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Electrochemical performance of graphene oxide synthesized from graphitic spent potlining for energy storage application
(Elsevier BV, 2024-11-10) Dzikunu, Perseverance; Arthur, Emmanuel Kwesi; Gikunoo, Emmanuel; Mensah-Darkwa, Kwadwo; Akinwamide, Samuel Olukayode; Fangnon, Eric A.K.; Vilaça, Pedro; Department of Mechanical Engineering; Materials to Products; Kwame Nkrumah University of Science and Technology
The hazardous nature of spent pot lining (SPL) generated from aluminum smelters poses environmental challenges due to its high fluoride and cyanide content. However, techniques and recent studies have shown that SPL can be converted into valuable carbon-based materials through innovative recycling with potential applications in energy storage devices. This work presents an electrochemical performance evaluation of graphene oxide (GO) electrodes derived from acid-treated SPL for supercapacitor applications. The SPL-derived graphene oxide (SPL-GO) was synthesized via a facile and scalable improved Tour method. SPL-GO electrodes were fabricated by drip-coating SPL-GO/PVDF/DMF suspension on nickel foams. The morphology, structure, and chemical composition of the SPL-GO were characterized using advanced techniques such as energy dispersive X-ray (EDX) spectroscopy, scanning electron microscopy (SEM), Fourier transforms infrared (FTIR) spectroscopy, Raman spectroscopy, and X-ray diffraction (XRD). The electrochemical properties of the SPL-GO in 3 M KOH were characterized with a three-electrode system using cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) measurements. The results demonstrate that SPL-GO exhibits a relatively high specific capacitance of 762.90 F/g at 1 A/g with corresponding power and energy densities of 502 W/kg and 106.60 Wh/kg, respectively. Additionally, excellent cycling stability of 85.4 % was achieved after 10,000 cycles with a coulombic efficiency of 95.23 %. These results suggest a superior rate capability of SPL-GO, rendering it a viable candidate for supercapacitor applications. Furthermore, this work sets the foundation for the sustainable use of industrial waste in energy storage devices, suggesting a novel, eco-friendly material for practical supercapacitor applications.
DEEP RECURRENT NEURAL NETWORK ALGORITHM FOR ACTIVE SOUND QUALITY CONTROL OF WIPER-WINDSHIELD FRICTION NOISE
(2024) Guo, Hui; Fan, Huizhi; Wang, Yansong; Ma, Minghui; Huang, Shuang; Liu, Ningning; Cheng, Qiang; Department of Mechanical Engineering; van Keulen, Wim; Kok, Jim; Energy Conversion and Systems; Shanghai University of Engineering Science
The effectiveness of traditional Adaptive Noise Equalizer (ANE) algorithm and its extension algorithms for Active Sound Quality Control (ASQC) systems is unsatisfied in engineering application, especially for the nonlinear problems. In this paper, a nonlinear active sound quality control algorithm based on Deep Recurrent Neural Network (DRNN) is proposed for the wiper-windshield friction noise. A DRNN model based on Long Short-Term Memory (LSTM) neutral network is constructed to perform nonlinear mapping on the input signal through setting an objective function. The trained output secondary signal is counteracted with the expected signal to obtain the minimum error signal. Thus, the linear filter in traditional algorithms is replaced by the proposed DRNN model. Setting the wiper-windshield friction noise of an actual vehicles as the input signal for the DRNN algorithm, simulation analysis was conducted. The results were compared with the input original signal in terms of control effectiveness in both time-domain and frequency-domain. Meanwhile, the psychoacoustic attribute metrics such as loudness, roughness, and sharpness are calculated for the simulating output signals. The results demonstrate that the proposed DRNN active sound quality control algorithm has a good control effect on the wiper-windshield friction noise, especially for the frequency range of 0-500Hz, which is 67.39%, 62.58%, and 56.38% lower than the original noise in terms of loudness, roughness, and sharpness, respectively. The amplitude of the noise is simultaneously reduced. Therefore, the proposed algorithm has significant advantages in improving the vehicle interior sound quality by controlling the wiper-windshield friction noise.
High-Resolution Fracture Dynamics Simulation of Pack-Ice and Drift-Ice Formation During Sea Ice Break up Events Using the HiDEM2.0 Code
(John Wiley & Sons, 2024-09-28) Åström, Jan; Polojärvi, A.; Department of Mechanical Engineering; Marine and Arctic Technology; CSC - IT Center for Science Ltd.
