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The optimal design of an airfoil varies across flight conditions, motivating the search for ways to implement adaptive designs. This study proposes an integrated framework for morphing airfoils using shape-memory alloy (SMA) actuators, targeting improved lift-to-drag (L/D) ratios during quasi-steady flight. Airfoils are parameterized using the PARSEC method and optimized using particle swarm optimization (PSO), with CFD evaluations conducted in COMSOL. A PID-controlled SMA model implements the resulting shapes through voltage-controlled deformation, simulated in Simscape. This setup allows aerodynamic performance to be optimized while respecting actuation and control constraints. Validation against benchmark data confirms solver accuracy, and actuator tracking performance is demonstrated with displacement errors below 0.6%. The framework bridges aerodynamic design and real-time implementation, highlighting SMA's suitability for cruise-phase morphing. While the current study focuses on fixed-wing applications, future work may extend the approach to adaptive UAVs or other domains requiring geometry-responsive actuation.

Sustainability has become one of the most significant considerations in everyday work, including energy production. The fast-growing trend of wind energy around the world has increased the demand for efficient and optimized airfoils, which has paved the way for energy harvesting systems. The present manuscript proposes an aerodynamically optimized design of the well-known existing NREL S809 airfoil for performance enhancement of the blade design for wind turbines. An integrated code, based on a genetic algorithm, is developed to optimize the asymmetric NREL S809 airfoil by class shape transformation (CST) and the parametric section (PARSEC) parameterization method, analyzing its aerodynamic properties and maximizing the lift of the airfoil. The in-house MATLAB code is further incorporated with XFOIL to calculate the coefficient of lift, coefficient of drag and lift-to-drag ratio at angles of attack of 0° and 6.2° by the panel technique and validated with National Renewable Energy Laboratory (NREL) experimental results provided by The Ohio State University (OSU). On the other hand, steady-state CFD analysis is performed on an optimized S809 airfoil using the Reynolds-averaged Navier–Stokes (RANS) equation with the K–ω shear stress transport (SST) turbulent model and compared with the experimental data. The present method shows that the optimized airfoil by CST is predicted, with an increment of 11.8% and 9.6% for the lift coefficient and lift-to-drag ratio, respectively, and desirable stability parameters obtained for the design of the wind turbine blades. These characteristics significantly improve the overall aerodynamic performance of new optimized airfoils. Finally, the aerodynamically improved results are reported for the design of the NREL Phase II, Phase III and Phase VI HAWT blades.

In the development of interactive aerodynamic optimization tools, the need to reduce the computational complexity of flow calculations has arisen. Computational complexity can be reduced by estimating the flow variables using machine learning, but that approach has a number of hindrances. Avoiding these hindrances through lowering the computational complexity by stating the assumptions of inviscid incompressible potential flow is the focus of this article. The assumptions used restrict the applicability of this approach to only specific cases, but in engineering practice, these cases are quite widespread. The assumptions allowed the coupling of the adjoint method with parsec parametrization and the panel method, yielding a highly computationally efficient and robust tool for optimizing an airfoil’s lift coefficient (Cy). The optimization of the NREL S809 airfoil was carried out, and the results were verified using the Xfoil 6.99 software. The Xfoil verification showed that by making minimal changes to the airfoil’s shape, the Cy and lift-to-drag ratios were significantly improved. The improvement magnitude was over 94% for a 0 deg angle of attack (AoA) and over 16% for 6.2 deg AoA. This indicates an improvement in performance that is similar to that of some genetic algorithms, but with computational costs that are many orders of magnitude lower.

This study describes the use of artificial intelligence to predict the aerodynamic properties of wind turbine profiles. The goal was to determine the lift coefficient for an airfoil using its geometry as input. Calculations based on XFoil were taken as a target for the predictions. The lift coefficient for a single case scenario was set as a value to find by training an algorithm. Airfoil geometry data were collected from the UIUC Airfoil Data Site. Geometries in the coordinate format were converted to PARSEC parameters, which became a direct feature for the random forest regression algorithm. The training dataset included 60% of the base dataset records. The rest of the dataset was used to test the model. Five different datasets were tested. The results calculated for the test part of the base dataset were compared with the actual values of the lift coefficients. The developed prediction model obtained a coefficient of determination ranging from 0.83 to 0.87, which is a good prognosis for further research.

