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8th International Conference on Digital Information Management (ICDIM)

作者： Mubeen, M. Gilani, S. A. M. Zafar, K. Natl Univ Comp & Emerging Sci Dept Comp Sci Lahore Pakistan

ISBN: (纸本)9781479906154;9781479906130

This paper presents a novel digital watermarking algorithm using Contourlet and Discrete Cosine transforms to perceptually shape the watermark by taking care of two conflicting parameters imperceptibility and robustness. multi objective genetic algorithm is used to decide the optimal watermarking strength (alpha). Contourlet transform is used to get better quality by taking care of Human Visual System and better robustness under various kinds of linear and non-linear filtering attacks. Discrete Cosine Transform with Zigzag scanning is used to detect the watermark blindly. The watermark embedding process is applied over the sub-band coefficients that lie on the edges and around the edges where distortions are less noticeable. The proposed algorithm provides an excellent tradeoff between robustness and imperceptibility and it is image adaptive. The experimental results show that the proposed algorithm improved the resistance to attacks especially JPEG compression with 5% quality, Median filter with a mask of 6 X 6, resizing up to 20% and various non-linear filtering attacks. Motivation of presented work is to adapt a watermark sequence to those frequency components of an image which provide a transparent and robust watermark.

关键词： Digital watermark Contourlet transform multi objective genetic algorithm Perceptual shaping

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Hierarchical Framework for Space Exploration Campaign Schedule Optimization

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JOURNAL OF SPACECRAFT AND ROCKETS 2024年第5期61卷 1146-1164页

作者： Gollins, Nicholas Ho, Koki Georgia Inst Technol Daniel Guggenheim Sch Aerosp Engn Atlanta GA 30332 USA

Space exploration plans are becoming increasingly complex as public agencies and private companies target deep-space locations, such as cislunar space and beyond, which require long-duration missions and many supporting systems and payloads. Optimizing multimission exploration campaigns is challenging due to the large number of required launches as well as their sequencing and compatibility requirements, making conventional space logistics formulations unscalable. To tackle this challenge, this paper proposes an alternative approach that leverages a two-level hierarchical optimization algorithm: an evolutionary algorithm is used to explore the campaign scheduling solution space, and each of the solutions is then evaluated using a time-expanded multicommodity flow mixed-integer linear program. A number of case studies, focusing on the Artemis lunar exploration program, demonstrate how the method can be used to analyze potential campaign architectures. The method enables a potential mission planner to study the sensitivity of a campaign to program-level parameters such as logistics vehicle availability and performance, payload launch windows, and in situ resource utilization infrastructure efficiency.

关键词： Space Exploration and Technology Optimization algorithm Space Logistics Models Lunar Exploration Mixed Integer Linear Programming Evolutionary algorithm Propellant Mass Fraction multi objective genetic algorithm

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Dynamic Assessment and Optimization of Thermal Energy Storage Integration with Nuclear Power Plants Using Machine Learning and Computational Fluid Dynamics

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APPLIED ENERGY 2025年 391卷

作者： Faizan, Muhammad Afgan, Imran Khalifa Univ Sci & Technol Coll Engn Dept Mech & Nucl Engn POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Emirates Nucl Technol Ctr ENTC Abu Dhabi U Arab Emirates

This study integrates thermal energy storage (TES) systems with nuclear power plants (NPPs) utilizing phase change materials (PCMs). It employs computational fluid dynamics (CFD) simulations and machine learning techniques to improve the overall efficiency and profitability of NPPs. The novelty of this research extends beyond merely analyzing the influence of PCM thermophysical properties, design parameters, and input conditions on system performance. The objective is to develop a TES system that can be effectively integrated with NPPs by addressing critical challenges, including the dynamic assessment of input parameters and the utilization of available excess energy in response to real-time demand fluctuations. For the analysis, 2,500 CFD simulations were performed to assess the phase change behavior within a vertical annular channel. Key design factors, such as heat transfer fluid injection conditions and various PCM properties, were systematically analyzed. The extensive dataset from numerical simulations was utilized to employ an artificial neural network (ANN) and a multi-objective genetic algorithm (MOGA) for optimizing the TES system, with an emphasis on minimizing injection velocity, reducing charging duration, and maximizing stored energy. The numerical and machine learning models were also explicitly evaluated against experimental data under different conditions. The findings indicate that design parameters have a substantial effect on the performance of the TES system, with the ANN model effectively predicting PCM melting time and energy storage capacity. This study presents a complete framework for an optimized thermal energy storage integration strategy specifically designed for NPPS, offering a scalable and efficient method for improving energy storage and utilization of excess energy during off-peak hours.

