ABSTRACT. The proposed work focuses on developing a remote alert system designed to track and locate vehicles before they enter areas known as non-coverage zones. These zones are characterized by significantly weak or entirely absent signal strength, commonly found in remote locations like deserts. The core of the system relies on detecting weak radio frequencies (RF) to pre-emptively identify vehicles approaching such areas. At the heart of this system is an Arduino microcontroller, tasked with analysing RF signal strengths detected by an RF detector. This setup enables the system to assess the proximity of vehicles to non-coverage zones accurately. When a vehicle is detected nearing a low-signal area, the Arduino triggers an alert mechanism. Communication with control system administrators is facilitated through a combination of LoRaWAN and GPS technologies. These components work in tandem to send location data and status messages, ensuring that vehicle positions are accurately relayed to the control station in real-time. This capability is crucial for pre-empting the potential loss of signal and maintaining continuous vehicle tracking. To validate the feasibility and effectiveness of the design, simulation was conducted using Proteus software. The simulation incorporated an Arduino Uno, ESP8266 module (for Wi-Fi connectivity), a buzzer (for physical alerts), LoRaWAN and GPS modules (for long-range communication and geolocation, respectively), an RF detector (to assess signal strength), and a smart phone interface (for user interaction and notifications). This comprehensive system design aims to mitigate the risks associated with vehicle operation in non-coverage zones by enabling pre-emptive tracking and location updates, facilitating more reliable and safer vehicle management in remote areas.

ABSTRACT. In today's world, privacy is becoming a major concern, especially with the use of Unmanned Aerial Vehicles (UAVs) for surveillance. This paper presents a novel approach to privacy protection in UAV live-streaming by introducing an automated video blurring system that operates in near real-time, replacing time-consuming operations in the post-processing stage. Our method leverages the Scale Invariant Feature Transform (SIFT) algorithm to match live footage with a pre-constructed aerial template image, enabling the blurring of private properties in near real-time allowing our UAV greater freedom of mobility whilst preserving the privacy of residents at ground level. This solution aligns with the EU’s General Data Protection Regulation (GDPR), offering a balance between surveillance utility and privacy rights. The proposed framework has the potential to significantly aid the UAV industry by providing a practical tool for privacy-preservation during aerial surveys and recreation drone flights.

ABSTRACT. YOLOv7 is a power full deep learning-based object detection model with a novel architecture that balances model complexity with inference time. When compared to other YOLO models, YOLOv7 has a lightweight backbone network called ELMAN that allows it to learn more efficiently without affecting the gradient path. However, the use of YOLOv7 in dealing with the problem of face detection from UAV-captured images has not been investigated. UAV-based images present challenges due to variations in view and distance, especially when taken outside. A total of 266 images collected by a UAV-based camera were used in this study to evaluate YOLOv7's performance in addressing this problem. In addition, six YOLOv7-based models were investigated in this study: YOLOv7, YOLOv7-X, YOLOv7-W6, YOLOv7-E6, YOLOv7-D6, and YOLOv7-E6E. In the experiments, 100 images from the WIDER FACE dataset were used for training purposes. However, the 266 UAV-based images collected were used in the testing phase. According to the reported results, YOLOv7 produced the best detection accuracy with a 95% in F1 measure. Furthermore, when tested on a single GPU machine, YOLOv7 required a short inference time of 3.7 milliseconds per image. The analysis revealed that YOLOv7 outperformed RetinaFace and MTCNN, one of the most popular pre-trained deep face detection models. Nonetheless, YOLOv7 fails to localize faces in low-resolution images, indicating that there is still room for improvement in terms of improving recall rates.

