Hasil untuk "Low temperature engineering. Cryogenic engineering. Refrigeration"

Patricia G. F. Matsubara, Tayana Conte

Over twenty years ago, the Software Engineering (SE) research community have been involved with Evidence-Based Software Engineering (EBSE). EBSE aims to inform industrial practice with the best evidence from rigorous research, preferably from systematic literature reviews (SLRs). Since then, SE researchers have conducted many SLRs, perfected their SLR procedures, proposed alternative ways of presenting their results (such as Evidence Briefings), and profusely discussed how to conduct research that impacts practice. Nevertheless, there is still a feeling that SLRs' results are not reaching practitioners. Something is missing. In this vision paper, we introduce Evidence to Decision (EtD) frameworks from the health sciences, which propose gathering experts in panels to assess the existing best evidence about the impact of an intervention in all relevant outcomes and make structured recommendations based on them. The insight we can leverage from EtD frameworks is not their structure per se but all the relevant criteria for making recommendations to practitioners from SLRs. Furthermore, we provide a worked example based on an SE SLR. We also discuss the challenges the SE research and practice community may face when adopting EtD frameworks, highlighting the need for more comprehensive criteria in our recommendations to industry practitioners.

Xuetong He, L. Tian, Jianjian Gong et al.

The global helium shortage and escalating costs in cryogenic engineering have intensified demands for helium-free refrigeration technologies. Adiabatic demagnetization refrigeration (ADR) based on the magnetocaloric effect (MCE) presents a viable solution, with its efficacy fundamentally dependent on advanced magnetocaloric materials. Here we present the successful synthesis of a novel gadolinium garnet Gd3Te2Li3O12 through solid-state reaction, which crystallizes in the cubic Ia3̄d space group. The integration of magnetic characterization results with density functional theory (DFT) calculations establishes Gd3Te2Li3O12 as an antiferromagnetic compound exhibiting ultra-low magnetic ordering below 0.4 K. A comprehensive evaluation of the sub-kelvin magnetocaloric parameters demonstrates advantageous characteristics compared to commercial gadolinium gallium garnet (GGG) benchmarks, featuring both reduced magnetic ordering temperature and optimized entropy variation in the sub-Kelvin regime. These metrics position Gd3Te2Li3O12 as a prime candidate for sub-Kelvin ADR systems, while the observed geometrically frustrated magnetic sublattice configuration suggests new design principles for next generation magnetocaloric materials.

赵巍, 韩雅倩, 张华 et al.

In order to investigate the critical snow formation height of the mixed single-aperture nucleator within the artificial snow machine, an industrial microscope was used to observe the microstructure of the snow crystals, measure the critical snow formation height threshold, and analyse the effect of the air-to-water pressure ratio (0.4MPa:0.4MPa, 0.5MPa:0.45MPa, and 0.5MPa:0.4MPa) and the ambient temperatures (-5℃, -10℃, and -15℃) on the critical snow formation height. The results showed that under the working condition of air-water pressure ratio of 0.4MPa:0.4MPa, when the temperatures were -5℃ and -10℃, the threshold of critical snow formation height did not exist, and when the temperature was -15℃, it was able to form snow, and the threshold of critical snow formation height was 50~55cm; when the air-water pressure ratios were 0.5MPa:0.45MPa, 0.5MPa:0.4MPa, the three ambient temperatures can form snow. And the gas-water pressure ratio and ambient temperature will have a certain effect on the critical snow height, under the same ambient temperature, the larger the gas-water pressure ratio, the smaller the critical snow height; The critical snow formation height increases as the ambient temperature increases from -15°C to -5°C while keeping the air-water pressure ratio constant. When the gas-water pressure ratio is 0.5MPa:0.45MPa, the trend of critical snow formation height with temperature is larger, and the temperature has a greater impact on the critical snow formation height. The results of the study can provide a basis for the design of the optimized arrangement between the nucleator and the nozzle of the snow-making machine.

Mohammed Saleh Alshaikh

The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies.

Suman Lata Yadav

The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Hayato Ishida, Amal Elsokary, Maria Aslam et al.