关键词： Thermal energy storage Melting process Phase Change Material Latent heat storage Artificial Neural Network multi objective genetic algorithm

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Design Exploration of Transonic Airfoils for Natural and Hybrid Laminar Flow Control Applications

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JOURNAL OF AIRCRAFT 2023年第3期60卷 716-732页

作者： Sudhi, Anand Radespiel, Rolf Badrya, Camli Inst Fluid Mech Hermann Blenk Str 37 D-38108 Braunschweig Germany Univ Calif Davis One Shields Ave Davis CA 95616 USA

Designing transonic laminar swept wing at high Reynolds numbers is challenging due to the premature flow transition from the prominent crossflow instabilities (CFIs). Hence, natural laminar flow (NLF) wings are limited to lower sweep angles. In this study, hybrid laminar flow control (HLFC) is used to extend this limit to design low-drag transonic infinite wing. A multi-objective genetic algorithm with competing drag components as objectives is used to design NLF and HLFC airfoils. Optimal design is a tradeoff between the drag reduction from skin friction drag, pressure drag, and suction drag, and does not resemble initial airfoils. Airfoil shape and suction distribution are coupled and optimized simultaneously. Euler flow solver with integral boundary-layer method is coupled with a higher-fidelity Linear Stability Analysis using 2.5D approximation for transition prediction. At wing sweep of 22.5 degrees, Mach number of 0.78, and Reynolds number of 30 million, optimum NLF airfoil has 27% lower drag than an optimum turbulent airfoil. The optimum HLFC airfoil showed a 25% lower total drag than the NLF airfoil. A higher optimum sweep angle was observed for low-drag HLFC airfoil (20.5 degrees) in comparison to the NLF airfoil (16.8 degrees) and both favored an undercut on the lower surface to dampen CFI.

关键词： multi objective genetic algorithm Boundary Layer Suction hybrid laminar flow control Aerodynamic Shape Optimization HLFC Laminar Turbulent Transition Transonic Flow airfoil optimization Drag Reduction Swept Wing

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Designing Satellite Constellations to Observe Earth's Time-Varying Gravity from Clock Frequency Comparisons

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第3期60卷 848-858页

作者： Lee, Kwonhee A. Gunter, Brian C. Georgia Inst Technol Atlanta GA 30332 USA Daniel Guggenheim Sch Aerosp Engn Atlanta GA USA

The nondominated sorting genetic algorithm III (NSGA-III) is used to design nonsymmetric satellite constellations to observe large-scale high-frequency gravity variations that would serve as complementary systems to other dedicated gravity field mapping missions such as the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions. The functionality of the NSGA-III method is validated by reproducing optimal surface-coverage satellite constellations from the literature. Then, NSGA-III is used to generate nondominated constellations tuned to detect short-term long-wavelength gravity field variations. The constellations are used to generate subweekly gravity field solutions from clock frequency comparison measurements. The effectiveness of NSGA-III for optimizing nonsymmetric constellations was demonstrated because the constellations obtained from NSGA-III were consistently superior in performance than traditional symmetric constellations. Furthermore, the results showed that the time-varying signals of interest (that is, gravity coefficients through the spherical harmonic degree and order of seven at daily intervals) can be unambiguously resolved by assuming an optical clock with a 4.8x10-18 fractional frequency uncertainty representative of current technology. The ability to monitor these short-term long-wavelength signals on a global scale would observe a spectrum of the gravity field inherently undetectable by GRACE-like missions, and it would provide new insight to the understanding of global mass redistribution processes.

