Generation Of Engines Research Articles

Thermoresponsive Polymers as Viscosity Modifiers: Innovative Nanoarchitectures as Lubricant Additives.

The world of lubricants is driven by the constant pursuit of improved performance in response of the requests of new engine generations. Engine oils play a critical role as lubricants in mitigating wear, reducing friction and ensuring optimal engine operation under diverse conditions. Modern commercial engine oils are complex formulations, comprising of a base oil, generally coming from petroleum sources, formulated with specific, important additives able to optimize the viscosity, thickening and shear stress in the operating temperature range. Such additives are produced in the thousand tons per year scale range. The most important class of additives for modern lubrication is made of organic polymers with variable architectures and topologies, generally referred as "viscosity modifiers" (VMs): they act as "moderators" of viscosity at different working temperatures. The tremendous advances in polymer science have been reflected in the realm of VMs, allowing the commercialization of products obtained by controlled polymerization techniques, and the experimentation of a broad variety of different macromolecular architectures and topologies as VMs. In this review we introduce the reader, together with the basic principles of viscosity modification and thermal-dependent rheological response, to the fascinating chemistry towards the improvement of VMs, through optimization of macromolecular design and architecture.

Revolutionizing smart textiles: a comprehensive review of conductive ink printing for sustainable and adaptive wearable applications

Purpose The demand of species monitoring for the benefit of various sectors such as industrial, medicinal and ecological has surged rapidly in the recent past. Therefore, the purpose of this paper is to articulate the major developments in synthesizing conductive inks for the structurization of miniaturized as well as disposable or reusable electrochemical equipments. Design/methodology/approach This section is not applicable to a review paper. Findings Numerous times the need for the use point becomes significant for achieving accurate as well as swift quantification. As an alternate, the wearable and effectual reuseable electrochemical sensors are being practiced. The technique of fabricating devices using conductive inks encompasses novelty, as it provides flexibility in designing the electrodes. The increase in the popularity of inks development is governed by its features of simplicity, reduced cost and waste generation, high production and eco-friendly engineering procedures. Further, the electrochemistry aspects of conductive inks highlighting the importance of their compounds and binders has also been discussed emphasizing on the conductive materials. Originality/value This paper is an original review work. This paper will be helpful for manufacturers/researchers from smart wearable textile sector in envisaging innovative developing techniques of sensors as well as biosensors through conductive inks.

Towards a comprehensive understanding of science practices: a heuristic for increasing educator familiarity across a series of science education frameworks

Scientific practices describe behaviors scientists engage in while investigating the natural world. Educators should be aware of different science practice frameworks to better understand their students' progression for learning science practices across the K-20 spectrum. For example, a high school AP instructor would benefit from understanding both the K-12 frameworks their students have already experienced and the future undergraduate frameworks their students are moving toward. We were interested in exploring educator familiarity with the scientific practices outlined in the Next Generation Science and Engineering Practices, the Advanced Placement Science Practices, and Vision & Change Core Competencies. In practice, all science educators are teaching the same set of core principles around which these different frameworks are built. Under this view, it is a missed opportunity to not consider or draw on different frameworks to expand one’s teaching practice. Using a cognitive constructionist approach, we describe the implementation of the “Science Practice Frameworks Concept Map Activity,” designed to (1) introduce educators to scientific practices contained within Science Practice Frameworks and (2) show educators that despite these science practices targeting different student populations they are, at their core, very similar and often overlap. We present data showing a positive shift in educator familiarity with Science Practice Frameworks and present examples of participant concept maps that help us begin to understand how educators perceive the frameworks and the relationships between them, ensuring that educators are better prepared to guide students through a comprehensive understanding of science practices.

Interface Engineering for Efficient Photocarrier Generation and Transfer in Strongly Coupled Metallic/Semiconducting 1T'/2H MoS2 Heterobilayers.

