Search Results (107)

Dynamic pressure measurement is an important component in the turbo engine testing process. This paper presents a comparative analysis between two types of multichannel electronic pressure measurement systems, commonly known as pressure scanners, used for this purpose: ZOC17/8Px, with analog amplification per channel, and MPS4264, a modern digital system with integrated A/D conversion. The study was conducted in two stages: a metrological verification and validation in static mode, using a high-precision pressure standard, and an experimental stage in dynamic mode, where data was acquired from a turbojet engine test stand, in constant engine speed mode. The signal stability of the pressure scanners was statistically analyzed by determining the coefficient of variation in the signal and the frequency spectrum (FFT) for each channel of the pressure scanners. Furthermore, comprehensive uncertainty budgets were calculated for both systems. The results highlight the superior stability and reduced uncertainty of the MPS4264 pressure scanner, attributing its enhanced performance to digital integration and a higher resilience to external noise. The findings support the adoption of modern digital systems for dynamic applications and provide a robust metrological basis for the optimal selection of measurement systems. Full article

Internal combustion engines remain a predominant source of global energy consumption, contributing substantially to both operational costs and greenhouse gas emissions. This work evaluates a nanomaterial-based engine oil additive that reduces friction and wear and increases torque, horsepower, and fuel efficiency. This novel nano oil additive contains functionalized carbon nanotubes and hexagonal boron nitride nanosheets that are dispersed in base oil using a proprietary ultrasonication process. Block-on-ring tests performed by multiple testing facilities demonstrated up to a 17% decrease in coefficient of friction and up to a 78% decrease in wear compared to the base oil after treating with the nano oil additive. Thermal properties enhancement by the nano oil additive was evaluated and increases up to 17 °C in thermal stability were obtained. Additionally, the nano oil additive increased torque and horsepower by an average of 7% in motorcycles and 2.4% in pickup trucks. Most importantly, the nano oil additive demonstrated improvements in fuel economy in both gasoline and diesel engines, with laboratory tests reporting 3–5% increases and practical field tests on a commercial truck fleet reporting an average of a 6% increase. The improved engine efficiency leads to reduced turbo temperature in heavy diesel engines and prolonged engine life expectancy and will significantly improve global environmental sustainability. Full article

This study explores the effects of electrically assisted turbochargers (EAT) on the performance of diesel engines by incorporating an electrical motor/generator into a conventional turbocharged model. The engine simulations were conducted at three different power levels of 2, 2.5, and 3 kW to assess the impact of electrical assistance. The results demonstrated that EAT significantly boosts engine performance, with an increase in boost pressure of up to 58.9% at 1000 rpm and an average increase of 30.9% across the low engine speed range (1000–2200 rpm). Additionally, the maximum turbocharger speed was achieved at lower engine speeds, dropping from 2400 rpm to as low as 1600 rpm with 3 kW assistance. Engine torque improved by up to 28.2% at 1000 rpm, and brake-specific fuel consumption (BSFC) was reduced by as much as 8.1%. Transient simulations showed notable improvements in response times, with turbo lag reduced by up to 53% under acceleration conditions. Overall, EAT technology provides significant enhancements in engine efficiency, torque output, fuel economy, and transient response, positioning it as a promising solution for improving diesel engine performance, particularly in addressing turbo lag and low-speed inefficiencies. Full article

This study presents a fine-tuned Large Language Model approach for predicting band gap and stability of transition metal sulfides. Our method processes textual descriptions of crystal structures directly, eliminating the need for complex feature engineering required by traditional ML and GNN approaches. Using a strategically selected dataset of 554 compounds from the Materials Project database, we fine-tuned GPT-3.5-turbo through nine consecutive iterations. Performance metrics improved significantly, with band gap prediction R² values increasing from 0.7564 to 0.9989, while stability classification achieved F1 > 0.7751. The fine-tuned model demonstrated superior generalization ability compared to both GPT-3.5 and GPT-4.0 models, maintaining high accuracy across diverse material structures. This approach is particularly valuable for new material systems with limited experimental data, as it can extract meaningful features directly from text descriptions and transfer knowledge from pre-training to domain-specific tasks without relying on extensive numerical datasets. Full article

Unified Modeling Language (UML) diagrams serve as essential tools for visualizing system structure and behavior in software design. With the emergence of Large Language Models (LLMs) that automate various phases of software development, there is growing interest in leveraging these models for UML diagram generation. This study presents a comprehensive empirical investigation into the effectiveness of GPT-4-turbo in generating four fundamental UML diagram types: Class, Deployment, Use Case, and Sequence diagrams. We developed a novel rule-based prompt-engineering framework that transforms domain scenarios into optimized prompts for LLM processing. The generated diagrams were then synthesized using PlantUML and evaluated through a rigorous survey involving 121 computer science and software engineering students across three U.S. universities. Participants assessed both the completeness and correctness of LLM-assisted and human-created diagrams by examining specific elements within each diagram type. Statistical analyses, including paired t-tests, Wilcoxon signed-rank tests, and effect size calculations, validate the significance of our findings. The results reveal that while LLM-assisted diagrams achieve meaningful levels of completeness and correctness (ranging from 61.1% to 67.7%), they consistently underperform compared to human-created diagrams. The performance gap varies by diagram type, with Sequence diagrams showing the closest alignment to human quality and Use Case diagrams exhibiting the largest discrepancy. This research contributes a validated framework for evaluating LLM-generated UML diagrams and provides empirically-grounded insights into the current capabilities and limitations of LLMs in software modeling education. Full article

This paper investigates how octanol, used as a renewable additive in Jet A fuel, influences the performance and emissions of aviation micro-turbo engines. Blends containing 10%, 20%, and 30% octanol, with an additional 5% n-heptane, were tested to closely replicate Jet A’s physical–chemical properties. Mathematical models validated using density and viscosity data achieved accurate predictions, with maximum absolute errors of 0.0018 g/cm³ for density and 0.4020 mm²/s for viscosity. Performance assessments showed that fuel consumption increased due to octanol’s lower calorific value, requiring higher fuel flow to sustain engine speed. Combustion temperature variations ranged from a decrease of 5.38% in Regime 1 (30% octanol) to increases of up to 1.47% and 1.13% in Regimes 2 and 3, respectively, without compromising engine stability. Thrust variations were minimal, with decreases up to 0.72% observed at 30% octanol concentration. Emission analysis indicated significant reductions in CO and NO_x levels with increased octanol content, attributed to enhanced combustion completeness and additional oxygen availability. SO₂ emissions also decreased slightly due to the lower sulfur content. Thermal efficiency marginally declined from 5.04% (Jet A) to approximately 4.92–4.97% for octanol blends. These findings support octanol as a viable sustainable additive, offering substantial emission benefits with only minor efficiency trade-offs. Full article

This paper introduces a novel framework enabling natural language question answering on Piping and Instrumentation Diagrams (P&IDs), addressing a critical gap between engineering design documentation and intuitive information retrieval. Our approach transforms static P&IDs into queryable knowledge bases through a three-stage pipeline. First, we recognize entities in a P&ID image and organize their relationships to form a base entity graph. Second, this entity graph is converted into a Labeled Property Graph (LPG), enriched with semantic attributes for nodes and edges. Third, a Large Language Model (LLM)-based information retrieval system translates a user query into a graph query language (Cypher) and retrieves the answer by executing it on LPG. For our experiments, we augmented a publicly available P&ID image dataset with our novel PIDQA dataset, which comprises 64,000 question–answer pairs spanning four categories: (I) simple counting, (II) spatial counting, (III) spatial connections, and (IV) value-based questions. Our experiments (using gpt-3.5-turbo) demonstrate that grounding the LLM with dynamic few-shot sampling robustly elevates accuracy by 10.6–43.5% over schema contextualization alone, even under high lexical diversity conditions (e.g., paraphrasing, ambiguity). By reducing barriers in retrieving P&ID data, this work advances human–AI collaboration for industrial workflows in design validation and safety audits. Full article