ABSTRACT. The use of hydrogen as a fuel in internal combustion engines holds promise for reduced emissions and improved efficiency. However, the injection and spray characteristics of gaseous hydrogen in high-pressure environments require further study to enable the development of optimized hydrogen injectors. Numerical simulations can provide such deep insight, however, models developed for hydrogen spray in the literature are scarce, as well as they are commonly validated using experimental data of working fluids other than hydrogen. In this work, a two-dimensional computational fluid dynamics (CFD) model, incorporating a discrete phase model (DPM), was developed to quantify the spray characteristics, such as penetration distance and spray angle, of directly injected hydrogen into a chamber filled with pressurized argon gas. The paper aims to evaluate the performance of the model by comparing the numerical results against experimental data of hydrogen spray. The results showed that the 2D CFD-DPM model was able to reasonably capture the penetration length of the hydrogen spray. However, the model failed to accurately represent the angular dispersion of the hydrogen spray within the pressurized argon medium. To address the limitations of the 2D approach, future work will focus on extending the model to a three-dimensional (3D) domain, as well as implementing other advanced multiphase models, such as Volume of Fluid (VOF). These improvements are expected to provide a deeper understanding of the complex hydrogen spray characteristics in high-pressure environments, which can aid in the design and optimization of hydrogen injectors for internal combustion engine applications.

ABSTRACT. Flash floods are sudden, large amounts of water that happen in a short amount of time. They are usually caused by heavy rain, storms, or sudden melting. The frequency ratio (FR) model is used in this study to make maps showing how likely it is that flash floods will happen in the Shigar Valley in north Pakistan. The primary aim of this study is to identify the elements that influence flash floods and to develop a zonation based on flash flood vulnerability within the designated study region. The Global Position System (GPS) was used to determine the locations of 428 flash floods to establish targets and conduct a field study. A flood danger inventory map was developed using Geographic Information System (ArcGIS 10.5) software, based on the specific locations of flash floods. The sites of flash flood hazards were randomly allocated into training and validation datasets. The models were constructed using 70% (300) of the data, which pertained to the sites of flash flood threats. The remaining 30% (128) of flash flood locations were allocated for model testing purposes. The present study included a set of 11 conditioning parameters, including slope, elevation, aspect, curvature, and land use/land cover, etc. In addition, the effects of each conditioning parameter are evaluated by testing all values. The model has success and accuracy rates of 79.41% and 77.09%, respectively. This outcome is classified as a good category. Based on the findings, it can be concluded that the frequency ratio model is appropriate for the zonation of flash flood susceptibility. The results indicate that low-elevation and flat regions are the most susceptible locations, necessitating land use planning, and infrastructure enhancements to effectively mitigate flash flood dangers.

ABSTRACT. The Sunda Strait is one of the tsunamigenic regions in Indonesia and is part of the Sunda Megathrust, known to be highly vulnerable to earthquakes and tsunamis. The maximum earthquake in the Sunda Megathrust can reach 8.2-8.7 Mw. This research examines the tsunami hazard in Cilegon City, Banten, Indonesia, which directly borders the Sunda Strait. The purpose of this study is to determine the tsunami wave run-up height and analyse the inundation area due to tsunami waves in Cilegon City. The methods used in this research include tsunami modelling with three earthquake scenarios of magnitudes 8.2 Mw, 8.45 Mw, and 8.7 Mw using COMCOT software and H-loss calculations. Based on the results of the tsunami hazard analysis in Cilegon City using a geographic information system involving COMCOT software and H-loss calculations, the tsunami hazard in Cilegon City is relatively low. The wave arrival time at the Cilegon City coast from each scenario is 141.9 minutes, 150 minutes, and 84.2 minutes respectively, with run-up heights not exceeding 3 meters and inundation areas less than 200 hectares in the Ciwandan, Citangkil, and Grogol Districts.

