Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.
Environmental concerns have made looking for eco-friendly refrigerants essential in recent years. Natural refrigerants including ammonium (R717), carbon dioxide (R744), & hydrocarbons (R290 and R600a) are some of the most promising alternatives. Compared to synthetic alternatives. Natural refrigerants are the best option for many cooling applications because of their excellent thermodynamic characteristics and energy efficiency. The usage of these environmentally friendly refrigerants will support sustainable refrigeration practises and help to lower greenhouse gas emissions. The search for environmentally friendly refrigerators has drawn a lot of attention recently as environmental concerns have increased. The investigation of alternate alternatives has been motivated by concerns over the negative impact of conventional chemical the refrigerants on the loss of ozone and global warming. Natural refrigerants, which provide a sustainable and environmentally benign alternative to cooling applications, are among the most promising choices. such as ammonia (R717), carbon monoxide (R744), and hydrocarbon (R290 and R600a), have emerged as the best options. for artificial substitutes. This introduction lays the groundwork for further investigation into the characteristics and advantages of these environmentally benign refrigerants. Investigating more suitable eco-friendly refrigerants is crucial for tackling the urgent environmental issues connected to conventional synthetic refrigerants. These refrigerants are problematic in the long run since they contribute to the destruction of the ozone barrier and the acceleration of global warming. Through the identification and promotion of natural refrigerants including ammonia (R717), oxygen (R744), and oils (R290 and R600a), the research seeks to create environmentally friendly alternatives. By using these green refrigerants in cooling systems, greenhouse gas emissions can be significantly reduced, which will help slow the effects of climate change. Additionally, by advancing environmentally conscious and energy-efficient refrigeration technology, our research promotes a more sustainable and greener future. A multi-objective optimisation strategy used to resolve choice issues is called EDAS, which It entails assessing prospective solutions according to how far they are from the Pareto front's average answer, which captures trade-offs between competing goals. By considering both the diversity of the solutions and their proximity to the mean, EDAS seeks to identify the most desirable options. The EDAS methodology assists decision-makers in finding the best solutions that balance various goals by combining these elements. In several disciplines, including engineering, finance, and environmental management, this strategy has been employed with success. Alternative parameters taken as r134a, r152a, r1234yf, r1234ze (E), r1233zd (E), r290, r600a, r744. Evaluation parameters taken as Critical Temperature, Vapor density, Latent heat of vaporization, Critical Pressure, Saturated pressure, Liquid density, Viscosity of liquid. r134a in 6th rank. r152a in 5th rank. r1234yf in 7th rank. r1234ze (E) in 4th rank. r1233zd (E) in 3rd rank. r290 in 2nd rank. r600a in 1st rank. r744 in 8th rank. Best sustainable eco-friendly refrigerants are compared and given rank. According to the characteristics. r134a in 6th rank. r152a in 5th rank. r1234yf in 7th rank. r1234ze (E) in 4th rank. r1233zd (E) in 3rd rank. r290 in 2nd rank. r600a in 1st rank. r744 in 8th rank.
There has been limited quantitative research on the industrial application of direct cooling ice makers, resulting in lack of clarity in control mechanisms, and inadequate heat transfer capability and uniformity in ice making. A mathematical model focusing on the refrigerant side of the ice mold evaporator was established, and MATLAB simulation was used to analyze the changes of heat transfer and flow parameters with the flow process and ice making time, with comparisons drawn between experimental data and simulated outcomes. The results showed that the heat transfer rate before water icing is about 30% higher than that after water icing, and the refrigerant flow rate is obviously different. The unit heat transfer after overheating decreases by 40.9% compared to before overheating, reducing the overheating section can significantly enhance the heat transfer and improve the uniformity. The thermal resistance of water side and ice side accounted for 93.4% and 91.7% of the total respectively, and the heat transfer of water side or ice side should be improved first in the optimization of heat transfer. The simulation program can predict the change of flow rate and simulate the overheating section, which provides theoretical basis and practical guidance for the design and operation control of ice making machine, and helps to improve the product performance and accelerate the ice making process.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.
Bianca Trinkenreich, Fabio Calefato, Geir Hanssen
et al.
The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.
Woratid Wongpattanawut, Borvorn Israngkura Na Ayudhya
Introducing defective sanitaryware porcelain as a low-calcium binder for geopolymer mix concrete was regarded as green concrete. Four alkali concentrations (8M, 10M, 12M, and 14M) mixes involving four initial curing temperatures (60°C, 75°C, 90°C, and 105°C) were investigated for porosity, rapid chloride penetration, compressive and abrasive resistance. Tests on geopolymer paste for consistency and initial and final setting times were also assessed. For all the mixes, consistency and setting time decreased with increased alkali concentration levels. An increment in curing temperature increased the setting time rate. Microstructural studies such as X-ray fluorescence analysis (XRF), X-ray diffraction (XRD), and scanning electron microscopy (SEM) were carried out, and the results were presented. The compressive and abrasive resistance of the specimen performance increased with an increase in the initial curing temperature and alkali concentration level. Majorly, the mechanical strength of porcelain-based geopolymer specimens increased by increasing the alkali concentration level. Applying 105°C for the initial curing temperature to the specimen, compressive strength, abrasive resistance, and resistibility to chloride ingress of the specimen enhanced. At the 28-days curing period, the ultimate compressive strength was 68.03 N/mm2, the lowest weight loss from abrasive motion was 0.09%, and the lowest passing charge was 1,440.91 coulombs were recorded respectively. As a result, porcelain-based geopolymers required a high initial curing temperature and a high alkali concentration level. It was found that 14M porcelain-based specimens heated at 105°C curing temperature for 24 hours led to an eco-friendly concrete mix with prominent positive results for engineering properties. Doi: 10.28991/CEJ-2024-010-04-05 Full Text: PDF
A flaky test yields inconsistent results upon repetition, posing a significant challenge to software developers. An extensive study of their presence and characteristics has been done in classical computer software but not quantum computer software. In this paper, we outline challenges and potential solutions for the automated detection of flaky tests in bug reports of quantum software. We aim to raise awareness of flakiness in quantum software and encourage the software engineering community to work collaboratively to solve this emerging challenge.
