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

Chen Guanghao He Yunyun Chen Jianyong Chen Ying Luo Xianglong, Liang Yingzong He Jiacheng

Vapor-liquid separation technology can enhance heat transfer while reducing pressure drop. The vapor-liquid separation unit is key to achieving efficient vapor-liquid separation. A visualization experiment of the header-orifice separator is conducted in this study using the zeotropic mixture R1234ze（E）/R32 （mass fraction ratio， 80/20） to investigate the vapor-liquid separation characteristics under different conditions and obtain the effective separation range. The results show that increasing the inlet dryness vapor quality， reducing the inlet mass-flow rate， increasing the flow cross-sectional area of the lower outlet branch， and expanding the separation aperture can improve the separation efficiency， among which the inlet mass-flow rate contributes the most significantly to the separation efficiency. In the effective separation area， when the flow rate increases from 18 to 12 g/s， the separation efficiency increases by 14.0%. The inlet mass-flow rate， valve opening， and separation aperture minimally affects the size of the effective separation dryness range； however， for the deviation of the effective separation area dryness range， the inlet mass-flow rate exerts the greatest impact， followed by the separation aperture， whereas the valve opening exerts the least impact.

Lei Zhang

The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.

Alexander Korn, Lea Zaruchas, Chetan Arora et al.

Large Language Models, particularly decoder-only generative models such as GPT, are increasingly used to automate Software Engineering tasks. These models are primarily guided through natural language prompts, making prompt engineering a critical factor in system performance and behavior. Despite their growing role in SE research, prompt-related decisions are rarely documented in a systematic or transparent manner, hindering reproducibility and comparability across studies. To address this gap, we conducted a two-phase empirical study. First, we analyzed nearly 300 papers published at the top-3 SE conferences since 2022 to assess how prompt design, testing, and optimization are currently reported. Second, we surveyed 105 program committee members from these conferences to capture their expectations for prompt reporting in LLM-driven research. Based on the findings, we derived a structured guideline that distinguishes essential, desirable, and exceptional reporting elements. Our results reveal significant misalignment between current practices and reviewer expectations, particularly regarding version disclosure, prompt justification, and threats to validity. We present our guideline as a step toward improving transparency, reproducibility, and methodological rigor in LLM-based SE research.

Alexandra González, Xavier Franch, Silverio Martínez-Fernández

Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.

Jun Zhao, Yue Guo, Peisi Chen et al.

Jinqiao Liang

Accurate soil moisture prediction is essential for intelligent irrigation and optimal agricultural water resource allocation. To address the limitation of traditional linear models in capturing complex nonlinear relationships between meteorological factors and soil moisture, this study developed an Extreme Gradient Boosting (XGBoost)-based soil moisture prediction model using hourly meteorological and soil moisture data from a 1-hectare multi-crop farm, achieving high-precision prediction through in-depth data preprocessing, multi-dimensional feature engineering, and systematic model training and validation. Results indicate that with 44 optimal features selected, the model achieves a coefficient of determination (R²) of 0.673 on the test set, with low mean squared error (MSE) and mean absolute error (MAE), outperforming traditional linear models significantly; feature importance analysis identifies daily average temperature, previous day's soil moisture, and total daily precipitation as key driving factors (consistent with soil water evaporation and replenishment mechanisms); and the model predicts 5 cm depth soil moisture of 0.2435 (meeting the minimum crop survival threshold) for the target date. This model provides reliable data support for dynamic decision-making in intelligent irrigation systems, with great practical value for improving agricultural water use efficiency and advancing precision agriculture.

Shavindra Wickramathilaka, John Grundy, Kashumi Madampe et al.

The use of diverse mobile applications among senior users is becoming increasingly widespread. However, many of these apps contain accessibility problems that result in negative user experiences for seniors. A key reason is that software practitioners often lack the time or resources to address the broad spectrum of age-related accessibility and personalisation needs. As current developer tools and practices encourage one-size-fits-all interfaces with limited potential to address the diversity of senior needs, there is a growing demand for approaches that support the systematic creation of adaptive, accessible app experiences. To this end, we present AdaptForge, a novel model-driven engineering (MDE) approach that enables advanced design-time adaptations of mobile application interfaces and behaviours tailored to the accessibility needs of senior users. AdaptForge uses two domain-specific languages (DSLs) to address age-related accessibility needs. The first model defines users' context-of-use parameters, while the second defines conditional accessibility scenarios and corresponding UI adaptation rules. These rules are interpreted by an MDE workflow to transform an app's original source code into personalised instances. We also report evaluations with professional software developers and senior end-users, demonstrating the feasibility and practical utility of AdaptForge.