ABSTRACT. With the development of technology nowadays, many business sectors and community services are enhanced with a good adoption of technologies and tools, which leads to providing demandable services, a user-friendly environment, and on the business side, a good economy, new investments, better services to the community, and building smart, sustainable systems has a positive impact on the environment. Smart sensing technology has a wide effect on today's work, business, and life. Basically, sensors exist in wide applications, starting from the smart hand sanitizer system to autonomous vehicles. Water purification has been benefiting from the smart sensing technology with the innovative Seaswarm robot, which can absorb a huge amount of gas and oil spoiled from oil barrels that travel every day between countries for the purpose of commerce. Traditional procedures used to remove the oil from water was hazard to workers life, time consuming, and do not give the result that is requested. With Seaswarm robot, all these obstacles has been solved, and as a result, maintain a clean water system to save the life of marine animals. Smart sensors work based on receiving input from the physical environment and the system needs that input, processing them using built in models, and taking appropriate action based on data received from the input. Sensors are becoming an essential tool playing an important role in developing traditional systems, in a way that makes them more advanced and developed. This paper aims to highlight the importance of sensors and how they are a crucial part of IoT technology. Smart sensing technology keeps growing to provide efficient and sustainable use of energy with high-performance processing.

ABSTRACT. Electric Vehicle Wireless Power Transfer (EV-WPT) systems promise to revolutionize sustainable transportation by facilitating efficient charging wirelessly. The EV-WPT efficiency is closely related to the coil configuration and can be improved by introducing ferrite cores. In this study based on Ansys software, we have characterized the three major geometries used in literature and compared their efficiency with and without ferrite cores. Equivalent and inequivalent scenarios have also been considered. To ensure that the one-block planar coils approach is reliable compared to the spiral one (computationally intensive), results comparing the two approaches showed that despite a lower EMF, the one-block coils were almost identical to the spiral coils when it comes to the coupling coefficient. Hence, the choice to continue our characterization using the one-block approach for all the considered scenarios. Various coil heights were simulated to optimize design while minimizing costs and weight, confirming only a slight increase in coupling coefficient resulted in choosing a smaller height for further research. Multiple adjustments of ferrite core parameters were necessary to establish an optimal configuration. The inequivalent configuration led to an increase in the coupling coefficient of up to 73% for circular coils, 105% for rectangular coils, and 83% for squared coils compared to the equivalent configuration. Results have also shown that the circular coil geometry was the only one able to achieve a coupling coefficient higher than 0.5.

ABSTRACT. The drastic adoption of electric fleets (EF) highlights the need to address several infrastructural and planning challenges, especially EF charging station (CS) scheduling and incorporation of renewable energy sources. This study considers adopting wireless power transmission (WPT) technology to utilize fluctuating renewable energy sources for charging electric fleets in transit, offering an inventive way to optimize the allocation of charging stations, charging times, and profitability. The model considers operational-level information, lowers infrastructure costs, offers guidance for grid policies, and encourages the integration of renewable energy sources by utilizing Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) energy flow networks. The novel model simultaneously optimizes the state of charge, energy flows to and from grid and reservoir, energy consumption, and profit count-ability by utilizing its mixed integer linear programming (MILP), expanding to include demand uncertainty and robust counterpart optimization. These results provide useful information for upcoming fieldwork and give stakeholders a framework for improving sustainability and efficiency in the operations of public electric fleets

ABSTRACT. Unmanned Aerial Vehicles (UAVs) heavily rely on Global Positioning Systems (GPS) for navigation, limiting their functionality in indoor GPS-denied environments. This paper investigates the application of Large Language Models (LLMs) for visual path planning in such scenarios. This work proposed a new LLM-based approach for understanding visual data captured by the UAV's camera. By analyzing this data in terms of the positions of the detected persons and depth information, the fine-tuned LLM would generate safe and efficient flight paths. To validate the proposed approach, we have created an indoor virtual navigation environment for the entrance of our center (JRCAI) with 3 standing persons and 2 randomly moving. Guided by LLMs, the mission of UAVs is to reach the target goals that result in the minimum collisions. The reported results clearly showed that the proposed LLMs achieved better results than the standard deep reinforcement learning DQN model in both the average number of collisions as well as the traveled distance toward the goal point.