Realisation of significant advances in capabilities of sensors, computing, timing, and communication enabled by quantum technologies is dependent on engineering highly complex systems that integrate quantum devices into existing classical infrastructure. A systems engineering approach is considered to address the growing need for quantum-secure telecommunications that overcome the threat to encryption caused by maturing quantum computation. This work explores a range of existing and future quantum communication networks, specifically quantum key distribution network proposals, to model and demonstrate the evolution of quantum key distribution network architectures. Leveraging Orthogonal Variability Modelling and Systems Modelling Language as candidate modelling languages, the study creates traceable artefacts to promote modular architectures that are reusable for future studies. We propose a variability-driven framework for managing fast-evolving network architectures with respect to increasing stakeholder expectations. The result contributes to the systematic development of viable quantum key distribution networks and supports the investigation of similar integration challenges relevant to the broader context of quantum systems engineering.

Arhaan Nawab, Adnan Zafar, Mohammed Yousuf Majid et al.

In the recent past, the cryogenic system has become significant because of its extensive applications across various industries, including liquefaction, medical imaging, aerospace, etc There are many thermodynamic cycles used to achieve ultra-low temperatures, but the Linde-Hampson cycle is known for its simplicity compared to other complex cycles, such as the Claude or Kapitza cycles. The present study focuses on the design and development of a cryocooler using the Linde-Hampson refrigeration cycle to achieve ultra-low temperatures. A thermodynamic analysis of the Linde-Hampson cryogenic refrigeration cycle has been carried out using an isenthalpic expansion via a Joule-Thomson (JT) valve. The study investigates the behavior and performance of four different working gases: nitrogen, oxygen, methane, and air. Simulations were carried out using Engineering equation Solver (EES) to evaluate each gas under actual operating conditions. The work input, COP, liquefaction fraction, exergy efficiency, and efficiency are identified as key performance metrics for assessing system performance. Nitrogen and oxygen exhibited higher liquefaction efficiencies; however, methane shows more refrigeration potential. Further, an experimental setup has been developed to achieve ultra-low temperatures with certain limitations of the compressor. Experiments were conducted using air and water as a secondary fluid in the evaporator to carry the refrigeration. The water medium showed a higher COP than air. These findings help to improve the efficiency and viability of gas liquefaction operations by providing a better understanding of gas-specific thermodynamic behaviour.

J. Adams, Nathaniel O’Connor, Matthew Jones et al.

The acoustic expander is an innovative cryogenic component that uses pressure waves for work transfer as part of a continuous flow, recuperative cycle refrigerator. This expander uses passive reed-valves coupled to an acoustic resonator to produce refrigeration. The passive reed-valves are pressure-controlled by the imposed, static pressure difference across the expander and the natural oscillating pressure in the resonator. The resonator is a series of tubes and cones. The practical implications of these simple components are that the acoustic expander does not require controlled valving or close-tolerance sliding seals at low-temperature, unlike existing piston- or turbo-expanders. This work compares two resonator designs, a harmonic resonator and a non-harmonic resonator. The non-harmonic resonator is excited by a single-frequency allowing for operation at an expansion pressure-ratio of 2.4. These expanders are expected to be useful in medium-scale refrigeration applications that are not well served by current small-scale Stirling cryocoolers or large-scale turbo-expander refrigerators.

W. Pan, S. Q. Yang, X. J. Xie et al.

Large cryogenic refrigeration systems are the only means to achieve a low-temperature environment for large scientific devices. As an important part of the China Initiative Accelerator Driven System (CiADS), an 18kW@4.5K/4kW@2K large helium cryogenic refrigerator is primarily used to cool down superconducting magnetic cryostats. It has been designed by Technical Institute of Physics and Chemistry, Chinese Academy of Sciences at the end of 2023. This paper gives an overview on the performance characteristics and working principle of the 18kW@4.5K/4kW@2K large helium cryogenic system. The integrated design of this helium cryogenic refrigerator is introduced. The overall engineering layout design based on the experimental building at Zhongshan Institute of Advanced Cryogenic Technology has been completed. The design result has been used to guideline the engineering and manufacturing phase. Its commissioning tests will be carried out and completed at the end of this year.

Zhongyu Zou, Zhigang Zhu, Qiyong Zhang et al.