Creating accurate predictive models for drift and pack ice is crucial for a wide array of applications, from improving maritime operations to improving weather prediction and climate simulations. Traditional large-scale sea ice dynamics models rely on phenomenological ice rheology to simulate ice movements. These models are efficient on large scales but struggle to depict smaller-scale ice features. In our study, we use a new version of the HiDEM discrete element model software to examine the formation of drift and pack ice under various stress conditions. Our findings show that high-resolution size distributions of ice floes are universal and multimodal, and that compression ridges form three distinct zones. Reproducing complex characteristics of this nature in a standard rheology model is challenging, suggesting that a combination of models may be necessary for more precise predictions of sea ice dynamics. We propose a potential hybrid algorithm that integrates these approaches.
Optimal design of building envelope towards life cycle performance : Impact of considering dynamic grid emission factors
(Elsevier Science Inc., 2024-11-15) Li, Changqi; Pan, Yiqun; Liu, Zhichao; Liang, Yumin; Yuan, Xiaolei; Huang, Zhizhong; Zhou, Nan; Department of Mechanical Engineering; Energy Conversion and Systems; Tongji University; Carnegie Mellon University; University of Hong Kong; Lawrence Berkeley National Laboratory
Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.
Multiscale Modeling of Plasma-Assisted Non-Premixed Microcombustion
(MDPI AG, 2024-09) Cinieri, Giacomo; Mehdi, Ghazanfar; De Giorgi, Maria Grazia; Department of Mechanical Engineering; University of Salento
This work explores microcombustion technologies enhanced by plasma-assisted combustion, focusing on a novel simulation model for a Y-shaped device with a non-premixed hydrogen-air mixture. The simulation integrates the ZDPlasKin toolbox to determine plasma-produced species concentrations to Particle-In-Cell with Monte Carlo Collision analysis for momentum and power density effects. The study details an FE-DBD plasma actuator operating under a sinusoidal voltage from 150 to 325 V peak-to-peak and a 162.5 V DC bias. At potentials below 250 V, no hydrogen dissociation occurs. The equivalence ratio fitting curve for radical species is incorporated into the plasma domain, ensuring local composition accuracy. Among the main radical species produced, H reaches a maximum mass fraction of 8% and OH reaches 1%. For an equivalence ratio of 0.5, the maximum temperature reached 2238 K due to kinetic and joule heating contributions. With plasma actuation with radicals in play, the temperature increased to 2832 K, and with complete plasma actuation, it further rose to 2918.45 K. Without plasma actuation, the temperature remained at 300 K, reflecting ambient conditions and no combustion phenomena. At lower equivalence ratios, temperatures in the plasma area consistently remained around 2900 K. With reduced thermal power, the flame region decreased, and at Φ = 0.1, the hot region was confined primarily to the plasma area, indicating a potential blow-off limit. The model aligns with experimental data and introduces relevant functionalities for modeling plasma interactions within microcombustors, providing a foundation for future validation and numerical models in plasma-assisted microcombustion applications.
Comparative Study on Heat Dissipation Performance of Pure Immersion and Immersion Jet Liquid Cooling System for Single Server
(MDPI AG, 2024-09) Yuan, Linhui; Wang, Yu; Kosonen, Risto; Yang, Zhengchao; Zhang, Yingying; Wang, Xincheng; Department of Mechanical Engineering; Energy Conversion and Systems; Nanjing Tech University
Heat dissipation has emerged as a critical challenge in server cooling due to the escalating number of servers within data centers. The potential of immersion jet technology to be applied in large-scale data center server operations remains unexplored. This paper introduces an innovative immersion jet liquid cooling system. The primary objective is to investigate the synergistic integration of immersion liquid cooling and jet cooling to enhance the heat dissipation capacity of server liquid cooling systems. By constructing a single-server liquid cooling test bench, this study compares the heat dissipation efficiencies of pure immersion and immersion jet liquid cooling systems and examines the impact of inlet water temperature, jet distance, and inlet water flow rate on system performance. The experimental outcomes show that the steady-state surface heat transfer coefficient of the immersion jet liquid cooling system is 2.6 times that of the pure immersion system, with increases of approximately 475.9 W/(m2·K) and 1745.0 W/(m2·K) upon adjustment of the jet distance and flow rate, respectively. Furthermore, the system model is streamlined through dimensional analysis, yielding a dimensionless relationship that encompasses parameters such as inlet water temperature, jet distance, and inlet water velocity. The correlation error is maintained below 18%, thereby enhancing the comprehension of the immersion jet cooling mechanism.
