation problem in the rail segment and the crowd-courier routing problem in the road segment. The freight allocation problem is formulated based on the concept of transportation plans. The transportation plans with and without transshipments are found by a depth-first search-based algorithm in the rail segment. A customized heuristic algorithm based on the adaptive large neighborhood search with multiple operators is designed to find a near-optimal solution. A set of numerical experiments demonstrates the speed and accuracy with which the proposed method solves different-sized versions of problem. A sensitivity analysis based on a real HSR network is also conducted to provide more reliable managerial insights for operators.

Fisher-informed continual learning for remaining useful life prediction of machining tools under varying operating conditions

Accurate prediction of remaining useful life (RUL) of equipment has become an essential task in manufacturing. It not only helps prevent unexpected failures but also enables maximal utilization of available life, thus improving process efficiency. In practice, however, the use of multiple operating conditions that vary by time impedes efficient data-driven RUL prediction. Unlike conventional supervised learning setups, varying operating conditions generate heterogeneous data with time-varying generating distributions. Thus, existing approaches cannot be effectively applied due to increasing modeling and memory costs. One of the domains that suffer from this issue is machining, where RUL prediction of cutting tools is crucial for productivity. Considering realistic circumstances with varying operating conditions, this work proposes a method named Fisher-informed continual learning (FICL), which enables efficient tool RUL prediction that adaptively learns as conditions change without storing previous data and models. FICL uses Fisher information to improve generalization via sharpness-aware minimization and transfer knowledge between operating conditions through structural regularization. Experiments using datasets from real-world machining processes under five distinct operating conditions prove FICL’s efficacy, indicating its superior prediction performance to existing methods for all operating conditions. Particularly, FICL manifests the least catastrophic forgetting, implying it effectively retains informative knowledge from varying operating conditions.

Task Characteristics Associated with Mathematical Word Problem-Solving Performance Among Elementary School-Aged Children: A Systematic Review and Meta-Analysis

Mathematical word problem-solving skills are crucial for students across their lives, yet solving such tasks poses challenges for many. Therefore, understanding the characteristics of mathematical word problems that are associated with students’ performance is important. The objective of this systematic review and meta-analysis was to evaluate the effects of linguistic and numerical task characteristics associated with mathematical word problem-solving performance among elementary school-aged children (Grades 1 to 6). The systematic review was based on five electronic databases and citation searching. Reporting was conducted following The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The findings (K = 69) showed that five of the six investigated linguistic task characteristics (i.e., the position of the unknown, schematic structure, irrelevant information, realistic considerations, and lexical consistency) and one of the two numerical task characteristics (i.e., number of operations) were related (g = 0.39 to 4.26) with elementary school-aged children’s mathematical word problem-solving. However, the findings did not provide support for a general association between a familiar situational narrative or the required operation with mathematical word problem-solving. The findings highlight that elementary school-aged children especially struggle with mathematical word problems requiring realistic considerations or multiple mathematical operations, containing lexical inconsistency, and problems in which the position of the unknown is the first value. This further understanding of elementary schoolers’ word problem-solving performance may guide the design of appropriate and progressive instruction and assessment tools and steer research into the interactions within task characteristics and with individual characteristics.

Calcium Carbonate Deposition Model Supporting Multiple Operating Conditions Based on the Phase-Field Method for Free-Surface Flows

The drainage systems of tunnels situated in limestone regions frequently encounter crystallization blockages. Numerous studies have addressed this issue, and their findings identified factors such as the flow velocity and temperature as influencing the crystallization process. However, these studies could not predict the occurrence of crystallization. Regarding theoretical approaches, most studies have focused on full-pipe operations or have solely considered flow-field dynamics without including simulations of the chemical reactions and mass transfers. This study introduces a mass-transfer model for drainage pipes based on a two-phase flow (water and air) with a free surface and non-full pipe flow that simulates the crystallization deposition process in drainage pipes. This model can predict the deposition conditions at varying flow velocities and intuitively visualize the crystallization process under the influence of various factors. The impact of flow velocity on the overall crystallization deposition process can be directly analyzed through simulations developed using this model. The results show that under conditions of incomplete pipe flow with no pressure at the outlet, the weight of the deposition first increases and then decreases within a certain velocity. This model can depict the variations within a 30 d period.

Numerical Evaluation of the Effectiveness of the Use of Endplates in Front Wings in Formula One Cars under Multiple Track Operating Conditions

The last change in the technical regulations of Formula One that came into force in 2022 brought with it significant changes in the aerodynamics of the vehicle; among these, those made to the front wing stand out since the wing was changed to a more straightforward shape with fewer parts but with no less efficiency. The reduction in its components suggests that if one part were to suffer damage or break down, the efficiency of the entire front wing would be affected; however, from 2022 to date, there have been occasions in which the cars have continued running on the track despite losing some of the endplates. This research seeks to understand the endplates’ impact on the front wing through a series of CFD simulations using the k-ω SST turbulence model. To determine efficiency, the aerodynamic forces generated on the vehicle’s front wing, suspension, and front wheels were compared in two different operating situations using a model with the front wing in good condition and another in which the endplates were removed. The first case study simulated a straight line at a maximum speed where the Downforce is reduced by 2.716% while the Drag and Yaw increase by 7.092% and 96.332%, respectively, when the model does not have endplates. On the other hand, the second case study was the passage through a curve with a decrease of 17.707% in Downforce, 6.532% in Drag, and 22.200% in Yaw.