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

Chuqiao Song, Jinjia Liu, Ruihang Wang et al.

Zhang Yiqi, Huang Shuoquan, Li Xiuming et al.

Applying data-driven fault-diagnosis models to data center air-conditioning systems can significantly improve operational reliability. However， these models often lack diagnostic interpretability， which limits their application. This study develops a composite fault-diagnosis model based on typical machine-learning algorithms， compares the diagnostic performance of different models， and conducts interpretability research on the diagnostic models using the Shapley additive explanation method. The results demonstrate that the convolutional neural network （CNN）-based fault-diagnosis model achieves optimal performance in both the heat-pipe and vapor-compression modes， with F-1 scores exceeding 0.999 across all classifications. In the heat-pipe mode， the diagnosis of the CNN model primarily relies on the condenser-fan frequency， outdoor temperature， and refrigerant-pump power consumption as key features， whereas in the vapor-compression mode， the dominant features are the outdoor temperature， compressor frequency， and subcooling degree.

Qing‐Tai Zhao, Yisong Han, Hung-Chi Han et al.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

Nasir U. Eisty, Jeffrey C. Carver, Johanna Cohoon et al.

In the evolving landscape of scientific and scholarly research, effective collaboration between Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) is pivotal for advancing innovation and ensuring the integrity of computational methodologies. This paper presents ten strategic guidelines aimed at fostering productive partnerships between these two distinct yet complementary communities. The guidelines emphasize the importance of recognizing and respecting the cultural and operational differences between RSEs and SERs, proactively initiating and nurturing collaborations, and engaging within each other's professional environments. They advocate for identifying shared challenges, maintaining openness to emerging problems, ensuring mutual benefits, and serving as advocates for one another. Additionally, the guidelines highlight the necessity of vigilance in monitoring collaboration dynamics, securing institutional support, and defining clear, shared objectives. By adhering to these principles, RSEs and SERs can build synergistic relationships that enhance the quality and impact of research outcomes.

Oz Levy, Ilya Dikman, Natan Levy et al.

This paper explores how generative AI can help automate and improve key steps in systems engineering. It examines AI's ability to analyze system requirements based on INCOSE's "good requirement" criteria, identifying well-formed and poorly written requirements. The AI does not just classify requirements but also explains why some do not meet the standards. By comparing AI assessments with those of experienced engineers, the study evaluates the accuracy and reliability of AI in identifying quality issues. Additionally, it explores AI's ability to classify functional and non-functional requirements and generate test specifications based on these classifications. Through both quantitative and qualitative analysis, the research aims to assess AI's potential to streamline engineering processes and improve learning outcomes. It also highlights the challenges and limitations of AI, ensuring its safe and ethical use in professional and academic settings.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Max Neuwinger, Dirk Riehle

The design of effective programming languages, libraries, frameworks, tools, and platforms for data engineering strongly depends on their ease and correctness of use. Anyone who ignores that it is humans who use these tools risks building tools that are useless, or worse, harmful. To ensure our data engineering tools are based on solid foundations, we performed a systematic review of common programming mistakes in data engineering. We focus on programming beginners (students) by analyzing both the limited literature specific to data engineering mistakes and general programming mistakes in languages commonly used in data engineering (Python, SQL, Java). Through analysis of 21 publications spanning from 2003 to 2024, we synthesized these complementary sources into a comprehensive classification that captures both general programming challenges and domain-specific data engineering mistakes. This classification provides an empirical foundation for future tool development and educational strategies. We believe our systematic categorization will help researchers, practitioners, and educators better understand and address the challenges faced by novice data engineers.

Ye Zhang, Hai Mou, Qiu Huang et al.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

V. Sokolenko

The review presents the most significant scientific results obtained at the NSC KIPT NAS of Ukraine during low-temperature studies of the physical and mechanical properties of a wide range of metals and alloys over the period from the second half of the 30 s of the 20th century to recent years. During this period, a school in the field of physics of strength and plasticity was formed at the Institute, at the origins of which were I. V. Obreimov, R. I. Garber, V. I. Startsev, V. I. Khotkevich, B. G. Lazarev, and I. A. Gindin. As a result of the studies of these famous scientists, their students and followers, fundamental concepts in the field of low-temperature physics of strength and plasticity of solids and cryogenic materials science have been developed. The relevance of the results obtained is also confirmed by the development of modern cryogenic and aerospace engineering, nuclear and hydrogen energy, electronics, etc.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.