Is Construction Industry Still Performing Worse Than Other Industries?
(Lean Construction Institute, 2022) Elfving, Jan A.; Seppänen, Olli; Department of Civil Engineering; Performance in Building Design and Construction; Skanska Finland Oy
Research Questions: Is the construction industry improving performance in Finland? Is construction industry performing worse than other industries? Why is the industry scoring high on customer satisfaction, even if it scores low in many other cross-industry performance measurements? What is different from other countries? Purpose: Is to introduce a set of benchmarking measurements that the industry and academia have agreed to report annually in Finland and to review other industry-level metrics in use. Research Method: A design science approach was used. The first step was to review industry metrics typically collected in Finland. Then a group of companies co-created the performance metrics in collaboration with the researchers. The developed metrics were validated by collecting data from construction sites. Findings: We established a baseline for the Finnish construction industry regarding reliability, user experience, sustainability, productivity, and customer satisfaction, and also compared to other industries. There are already some interesting points to be lifted, like schedule reliability in Finland seems to be higher than in studies in other countries. Another interesting observation is customer satisfaction and Net Promotor score, where construction industry scores higher than most other industries. Limitations: The study is limited to Finnish construction industry. After first year of measurement, it is too early to say if the industry has improved compared to past years. Implications: The study proposes to continue the measurement and analysis of results at least in Finland and encourages to conduct similar studies in other countries. Value for Practitioners: Practitioners will understand how their perform against their peers. The real success of the measurement will be tested in the future if the industry and academia together can learn and improve the baseline results.
Uncovering and Visualizing Work Process Interruptions through Quantitative Workflow Analysis
(Lean Construction Institute, 2022) Görsch, Christopher; Al Barazi, Alaa; Seppänen, Olli; Abou Ibrahim, Hisham; Department of Civil Engineering; Performance in Building Design and Construction
Question: Continuous improvement requires visualizing process constraints which interrupt workflows. Production control from a management perspective often operates at lower levels of information granularity than required at operational levels to perform work without interruptions. How can workflow interruptions in plumbing work be analysed and explained? How can an analysis of workflow interruptions help to close the information granularity gap between operational and management levels? Purpose: The purpose of this paper is to evaluate methods for their fit in revealing and closing the information granularity gap between between operational and management levels. Research Method: The paper examines workflows of plumbing work from video footage. This video data is classified and analyzed for frequency, causes, and effects of work interruptions. Findings: Results indicate that value-supporting activities caused the largest proportion of interruptions. Moreover, the proportion of non-value-adding activities increases when durations of interruptions rise. Limitations: The analyzed and tested data includes one working day of one worker in one certain construction project, which limits the meaningfulness of these results and explanations. Implications: The conducted time-motion-study and its classified data set made it possible to develop an agent-based simulation model of construction workers behavior. Value for authors: This paper provides a framework to examine workflow interruptions in craft work so that the information gap between operational and management levels is closed.
Physically based modelling of spectral transmittance through forest canopies
(John Wiley & Sons, 2024-10) Hovi, Aarne; Janoutová, Růžena; Malenovský, Zbyněk; Schraik, Daniel; Gastellu-Etchegorry, Jean Philippe; Lauret, Nicolas; Novotný, Jan; Rautiainen, Miina; Department of Built Environment; Geoinformatics; Czech Academy of Sciences; University of Bonn; Université de Toulouse; Natural Resources Institute Finland (Luke)
Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance. The approaches were (i) three-dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p-theory), and were implemented using DART-FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear-sky conditions in forest plots across central and Northern Europe. In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART-FT in broadleaved forests. We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p-theory constitutes a computationally efficient and easy-to-parameterize alternative to three-dimensional radiative transfer.
