H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley
Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.
Dhiraj Neupane, Richard Dazeley, Mohamed Reda Bouadjenek
et al.
Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement Learning to train a discriminator that distinguishes between normal (expert) and policy-generated transitions. The discriminator's learned reward serves as an anomaly score, indicating deviations from normal operating behaviour. When evaluated on three run-to-failure benchmark datasets (HUMS2023, IMS, and XJTU-SY), the model consistently assigns low anomaly scores to normal samples and high scores to faulty ones, enabling early and robust fault detection. By aligning RL's sequential reasoning with MFD's temporal structure, this work opens a path toward RL-based diagnostics in data-driven industrial settings.
آسیب ساختاری، چالش مهمی را برای ایمنی و کیفیت پرواز هواپیما ایجاد میکند. این نوع آسیب باعث تغییر گسترده در پارامترهای اصلی پرنده همچون ضرایب آیرودینامیکی و مشخصههای جرمی میشود. در این مقاله، رویکرد طراحی سیستم کنترل تحملپذیر عیب مبتنی بر قانون کنترل وارون دینامیک غیرخطی ارائه شده است. رویکرد پیشنهادی، خرابی در دم عمودی را بررسی کرده است. جهت مدلسازی آسیب، دو پارامتر جدید با عناوین ضریب درجه آسیب و عدمقطعیت ناشی از آسیب معرفی شده است. پارامتر عدم قطعیت بیانکننده کوپلینگ بین معادلات طولی و عرضی-سمتی ناشی از عدم تقارن در پیکربندی هواپیمای آسیبدیده است و ضریب درجه آسیب، میزان فیزیکی آسیب را نشان میدهد. در فرآیند حل مشکل آسیب، یک واحد تشخیص عیب مبتنی بر فیلتر کالمن تعمیمیافته به سیستم کنترل افزوده شده است. با استفاده از مقادیر تخمینی فیلتر، مشتقات پایداری و پارامتر عدمقطعیت در ساختار کنترلی بروزرسانی شده و کنترلکننده با شرایط آسیب تطبیق داده شده است. روش پیشنهادی در این رساله یعنی اصلاح برخط ساختار کنترلکننده بر مبنای تشخیص فیزیکی میزان آسیب و پارامتر جدید عدمقطعیت، میتواند یک رویکرد جدید در حوزه آسیبهای ساختاری به شمار برود. در انتها، سناریوهای مختلف پروازی برای مدل غیرخطی هواپیمای بوئینگ 747 شبیهسازی شده است. نتایج، توانایی سیستم کنترل تحملپذیر عیب در بهبود نسبی عملکرد هواپیمای آسیبدیده را نشان میدهند.
Loan Thi Do, Trung Ngoc Nguyen, Quynh T. Tran
et al.
Batteries energy storage systems (BESS) are becoming a common trend worldwide supporting an increase in the power system's renewable energy (RE). Storing energy is not applied and has been in the research process in Vietnam. This study aims to evaluate the economic performance of a solar power plant (SPP) in Vietnam both before and after integrating a BESS through key metrics including the levelized cost of electricity (LCOE), net present value (NPV), and electrical productivity. Furthermore, this study incorporates sensitivity analysis to the metrics under variations in transmission line limitation (TLL), capital expenditure (CAPEX), and subsidies in this project. The results show that the solar photovoltaic (PV) system in the chosen SPP has an LCOE of 6.13 cents/kWh and an NPV of 7.52 million USD. The NPV will decrease to zero in the TLL from 22.2 MW. If this SPP is installed with a BESS of 2 MW 2 MWh, the LCOE increases to 6.38 cents/kWh, the NPV decreases to 5.5 million USD, and the levelized storage cost (LCOS) of 126.61 cents/kWh. The PV-BESS system is no longer economically efficient when the BESS reaches 12 MWh or larger. When TLL falls below 24 MW, BESS holds a significant role in improving the system's output. CAPEX's reductions of BESS have a negligible impact on LCOE, but a significant on LCOS. Investment-based incentives (IBI) starting at 7 %, or Capital-based incentives (CBI) starting at 4 cents/W can enhance the financial attractiveness of the PV-BESS system.