The aircraft conceptual design step requires a substantial number of aerodynamic configuration evaluations. Since the wing is the main aircraft lifting element, the focus is on solving direct and reverse design problems. The former could be solved using a low-cost computational model, but the latter is unlikely, even for these models. Surrogate modeling is a technique for simplifying complex models that reduces computational time. In this work, a surrogate aerodynamic model, based on the implementation of a multilayer perceptron (MLP), is presented. The input data consist of geometrical characteristics of the wing and airfoil and flight conditions. Some of the MLP hyperparameters are defined using evolutionary algorithms, learning curves, and cross-validation methods. The MLP predicts the aerodynamic coefficients (drag, lift, and pitching moment) with high agreement with the substituted aerodynamic model. The MLP can predict the aerodynamic characteristics of compressible flow up to 0.6 M. The developed MLP has achieved up to almost 800 times faster in computing time than the model on which it was trained. The application of the developed MLP will enable the rapid study of the effects of changes in various parameters and flight conditions on flight performance, related to the design and modernization of new vehicles.

The novel enhanced dynamic game method (EDGM) is proposed to advance game-based design approaches, with a focus on enhancing solution distribution, precision, and the ability to reveal the dynamic influence sensitivity of design variables on objective functions. An integrated mathematical model is developed by combining EDGM with PARSEC and CST parameterization methods, forming a systematic framework for offshore wind turbine airfoil optimization. Targeting airfoils with approximately 30% and 35% thickness, the study aims to improve annual energy production (AEP) and optimize the polar moment of inertia. Redesigned airfoils using the EDGM-integrated model exhibit significant enhancements in aerodynamic performance and anti-flutter capability compared to baseline airfoils DU97W300 and DU99W350. The methodology’s superiority is validated through analyses of pressure distributions, lift-to-drag ratios, and streamline patterns, as well as comparative evaluations using HV and Spacing metrics, demonstrating EDGM’s potential for broader engineering applications in complex multi-objective optimization scenarios.

Typical communication networks for closed-loop control application in factory automation are designed to grant short cycle times, precise synchronicity in the microseconds range and high reliability with low packet error rates. However, new requirements for greater flexibility and scalability can only be fulfilled in combination with wireless networks. Therefore, cascaded communication networks with combinations of wired and wireless subnetworks will arise. However, combining heterogeneous communication protocols will lead to additional latencies. In order to reduce their influence on the real-time behavior of the overall network as far as possible, this latency must be completely known to the application and minimized as far as possible. In this paper, we analyze one additional source of latencies resulting from frame conversion for different subnetworks. With an abstract network model we introduce, we are able to analyze the timing independent of a specific protocol implementation. Using this, we are able to show different existing frame conversion concepts with their properties regarding latency and jitter. By simulating a typical network for automation technology and a currently developed wireless real-time communication network, we were able to verify our investigations.

Single-ISA heterogeneous multi-core processors trade-off power with performance; however, threads that co-run on shared resources suffer from resource contention, which induces performance degradation and energy inefficiency. The authors introduce a novel approach to optimise the co-scheduling of multi-threaded applications on heterogeneous processors. The approach is based on the concept of stakes function, which represents the trade-off between isolation and sharing of resources. The authors also develop a co-scheduling algorithm that use stakes functions to optimise resource usage while mitigating resource contention, thus improving performance and energy efficiency. They validated the approach using applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite, obtaining up to 12.88% performance speed-up, 13.65% energy speed-up and 28.29% energy delay speed-up with respect to the standard Linux heterogeneous multi-processing scheduler.

Spintronic memory [spin-transfer torque-magnetic random access memory (STT-MRAM)] is an attractive alternative technology to CMOS since it offers higher density and virtually no leakage current. Spintronic memory continues to require higher write energy, however, presenting a challenge to memory hierarchy design when energy consumption is a concern. This study motivates the use of STT-MRAM for the first-level caches of a multicore processor to reduce energy consumption without significantly degrading the performance. The large STT-MRAM first-level cache implementation saves leakage power. Moreover, the use of small level-0 cache regains the performance drop due to STT-MRAM long write latencies. The combination of both reduces the energy-delay product by 65% on average compared with CMOS baseline. The proposed STT hierarchy also shows good scalability over the CMOS with a few benchmarks which scale significantly better. The PARSEC and Splash2 benchmark suites are analysed running on a modern multicore platform, comparing performance, energy consumption and scalability of the spintronic cache system to a CMOS design.