Florian Schummer, Maximilian Hyba

Model-Based Systems Engineering aims at creating a model of a system under development, covering the complete system with a level of detail that allows to define and understand its behavior and enables to define any interface and workpackage based on the model. Once such a model is established, further benefits can be reaped, such as the analysis of complex technical correlations within the system. Various insights can be gained by displaying the model as a formal graph and querying it. To enable such queries, a graph schema needs to be designed, which allows to transfer the model into a graph database. In the course of this paper, we discuss the design of a graph schema and MBSE modelling approach, enabling deep going system analysis and anomaly resolution in complex embedded systems. The schema and modelling approach are designed to answer questions such as what happens if there is an electrical short in a component? Which other components are now offline and which data cannot be gathered anymore? Or if a condition cannot be met, which alternative routes can be established to reach a certain state of the system. We build on the use case of qualification and operations of a small spacecraft. Structural and behavioral elements of the MBSE model are transferred to a graph database where analyses are conducted on the system. The schema is implemented by an adapter for MagicDraw to Neo4j. A selection of complex analyses are shown on the example of the MOVE-II space mission.

B. Ghorbani, Armin Ebrahimi, M. Moradi et al.

Abstract Continuous cryogenic refrigeration is the need for different industries. The purpose of this study is to produce continuous refrigeration at a temperature of 171 K. To this end, a two-stage ejector cooling system is used. The first ejector cycle with propane as working fluid provides the refrigeration up to a temperature of 233 K. The second ejector cycle with ethylene as working fluid utilizing the refrigeration provided at the previous cycle, as a heat source, provides refrigeration up to a temperature of 171 K as the final product of the cycle. Kalina power cycle is used to reduce the cycle’s consumed power, so that utilizing the ejector cycle’s excess heat produces a power of 2753 kW and, as a result, the coefficient of performance of the refrigeration cycle increases from 0.7821 to 0.8277. By employing solar energy through the photovoltaic panels, the power required by the system is provided. With the use of photovoltaic system software, a 48 MW grid-connected monocrystalline photovoltaic unit for a geographical location of Chabahar, Iran is simulated. This system has an annual performance ratio of 79.3% and, on average, produces energy of 80,224 MWh per year. Phase change material is used to provide end-user cold duty continuously; so that during the day the half of produced cold duty is delivered to the end-user, and the remaining is stored at phase change material, and during the night, this stored cold duty is recovered and delivered to the end-user. The exergy analysis shows that the maximum share dedicated to photovoltaic panels is 84.45% of the total exergy destruction of the system, which followed by compressors, heat exchangers, and ejectors with a value of 7.710%, 4.270%, and 1.24% of total exergy destruction of the system. The exergy efficiency of the total system is 28.97%. The results obtained from sensitivity analysis indicate that by increasing the operating pressure of the Kalina cycle up to 1500 kPa, the consumed power of the total system decreases by 12.37%. Besides, the coefficient of performance of the refrigeration cycle reaches to 0.9150.

Wang Fenghao, Cai Wanlong, Wang Ming et al.

Geothermal energy is widely used in building heating owing to its stability, large reserves, and wide distribution. Beginning from the classification of geothermal energy heating technology, this paper elaborates on the basic concepts, development history, and application status of shallow ground source heat pump technology, hydrothermal heating technology, and medium-deep borehole heat exchanger heating technology. Based on the reported research, future directions for investigation of geothermal energy heating technology are summarized, from the perspective of the operation mechanism and application practice. These future research directions mainly include heat balance analysis of large-scale shallow borehole heat exchanger arrays, high-efficiency water recharge technology of hydrothermal heating, and evaluation of the heat transfer performance of medium and deep borehole heat exchanger arrays.

Alex Serban, Koen van der Blom, Holger Hoos et al.