ABSTRACT. This study presents a dual-band microstrip patch antenna specifically designed for Vehicle-to-Vehicle (V2V) communications. Simulated on an FR-4 substrate and incorporating strategically placed slots along the feedline, the antenna resonates at critical frequencies (0.915 GHz and 3.5 GHz) essential for various V2V scenarios. The design achieves effective impedance matching, showcasing bidirectional radiation patterns with a peak gain of 4.03 dBi at 3.5 GHz. Furthermore, optimized surface current distribution enhances the antenna’s adaptability to diverse vehicle orientations. With radiation efficiency values surpassing 80% in each operational band, the antenna efficiently converts electrical power into radiated signals. The proposed antenna stands out as a versatile and dependable solution for V2V communications, marking a significant contribution to antenna technology for vehicular communication applications.

ABSTRACT. This research paper presents an approach to enhance efficiency in agriculture by combining advanced routing algorithms with Unmanned Aerial Vehicle (UAV) technology capabilities. By positioning the sensor nodes throughout the agricultural field, crucial factors like soil moisture, temperature, and nutrient levels are continuously monitored. The key innovation here lies in promoting a routing algorithm adapted from the Enhanced Latency Objective Function (OF) designed initially for Low Power and Lossy Networks (LLNs), cleverly repurposed for the specific needs of modern precision agriculture. The system considers factors like latency, connection quality, remaining energy, and congestion to optimize data routing and establish strong and dependable communication channels for tracking and alert purposes. The paper explains how this integrated system creates, implements, and evaluates the potential to enhance production methods and transform crop management practices. The practicality and effectiveness of this approach are supported by results that demonstrate its practicality and lead to reduced latency levels—a significant step forward in advancing farming techniques for today's needs.

ABSTRACT. This study examines the sustainability alignment of smart mobility projects with the United Nations Sustainable Development Goals and targets. A total of 32 smart mobility projects from Finland were analyzed. The results highlight commendable efforts in sustainable urbanization and infrastructure development but significant gaps in environmental sustainability and responsible consumption in urban areas. Projects in rural areas focus on innovation and resilient infrastructure, while urban projects aim to enhance inclusive, sustainable cities and ensure safe, affordable, and accessible transport systems. The UN’s specific SDG targets, especially those related to the responsible use of renewable energy, require constant attention in both rural and urban contexts. The findings underscore the need for a comprehensive, integrated approach to address the identified gaps.

ABSTRACT. Digitalization offers the potential to increase through new/developed technologies that can gather and analyze datasets then use it to improve or change the physical behavior of indoor or outdoor environments either automatically or through human intervention. Technologies are evaluated and compared based on the nine parameters on the TRL. The Technology Readiness Level (TRL) assessment is based on the type and size of the entity developing the technologies. This study presented an approach to measuring TRL on digital technologies related to building energy efficiency. An expert survey assessed the technology readiness level in buildings energy efficiency digital technologies. This paper contributes to the widening and systematizing of knowledge on the maturity and understanding digital technologies related to energy efficiency. This study aims to inform researcher, user, and industry to support energy efficiency technology development focusing on digital technologies.

ABSTRACT. Industry 4.0 plays a crucial role in fostering and attaining the goals of sustainable development. Globally, sustainability is regarded as achievable through an emerging paradigm known as "Green Governance (GG)”. Green governance is a vital driver for reshaping economic growth and bringing about a sustainable technology revolution. Therefore, governance effectiveness and mechanisms are needed in adopting green practices at all levels and domains to achieve sustainable development goals (SDG). An exhaustive literature review was carried out, limited studies were reported, and academics were pressured to develop sustainable green governance frameworks. GG is gaining attention as organizations strive to meet the SDGs and become carbon neutral. This study sought to confirm if GG practices of enterprises assist SDG objectives. The significance of our research lies in the fact that it offers organizations and governments concrete proof to back up the claim that adopting environmentally sustainable business practices and renewable energies can lead to long-term economic growth, for nations working in greening their economies by creating value that is sustainable for all parties involved. The current study is anticipated to contribute to addressing the knowledge gap on sustainability in Nigeria. Likewise, to push companies to start more sustainable practices in the future, and improve the socioenvironmental situation in Nigeria, thereby encouraging green governance-related initiatives for managers, regulators, societies, practitioners, and policymakers from theoretical and practical insights as they attempt to adopt a Sustainable green governance framework to achieve SDG13.

ABSTRACT. In recent years, there has been substantial growth in multimedia and visual data management. This increase in data volume presents several challenges, notably protecting multimedia content from tampering or manipulation. Images, as a prevalent form of multime- dia content, often requires protective techniques such as watermarking and blockchain, especially in medical contexts. Addressing challenges like watermark size and manipulations without third parties in multimedia data, including medical images, is crucial. This paper proposes a medical image protection scheme using a combination of watermark and blockchain techniques based on the k-means algorithm. This scheme utilizes k-means to compress the watermark image into an actual binary image, which is then embedded into the medical image using the least significant bit (LSB) technique, and then stored on the blockchain network. also, employing a bitwise XOR operation is applied to detect any manipulations by comparing it with the originally embedded binary image. The technique’s efficacy is evaluated using the Peak Signal-to-Noise Ratio (PSNR) and subjected to various attacks, includ- ing different types of noise. Experimental and analytical results demonstrate that the proposed scheme effectively detects image manipulations and reduces the watermark size, enhancing security for medical images without third parties.

ABSTRACT. Due to the diversity of devices, vast attack surfaces, and the critical nature of healthcare data. Securing healthcare networks within the Internet of Things (IoT) presents a challenging task. This study addresses the heightened cybersecurity risks associated with the growth of IoT devices in healthcare by developing a custom RL cybersecurity IoT environment for healthcare. Our methodology utilizes Microsoft CyberBattleSim, an RL simulation environment, and Gymnasium to construct a healthcare IoT environment that includes various devices, vulnerabilities, and security measures. Involves the simulation of attack and defense strategies through RL models, Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C) to assess network resilience and identify effective cybersecurity practices. The application of these algorithms has demonstrated the capacity of the models to adapt to complex cyber threats, thereby shedding light on essential defense mechanisms and the prevalent vulnerabilities of the network. However, the simulated nature of our dataset, tailored to the CyberBattleSim environment, presents limitations in fully capturing the real-world intricacies and novel threats facing IoT healthcare networks. Despite these constraints, our findings significantly contribute to the conversation on IoT healthcare cybersecurity, providing a foundation for further research and practical implementation to fortify these critical networks.

ABSTRACT. Direct current microgrids (DCMG) have garnered immense popularity due to the absence of frequency synchronization, reactive power compensation, and skin effect issues. However, challenges associated with the voltage regulation of dc bus persist in DCMG. This paper introduces a DCMG consisting of wind, photovoltaic, hydrogen storage system, ultracapacitor, and battery. The primary objectives are to sustain the dc bus voltage and maintain the power balance. Hence, to achieve the desired objectives and for the reliable operation of DCMG, an integral sliding mode controller (ISMC) is proposed. To evaluate the performance of ISMC, DCMG is set up and simulated in Matlab/Simulink. Through comprehensive simulations, the effectiveness of the proposed ISMC is demonstrated. Furthermore, the efficacy of the implemented control approach is validated through the real-time experiments conducted with hardware in the loop.

