Hasil untuk "Systems engineering"

Sophie Papst

A. Madni, C. C. Madni, Scott Lucero

Digital twin, a concept introduced in 2002, is becoming increasingly relevant to systems engineering and, more specifically, to model-based system engineering (MBSE). A digital twin, like a virtual prototype, is a dynamic digital representation of a physical system. However, unlike a virtual prototype, a digital twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the physical system’s life cycle. This paper presents an overall vision and rationale for incorporating digital twin technology into MBSE. The paper discusses the benefits of integrating digital twins with system simulation and Internet of Things (IoT) in support of MBSE and provides specific examples of the use and benefits of digital twin technology in different industries. It concludes with a recommendation to make digital twin technology an integral part of MBSE methodology and experimentation testbeds.

Le Cong, F. Ran, David B. T. Cox et al.

Christine Legner, Torsten Eymann, T. Hess et al.

R. Messenger, A. Abtahi

E. Pistikopoulos, A. Barbosa‐Póvoa, Jay H. Lee et al.

Process Systems Engineering (PSE) is the scientific domain within chemical engineering, of describing and analyzing the behavior of a physicochemical system via mathematical modeling, data analytics, design, optimization and control. The webinar will provide a guide towards the evolution of PSE by looking at its history, core competencies, current status and future trends. We will first briefly present some of the key theoretical developments and computational tools in PSE. We will then argue that the versatility and effective employment of PSE methods and tools can offer a systematic platform to address current and future societal, industrial and scientific challenges that require a holistic, systems approach, in energy, the environment, the ‘industry of tomorrow’, and sustainability. We will finally outline the foundations of a Circular Economy Systems Engineering paradigm, that may provide The Generation Next of PSE’s thinking and practice.

P. Daoutidis, Jay H. Lee, Srinivas Rangarajan et al.

This “white paper” is a concise perspective of the potential of machine learning in the process systems engineering (PSE) domain, based on a session during FIPSE 5, held in Crete, Greece, June 27-29, 2022. The session included two invited talks and three short contributed presentations followed by extensive discussions. This paper does not intend to provide a comprehensive review on the subject or a detailed exposition of the discussions; instead its aim is to distill the main points of the discussions and talks

M. González-Castaño, Bogdan Dorneanu, H. Arellano‐Garcia

The catalytic reduction of CO2 into value-added products has been considered a compelling solution for alleviating global warming and energy crises. The reverse water gas shift (RWGS) reaction plays a pivotal role among the various CO2 utilization approaches, due to the fact that it produces syngas, the building block of numerous conversion processes. Although a lot of work has been carried out towards the development of a RWGS process, ranging from efficient catalytic systems to reactor units, and even pilot scale processes, there is still a lack of understanding of the fundamental phenomena that take place at the various levels and scales of the process. This contribution presents the main solutions and remaining challenges for a structured, trans- and multidisciplinary framework in which catalysis engineering and process systems engineering can work together to incorporate understanding and methods from both sides, to accelerate the investigation, creation and operation of an efficient industrial CO2 conversion process based on the RWGS reaction.

Kaitlin Henderson, A. Salado

Traditional document‐based practices in systems engineering are being transitioned to model‐based ones. Adoption of model‐based systems engineering (MBSE) continues to grow in industry and government, and MBSE continues to be a major research theme in the systems engineering community. In fact, MBSE remains a central element in the International Council on Systems Engineering (INCOSE)’s vision for 2025. Examining systems engineering literature, this paper presents an assessment of the extent to which benefits and value of MBSE are supported by empirical evidence. A systematic review of research and practice papers in major systems engineering archival journals and conference proceedings was conducted. Evidence was categorized in four types, two of which inductively emerged from the results: measured, observed (without a formal measurement process), perceived (claimed without evidence), and backed by other references. Results indicate that two thirds of claimed MBSE benefits are only supported by perceived evidence, while only two papers reported measured evidence. The aggregate assessment presented in this paper indicates that claims about the value and benefits of MBSE are mainly based on expectation. We argue that evidence supporting the value and benefits of MBSE remains inconclusive.

Dahai Liu

Mayuranath SureshKumar, Hanumanthrao Kannan

Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.

A. Badiru

Siemens Digital Industries Software believes that today’s complex automotive products require a systems-driven approach to product development that combines systems engineering with an integrated product definition and the ability to unify your product development framework with your manufacturing and shop floor operations. To facilitate model-based systems engineering, Siemens Digital Industries Software provides functional networking, a consistent process-enabled framework, advanced modeling and simulation, an intuitive user experience and an open product lifecycle management (PLM) environment. Siemens Digital Industries Software

Mengshang Liang, Changxin Xu, Mingxian Li et al.