Effects of Damage Evolution on Edge Crack Sensitivity in Dual-Phase Steels
(Wiley-VCH Verlag, 2024-10) Habibi, Niloufar; Beier, Thorsten; Lian, Junhe; Tekkaya, Berk; Koenemann, Markus; Muenstermann, Sebastian; Department of Mechanical Engineering; Materials to Products; RWTH Aachen University; Thyssen Stahl AG
The present study aims to thoroughly investigate the edge-cracking phenomenon in high-strength sheets. Hence, the edge crack sensitivity of three dual-phase steels is studied in various combinations of edge manufacturing and forming processes. Finite element simulations are performed to elaborate the study. In this regard, the Yoshida–Uemori kinematic hardening model is employed to describe the plasticity behavior of the materials under multistep processes. A stress-state fracture model is coupled with this plasticity model to illustrate the distinguished local fracture strains of each material. Moreover, the effects of strain rate and the consequent temperature rise on hardening and damage are taken into account, which play significant roles during shear-cutting. The results show that although the shear-cutting processes are applied at very low speed, the strain rate and induced temperature are still high at the cutting area. The hole expansion results show different fracture behaviors for different cases. In brief, cracking is initiated at a location, which shows the highest damage accumulation during edge manufacturing plus the subsequent forming process. Such a complicated situation can only be successfully predicted by using a computer-aided approach along with proper material modeling, like the applied model in this study.
Assessing the performance of a hybrid max-weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts
(Institution of Engineering and Technology, 2024-10-02) Liaquat, Muwahida; Vosough, Shaghayegh; Roncoli, Claudio; Charalambous, Themistoklis; Department of Built Environment; Department of Electrical Engineering and Automation; Planning and Transportation; Distributed and Networked Control Systems
Max-weight (or max-pressure) is a popular traffic signal control algorithm that has been demonstrated to be capable of optimising network-level throughput. It is based on queue size measurements in the roads approaching an intersection. However, the inability of typical sensors to accurately measure the queue size results in noisy queue measurements, which may affect the controller's performance. A possible solution is to utilise the noisy max-weight algorithm to achieve a throughput optimal solution; however, its application may lead to decreased controller performance. This article investigates two variants of max-weight controllers, namely, acyclic and cyclic max-weight (with and without noisy queue information) in simulated scenarios, by examining their impact on the throughput and environment. A detailed study of multiple pollutants, fuel consumption, and traffic conditions, which are proxied by a total social cost function, is presented to show the pros and cons of each controller. Simulation experiments, conducted for the Kamppi area in central Helsinki, Finland, show that the acyclic max-weight controller outperforms a fixed time controller, particularly in avoiding congestion and reducing emissions in the network, while the cyclic max-weight controller gives the best performance to accommodate maximum vehicles flowing in the network. The complementary positive characteristics motivated the authors to propose a new controller, herein called the hybrid max-weight, which integrates the characteristics of both acyclic and cyclic max-weight algorithms for providing better traffic load and performance through switching.
An exploration of e-scooter injuries and severity: Impact of restriction policies in Helsinki, Finland
(Elsevier Ltd, 2024-12) Dibaj, Samira; Vosough, Shaghayegh; Kazemzadeh, Khashayar; O'Hern, Steve; Mladenović, Miloš; Department of Built Environment; Planning and Transportation; University of Cambridge; University of Leeds
Introduction: The emergence of shared electric scooter (e-scooter) services has introduced a new mobility option in numerous urban areas worldwide. Safety concerns surrounding e-scooter riding have prompted some cities to impose bans or restrictions on shared e-scooters. This study aims to assess the impact of e-scooter restriction policies, on the spatiotemporal distribution of e-scooter injuries and factors influencing injury severity in Helsinki, Finland, in 2021 and 2022. These restrictions include banning shared e-scooter use from midnight to 5 a.m. on weekends and reducing speeds during certain hours. Method: This study employed an ordered logit model, heatmap analysis of crash locations, and temporal analysis across different time frames to achieve these objectives. Results: The findings indicate a 64% reduction in the number of e-scooter injuries after the restrictions. However, the severity of injuries experienced only a slight decrease. Notably, the trend of injury severity appeared smoother in 2022 compared to 2021, with spikes occurring from Friday to Sunday. The spatial distribution of crashes revealed that, in 2021, most crashes were concentrated in the city center, while in 2022, the crash locations were more scattered, partly due to the increased area serviced by e-scooters. The results also underscored the substantial impact of alcohol intoxication, as it significantly increased the probability of more severe injuries. Furthermore, higher age groups and people using e-scooters from 4 p.m. to 9 p.m. are more likely to experience higher injury severity after the restrictions were implemented. These research outcomes offer valuable insights for other cities, providing lessons on how to tailor policies to effectively reduce the number of e-scooter-related injuries.
Operations Management of Additive Manufacturing
(2024-09-08) Khajavi, Siavash Haghighat; Salmi, Mika; Holmström, Jan; Department of Industrial Engineering and Management; Department of Mechanical Engineering; Thürer, Matthias; Riedel, Ralph; von Cieminski, Gregor; Romero, David; Materials to Products
This article reviews the growing literature on additive manufacturing (AM) operations management and sheds light on the emerging research areas in this field. As the AM use cases of final parts rapidly expand, it is essential to focus on the operations management of this technology and determine the primary current and future research streams. A literature study method is utilized to select, review, and categorize articles in the field of AM. The 108 articles selected after the initial evaluation were carefully examined and categorized. The selected papers evaluate AM from an operations management perspective. This article categorizes the body of knowledge studying the application and operations management of additive manufacturing into three categories: studies concerned with the industry's current state, forward-looking studies with a conceptual approach, and forward-looking papers with empirical grounding. Different AM processes studied are also considered. Our categorization showed that the latter category is still under-researched and presents an opportunity for future investigations. Moreover, six emerging streams of research in the third category were recognized. In addition to pointing out the areas of research that require more attention, this article aims to assist the researchers in better positioning their research.
Evaluating the influence of cyclists’ route choices incorporation into travel demand modelling: A case study in greater Helsinki
(Elsevier, 2024-09) Tarkkala, Konsta; Vosough, Shaghayegh; West, Jens; Roncoli, Claudio; Department of Built Environment; Planning and Transportation; Liikenne- ja viestintävirasto Traficom
Cycling is a sustainable transport mode that endorses an active lifestyle. While cycling shows great potential, it is essential for urban planning to consider attributes influencing the choices that cyclists act upon. Cyclists’ route choices have been studied since the Eighties with knowledge being applied in cycling network planning. Yet, the role of cycling as a sustainable transportation mode has been largely absent from travel demand modelling. This paper researches cyclists’ route choice preferences and evaluates the opportunity of incorporating route choice modelling into travel demand modelling to improve the accuracy of cycling route choice. To this end, a route choice framework is developed in which a stated preference survey for data collection is conducted, a multinomial Logit model is applied to the data to identify the factors that significantly influence cyclists’ route choice behaviour. The generated route choice utility models are further integrated into an existing regional travel demand model to evaluate the performance of cyclists’ route choice modelling in the presence of additional factors. Then, the route choice model outputs are validated against two sets of external data. The results show that bike facilities, traffic volume, and trip length are the key factors influencing cyclists’ route choice preferences, and the generated route choice models can be an applicable improvement in incorporating the influences of cyclists’ preferences into travel demand modelling.
Systems driven intelligent decision support methods for ship collision and grounding prevention : Present status, possible solutions, and challenges
(Elsevier Ltd, 2025-01) Zhang, Mingyang; Taimuri, Ghalib; Zhang, Jinfen; Zhang, Di; Yan, Xinping; Kujala, Pentti; Hirdaris, Spyros; Department of Mechanical Engineering; Marine and Arctic Technology; Wuhan University of Technology; Napa Ltd; Tallinn University of Technology; American Bureau of Shipping, Greece
Despite advancements in science and technology, ship collisions and groundings remain the most prevalent types of maritime accidents. Recent developments in accident prevention and mitigation methods have been bolstered by the rise of autonomous shipping, digital technologies, and Artificial Intelligence (AI). This paper provides an exhaustive review of the characteristics of fleets at risk over the past two decades, emphasizing the societal impacts of preventing collisions and groundings. It also delves into the key components of decision support systems from a ship's perspective and undertakes a systematic literature review on the foundations and applications of systems-driven decision support methods for ship collision and grounding prevention. The study covers risk analysis, damage evaluation, and ship motion prediction methods from 2002 to 2023. The conclusions indicate that modern ship science methods are increasingly valuable in ship design and maritime operations. Emerging multi-physics systems and AI-enabled predictive analytics show potential for future integration into intelligent decision support systems. The strategic research challenges include (1) underestimating the impacts of real operational conditions on ship safety, (2) the inherent limitations of static risk analysis and finite numerical methods, and (3) the need for rapid, probabilistic assessments of damage extents. The demands and trends suggest that leveraging big data analytics and rapid prediction methods, underpinned by digitalization and AI technologies, represents the most feasible way forward.
Health in mobility planning : An assessment of how health is considered in Sustainable Urban Mobility Plans
(Elsevier, 2024-11) Kasraian, Dena; Murdock, Hannah E.; Faghih Imani, Ahmadreza; Yu, Yurong; de Nazelle, Audrey; Stead, Dominic; Kahlmeier, Sonja; Department of Built Environment; Planning and Transportation; Eindhoven University of Technology; Imperial College London; Swiss Distance University of Applied Sciences
Introduction: Urban mobility can detrimentally impact city dwellers' health and quality of life, e.g. through air pollution, noise and traffic injuries, but offers opportunities for health promotion, e.g., through active travel. While the health impacts of transport are well known, the extent to which health is considered in mobility plans is less obvious. The European Commission encourages cities to develop Sustainable Urban Mobility Plans (SUMPs) to improve residents’ quality of life. We assess how health is addressed in SUMPs by examining: i) key health and health equity terminology, ii) explicit transport pathways to health, iii) health targets and key performance indicators, and iv) the health-rationale of actions and measures. Methods: Using a customised health dictionary, we perform a quantitative text analysis of SUMPs issued from 2006 to 2023 (n = 230) from 31 European countries listed on the European Local Transport Information Service (Eltis) City Database. We further validate this by an in-depth qualitative analysis of a purposive sub-sample (n = 13). Results: The findings show that while the prominence of health in SUMPs seems to be increasing, the link between transport and equity, and social and mental wellbeing is not frequently discussed. Detailed targets and KPIs for several health pathways are scarce or missing, as are the health rationale and health outcomes for proposed measures. Overwhelmingly SUMPs’ health aspirations focus on minimising detrimental health impacts of transport, primarily from traffic injuries and to a lesser extent from air pollution. Health related concepts such as accessibility and active travel feature prominently but are not explicitly identified as an opportunity to enhance health. Conclusion: Urban mobility planning across Europe seems to miss an opportunity to embrace mobility as a driver of health promotion.
Physics-guided metamodel for vertical bending-induced fatigue damage monitoring in container vessels
(Elsevier Ltd, 2024-11-15) Lang, Xiao; Zhang, Mingyang; Zhang, Chi; Ringsberg, Jonas W.; Mao, Wengang; Department of Mechanical Engineering; Marine and Arctic Technology; Chalmers University of Technology
This study proposes a novel physics-guided metamodel to predict vertical bending-induced fatigue damage in a 2800TEU container vessel navigating the North Atlantic, based on data from the vessel's hull monitoring system. The metamodel combines two XGBoost-based base learners: a black-box model utilizing ship heave and pitch motion measurements, and a gray-box model using spectral moments from numerical analysis. Predictions from both models are refined through a meta learner Gaussian process regression to enhance accuracy. The metamodel was evaluated against black-box and gray-box models across various training data volumes. The proposed model adapts to varying data volumes, from months to over 2 years, effectively integrating the strengths of both base learners to provide reliable predictions in both seen and unseen scenarios. The model consistently demonstrated superior performance, enhancing fatigue damage accumulation accuracy by up to 35% over traditional machine learning methods. This advancement can aid the maritime industry in effectively monitoring ship fatigue and implementing predictive maintenance strategies, marking a significant step forward in applying data-driven techniques in shipping.
An exploration of biodiversity limits to grazing ruminant milk and meat production
(Nature Publishing Group, 2024-09) Resare Sahlin, Kajsa; Gordon, Line J.; Lindborg, Regina; Piipponen, Johannes; Van Rysselberge, Pierre; Rouet-Leduc, Julia; Röös, Elin; Department of Built Environment; Water and Environmental Eng.; Stockholm University; Swedish University of Agricultural Sciences; Leipzig University
The production and consumption of animal-source foods must be transformed to mitigate negative environmental outcomes, including greenhouse gas emissions and land-use change. However, livestock are also key for food production and for livelihoods in some settings, and they can help preserve biodiversity and certain ecosystems. Previous studies have not yet fully explored sustainability limits to the use of grazing lands for food production in the context of biodiversity. Here we explore ‘biodiversity limits’ to grassland ruminant production by estimating the meat and milk production from domestic ruminants limited to grazing areas and stocking densities where livestock can contribute to the preservation or restoration of biodiversity. With biodiversity-friendly grazing intensities at 0–20% biomass removal depending on aridity, this take on biodiversity limits corresponds to 9–13% and 26–40% of the current grassland-based milk and meat production, respectively. This equals only 2.2 kg of milk and 0.8 kg of meat per capita per year, globally, but altered management and moving from meat-specialized to meat-and-dairy systems could increase the potential production while still remaining within this approach to biodiversity limits.