关键词： multi objective genetic algorithm Gravity Recovery and Climate Experiment Geophysics Satellite Geodesy Satellite Constellations Spherical Harmonics multi-objective genetic algorithm Satellite Remote Sensing

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Shape Optimization of Pin-Fins by Free-Form Deformation and Active Learning Surrogate Model

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2023年第2期37卷 448-456页

作者： Yang, Fan Chen, Zhaolin Nanjing Univ Aeronaut & Astronaut Nanjing 210016 Peoples R China

The shapes of pin-fins play an essential role in enhancing overall and local forced convective heat removal. This study presents a new design optimization scheme based on the free shape deformation method, which gives rise to nonuniform pin-fin shapes depending on local flow structures interacting with each local pin-fin. The grid of pin-fins is embedded in a parameter space formed by basic function and control points. By moving the position of the control points, the shapes of the pin-fins and the neighborhood grid are deformed during optimization. The optimization objective function is the heat transfer coefficient of the pin-fin arrays at a limited pressure drop to ensure cycle efficiency. In addition, to reduce the computational resources, the active learning surrogate model for constraint global optimization is adopted, which is combined with computationally expensive computational fluid dynamics simulations. To exemplify the effectiveness of the proposed method on optimizing the pin-fin shape, two-dimensional pin-fins in inline arrangements are considered. Finally, an improvement in the heat transfer of 33.4% is achieved as compared to the baseline. Therefore, the proposed method of pin-fin optimization is effective in improving thermal performance.

关键词： Numerical Modeling Heat Transfer Coefficients Computational Fluid Dynamics Simulation Thermal Performance Heat Sinks Shape Optimization Sensitivity Analysis Finite Volume Method multi objective genetic algorithm Probability Density Functions

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Parameter Space Exploration of Cellular Mechanical Metamaterials Using genetic algorithms

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AIAA JOURNAL 2023年第8期61卷 3633-3643页

作者： Liu, Sheng Acar, Pinar Virginia Polytech Inst & State Univ Dept Mech Engn Blacksburg VA 24061 USA

Cellular materials widely exist in natural biologic systems such as honeycombs, bones, and woods. With advances in additive manufacturing, research on cellular metamaterials is emerging due to their unique mechanical performance. However, the design of on-demand cellular metamaterials usually requires solving a challenging inverse design problem for exploring complex structure-property relations of microstructured representative volume elements (RVEs) in the design domain. Here, we propose an experience-free and systematic methodology for exploring a parametrized system for microstructures of cellular mechanical metamaterials using a multiobjective genetic algorithm (GA). Globally, by considering the importance of the initial population selection for a population-based heuristic optimization method, we study the impact of the populations initialized by the different sampling methods on the optimal solutions. Locally, we develop our method by using a micro-GA with a new searching strategy, which requires the standard genetic algorithm to be conditionally run for a sufficient number of times with a small population size during the global searching process. We have applied our method to explore optimal solutions for applications mapped on two different parameter spaces of the cellular mechanical metamaterials with periodic and nonperiodic RVEs effectively and accurately.

关键词： multi objective genetic algorithm Mechanical Properties Cellular Metamaterials Topology Optimization Parameter Space Initial Population Random Number Generation Homogenization Inverse Design

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CFD Surrogate Assisted Optimization of an Electrically Driven Rocket Engine Propellant Pump

CFD Surrogate Assisted Optimization of an Electrically Drive...

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AIAA SciTech Forum

作者： Reddy, Vedanth Singh, Yashik Cooper, Ryan Snedden, Glen Brooks, Michael Univ KwaZulu Natal Aerosp Syst Res Inst ASRI Sch Engn ZA-4041 Durban South Africa