Premature seal failure induced by the unevenness of interstage pressure distribution in multi-stage brush seals significantly compromises the sealing efficiency of Air-Turbo Rocket (ATR) engines operating under high-pressure (megapascal-level) differential conditions. Conventional pressure equalization designs for such seals often result in significant leakage rate increases. This study addresses the pressure imbalance phenomenon in four-tooth three-stage brush composite seals through a novel fractal–geometric porous-media model, rigorously validated against experimental data. Systematic investigations were conducted to elucidate the effects of structural parameters and operational conditions on both sealing performance and pressure distribution characteristics. Key findings reveal that, under the prototype structure parameter, the first-, second-, and third-stage brush bundles account for 18.3%, 30.0%, and 43.3% of the total pressure drop, respectively, with grate teeth contributing 8.4%, demonstrating an inherent pressure imbalance. Axial brush spacing exhibits a minimal impact on the pressure distribution, while the gradient thickness settings of the brush bundles show limited influence. Radial clearance optimization and gradient backplate height adjustment effectively regulate pressure distribution, albeit with associated leakage rate increases. Structural modifications based on these principles achieved only a 5.8% leakage increment while reducing the maximum bundle pressure drop by 23%, demonstrating effective pressure balancing. A simplified analysis of entropy reveals that the fundamental mechanism governing the pressure imbalance stems from non-uniform entropy generation caused by aerodynamic damping dissipation across sequential brush stages. These findings establish a dampened dissipation-based theoretical framework for designing high-performance multistage brush seals in aerospace applications, providing critical insights for achieving an optimal balance between leakage control and pressure equalization in extreme-pressure environments. Full article

The aim of the research was to determine the impact of antifriction coatings on the technical condition of marine diesel engine bearings. Various epilams were used as antifriction coatings, with a thin layer applied to the surfaces of the bearings of the marine diesel engines 12V32/40 MAN-Diesel&Turbo. The thickness of the epilam coating adsorbed on the metal surface was controlled by ellipsometry. It was found that the thickness of the epilam layer on the surfaces of marine diesel engine bearings could reach 11.2 nm to 17.0 nm. The adsorption time required does not exceed 10 min. It was shown that the epilam nanolayer applied to the metal surface led to an increase in the structural characteristics of the oil boundary layer (thickness: from 12.3 µm to 15.2–18.3 µm; contact angles: from 10.2 deg to 15.8–17.4 deg). It was experimentally confirmed that the epilam coating of bearing surfaces significantly reduced their wear. For the 12V32/40 MAN-Diesel&Turbo marine diesel engine, in the case of epilaminating, the wear of the bearing shell surface was reduced by 6.1–27.6%, with the greatest reduction in wear occurring for the stern (most loaded) bearings. This helped to maintain the technical condition of the bearings of marine diesel engines. Full article

In the product design and manufacturing process, the effective management and representation of system requirements (SRs) are crucial for ensuring product quality and consistency. However, current methods are hindered by document ambiguity, weak requirement interdependencies, and limited semantic expressiveness in model-based systems engineering. To address these challenges, this paper proposes a prompt-driven integrated framework that synergizes large language models (LLMs) and knowledge graphs (KGs) to automate the visualization of SR text and structured knowledge extraction. Specifically, this paper introduces a template for information extraction tailored to arbitrary requirement documents, designed around five SysML-defined SR categories: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. By defining structured elements for each category and leveraging the GPT-4 model to extract key information from unstructured texts, the system can effectively extract and present the structured requirement information. Furthermore, the system constructs a knowledge graph to represent system requirements, visually illustrating the interdependencies and constraints between them. A case study applying this approach to Chapters 18–22 of the ‘Code for Design of Metro’ demonstrates the effectiveness of the proposed method in automating requirement representation, enhancing requirement traceability, and improving management. Moreover, a comparison of information extraction accuracy between GPT-4, GPT-3.5-turbo, BERT, and RoBERTa using the same dataset reveals that GPT-4 achieves an overall extraction accuracy of 84.76% compared to 79.05% for GPT-3.5-turbo and 59.05% for both BERT and RoBERTa. This proves the effectiveness of the proposed method in information extraction and provides a new technical pathway for intelligent requirement management. Full article