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Max Ofsa, Taylan G. Topcu

Systems engineering (SE) is evolving with the availability of generative artificial intelligence (AI) and the demand for a systems-of-systems perspective, formalized under the purview of mission engineering (ME) in the US Department of Defense. Formulating ME problems is challenging because they are open-ended exercises that involve translation of ill-defined problems into well-defined ones that are amenable for engineering development. It remains to be seen to which extent AI could assist problem formulation objectives. To that end, this paper explores the quality and consistency of multi-purpose Large Language Models (LLM) in supporting ME problem formulation tasks, specifically focusing on stakeholder identification. We identify a relevant reference problem, a NASA space mission design challenge, and document ChatGPT-3.5's ability to perform stakeholder identification tasks. We execute multiple parallel attempts and qualitatively evaluate LLM outputs, focusing on both their quality and variability. Our findings portray a nuanced picture. We find that the LLM performs well in identifying human-focused stakeholders but poorly in recognizing external systems and environmental factors, despite explicit efforts to account for these. Additionally, LLMs struggle with preserving the desired level of abstraction and exhibit a tendency to produce solution specific outputs that are inappropriate for problem formulation. More importantly, we document great variability among parallel threads, highlighting that LLM outputs should be used with caution, ideally by adopting a stochastic view of their abilities. Overall, our findings suggest that, while ChatGPT could reduce some expert workload, its lack of consistency and domain understanding may limit its reliability for problem formulation tasks.

Yunyang Zhang, Zhiqiang Gong, Weien Zhou et al.

Temperature field prediction is of great importance in the thermal design of systems engineering, and building the surrogate model is an effective way for the task. Generally, large amounts of labeled data are required to guarantee a good prediction performance of the surrogate model, especially the deep learning model, which have more parameters and better representational ability. However, labeled data, especially high-fidelity labeled data, are usually expensive to obtain and sometimes even impossible. To solve this problem, this paper proposes a pithy deep multi-fidelity model (DMFM) for temperature field prediction, which takes advantage of low-fidelity data to boost the performance with less high-fidelity data. First, a pre-train and fine-tune paradigm are developed in DMFM to train the low-fidelity and high-fidelity data, which significantly reduces the complexity of the deep surrogate model. Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process. Two diverse temperature field prediction problems are constructed to validate the effectiveness of DMFM and PD-DMFM, and the result shows that the proposed method can greatly reduce the dependence of the model on high-fidelity data.

Gajalakshmi G, Meenakshi S

This paper explores the intricate relationship between man and nature in Ernest Hemingway’s The Old Man and the Sea through the lens of deep ecology. It challenges the traditional anthropocentric interpretation of the novella, proposing that the protagonist Santiago’s struggle is not merely a tale of human triumph over nature but a journey towards understanding and coexisting with the natural world. By applying the principles of deep ecology, the study reveals how Santiago’s evolving relationship with the marlin and other sea elements reflects a broader ecological consciousness. The analysis also draws parallels between Santiago’s experience and the Biblical narrative of Jonah, suggesting that Santiago’s success is not solely due to his physical endurance but also the cosmic forces that aid him. This paper ultimately rethinks the themes of struggle and victory in the novella, emphasising the need for a harmonious relationship between humanity and the environment.

Diana Robinson, Christian Cabrera, Andrew D. Gordon et al.

What if end users could own the software development lifecycle from conception to deployment using only requirements expressed in language, images, video or audio? We explore this idea, building on the capabilities that generative Artificial Intelligence brings to software generation and maintenance techniques. How could designing software in this way better serve end users? What are the implications of this process for the future of end-user software engineering and the software development lifecycle? We discuss the research needed to bridge the gap between where we are today and these imagined systems of the future.

Marco Autili, Martina De Sanctis, Paola Inverardi et al.

As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.

Oluwatosin Ogundare, Srinath Madasu, Nathanial Wiggins

Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.

Chong Wang, Xinyu Ye, Yitao Zhao et al.

Tumor resection is widely used to prevent tumor growth. However, the defected tissue at the original tumor site also causes tissue or organ dysfunction which lowers the patient’s life quality. Therefore, regenerating the tissue and preventing tumor recurrence are highly important. Herein, according to the concept of ‘first kill and then regenerate’, a versatile scaffold-based tissue engineering strategy based on cryogenic 3D printing of water-in-oil polyester emulsion inks, containing multiple functional agents, was developed, in order to realize the elimination of tumor cells with recurrence suppression and improved tissue regeneration sequentially. To illustrate our strategy, water/poly(lactic-co-glycolic acid)/dichloromethane emulsions containing β-tricalcium phosphate (β-TCP), 2D black phosphorus (BP) nanosheets, low-dose doxorubicin hydrochloride (DOX) and high-dose osteogenic peptide were cryogenically 3D printed into hierarchically porous and mechanically strong nanocomposite scaffolds, with multiple functions to treat bone tumor, resection-induced tissue defects. Prompt tumor ablation and long-term suppression of tumor recurrence could be achieved due to the synergistic effects of photothermotherapy and chemotherapy, and improved bone regeneration was obtained eventually due to the presence of bony environment and sustained peptide release. Notably, BP nanosheets in scaffolds significantly reduced the long-term toxicity phenomenon of released DOX during in vivo bone regeneration. Our study also provides insights for the design of multi-functional tissue engineering scaffolds for treating other tumor resection-induced tissue defects.

Safaa M Ali, Maathe A. Theeb

Refrigeration, and air conditioning systems are designed to transport heat from internal spaces or products and discard it into the surrounding. Refutation of heat might happen in a straight line to the air, as in the case of most conventional units of air-source, or to water flowing from a cooling tower. This heat is of a "low-grade diversity", it still signifies wasted energy. According to the viewpoint of energy conservation, it would be necessary to regain this heat in a serviceable form. In this research, a practical study aims to collect the heat emitted from the cooling condenser by 35% and exploit it in heating hot water for domestic purposes, in addition to other uses, including reducing the consumption of electrical energy as a result of exploiting and collecting waste heat, from cooling devices using a system consisting of two types of helical heat exchangers and two stages. The first stage consists of a tube-in-tube helical exchanger and the second stage includes a tube-in-shell heat exchanger using R410A refrigerant. The EES engineering equation solving program was used to solve the numerical equations that are used to solve problems related to thermodynamics, due to its high accuracy in solving equations, extracting values and graphs, and calculating the performance coefficient of the COP to obtain better efficiency for the work of the system, as it was found that the higher the temperature. As a result of increasing the flow rate of deionized water, the percentage of performance coefficient of COP has increased.

H. Su, C. Lv, J. Shang et al.

As a clean and efficient energy, liquid hydrogen has become an important solution for the energy restructuring in the future. The centrifugal liquid hydrogen pump has been applied in the storage, transportation and distribution of liquid hydrogen. As the support of pump rotor, the performance of cryogenic bearings are essential to the dynamic characteristics and design optimization of liquid hydrogen pump. In this paper, an improved quasi-static model for cryogenic bearing in liquid hydrogen pump was introduced, in which the effects of high-speed centrifugal expansion and shrinkage under cryogenic condition on bearing rings were considered. Based on this model, the effects of rotating speed and axial preload on bearing dynamic characteristics were studied. At the same time, the friction within the cryogenic bearing was analysed and calculated. Finally, the load variation of bearing preload disc spring at cryogenic temperature was calculated and experimentally studied. The results of this paper will provide theoretical suggestions for further engineering design and applications of cryogenic bearings in liquid hydrogen pump.

Cody Karcher, Robert Haimes

A method of Sequential Log-Convex Programming (SLCP) is constructed that exploits the log-convex structure present in many engineering design problems. The mathematical structure of Geometric Programming (GP) is combined with the ability of Sequential Quadratic Program (SQP) to accommodate a wide range of objective and constraint functions, resulting in a practical algorithm that can be adopted with little to no modification of existing design practices. Three test problems are considered to demonstrate the SLCP algorithm, comparing it with SQP and the modified Logspace Sequential Quadratic Programming (LSQP). In these cases, SLCP shows up to a 77% reduction in number of iterations compared to SQP, and an 11% reduction compared to LSQP. The airfoil analysis code XFOIL is integrated into one of the case studies to show how SLCP can be used to evolve the fidelity of design problems that have initially been modeled as GP compatible. Finally, a methodology for design based on GP and SLCP is briefly discussed.

Daniel Regulin, Thomas Aicher, Birgit Vogel-Heuser

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.