Renewable energy sources, Environmental engineering
Centrifugal fans are widely used in the ventilation and domestic appliance industries. Their aerodynamic and aeroacoustic characteristics vary significantly in different application scenarios and operating conditions. This study applied a double-suction multiblade centrifugal fan to a range hood. The full three-dimensional flow and acoustic field were calculated synchronously using direct computational aeroacoustics (CAA) based on the lattice Boltzmann method (LBM) to investigate the internal flow, aerodynamic noise, and acoustic source characteristics of the fan under different operating conditions. We focused on two typical operating conditions: the maximum volume flow rate and working volume flow rate. The accuracy of the numerical simulation was verified using experimental data measured from the performance test bench and the semianechoic chamber. The flow field results show that more than 70% of the airflow enters the volute from the main wind inlet; this asymmetric wind intake condition creates an asymmetric flow pattern inside the volute. Acoustic waves radiate to the far-field mainly through the inlet and outlet of the range hood. The propagation characteristics of a dipole source are not very obvious and the tonal noise associated with the blade passage frequency (BPF) is not significant. In addition to the acoustic sources identified in the impeller region, the volute tongue, and the gap between the impeller and the inlet nozzle, two other significant acoustic sources are identified in the outlet collector and inlet nozzle regions.
This paper investigates the economic and technical feasibility of integrating Vehicle-to-Grid (V2G) technology in the Non-Road Mobile Machinery (NRMM) sector. These often-idling assets, with their substantial battery capacities, present a unique opportunity to participate in energy markets, providing grid services and generating additional revenue. A novel methodology is introduced that integrates Bayesian Optimization (BO) to optimize the energy infrastructure together with an operating strategy optimization to reduce the electricity costs while enhancing grid interaction. While the focus lies on the methodology, the financial opportunities for the use-case of an electric NRMM rental service will be presented. However, the study is limited by the availability of real-world data on the usage of electric NRMM and does not address regulatory challenges of V2G. Further research is needed to extend the model accuracy and validate these findings.
The boundary layer's separation loss in compressor cascades constitutes a significant portion of profile loss, critically influencing aerodynamic performance optimization and control. This study employs Large Eddy Simulation (LES) to examine separation losses at varying attack angles, focusing on a rectangular compressor cascade. Specifically, it explores the long separation bubble at a 45% blade height cross-section under designed incidence. Analysis of the separation bubble's transition process revealed a notable surge in total pressure loss rate prior to transition, which stabilized following reattachment. The study thoroughly investigates the evolution of long bubbles, employing quadrant analysis of Reynolds stress, critical point theory, and an in-depth examination of individual vortex dynamics. The findings indicate that the peak of cross-flow within the separation bubble acts as the primary mechanism initiating the transition. This insight is corroborated by DNS calculations of natural transitions on flat plates. Building upon these findings, the study discusses the effects of varying attack angles on transition processes. Notably, increased incidence prompted the upstream migration of the long separation bubble, transforming it into a short bubble at the leading edge. This shift led to a fivefold increase in separation loss and doubled the frequency of transverse flow fluctuations.
Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt engineering techniques. This paper presents an extensive empirical study that reevaluates various prompt engineering techniques within the context of these advanced LLMs. Focusing on three representative SE tasks, i.e., code generation, code translation, and code summarization, we assess whether prompt engineering techniques still yield improvements with advanced models, the actual effectiveness of reasoning models compared to non-reasoning models, and whether the benefits of using these advanced models justify their increased costs. Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models. In reasoning LLMs, the ability of sophisticated built-in reasoning reduces the impact of complex prompts, sometimes making simple zero-shot prompting more effective. Furthermore, while reasoning models outperform non-reasoning models in tasks requiring complex reasoning, they offer minimal advantages in tasks that do not need reasoning and may incur unnecessary costs. Based on our study, we provide practical guidance for practitioners on selecting appropriate prompt engineering techniques and foundational LLMs, considering factors such as task requirements, operational costs, and environmental impact. Our work contributes to a deeper understanding of effectively harnessing advanced LLMs in SE tasks, informing future research and application development.
Active Learning (AL) is a well-known teaching method in engineering because it allows to foster learning and critical thinking of the students by employing debate, hands-on activities, and experimentation. However, most educational results of this instructional method have been achieved in face-to-face educational settings and less has been said about how to promote AL and experimentation for online engineering education. Then, the main aim of this study was to create an AL methodology to learn electronics, physical computing (PhyC), programming, and basic robotics in engineering through hands-on activities and active experimentation in online environments. N=56 students of two engineering programs (Technology in Electronics and Industrial Engineering) participated in the methodology that was conceived using the guidelines of the Integrated Course Design Model (ICDM) and in some courses combining mobile and online learning with an Android app. The methodology gathered three main components: (1) In-home laboratories performed through low-cost hardware devices, (2) Student-created videos and blogs to evidence the development of skills, and (3) Teacher support and feedback. Data in the courses were collected through surveys, evaluation rubrics, semi-structured interviews, and students grades and were analyzed through a mixed approach. The outcomes indicate a good perception of the PhyC and programming activities by the students and suggest that these influence motivation, self-efficacy, reduction of anxiety, and improvement of academic performance in the courses. The methodology and previous results can be useful for researchers and practitioners interested in developing AL methodologies or strategies in engineering with online, mobile, or blended learning modalities.