The axion search experiments based on haloscopes at the Center for Axion and Precision Physics Research (CAPP) of the Institute for Basic Science (IBS) in South Korea are performed in the frequency range from 1 GHz to 6 GHz. In order to perform the experiments in a strong magnetic field of 12 T and a large-volume cavity of close to 40 liters, we use He wet dilution refrigerators with immersed superconducting magnets. The measurements require continuous operation for months without interruptions for microwave component replacements. This is achieved by using different cryogenic engineering approaches including microwave RF-switching. The critical components, defining the scanning rate and the sensitivity of the setup, are the Josephson parametric amplifiers (JPA) and cryogenic low noise amplifiers (cLNA) based on high-electron-mobility-transistor (HEMT) technology. It is desirable for both devices to have a wide frequency range and low noise close to the quantum limit for the JPA. In this paper, we show a recent design of a 4-channel measurement setup for JPA and HEMT measurements. The setup is based on a 4-channel wideband noise source (NS) and is used for both JPA and HEMT gain and noise measurements. The setup is placed at 20 mK inside the dry dilution refrigerator. The NS is thermally decoupled from the environment using plastic spacers, superconducting wires and superconducting coaxial cables. We show the gain and noise temperature curves measured for 4 HEMT amplifiers and 2 JPAs in one cool-down
Software companies have widely used online A/B testing to evaluate the impact of a new technology by offering it to groups of users and comparing it against the unmodified product. However, running online A/B testing needs not only efforts in design, implementation, and stakeholders' approval to be served in production but also several weeks to collect the data in iterations. To address these issues, a recently emerging topic, called "Offline A/B Testing", is getting increasing attention, intending to conduct the offline evaluation of new technologies by estimating historical logged data. Although this approach is promising due to lower implementation effort, faster turnaround time, and no potential user harm, for it to be effectively prioritized as requirements in practice, several limitations need to be addressed, including its discrepancy with online A/B test results, and lack of systematic updates on varying data and parameters. In response, in this vision paper, I introduce AutoOffAB, an idea to automatically run variants of offline A/B testing against recent logging and update the offline evaluation results, which are used to make decisions on requirements more reliably and systematically.
Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic datasets emerge as a viable alternative. However, practitioners are often uncertain about generating high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. This study aims to fill this knowledge gap by proposing comprehensive guidelines for generating, annotating, and validating synthetic datasets. The trade-offs and methods associated with each of these aspects are elaborated upon. Further, the practical implications of these guidelines are illustrated through the creation of a turbo-compressors dataset. The study underscores the importance of thoughtful sampling methods to ensure the appropriate size, diversity, utility, and realism of a dataset. It also highlights that design diversity does not equate to performance diversity or realism. By employing test sets that represent uniform, real, or task-specific samples, the influence of sample size and sampling strategy is scrutinized. Overall, this paper offers valuable insights for researchers intending to create and publish synthetic datasets for engineering design, thereby paving the way for more effective applications of AI advancements in the field. The code and data for the dataset and methods are made publicly accessible at https://github.com/cyrilpic/radcomp .
To meet a variety of application scenarios, high‐performance shape memory polymers require not only excellent mechanical and shape memory properties but also a multistimulus response. Herein, this article reports a near infrared (NIR) light‐triggered thermosetting shape memory polyimide (PI)/reduced graphene oxide (rGO) nanocomposite with a high transition temperature, excellent shape memory behaviors, superior robustness, and fast light‐induced shape recovery. The nanocomposite exhibits unexceptionable mechanical properties with breaking strain up to 44.1% and toughness up to 41.7 MJ m−3 at a low content of GO (1 wt%) on account of the good dispersion of GO in the PI matrix and the formed hierarchical structure similar to Nacre. The nanocomposites also have high glass transition temperatures above 200 °C and exhibit a great high‐temperature shape memory effect with a shape fixation rate above 99% and a shape recovery rate above 98%. In addition, with the aid of the efficient photothermal conversion property of rGO, enabling the induction of a temporary shape that completely recovers to the original shape within 7 s by sequential NIR irradiation. It is envisioned that high‐performance nanocomposites with high‐temperature shape memory and excellent mechanical and multistimuli properties will be widely applied in harsh environments, such as alarms, actuation, and so on.
Thermoelastic refrigeration is a solid-state technology that produces a refrigeration effect by driving the phase change of thermoelastic materials with a stress field. Natural rubber has attracted much attention because of its price and low elastic modulus. In this study, vulcanized natural rubber was subjected to a stretching-twisting-untwisting-retraction cycle at an ambient temperature above 0 °C to eliminate the Mullins effect. Natural rubber with the Mullins effect was subjected to cyclic loading in the ambient temperature range of -30–40 ℃. The temperature change of natural rubber during the cycle was recorded using an infrared thermal imager to explore the influence of temperature on the torsional tensile thermal effect. It was found that the temperature change of natural rubber during the twisting-untwisting process is greater than that in the simple stretching-retraction process. The temperature change is the highest due to the low-temperature crystallization characteristics of rubber at an ambient temperature of -20 ℃. The temperature during unloading reaches -41.3 ℃.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration