Generator System Research Articles

Investigating the power and efficiency of the 3D model of the concentrated photovoltaic thermoelectric hybrid system and estimating the power consumption of the water pump for cooling

Solar energy is one of the most useful types of renewable energy. Solar cells are being used to convert solar energy into electricity. By focusing the radiation on the cells, more energy is radiated in a smaller area of the cell which is called concentrated photovoltaic (CPV). Due to the concentration of radiation, the temperature of the cell rises which can be converted into electricity through thermoelectric generators (TEG). The present study is a hybrid system of solar cell, thermoelectric generator, and heat exchanger (CPV-TEG). By this survey being created, it was revealed that the combined power produced by the CPV-TEG reaches its maximum value at concentration ratio of 40. This increase continues with the increase in fluid flow rate. however, according to the power consumption of the pump, it was observed that in all types of heat exchangers, an increase in flow rate of more than 8 liters per hour reduces the output power of the system. The replacement of the secondary heat exchanger with the primary one illustrated that the increase of the channels in the exchanger, despite the increase in the power consumption of the pump, causes a very small increase in the output power and efficiency of the system. Moreover, replacing the tertiary heat exchanger instead of the secondary exchanger showed that changing the direction of fluid output increases the power consumption of the pump in high flow rates.

Enhancing ethical codes with artificial intelligence governance – a growing necessity for the adoption of generative AI in counselling

ABSTRACT The advent of generative Artificial Intelligence (AI) systems, such as large language model chatbots, is likely to have a significant impact in psychotherapy and counselling in the future. In this paper we consider the current state of AI in psychotherapy and counselling and the likely evolution of this field. We examine the ethical codes of practice for counselling in four countries in different parts of the world, namely the UK, the USA, Australia and Malaysia, and identify aspects of these codes that will need enhancement to reflect good AI governance. Using the Model Artificial Intelligence Governance Framework as an example, we have identified how the key elements of the AI framework relate to the core elements of the ethical codes, as a pointer to how such ethical codes will need to be enhanced if generative AI systems are to be adopted by the counselling profession.

Investigating the impact of magnetic air gap variations on short circuit characteristics of partially high temperature superconducting generator

Partially high-temperature superconducting generators (PHTSGs) feature large magnetic air gaps imposed by using cryostats for HTS field windings. This increased air gap significantly affects end winding inductance and can lead to elevated fault torque levels. This study investigates the influence of magnetic air gap variation on generator design and end winding inductance and its implications for short-circuit faults in PHTSGs. Through a combination of numerical simulations and analytical analysis, the study explores how changes in the magnetic air gap affect end winding inductance and subsequently influence short-circuit fault behavior. The results reveal a direct correlation between magnetic air gap length, end winding inductance, and key short-circuit parameters such as stator current, field currents, and electromagnetic torque. Notably, an increase in the magnetic air gap is observed to elevate stator and field currents and electromagnetic torque during short-circuit events. These insights underscore the importance of considering magnetic air gap variation and its impact on end winding inductance and its relationship with short circuit characteristics in the design and operation of PHTSGs, providing valuable insights for enhancing the resilience and performance of high-temperature superconductor-based generator systems.

A reinforcement learning method for two‐layer shipboard real‐time energy management considering battery state estimation

AbstractIncreasing global environmental concerns encourage a continuous reduction in carbon emissions from the shipping industry. It has become an irreversible trend to replace traditional fossil fuels with advanced energy storage technology. However, an improper energy management leads to not only energy waste but also undesired costs and emissions. Accordingly, the authors develop a two‐layer shipboard energy management framework. In the initial stage, a shipboard navigation planning problem is formulated that considers battery state estimation and is subsequently solved using particle swarm optimisation to obtain an optimal speed trajectory. To track the scheduled speed, a reinforcement learning method based on a deep Q‐Network is proposed in the second stage to realise real‐time energy management of the diesel generator and energy storage system. This approach ensures that the state of charge remains within a safe range and that the performance is improved, avoiding excessive discharge from the energy storage systems and further enhancing the efficiency. The numerical results demonstrate the necessity and effectiveness of the proposed method.

Beyond the Turing Test: Exploring the implications of generative AI for category construction

As generative AI systems move beyond Turing’s benchmark for whether a machine exhibits human-like intelligence, what implications does this technological milestone have for organization theory? We engage with this question by considering how the increasing creativity and social competence exhibited by generative AI impacts processes of social construction and cultural evolution that have, up to this point, been the exclusive domain of humans. More specifically, we consider what it means to have intelligent machines capable of category work, which we define here as both the culturally savvy use of categories and purposeful participation in the processes of construction that underpin systems of categories more generally. We go on to explore some of the implications for individuals, organizations and societies of the appearance of this new class of artificial participants in the processes that constitute category systems.

Thermal, Energy and Exergy Analysis of Thermoelectric Generator System for Waste Heat Recovery Applications

Thermal, Energy and Exergy Analysis of Thermoelectric Generator System for Waste Heat Recovery Applications

Model‐Based Systems Engineering Approach for Designing an Artificial Magnetic Field Generator System for Spacecraft Radiation Protection

AbstractThe hazardous environment of space, dominated by cosmic and solar radiation, poses a significant threat to spacecraft and their occupants. Traditional radiation shielding methods, like passive materials, have limitations in weight and effectiveness. An artificial magnetic field generator system emerges as a promising solution to replicate Earth's magnetosphere, providing a protective magnetic shield against harmful radiation. (Dick, Launius, 2007) This paper presents a Model‐Based Systems Engineering (MBSE) approach to the holistic modeling and design of such a system.Utilizing the MBSE approach, this study models the system's components, their interactions, and the offered services, incorporating Radiation Monitoring, Magnetic Shielding, Power Management, System Health & Diagnostics, and Crew Communication services. The conceptual data model captures key entities and their relationships, ensuring a coherent integration of the system's parts. The activity diagram illustrates the operational flow, providing clarity on the system's dynamic behavior under varying radiation conditions and power reserves. A services taxonomy is developed to hierarchically categorize and ensure the comprehensive functionality of the system.The application of MBSE methodology provides numerous advantages, including a unified visualization of the system's complexities, enhanced stakeholder communication, and a streamlined validation and verification process. Furthermore, the flexibility inherent in the MBSE approach ensures that the system can be easily updated or scaled based on advancements in technology or changing mission requirements. (ECLIPSE Suite, 2022) The MBSE approach's application to the design of an artificial magnetic field generator system for spacecraft presents a robust and systematic method to ensure optimal performance, safety, and adaptability in the perilous realm of space.

Gas emission optimization and optimal energy management of a microgrid operating by renewable and sustainable generation systems

The economic operation of electric energy generating systems is one of predominant problems in energy systems. Due to the need for better reliability, high energy quality, lower losses, lower cost and clean environment, the application of renewable and sustainable energy sources such as wind energy, solar energy, fuel cells, etc. in recent years has become more widespread mainly. In this work, one of most general of all swarm intelligence algorithms inspired by collective intelligent behavior of natural or artificial systems called the Particle Swarm Optimization (PSO) is applied to solve the Gas emission Optimization (GEO) and the Optimal Energy Management (OEM) problems of a micro-grid (MG) operating by Renewable and Sustainable Generation Systems (RSGS). Our main goal is to minimize nonlinear objective function of an electrical micro-grid taking into account of equality and inequality constraints. The PSO approach was examined and tested on a standard MG system composed of different types of RSGS such as wind turbines (WT), photovoltaic systems (PV), fuel cells (FC), micro turbine (MT), diesel generator (DEG) and loads with energy storage systems (ESS). The results are promising and show the effectiveness and robustness of proposed approach to solve the GEO and the OEM problems. The results of proposed method have been compared and validated with those known references published recently.

Analisis Commissioning Pembangkit Listrik Tenaga Surya Kapasitas 20.5 kWp On Grid PT. Quickprint Office

Solar power plants (PLTS) are one of the renewable energy plants that continue to be developed in Indonesia to meet electrical energy needs. The Indonesian government issued the Electricity Supply Business Plan (RUPTL) as a national energy management framework designed to resolve energy needs challenges and problems by utilizing new renewable energy sources (EBT). In the construction of a Solar Power Plant (PLTS) at PT. Quick Print Office built PLTS with a capacity of 20.5 kWp which is used for the electrical energy needs used by PT. Quick Print Office with a total power of 10 kWp, generator system with an on grid system which means it is directly connected to the PLN network. In the feasibility study, the performance ratio (PR) obtained from manual calculations for inverter 1 was 71.9% and inverter 2 was 26.2%. Meanwhile, to improve the performance ratio of the PLTS system, maintenance of the PLTS system is required, starting from cleaning the panels or checking the cable installation on the PLTS. For the calculation results, the efficiency on inverter 1 was 94.6% and on inverter 2 the efficiency was 90.8% when measured at 11.18 – 14.53.