Raúl de la Fuente, Luciano Radrigan, Anibal S Morales
Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), a hierarchical inference framework across edge devices, gateways, and cloud services for real-time condition monitoring. The system dynamically adjusts inference locations--on-device, on-gateway, or on-cloud--based on trade-offs among accuracy, latency, and battery life, leveraging Tiny Machine Learning (TinyML) techniques for model optimization on resource-constrained devices. Performance evaluations showed that on-sensor and on-gateway inference modes achieved over 90\% classification accuracy, while cloud-based inference reached 99\%. On-sensor inference reduced power consumption by approximately 44\%, enabling up to 104 hours of operation. Latency was lowest for on-device inference (3.33 ms), increasing when offloading to the gateway (146.67 ms) or cloud (641.71 ms). The ESN-PdM framework provides a scalable, adaptive solution for reliable anomaly detection and PdM, crucial for maintaining machinery uptime in remote environments. By balancing accuracy, latency, and energy consumption, this approach advances PdM frameworks for industrial applications.
AbstractIn order to study the anti-detonation performance of welded joint structure of armored steel, the finite element analysis of welded joint structure under explosion impact was carried out by using material assignment method and structured ALE algorithm (S-ALE), and the accuracy of the model was verified by explosion impact test. The central deformation and failure form of each weld structure under different equivalent loads of a single explosion are studied. Repeated explosive loading of weld structure based on complete restart technology; The cumulative analysis of plastic deformation of weld structure under multiple explosions is realized, and the relationship between central deformation and explosion times and explosion equivalent is obtained.
AbstractIn order to study the influence of roughness on the nanoindentation test, finite element method was used to simulate the nanoindentation test process, and secondary indentation method was further proposed to suppress the influence of roughness on the indentation. After optimizing the indentation parameters, the relative error between the results of nanoindentation simulation for rough surface samples and smooth surface samples is stable within 5%. The secondary indentation method can effectively reduce the influence of roughness on nanoindentation test results.
Beside interaction, attitude is another factor that cause the lack of understanding in learning process. Learning attitude is needed for success and smoothness of the learning processed. The purposed of the studied was to determines the correlation between attitudes and learning outcomes and how much influence the two variables had. Quantitative research with correlation analysis is applied to find out the relationships between two variable. Result from data analysis using a hypothesis test with products moment correlations analysis obtaineds a correlations coefficient values of 0.454 with a sig value of 0.0009. From the result concludes thats theres is a relationship betweens the two variables, this happens because the value of sig < 0.005 is strengthened by the calculated r value of 0.361 and the r table value of 0.2706 meaning Ha is accept and Ho is reject whiled the value of the magnitudde of the influences betweens the two variables is 13, 06%, which means that attitude is one of the determine factor in the student learnings outcomes in this studied. The conclusions is thats there’s a positived correlation between attitudes and learn outcome of student of class X Mechanical Engineering at SMK N 5 Padang and from the result it’s known thats one of the factor in improving learning outcomes is attitude in learning. Learning outcomes can increase with a good learning attitude and vice versa.
AbstractAt present, for the study of the dynamic characteristics of the hydro-pneumatic suspension of vehicles, the elastic force is mainly modeled by the variable gas equation of state, and the damping force is modeled by thin-walled orifice theory, which only considers the turbulent flow. Here, based on expressing the whole flow field including laminar flow, transition flow, and turbulence with piecewise function, the turbulence region is modeled by the Brasius formula and thin-walled orifice theory respectively. By applying vibration signals collected from real roads, the responses of two piecewise function damping force models and traditional thin-walled orifice model of 1/4 suspension system in the time domain and frequency domain respectively are calculated. The average absolute error MAE and root mean square error RSME are used to compare them with the real upper fulcrum data of the suspension cylinder. The results show that different models can simulate suspension vibration well in the low-frequency range, but there are obvious deficiencies in the middle and high-frequency range, while the short-hole flow theoretical model in the form of a piecewise function is closer to the real value in the frequency domain.