ABSTRACT. Worldwide, microgrids (MGs) become crucial in enhancing the reliability and cost-effectiveness of energy supply, particularly during significant circumstances in utility grid. Despite their potential, the literature has not extensively explored the resilience benefits of MGs, especially in terms of net energy metering (NEM), demand response (DR), outages, and consequent economic returns. This study addresses this gap by optimizing and simulating a smart and resilient solar PV/battery MG for office facilities and integrating electric vehicles (EVs) to support e-mobility applications. The influence of NEM and DR on the MG is assessed demonstrating that the proposed MG can fulfill the office and EVs demands during the normal days, weekends, peak demand, outages, and DR events with best techno-envrio-economic performance. The annual revenue attained with DR and NEM smart grid mechanism has reduced the energy cost by 6.25% and annual electricity charges by 6.43% compared to the based case without DR and NEM. Also, this proposed MG system cuts the annual greenhouse gas emissions by 4%. Moreover, the MG optimally utilizes about 93.6% of its generated PV electricity on-site, exporting the excess to the grid, which underscores NEM's role in minimizing power curtailment and maximizing efficiency. In our opinion, the alignment of this study with the objectives of Saudi Vision 2030 in promoting sustainable power and transportation solutions and enhancing energy system resilience highlights the pivotal role of MGs in shaping the future energy landscape.

ABSTRACT. The rising adoption of electric vehicles (EVs) demands the development of efficient and sustainable charging infrastructure. This paper explores designing and optimizing renewable energy-based microgrids tailored for EV charging. It focuses on a case study in Saharanpur City, Uttar Pradesh, India, which benefits from favorable solar irradiance. Through a techno-economic analysis, this study evaluates EV load patterns and the availability of solar and wind resources, ensuring the economic viability of the proposed grid-tied microgrid. A hybrid optimization model is employed to simulate and assess various configurations, emphasizing cost-effectiveness and sustainability. Analysis of 1164 simulated microgrid solutions highlights the critical importance of precise component selection and system design for economic viability, with the net present cost (NPC) ranging between $70.2M and $71.3M. Furthermore, the net financial gain from energy trade costs ranged from $468,726.70 in June to $1,276,238.72 in March. It is evident that the proposed grid-tied microgrid models achieving over 80% renewable fractions demonstrate reduced greenhouse gas emissions and promote economic growth, thereby supporting sustainable energy infrastructure in EV charging.

ABSTRACT. In order to estimate the state-of-charge (SoC) of lithium-ion batteries, this study presents a fractional-order sliding mode (SM) observer. Due to nonlinearities and uncertainty in the dynamical models of these power sources, a precise estimation of their state of charge (SoC) necessitates the implementation of an effective and nonlinear observer. In their performance, the sliding mode observers exhibit chattering behaviors and extended convergence time. However, the proposed fractional order sliding mode observer addresses these issues by providing reduced chattering and convergence time. Next, we select a suitable control rule based on the principles of Lyapunov stability theory to guarantee a decrease in the Lyapunov function. In order to create the intended FNTSM observer, we take into account an equivalent circuit model (ECM) for the battery, which incorporates uncertainty. We validated the stability and effectiveness of the developed technique using simulation results.

ABSTRACT. In the growing telemedicine and remote diagnosis field, ensuring the integrity and rights management of shared medical image data across hospitals, institutions, and insurance companies is a critical challenge. This paper proposes a novel approach combining data compression, watermarking, encryption, and blockchain technology to address these concerns. The technique involves compressing a watermark into vectors and embedding them into the host medical image using the Discrete Wavelet Transform (DWT). These vectors are then inserted into the LL2 segment of the image via the Least Significant Bit (LSB) method, creating a securely watermarked image. The watermarked images are encrypted and stored on a blockchain using smart contracts on platforms like Ethereum to enhance security further. This dual-layer security strategy preserves data integrity, and privacy, and enables equitable sharing among stakeholders. The approach’s efficacy is demonstrated by an exceptional peak signal-to-noise ratio (PSNR) of 63.28298 dB and a structural similarity index (SSIM) of 0.9999. These results affirm the method’s effectiveness in securely exchanging medical images, positioning it as a superior solution in the digital healthcare landscape by addressing integrity, rights management, and secure sharing of sensitive medical data across entities.