The Impact of the Development of Artificial Intelligence with Generative Ability on Education

One kind of artificial intelligence technology called generative AI is used to create new text, picture, audio, and video material. It may be used for many different things in education, such creating material, enhancing data, personalizing learning, simulating situations, and providing training. It also raises moral questions about prejudices, veracity, false information, intellectual property, loss of employment, and potential future developments like more realism and responsiveness. Content creation, personalized learning, administrative work automation, interactive learning environments, feedback and evaluation, natural language processing (NLP), forecasting and prediction, and collaborative learning are some of the educational applications of generative AI approaches. These technological advancements are intended to improve educational opportunities, streamline administrative duties, and provide individualized course materials. But there are still issues to be resolved, like protecting data privacy, managing human - AI interaction well, and preventing biases in information produced by AI. The process of creating a generative AI system for teaching includes gathering data, choosing a model, training it, and deploying it. Although scalable, personalized, and engaging learning solutions offered by generative AI hold great promise for revolutionizing education, there are a number of drawbacks that may restrict the technology's applicability and prevent it from being widely used. The difficulty of maintaining and upgrading these systems, ethical and privacy problems, and the caliber and bias of the produced information are examples of technical constraints. The applications, legal frameworks, and social consequences of generative AI will be shaped by its technological limits. To fully realize the benefits of AI in education, issues including data privacy breaches, possible bias in AI systems, and the digital divide must be resolved.

Evaluating Transferability of Attacks across Generative Models

The need for adversarial sample transferability is to attack black-box deep learning models. Whereas much recent work focuses on making untargeted adversarial attacks more transferable, there has been scarce research on the creation of transferable targeted adversarial instances that can trick models into believing they are of a particular class. The present transferable targeted adversarial attacks are not transferable since they cannot sufficiently define the distribution of target classes. In this paper, we propose a generative adversarial training system consisting of a feature-label dual discriminator to identify the adversarial instances formed from the target class images and a generator to construct targeted adversarial examples. It is concluded that adversarial scenarios have significant real-world applications in safety-critical fields like biometrics and autonomous driving. In addition, it is demonstrated that the current networks' susceptibility to hostile attacks, even under the worst black-box conditions has far-reaching societal consequences. We intend to further encourage more research into the inner workings of neural networks in the face of adversarial attacks, whereby people might use this knowledge to build robust defense mechanisms.

‘No, Alexa, no!’: designing child-safe AI and protecting children from the risks of the ‘empathy gap’ in large language models

ABSTRACT Rapid advancements in large language models makes child-safe design for their youngest users crucial. This article therefore offers child-centred AI design and policy recommendations to help make large language models (LLMs) utilised in conversational and generative AI systems safer for children. Conceptualising the risk of LLMs as ‘an empathy gap’, this research-based conceptual article focuses on the need to design LLMs that prevent or mitigate against the risks of responding inappropriately to children's personal disclosures or accidentally promoting harm. The article synthesises selected cases of human chatbot interaction and research findings across education, computer science and human-computer interaction studies. It concludes with practical recommendations for child-safe AI across eight dimensions of design and policy: content and communication; human intervention; transparency; accountability; justifiability; regulation; school-family engagement; and child-centred design methodologies. These eight dimensions are tailored to a variety of stakeholders, from policymakers and AI developers to educators and caregivers.

A highly efficient self-powered variable impendence system

As an efficient mechanical energy harvester, the triboelectric-electromagnetic hybrid generator (TEHG) stands as a cornerstone in self-powered systems. Nevertheless, significant impedance disparities between triboelectric nanogenerators (TENGs) and electromagnetic generators (EMGs) often hamper systems’ energy utilization efficiency, attributed to impedance mismatch at the load. Here, a variable impedance strategy is proposed, aimed at maximizing the utilization of mechanical energies converted by TEHG. This approach capitalizes on electronic components with dynamic impedance from GΩ to kΩ in response to OFF-ON state transitions, thus matching the impedance of TENG and EMG. Experimentally, an ultraviolent gas discharge tube (UV-GDT) is integrated into the self-powered variable impedance system. Operated at 240 rpm, the TEHG-driven UV-GDT extracts energy amounting to 1304.27 mJ with an 87.5 % utilization efficiency. These metrics outperform the situation where UV-GDT is individually powered by either EMG (0 mJ, 0 %) or TENG (18.24 mJ, 60.7 %). Furthermore, the mechanical energy-activated UV system demonstrates promise for sterilization, curing, and photo-chemical reactions. This variable impedance strategy resolves the impendence mismatch between TEHG and load, more importantly, provides a valuable guideline for developing hybrid generator systems with enhanced energy utilization efficiency.

New Mini Neutron Tubes with Multiple Applications

Recent experimental investigations have demonstrated that a substantial amount of H−/D− ions can be formed by thermal desorption processes. Based on these new findings, new mini axial and coaxial-type neutron tubes have been developed for the production of high or low-energy neutrons via the d-d, d-10B, d-7Li or p-7Li nuclear reactions. By operating these mini neutron tubes with a high frequency AC high-voltage supply, short pulses of high intensity neutron beams can be generated. Multiple applications, such as carbon and well logging, neutron imaging, cancer therapy, medical isotope production, fission reactor start-up, fusion reactor material evaluation, homeland security and space exploration can be performed with the subcompact neutron generator system. It is shown that the performance of these new mini neutron tubes can exceed those of the conventional plasma-based neutron sources.

Maximizing renewable energy and storage integration in university campuses

The worldwide demand increase and climate change constraints are leading to integration and maximization of renewable energy sources (RESs) share in the energy production. This represents a key aspect to reduce the pollutant emissions and facilitate the transition to a cleaner future. Many agreements and incentives were discussed and imposed in order to limit the fossil fuel use and thus reducing the pollutant emissions to limit global warming. With this considerations, the renewable energy sources are installed with continuous share increase. To support this implementation and used simultaneously with providing a power reserve for these intermittent power sources, energy storage systems are becoming more and more used and necessary. Simultaneously with these changes, hydrogen is becoming more and more used, being a means by which electricity can be produced without generating greenhouse gas emissions (GGE). A power supply system with multiple sources is presented and its operation is analysed, the system contains photovoltaic panels, wind system, fuel cell, hydrogen generator, electricity and hydrogen storage system to supply the consumption of a student dormitory from a university campus in Romania with prosumer possibilities.

Dual internal model control with improved load current feedback for enhanced dynamic performance in aircraft high‐voltage direct current brushless generator

AbstractIn order to enhance the dynamic performance of aircraft high‐voltage direct current wound rotor synchronous generator (WRSG) system under load variations, a dual internal model control (DIMC) method utilising improved load current feedback (ILCF) is proposed. It employs internal model controllers in both voltage and current loops, thereby significantly augmenting the system's disturbance rejection capabilities. Improved load current feedback is incorporated between the DC voltage loop and the field current loop. Additionally, a method for designing a feedback fitting function is established, enhancing the system's adaptability to changes in operational speed and load. The authors outline parameter design methods for voltage and current loops in DIMC, based on the internal model control principle. Adjustments to these parameters can further enhance the system's disturbance rejection capabilities. Finally, comparative experiments between the PI method with ILCF and the DIMC method with ILCF are conducted on a 15 kW WRSG experimental platform. These experiments demonstrate that the proposed DIMC method with ILCF optimises dynamic performance across the entire speed range, enhancing the competitiveness of WRSG systems in aircraft applications.

Investigating Gender and Racial Biases in DALL-E Mini Images

Generative artificial intelligence systems based on transformers, including both text generators such as GPT-4 and image generators such as DALL-E 3, have recently entered the popular consciousness. These tools, while impressive, are liable to reproduce, exacerbate, and reinforce extant human social biases, such as gender and racial biases. In this article, we systematically review the extent to which DALL-E Mini suffers from this problem. In line with the Model Card published alongside DALL-E Mini by its creators, we find that the images it produces tend to represent dozens of different occupations as populated either solely by men (e.g., pilot, builder, plumber) or solely by women (e.g., hairdresser, receptionist, dietitian). In addition, the images DALL-E Mini produces tend to represent most occupations as populated primarily or solely by White people (e.g., farmer, painter, prison officer, software engineer) and very few by non-White people (e.g., pastor, rapper). These findings suggest that exciting new AI technologies should be critically scrutinized and perhaps regulated before they are unleashed on society.

The Power Circuit of Mechanical Damping Device for Cooling System of 50 kW Pulse Diesel Generator

Diesel generators have advantages such as high reliability, superior economic performance, and a wide range of applications. However, its working environment is relatively sealed, and it is difficult for the internal heat of the motor to transfer to the outside. Prolonged high temperatures can affect the operational performance of the motor and even cause danger. Therefore, it is necessary to design a reasonable cooling system to ensure its efficient operation within the safe working temperature. The water-cooled cooling system has good heat dissipation performance, consisting of components such as a water pump, radiator, fan, water tank, and temperature controller. By precisely controlling the water flow rate and wind speed, effective control of the internal temperature of the generator is achieved, extending the service life of the equipment. This article studies the power circuit of the mechanical damping device in the cooling system of a 50kW pulse diesel generator, develops heat transfer and hydraulic energy circuits, and constructs the frequency response of the circuit. As the water quality increases in the circuit, the amplitude decreases at low frequencies (less than 4 rad/s) and frequencies close to 10 rad/s, and the amplitude aligns. For one parameter variable, the optimal frequency is 3-4 rad/s.

What distance learning students want from an AI Digital Assistant

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students’ perceptions regarding AI including self-reported benefits and concerns from their actual usage, in particular in distance learning contexts. Using a two-step, mixed-methods approach, we examined the perceptions of ten online and distance learning students from diverse disciplines regarding the design of a hypothetical AI Digital Assistant (AIDA). In the first step, we captured students’ perceptions via interviews, while the second step supported the triangulation of data by enabling students to share, compare, and contrast perceptions with those of peers. All participants agreed on the usefulness of such an AI tool while studying and reported benefits from using it for real-time assistance and query resolution, support for academic tasks, personalization and accessibility, together with emotional and social support. Students concerns related to the ethical and social implications of implementing AIDA, data privacy and data use, operational challenges, academic integrity and misuse, and the future of education. Implications for the design of AI-tailored systems are also discussed.

Study of Chaotic-based Audio Encryption Algorithms: A Review

Nowadays, multimedia communication has become very widespread and this requires it to be protected from attackers and transmitted securely for reliability. Encryption and decryption techniques are useful in providing effective security for speech signals to ensure that these signals are transmitted with secure data and prevent third parties or the public from reading private messages. Due to the rapid improvement in digital communications over the recent period up to the present, the security of voice data transmitted over various networks has been classified as a favored field of study in earlier years. The contributions to audio encryption are discussed in this review. This Comprehensive review mainly focuses on presenting several kinds of methods for audio encryption and decryption the analysis of these methods with their advantages and disadvantages have been investigated thoroughly. It will be classified into encryption based on traditional methods and encryption based on advanced chaotic systems. They are divided into two types, continuous-time system, and discrete-time system, and also classified based on the synchronization method and the implementation method. In the fields of information and communications security, system designers face many challenges in both cost, performance, and architecture design, Field Programmable gate arrays (FPGAs) provide an excellent balance between computational power and processing flexibility. In addition, encryption methods will be classified based on Chaos-based Pseudo Random Bit Generator, Fractional-order systems, and hybrid chaotic generator systems, which is an advantageous point for this review compared with previous ones. Audio algorithms are presented, discussed, and compared, highlighting important advantages and disadvantages. Audio signals have a large volume and a strong correlation between data samples. Therefore, if traditional cryptography systems are used to encrypt such huge data, they gain significant overhead. Standard symmetric encryption systems also have a small key-space, which makes them vulnerable to attacks. On the other hand, encryption by asymmetric algorithms is not ideal due to low processing speed and complexity. Therefore, great importance has been given to using chaotic theory to encode audio files. Therefore, when proposing an appropriate encryption method to ensure a high degree of security, the key space, which is the critical part of every encryption system, and the key sensitivity must be taken into account. The key sensitivity is related to the initial values and control variables of the chaotic system chosen as the audio encryption algorithm. In addition, the proposed algorithm should eliminate the problems of periodic windows, such as limited chaotic range and non-uniform distribution, and the quality of the recovered audio signal remains good, which confirms the convenience, reliability, and high security.