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The Aerospace Systems Research Institute (ASRI) is developing electric turbopumps for the South AFrican FIrst Rocket Engine (SAFFIRE) program. This aims to provide an indigenous pathway to microsatellite deployment. Dynamically scaled, water analog versions of these oxidizer and fuel pumps are important subsystems of this project that have been tested to acquire preliminary experimental data. This research details the numerical validation and surrogate optimization of the kerosene water-analog, scaled pump's performance. Benchmark simulations of the impeller and volute were first conducted in FINE (TM)/Open. Impeller optimization followed the objectives of improving hydraulic efficiency and cavitation reduction using the commercial software Numeca (TM) and Minamo (TM). The impeller geometry was first parametrized with the blade leading edge meridional profile and coordinates chosen as the free variables. Simulations were conducted on impeller geometries that varied according to the Latinized Centroidal Voronoi Tessellation (LCVT) sampling method. Tuned Radial Basis Function Networks (TRBFNs) were used to replicate and extend this design space. multi-objective genetic algorithms (MOGAs) were implemented to create optimized designs from the TRBFN surrogate model. Pareto-assisted techniques identified the globally optimum impeller design from the improved samples that increased hydraulic efficiency and minimum static pressure by 0.51% and 400.45 Pa, respectively. Manual modifications of the cutwater were made to attain short and long tongue configurations to further improve the global pump performance.

关键词： Rocket Engine Computational Fluid Dynamics Leading Edges Static Pressure Radial Basis Function Network Numerical Simulation multi objective genetic algorithm Propellant Turbopumps Kerosene

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Buffet Onset Optimization for Supercritical Airfoils with Prior-based Pressure Profile Prediction Model

Buffet Onset Optimization for Supercritical Airfoils with Pr...

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AIAA SciTech Forum

作者： Yang, Yunjia Lie, Runze Zhang, Yufei Chen, Haixin Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The transonic buffet is a critical phenomenon that limits the flight envelope of commercial aircraft. For years, RANS-based criteria like the lift-curve-break method have been applied to predict the buffet onset, but it requires flowfield simulations under multiple angles of attack, which is still too time-consuming for optimization. This paper presents a prior-based neural network model to predict pressure profiles under different angles of attack with reference to the one at cruise condition. The model is utilized to replace the offdesign CFD simulations in the lift-curve-break criterion so that only one simulation is needed to predict the buffet onset of an airfoil. It is then employed in a multi-objective genetic algorithm to optimize the buffet onset and the cruise lift-drag ratio simultaneously. To test the optimization procedure, the model is trained on an airfoil database and applied to optimize four airfoils not similar to the training database. The results show that all the optimizations receive positive gains of buffet onset, which affirm that the proposed model and optimization procedure can be reliably employed in search for airfoils with better buffet performance.

关键词： Supercritical Airfoils Aerodynamic Performance Freestream Conditions Flight Envelope CFD simulation multi objective genetic algorithm Neural Networks Commercial Aircraft Pareto Frontier Optimization algorithm

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Spacecraft Configuration Design Exploration Through Transformer-Based Reinforcement Learning

Spacecraft Configuration Design Exploration Through Transfor...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Demagall, Alexander Apaza, Gabriel Selva, Daniel Texas A and M Department of Aerospace Engineering Texas A and M University College StationTX United States Texas A and M University College StationTX United States University of Maryland College StationTX United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Configuration design, or the placement and orienting of components in a system, is an essential aspect of the engineering design process for many systems, and choices made in this stage can have a large downstream impact on cost and performance. Spacecraft are one such system, with many components that must be placed according to competing goals and requirements. Due to the complex and multi-domain nature of the problem, spacecraft configuration design currently relies heavily on the intuition and experience of a team of engineers. The problem addressed is, given a list of components and their characteristics (mass, dimensions, type, etc.), produce locations and orientations for each component which optimize on some given objectives. To solve this, a transformer model trained using a deep reinforcement learning (deep RL) implementation, Proximal Policy Optimization, is used. The transformer is benchmarked against a multi-objective genetic algorithm, NSGA-II, and random search. Results show the transformer model expands the Pareto front significantly more quickly than the genetic algorithm and the random search. This method could be used by spacecraft design teams to quickly perform a thorough tradespace exploration of the configuration design space. Additionally, with an appropriate set of components and objective and constraint functions, this method could be applied to other configuration design problems, such as chiplet design. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Spacecraft Configurations Reinforcement Learning Transformer multi objective genetic algorithm Attitude Control System Space Missions Heat Transfer Particle Swarm Optimization Heat Dissipation Neural Networks

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