Developing alternative two-dimensional (2D) metallic/semiconducting (M/S) van der Waals heterostructures (vdWHs) along with an understanding of interfacial photocarrier behavior is crucial for designing high-performance optoelectronic devices. Here, we comprehensively explored the photophysical model of photocarrier generation and interfacial transfer in as-grown 2D 1T'/2H MoS2 vdWHs using various spectroscopic characterizations. We demonstrated the transitions of activated photocarrier transfer trajectories by tuning the pump photon energies across the 2H MoS2 bandgap. The importance of confined bilayer transfer systems and strong interlayer coupling at vdW interfaces for transfer efficiency was elucidated. Additionally, the fluorophlogopite substrate was found to be an external method for regulating photocarrier generation in individual 2H layers through the p-doping effect at the substrate-2H layer interfaces, and this influence was alleviated after introducing the 2H-1T' vdW interface. Particularly, 1T' MoS2 as a broadband hot carrier absorber enabled the ultrafast (∼133 fs) injection and extraction of energetic hot carriers into the 2H layer via a photothermionic emission mechanism, achieving a high efficiency of ∼41% under 900 nm photoexcitation at room temperature. Our work offers fundamental insights into the complex interfacial carrier photophysics in 2D M/S vdWHs, providing a way of constructing advanced multifunctional devices by using these emerging materials as active components and interface engineering.

The Generative Generic-Field Design Method Based on Design Cognition and Knowledge Reasoning

Large language model (LLM) and Crowd Intelligent Innovation (CII) are reshaping the field of engineering design and becoming a new design context. Generative generic-field design can solve more general design problems innovatively by integrating multi-domain design knowledge. However, there is a lack of knowledge representation and design process model in line with the design cognition of the new context. It is urgent to develop generative generic-field design methods to improve the feasibility, innovation, and empathy of design results. This study proposes a method based on design cognition and knowledge reasoning. Firstly, through the problem formulation, a generative universal domain design framework and knowledge base are constructed. Secondly, the knowledge-based discrete physical structure set generation method and system architecture generation method are proposed. Finally, the application tool Intelligent Design Assistant (IDA) is developed, verified, and discussed through an engineering design case. According to the design results and discussion, the design scheme is feasible and reflects empathy for the fuzzy original design requirements. Therefore, the method proposed in this paper is an effective technical scheme of generative generic-field engineering design in line with the design cognition in the new context.

Effect of Ceramic Matrix Composites on the Thermal Efficiency of a Power Generation Turbine

Abstract Ceramic matrix composites (CMCs) offer higher allowable temperatures and reduced weight, making them an attractive prospect for parts in the hot section of a gas turbine engine. As CMCs are increasingly adapted into aero and land-based engines, there is a need to quantify the performance increase based on the potential for reduced cooling and increased firing temperatures. In this work, two static hot section components—the first-stage turbine vane and the first-stage turbine tip shroud (outer casing above the blade)—of a mid-sized power generation engine were modeled. Informed approximations about part geometry and cooling architectures were made to determine the cooling requirements of each part. Thermal boundary conditions for the turbine tip shroud and turbine vane were generated as a function of coolant mass flowrate using data from literature and applied to a 2D finite element analysis of the parts to determine maximum temperatures for both metallic and ceramic materials. A gas turbine cycle model was developed to simulate the performance of a mid-sized power generation turbine and used to determine the increase in efficiency due to a reduction in the cooling requirement for the CMC part compared to a conventional metal superalloy-based part. The potential reduction in chargeable cooling seen for the tip shroud ring was between 0.09% and 0.4% of the compressor mass flowrate, which corresponds to an increase in thermal efficiency between 0.11% and 0.45%. A similar analysis of the turbine vane resulted in a cooling reduction of 10.71% at the maximum turbine entry temperature considered which corresponds to a 3.4% increase in thermal efficiency.

Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework

Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications.

Exploring the synergy between generative AI and software engineering: Automating code optimization and bug fixing

As applied to software engineering, generative AI is quickly transitioning from a zero-sum industry game changer into the primary automation tool for code optimization, bug identification, and problem-solving. This technology takes advantage of artificial intelligence algorithms within machine learning models to analyze and write code, resulting in improved quality and speed of an application development process. The generative AI replenishes productivity in development work and enhances centralization between development work teams through code handling and intelligent suggestions for essential codes. However, the integration of AI in software engineering poses the following problems and ethical questions: the question of accuracy, bias, and data. This paper will review the existing knowledge on generative AI in software engineering regarding its current use, future evolutions and advancements, issues and limitations, and ethical factors in using this technology. This paper considers these aspects to give a global outlook on how generative AI will transform software development in the future and how responsible AI should be employed.