With the development of cryogenic engineering technology, cryogenic helium systems are increasingly used in nuclear fusion, high-energy particle physics, aerospace and other fields. As key equipment in cryogenic helium systems, plate-fin heat exchangers play an important role in the heat exchange process that reduces helium from room temperature to the temperature of liquid nitrogen, liquid helium or even superfluid helium. Therefore, its heat transfer and pressure drop performance have critical impact on the overall performance of cryogenic helium systems. In order to obtain the heat transfer and pressure drop characteristic curves of the helium gas flowing through the tested plate-fin heat exchangers, the experimental setup is established using low temperature helium as working medium. The experimental setup can provide testing conditions for liquid nitrogen temperature range and liquid helium temperature range without relying on a helium refrigeration unit. This paper completes design analysis and optimization of the experimental setup about thermal-hydraulic performance of plate-fin heat exchangers in cryogenic helium systems, including the process design and the design of the main components of the test cold box. In addition, the paper also utilizes Aspen HYSYSⓇ to optimize the cooling process of the experimental setup, with the objective of reducing the consumption of liquid helium.

H. Y. Chen, J. Shang, J. Z. Wang et al.

Large-scale helium cryogenic refrigeration facilities are indispensable equipment for many cutting-edge researches, and the cold compressor is the most advantageous pressurization scheme in superfluid helium systems with capacity of more than 200 watts. Due to the extremely low operating temperature zone of the cold compressor, the efficiency of the compression process decreases as the external heat leakage increases, especially when building systems with greater cooling capacity, this phenomenon becomes more significant. In order to reduce the influence of heat leakage, a conjugate heat transfer analysis is carried out on the cold compressor using commercial CFD software, and the internal structures, including thermal anchor, thermal insulation materials, are analysed and optimized base on the simulations.

Liang Anqi, Zeng Shuang, Ren Jiahang et al.

To improve the comprehensive utilization of regional energy and promote low-carbon development, this study constructs an integrated energy system for typical areas, such as parks, including a new energy power generation system driven by photovoltaic and wind power, heating and cooling energy supply systems for ground-source/air-source heat pumps, water chillers, and energy storage equipment. TRNSYS? software is used to simulate and study the dynamic characteristics of the system under six climate conditions in Beijing, and the game theory is used for intelligent operation, which is then compared with the logic control method. The results show that the logic control method can meet the load demand but cannot realize the efficient operation of the heat pump unit and the charge and discharge balance of the energy storage device. The integrated energy system after optimization via game theory can not only realize flexible energy scheduling and distribution through electric-thermal coordination, but also save the entire energy consumption of the heat pump unit and achieve the goal of regional energy economic benefits. The research presented in this paper provides an important theoretical basis for the intelligent operation of heat pump systems in integrated electric-thermal cooperative grids.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Dezhi Ran, Mengzhou Wu, Wei Yang et al.

By treating data and models as the source code, Foundation Models (FMs) become a new type of software. Mirroring the concept of software crisis, the increasing complexity of FMs making FM crisis a tangible concern in the coming decade, appealing for new theories and methodologies from the field of software engineering. In this paper, we outline our vision of introducing Foundation Model (FM) engineering, a strategic response to the anticipated FM crisis with principled engineering methodologies. FM engineering aims to mitigate potential issues in FM development and application through the introduction of declarative, automated, and unified programming interfaces for both data and model management, reducing the complexities involved in working with FMs by providing a more structured and intuitive process for developers. Through the establishment of FM engineering, we aim to provide a robust, automated, and extensible framework that addresses the imminent challenges, and discovering new research opportunities for the software engineering field.

Jie JW Wu

Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.

Lorenz Graf-Vlachy, Daniel Graziotin, Stefan Wagner

Context: Citations are a key measure of scientific performance in most fields, including software engineering. However, there is limited research that studies which characteristics of articles' metadata (title, abstract, keywords, and author list) are driving citations in this field. Objective: In this study, we propose a simple theoretical model for how citations come to be with respect to article metadata, we hypothesize theoretical linkages between metadata characteristics and citations of articles, and we empirically test these hypotheses. Method: We use multiple regression analyses to examine a data set comprising the titles, abstracts, keywords, and authors of 16,131 software engineering articles published between 1990 and 2020 in 20 highly influential software engineering venues. Results: We find that number of authors, number of keywords, number of question marks and dividers in the title, number of acronyms, abstract length, abstract propositional idea density, and corresponding authors in the core Anglosphere are significantly related to citations. Conclusion: Various characteristics of articles' metadata are linked to the frequency with which the corresponding articles are cited. These results partially confirm and partially go counter to prior findings in software engineering and other disciplines.