Following the recent surge in adoption of machine learning (ML), the negative impact that improper use of ML can have on users and society is now also widely recognised. To address this issue, policy makers and other stakeholders, such as the European Commission or NIST, have proposed high-level guidelines aiming to promote trustworthy ML (i.e., lawful, ethical and robust). However, these guidelines do not specify actions to be taken by those involved in building ML systems. In this paper, we argue that guidelines related to the development of trustworthy ML can be translated to operational practices, and should become part of the ML development life cycle. Towards this goal, we ran a multi-vocal literature review, and mined operational practices from white and grey literature. Moreover, we launched a global survey to measure practice adoption and the effects of these practices. In total, we identified 14 new practices, and used them to complement an existing catalogue of ML engineering practices. Initial analysis of the survey results reveals that so far, practice adoption for trustworthy ML is relatively low. In particular, practices related to assuring security of ML components have very low adoption. Other practices enjoy slightly larger adoption, such as providing explanations to users. Our extended practice catalogue can be used by ML development teams to bridge the gap between high-level guidelines and actual development of trustworthy ML systems; it is open for review and contribution

Toufique Ahmed, Premkumar Devanbu

Well-trained machine-learning models, which leverage large amounts of open-source software data, have now become an interesting approach to automating many software engineering tasks. Several SE tasks have all been subject to this approach, with performance gradually improving over the past several years with better models and training methods. More, and more diverse, clean, labeled data is better for training; but constructing good-quality datasets is time-consuming and challenging. Ways of augmenting the volume and diversity of clean, labeled data generally have wide applicability. For some languages (e.g., Ruby) labeled data is less abundant; in others (e.g., JavaScript) the available data maybe more focused on some application domains, and thus less diverse. As a way around such data bottlenecks, we present evidence suggesting that human-written code in different languages (which performs the same function), is rather similar, and particularly preserving of identifier naming patterns; we further present evidence suggesting that identifiers are a very important element of training data for software engineering tasks. We leverage this rather fortuitous phenomenon to find evidence that available multilingual training data (across different languages) can be used to amplify performance. We study this for 3 different tasks: code summarization, code retrieval, and function naming. We note that this data-augmenting approach is broadly compatible with different tasks, languages, and machine-learning models.

Sébastien Triqueneaux, James Butterworth, Johannes Goupy et al.

We present two different techniques for achieving low resistance ($<$20 n$\rm Ω$) contacts between copper and aluminium at cryogenic temperatures. The best method is based on gold plating of the surfaces in an e-beam evaporator immediately after Ar plasma etching in the same apparatus, yielding resistances as low as 3 n$\rm Ω$ that are stable over time. The second approach involves inserting indium in the Al/Cu joint. For both methods, we believe key elements are surface polishing, total removal of the aluminum oxide surface layer, and temporary application of large (typ. 11 kN) compression forces. We believe the values for gold plated contacts are the lowest ever reported for a Cu/Al joint of a few $\rm cm^{2}$. This technology could simplify the construction of thermal links for advanced cryogenics applications, in particular that of extremely low resistance heat switches for nuclear demagnetization refrigerators.

Sebastian Baltes, Paul Ralph

Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a critical review of the state of sampling in recent, high-quality software engineering research. The key findings are: (1) random sampling is rare; (2) sophisticated sampling strategies are very rare; (3) sampling, representativeness and randomness often appear misunderstood. These findings suggest that software engineering research has a generalizability crisis. To address these problems, this paper synthesizes existing knowledge of sampling into a succinct primer and proposes extensive guidelines for improving the conduct, presentation and evaluation of sampling in software engineering research. It is further recommended that while researchers should strive for more representative samples, disparaging non-probability sampling is generally capricious and particularly misguided for predominately qualitative research.

Manuel Rigger, Zhendong Su

Database Management Systems (DBMS) are used ubiquitously. To efficiently access data, they apply sophisticated optimizations. Incorrect optimizations can result in logic bugs, which cause a query to compute an incorrect result set. We propose Non-Optimizing Reference Engine Construction (NoREC), a fully-automatic approach to detect optimization bugs in DBMS. Conceptually, this approach aims to evaluate a query by an optimizing and a non-optimizing version of a DBMS, to then detect differences in their returned result set, which would indicate a bug in the DBMS. Obtaining a non-optimizing version of a DBMS is challenging, because DBMS typically provide limited control over optimizations. Our core insight is that a given, potentially randomly-generated optimized query can be rewritten to one that the DBMS cannot optimize. Evaluating this unoptimized query effectively corresponds to a non-optimizing reference engine executing the original query. We evaluated NoREC in an extensive testing campaign on four widely-used DBMS, namely PostgreSQL, MariaDB, SQLite, and CockroachDB. We found 159 previously unknown bugs in the latest versions of these systems, 141 of which have been fixed by the developers. Of these, 51 were optimization bugs, while the remaining were error and crash bugs. Our results suggest that NoREC is effective, general and requires little implementation effort, which makes the technique widely applicable in practice.