Decoding the Functional Interactome of Non-Model Organisms with PHILHARMONIC.

Protein-protein interaction (PPI) networks are a fundamental resource for modeling cellular and molecular function, and a large and sophisticated toolbox has been developed to leverage their structure and topological organization to predict the functional roles of under-studied genes, proteins, and pathways. However, the overwhelming majority of experimentally-determined interactions from which such networks are constructed come from a small number of well-studied model organisms. Indeed, most species lack even a single experimentally-determined interaction in these databases, much less a network to enable the analysis of cellular function, and methods for computational PPI prediction are too noisy to apply directly. We introduce PHILHARMONIC, a novel computational approach that couples deep learning de novo network inference with robust unsupervised spectral clustering algorithms to uncover functional relationships and high-level organization in non-model organisms. Our clustering approach allows us to de-noise the predicted network, producing highly informative functional modules. We also develop a novel algorithm called ReCIPE, which aims to reconnect disconnected clusters, increasing functional enrichment and biological interpretability. We perform remote homology-based functional annotation by leveraging hmmscan and GODomainMiner to assign initial functions to proteins at large evolutionary distances. Our clusters enable us to newly assign functions to uncharacterized proteins through "function by association." We demonstrate the ability of PHILHARMONIC to recover clusters with significant functional coherence in the reef-building coral P. damicornis, its algal symbiont C. goreaui, and the well-annotated fruit fly D. melanogaster. We perform a deeper analysis of the P. damicornis network, where we show that PHILHARMONIC clusters correlate strongly with gene co-expression and investigate several clusters that participate in temperature regulation in the coral, including the first putative functional annotation of several previously uncharacterized proteins. Easy to run end-to-end and requiring only a sequenced proteome, PHILHARMONIC is an engine for biological hypothesis generation and discovery in non-model organisms. PHILHARMONIC is available at https://github.com/samsledje/philharmonic.

Revolutionizing Precision Agriculture: Advanced Seed Prescription Generation and Cloud Infrastructure

This article explores the revolutionary impact of advanced seed prescription generation engines and cloud infrastructure on precision agriculture. It delves into the technical intricacies of enhancing seed prescription systems through cutting-edge data pipelines and scalable cloud platforms. The article discusses how these sophisticated systems analyze various factors such as soil composition, topography, historical yield data, and climate patterns to determine optimal seeding rates and patterns with unprecedented accuracy. It highlights the challenges faced in implementing these systems, including scalability, data processing, reliability, and monitoring, and presents innovative cloud-based solutions to address these issues. The benefits of these advancements, including improved efficiency, enhanced accuracy, scalability, reliability, and cost-effectiveness, are examined, along with their potential to reshape the global food production landscape and promote sustainable farming practices.

Measurement report: In-flight and ground-based measurements of nitrogen oxide emissions from latest-generation jet engines and 100 % sustainable aviation fuel

Abstract. Nitrogen oxides, emitted from air traffic, are of concern due to their impact on climate by changing atmospheric ozone and methane levels. Using the DLR research aircraft Falcon, total reactive nitrogen (NOy) in-flight measurements were carried out at high altitudes to characterize emissions in the fresh aircraft exhaust from the latest-generation Rolls-Royce Trent XWB-84 engine aboard the long-range Airbus A350-941 aircraft during the ECLIF3 (Emission and CLimate Impact of alternative Fuels 3) experiment. The impact of different engine thrust settings, monitored in terms of combustor inlet temperature, pressure and engine fuel flow, was tested for two different fuel types: Jet A-1 and, for the first time, a 100 % sustainable aviation fuel (SAF) under similar atmospheric conditions. In addition, a range of combustor temperatures and an additional blended SAF were tested during ground-based emission measurements. For the data measured during ECLIF3, we confirm that the NOx emission index increases with increasing combustion temperature, pressure and fuel flow. We find that as expected, the fuel type has no measurable effect on the NOx emission index. These measurements are used to compare to cruise NOx emission index estimates from three engine emission prediction methods. Our measurements thus help to understand the ground to cruise correlation of current engine emission prediction methods while serving as input for climate modelling and extending the extremely sparse data set on in-flight aircraft nitrogen oxide emissions to newer engine generations.