ABSTRACT. Electroencephalography techniques used in Brain computer interface to control external devices. Different BCI applications based on EEG were reported such as controlling the robotic arm, computer curser or gaming. Motor execution and motor imagery are both used as a EEG input control signals to control the peripheral devices. However, some applications are required to control the peripheral devices using thoughts (motor imagery) only. In this case, differentiation between actual and imagery action is crucial. In this work, the investigation of the changes of the power spectrum of the EEG bands including alpha and beta bands in the different brain regions. The public dataset recorded from 61 subjects in different movements task were used. Th Alpha (8-12 Hz) and Beta (13-30 Hz) were extracted from the recoded EEG signals. After the preprocessing, the power spectrum was calculated for each band in four brain regions including central sulcus, motor cortex, sensorimotor cortex. The results reveal that significant differences were found in the area in the alpha and beta bands in the motor cortex, frontal lobe and sensorimotor cortex. while in the central sulcus the differences were insignificant. The results of this study are still preliminary and more investigation using different EEG quantities and features are required. Additionally, this study needs to be validated with another dataset.

ABSTRACT. DC microgrids (MGs) have experienced swift growth, driven by the expanding integration of energy storage devices, renewable energy sources (RESs), and localized loads. However, it suffers from insufficient inertia owing to less rotating mass sources, which can lead to poor voltage stability. This paper introduces a control method that emulates both inertia and damping to mitigate fluctuations in DC voltage, thereby improving system stability and addressing the issue of low inertia. The proposed virtual inertia and damping (VID) control system is adopted through using a hybrid electric vehicle (HEV). The suggested HEV comprises three power sources: a battery, a fuel cell (FC), and a supercapacitor (SC). In this setup, both the battery and the FC are employed to supply virtual damping owing to their high energy density, and the SC is utilized to support virtual inertial characteristics due to its high power density. Through this approach, enhancements in the DC voltage stability of an islanded DC MG can be achieved. Simulations demonstrate that the proposed control system significantly outperforms existing works utilizing virtual inertia (VI) based on an EV's battery, as well as over systems lacking VI. The proposed control system effectively reduces voltage fluctuations and improves system stability, specifically in terms of the rate of change of voltage (ROCOV) and accelerated settling time.

ABSTRACT. Hydrogen fuel has sparked major stakeholders as an alternative to fossil fuel in industrial gas turbine or aerospace applications, considering its clean energy to reduce greenhouse gas emissions. Furthermore, hydrogen is known to have higher gravimetric energy density than conventional fuel. However, understanding hydrogen flames is particularly challenging due to the intricate nature of the turbulence characteristics. Hence, in this work, non-premixed turbulent hydrogen flames are numerically investigated using commercially available software. The aim of this work is to assess the predictability of various RANS turbulence models for an axisymmetric case, including standard, modified, realizable, and Pope correction models. Simulation of test cases has shown that the realizable model exhibits the best accuracy in the centerline region further downstream with high accuracy for the centerline mean temperature. The modified model demonstrates superior predictability near the injector exit plane in the radial direction. Overall, the findings suggest that RANS models are effective for initial data collection in the early stages of the design process, providing valuable insights into the behavior of hydrogen flames.

ABSTRACT. As rockets consume a lot of fuel, the rocket nozzle design is critical. This paper presents an exploration of performance optimization for expansion-deflection nozzles (EDNs) through the incorporation of a new design modification that includes an additional divergent section. Traditional EDNs experience a marked decline in thrust efficiency at specific nozzle pressure ratios (NPRs) due to the deflection of the supersonic flow near the nozzle exit plane, leading to flow separation and velocity reduction. To address this, we propose a strategic enhancement that integrates an additional divergent section designed to delay the onset of flow separation and improve overall efficiency. This design exploits expansion fan dynamics to re-expand and reattach the flow to the nozzle wall, thereby mitigating efficiency losses. Computational fluid dynamics (CFD) simulations were conducted to evaluate the modified EDN at NPRs of 70, 160, and 200, representing open wake, transition, and delayed closed wake modes, respectively. The results indicate a substantial improvement in thrust efficiency, with the modified design effectively delaying the mode transition and maintaining higher efficiency across a broader range of operating conditions.