With the deceleration of China’s economic growth, the crude economic model will progressively diminish in its competitive edge, thereby posing challenges for state-level economic and technological development zones (ETDZs) in terms of transitioning their development model and grappling with low levels of total factor productivity (TFP). This study aims to evaluate the TFP of prominent cities in China, examine the influence of the establishment of state-level ETDZs on urban TFP, and investigate the moderating effect of transportation infrastructure on this relationship. The results show that the aggregate TFP of Chinese urban areas declined from 1999 to 2020, a trend linked to structural economic adjustments and persistent underutilization of capital in several regions. The establishment of state-level ETDZs has been found to exert a notable positive influence on regional TFP. The presence of transportation infrastructure plays a moderating role in facilitating state-level ETDZs, thereby enhancing regional TFP. Among various modes of transportation, highways and railways are particularly prominent in this regard. These conclusions provide a theoretical basis and decision-making reference for further unleashing the policy potential of development zones in China.

Phat T. Nguyen, Duy C. Huynh, Loc D. Ho et al.

Amidst the rapid global expansion of smart grids, ensuring the safety and reliability of power transmission systems has become paramount. Insulators are critical components of high-voltage transmission lines, providing both electrical insulation and mechanical support. However, their exposure to electrical, mechanical, and environmental stressors renders them vulnerable point within the system. Defective insulators are a major cause of failures in power transmission systems. Consequently, the early and accurate detection of these defects is pivotal for maintaining the integrity and reliability of the power grid. To address this challenge, this study proposes InsDD-YOLO, a novel object detection architecture enhanced from the YOLOv13 framework. The model incorporates a suite of strategic enhancements, including an improved DSConv (IDSConv) module for robust feature extraction, a streamlined Neck architecture augmented with a feature stream from a shallower layer (B2) to improve small-target detection, and a direct Head connection mechanism to maximize the preservation of fine-grained details. Experimental results demonstrate that InsDD-YOLO achieves superior performance, reaching an mAP0.5 of 90.1% and an mAP<inline-formula> <tex-math notation="LaTeX">${}_{0.5:0.95}$ </tex-math></inline-formula> of 46.4%, outperforming the baseline YOLOv13 model by a significant 5.0% in mAP0.5. With an inference time of just 5.4 ms, the proposed model not only establishes a new benchmark for accuracy but also demonstrates an effective trade-off between performance and speed, underscoring its significant potential for deployment in real-time, automated power grid monitoring systems.

Khalil Ur Rehman, Wasfi Shatanawi, Lok Yian Yian

The heat transfer in Casson fluid with natural convection claims various applications namely thermal regulation in biological systems, solar collectors, polymer processing, and geothermal applications to mention just a few. Owing to such motivation, we have offered artificial intelligence-based solution outcomes for heat transfer aspects in Casson fluid flow in a partially heated square enclosure with free convection effect. The semi-heated triangular baffle is installed at the center of the cavity. The bottom and right walls have the same amount of heat. The left wall of the cavity is taken cold and the top wall is taken insulated. The surface of triangular baffle and cavity walls are carried with non-slip condition. Finite element method (FEM) with hybrid meshing is used to solve the developed flow equations. AI-based neural networks model is used to examine the variation in Nusselt number for the involved flow parameters. MSE=2.15008e-6, 5.81476e-5, and 3.51888e-4 for training, validation, and testing respectively, suggesting good model performance on Nusselt number data along the bottom and vertical walls. We have observed that the heat transfer coefficient improves as Rayleigh and Prandtl numbers increase. We believe that the present AI-based outcomes will be helpful for predicting natural convection phenomena subject to thermal engineering standpoints.

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.

Zirui Li, Stephan Husung, Haoze Wang

Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.

M. Palys, Hanchu Wang, Qi Zhang et al.

Synthetic ammonia is essential for agriculture, but its production at present is unsustainable. Ammonia synthesized with hydrogen from renewable-powered electrolysis and nitrogen separated from air has the potential to alleviate these sustainability concerns while also having promise as a low-cost storage medium for intermittent renewable energy. This paper reviews recent research and development on the topic of renewable ammonia production and utilization as fertilizer and as energy storage. We describe our vision for synergistically combining these renewable ammonia applications to improve sustainability. Furthermore, we outline opportunities for systems engineering to play a crucial role in advancing the adoption of renewable ammonia in a manner which is sustainable, economically competitive, and reliable.