MUSE: an open-access platform for urban expansion simulation with multitype patch generation engine and multilevel morphology regulation

Urban expansion models (UEMs) help unfold the mechanism, future pathways, and relevant repercussions of urban landscape dynamics. Although a plethora of UEMs have been developed, the field of open-access modeling platform for urban expansion simulation still presents gaps in terms of representing urban development events, regulating multilevel urban morphologies, and reflecting underlying regularities of physical urban growth. This study develops an open-access Multiengine Urban Expansion Simulation (MUSE) platform together with software that addresses some of these limitations. MUSE employs patch-based concepts and different patch generation engines to represent urban development events of distinctive types, enabling control over spatial morphology at landscape, class and patch levels. Moreover, MUSE incorporates two general regularities on urban development waves and urban diffusion and coalescence to govern physical urban expansion processes. Experiments in two cities examine MUSE’s ability in simulating realistic urban dynamics, and a series of synthetic experiments verify the operational behavior of the patch generation engines. These experiments show that MUSE can simulate a range of urban expansion types, including infill, edge growth and leapfrog expansion, and various morphologies, such as aggregated, sprawl, fragmented and compact. MUSE can empower practitioners to better project, analyze and understand complex urbanization dynamics.

A Product’s Kansei Appearance Design Method Based on Conditional-Controlled AI Image Generation

Accurately grasping users’ Kansei needs and rapidly transforming them into product design solutions are key factors in enhancing product competitiveness and sustainability. This paper proposes a product appearance design method based on Kansei engineering and AI image generation technology, integrating other approaches, with household indoor hydroponics as the research subject. First, the web crawler is used to obtain product image samples and user online reviews, and factor analysis (FA) is applied to quickly extract users’ Kansei needs. Second, product morphology is used to deconstruct and encode product appearances. Partial least squares regression (PLSR) is then employed to map and quantify the relationships between Kansei needs and design elements, yielding optimal design solutions and one-dimensional sketches. These sketches are subsequently used as controlled conditions in Stable Diffusion (SD), combined with a team-trained Lora model, to generate two-dimensional colored sketches in batches. Finally, evaluations verify that the generated design solutions are satisfactory and meet users’ Kansei needs. The results indicate that the proposed product appearance design method not only holds significant implications for the sustainable development of Kansei engineering in product design but also greatly enhances the efficiency of the design process, providing new insights into integrating new technologies and scientific research methods in the field of product design.

Digital deception: generative artificial intelligence in social engineering and phishing

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.

FECSG-ML: Feature Engineering for Nuclear Reaction Cross Sections Generation Using Machine Learning

In the field of nuclear science, obtaining and utilizing nuclear data, including nuclear reaction data, nuclear structure information, and radioactive decay data, is crucial. Neutron-induced nuclear reactions, particularly nuclear cross sections data, are essential for various applications, including reactor design. The EXFOR database is the only international repository for storing nuclear reaction experimental measurement information and data. However, experimental measurement data are often scarce, subject to discrepancies, or even errors, requiring human evaluation. This process can be prone to biases and significant uncertainties. To address these challenges, this study proposes a novel framework, Feature Engineering for Nuclear Reaction Cross Section Generation using Machine Learning (FECSG-ML), which employs machine learning methods to generate nuclear cross sections data, serving as a substitute for evaluating nuclear databases. Given the limited size of the EXFOR database, training a model solely on EXFOR data could lead to underfitting. Therefore, the proposed approach utilizes transfer learning, initially pre-training the model using the ENDF/B-VIII.0 dataset and subsequently fine-tuning it with the EXFOR database. This approach ensures high accuracy where real data are available and enables the learning of characteristics of the evaluation dataset where real data are lacking. Moreover, machine learning techniques are employed to transform discrete nuclear cross sections data into a continuous format, accommodating various isotopes and predicting multiple sets of cross sections data. The framework integrates various machine learning methods and utilizes ensemble learning for result optimization. Experimental results demonstrate that the regression curves generated by the FECSG-ML model align well with EXFOR data points, outperforming the ENDF/B-VIII.0 evaluation database. Furthermore, the nuclear cross sections data generated by the FECSG-ML model are applied in the OpenMC Monte Carlo simulation program to simulate pin fuel assemblies and CANDU reactors, confirming the effectiveness of the model. This study underscores the importance of accurate and reliable nuclear cross sections data and provides a method for substituting the evaluation of nuclear databases.