The present paper is a review of the use of different types of blends of Jet A aviation fuel with biodiesel and alcohols, respectively, as sustainable aviation fuels (SAF). The scientific literature published from 2017 to 2024 was addressed and highlighted that the use of Jet A fuel blended with alcohols and biodiesel has gained attention as a potential pathway to reducing aviation emissions and enhancing sustainability. Alcohol-blended Jet A fuels, such as ethanol and methanol mixtures, offer advantages including lower carbon monoxide (CO) and unburned hydrocarbon (HC) emissions due to their improved combustion efficiency. Similarly, biodiesel blends contribute to reduced particulate matter (PM) and CO emissions, while their oxygen content promotes cleaner combustion. Both types of blends have the potential to decrease the aviation sector’s carbon footprint and enhance fuel diversification. However, several gaps and limitations remain, including lower energy density leading to increased fuel consumption, material compatibility issues, increased nitrogen oxide (NO_x) emissions, and concerns over fuel stability. Further research is needed to optimize blend ratios, improve combustion control strategies, and ensure the safe and efficient integration of these alternative fuels in aviation. Full article

The ongoing global outbreak of mpox caused by clade IIb viruses has led to more than 100,000 confirmed cases around the world, highlighting the urgent need for antiviral research to combat current and future mpox outbreaks. Reporter viruses expressing fluorescent proteins to monitor viral replication and virus spreading in cell culture provide a powerful tool for antiviral drug screening. In this work, we engineered two recombinant mpox clade IIb viruses by inserting, under the control of the vaccinia early/late promoter 7.5, the coding sequence of two different fluorescent proteins (EGFP and TurboFP635) in a previously unreported location within the viral genome. These recombinant viruses replicate in BSC-1 cells at rates similar to those of the parental virus. We show how these reporter mpox viruses allow the discrimination of infected cells by cell flow cytometry and facilitate the quantification of viral spread in cell culture. Finally, we validated these reporter viruses with two previously known inhibitors of poxvirus replication, cytosine arabinoside (AraC) and bisbenzimide. Full article

The automation of educational and instructional assessment plays a crucial role in enhancing the quality of teaching management. In physics education, calculation problems with intricate problem-solving ideas pose challenges to the intelligent grading of tests. This study explores the automatic grading of physics problems through a combination of large language models and prompt engineering. By comparing the performance of four prompt strategies (one-shot, few-shot, chain of thought, tree of thought) within two large model frameworks, namely ERNIEBot-4-turbo and GPT-4o. This study finds that the tree of thought prompt can better assess calculation problems with complex ideas (N = 100, ACC ≥ 0.9, kappa > 0.8) and reduce the performance gap between different models. This research provides valuable insights for the automation of assessments in physics education. Full article

The European Union’s path towards zero carbon dioxide emissions for new passenger vehicles necessitates a transitional period in which conventional vehicles coexist with zero-emission alternatives. This shift requires targeted strategies from engineers and policymakers, particularly in the area of road design, to reduce pollution. This study aims to investigate the environmental benefits of converting a two-lane urban roundabout into a turbo-roundabout through a virtual microsimulation approach using PTV VISSIM. The simulated model was calibrated and validated with real-world daily traffic data by properly adjusting the driving behavior parameters and comparing observed and modeled traffic volumes and queues. The Vehicle-Specific Power (VSP) emission method was applied to model, calculate and illustrate emissions by analyzing vehicle trajectories for the examined scenarios. Results show a statistically significant reduction in emissions for nearly all trips, with emissions decreasing by up to 44% across the intersection and its surrounding areas, and up to 23% at the intersection itself. Emissions are largely influenced by trip duration and traffic efficiency, both of which are enhanced by the improved geometric configuration of the case study intersection. These findings highlight that turbo-roundabouts represent an effective, environmentally sustainable design solution for urban intersections. Full article

This study focuses on optimizing traffic flow at uncontrolled intersections by comparing the effectiveness of different intersection types: roundabouts, signal-controlled intersections, and turbo-roundabouts. The purpose is to determine which type offers the best solution for enhancing traffic efficiency, reducing delays, and improving safety. The research employs simulation-based modeling to analyze traffic performance under varying traffic conditions. Critical parameters such as vehicle flow rate, average delay time, and capacity are used to assess the performance of each intersection type. The results indicate that turbo-roundabouts outperform conventional roundabouts and signal-controlled intersections in terms of both capacity and reduction in delays. The findings suggest that implementing turbo-roundabouts at high-traffic intersections can significantly improve traffic flow and reduce congestion. However, the effectiveness of each solution is context-dependent, with signal-controlled intersections still being advantageous under specific conditions, particularly in highly urbanized areas. This study provides valuable insights for transportation planners and engineers, highlighting the importance of intersection design in traffic optimization. Full article