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.

Dual-engine-driven realizing high-yield synthesis of Para-Xylene directly from CO2-containing syngas

The direct synthesis of light aromatics, especially para-xylene (p-X), from syngas/CO2 is drawing strong interest, but improving the space-time yield (STY) of p-X is a significant challenge. Here, a dynamic “dual-engine-driven” (DED) catalytic system is designed by combining two partners of ZnCr and FeMn (named “dual-engine”) with Z5@SiO2 capsule zeolite. The DED catalyst of 1.0%FeMn&[ZnCr&Z5@SiO2] shows an extremely higher p-X STY of 36.1 gp-x·kgcat-1·h-1, about eight times higher than that of [ZnCr&Z5]. DED manipulates ZnCr engine for methanol formation and drives FeMn engine for light olefins generation together, and then the formed methanol and light olefins are coordinately converted in situ into p-X-rich aromatics over Z5@SiO2. The DED model boosts the driving force for syngas/CO2 conversion, simultaneously concerting the cooperation of “dual-engine” for p-X generation, resulting in extremely high STY of p-X. This study achieves non-petroleum p-X production at industrial-relevant level and advances knowledge in designing innovative heterogeneous catalysts.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Energy extraction potential from wave-induced ship motions using linear generators

This paper presents an assessment of the energy harvesting potential from wave-induced motions when producing electricity by linear generators installed on ships. The study estimates an upper maximum energy extraction potential by not considering the electro-mechanical coupling; neither is mechanical and electrical dissipation considered. The analysis of the harvested energy is made using simulated data in a case study investigating three different ships (by size). Specifically, the case study reveals that, in moderate to mildly severe sea states, the power harvested from the environment using linear generators may reach values around 1–2 kW/tons of seismic mass. Thus, it is unrealistic to imagine ship designs where linear generators are thought to provide a ship’s necessary propulsion power but, on the other hand, they may serve to supplement the main engine for auxiliary power generation.

Experimental Study on the Spray Characteristics of Diesel and Hydrotreated Vegetable Oil (HVO) Fuels under Different Injection Pressures

This investigation employed the diffused back-illumination (DBI) technique to analyze the spray characteristics of hydrotreated vegetable oil (HVO) fuel at three injection pressures and compared them with conventional diesel fuel. The results showed that as the injection pressure increased, the peak injection rates of both the HVO and diesel increased. At injection pressures above 120 MPa, the injection rates of both fuels were nearly identical, though differences were observed at lower pressures. Increasing the injection pressure reduced the injection delay. The HVO fuel exhibited a shorter spray tip penetration, lower equivalence ratio, larger spray angle, and spray volume, but its spray angle stability was lower than that of diesel. The ambient gas entrainment rate primarily occurred in two stages, significantly influenced by the spray breakup development stage. For diesel sprays, the injection pressure mainly affected the equivalence ratio near the nozzle with minimal downstream impact. Dent’s model provided better predictions of the penetration distance for diesel, while Hiroyasu’s model was more accurate in predicting the penetration distance of the HVO at 120 MPa and 180 MPa. Inagaki’s model performed better in predicting the spray angle for diesel, whereas Hiroyasu’s model was more accurate for the HVO spray angle predictions. Through this research, a better understanding of the spray characteristics of green fuels will be achieved, providing a reference for the design and optimization of new generation engines.