A novel method of estimating earthquake durations for the analysis of floor vibrations of nuclear power plants
(Elsevier Science Inc., 2024-12-01) Jussila, Vilho; Fülöp, Ludovic; Mäntyniemi, Päivi; Puttonen, Jari; School common, ENG; Department of Civil Engineering; Structures – Structural Engineering, Mechanics and Computation; VTT Technical Research Centre of Finland; University of Helsinki
Many low-seismicity countries such as Finland have adopted IAEA requirements and recommendations for seismic design of new and existing nuclear power plants (NPPs). In low seismic regions, the structural seismic design is associated with floor vibration of NPPs. The floor vibration analysis is usually conducted in the time domain for which maximum amplitudes are retrieved from design spectra while the duration of ground motion is estimated as an interval between 5% and 75% of accumulation of the Arias intensity. As this method was developed for active seismic regions, it often overestimates the duration for the regions with low seismicity. The present article introduces a new twofold method for estimating the duration. First, the Arias intensity is calculated for a complete and consecutively reduced accelerograms resulting in a deviation curve. Second, this curve is simplified by a piecewise linear regression fitting. The simplified deviation curve has a linear time frame that includes the most significant part of the Arias intensity. The length of the time frame defines the effective duration of a specific ground motion. This implies that the effective duration depends directly on the ground motion instead of predefined percentiles of the Aries intensity. In this study, the method was applied to a set of ground accelerations adopted from eastern Canada, which is geologically similar to the Fennoscandian Shield where appropriate recordings are absent. The results showed that the durations depend on distance, but they were insensitive of magnitude for short rupture distances. This indicates that smaller events can also be useful for estimating the durations even though they do not meet the requirement of design basis earthquake in terms of the peak ground acceleration. The durations obtained with the proposed method were typically shorter than those based on the 5%–75% criterion. The durations received can be used to generate the acceleration time histories compliant with the design response spectra. We also propose durations with different rupture distances for the seismic design of the structures, systems, and components of nuclear facilities in Finland. In a feasibility study, we calculated floor vibrations of a generic reactor building using 3D finite element analysis. The results show that floor accelerations are very similar, when the base accelerogram is complete or shortened to the length proposed in this study.
Innovation meets institutions : AI and the Finnish construction ecosystem
(Institute of Physics Publishing, 2024) Ainamo, A.; Peltokorpi, A.; Department of Civil Engineering; Performance in Building Design and Construction
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are technologies that have recently transformed many industries. The construction industry has traditionally been a laggard industry in terms of digital-technology adoption. When leading firms in this industry have experimented with these technologies, many of these experiments have met resistance. In this paper we take an institutional lens to study why and particular social structures appears to have contributed to the resistance and paucity of success stories. Within institutional research, we focus on research with traces to cognitive science and psychology. We have carried out a qualitative embedded multiple-case study on resistance to new technologies and how to overcome such resistance. The study involves four use cases in the Finnish construction industry: (1) automation of a material-product subcontractor's production planning; (2) business-model innovation by contractor on how to best work across multiple construction sites at once; (3) machine learning and automation of documentation by a software firm; and (4) promotion of a vision of information sharing across organizations by the above software firm. Based on within and cross-case analyses, preliminary empirical findings are that AI, ML and DL have in the Finnish construction industry challenged institutionalized forms of organizing and workflow established long since in the industry and, until about the time of this piece of research, taken for granted. Resistance was nonetheless beginning to be overcome at the time of writing this piece of research with small-group interaction across firms - such as those in this study - - in the industry ecosystem. Human-human mediation and face-to-face encounters were building trust in and across the organizations. The implication for practice and policy is that business transformation will not quickly and autonomously transform into "impersonal"or machine-machine exchange but, before that, requires human-human mediation. "In the long-term, AI and analytics have boundless potential use cases in E&C [i.e. engineering and construction]. Machine learning is gaining some momentum as an overarching use case (that is, one applicable to the entire construction life cycle, from preconstruction through O&M 8i.e. operations and management), particularly in reality capture (for example, in conjunction with computer vision) as well as for comparison of in situ field conditions with plans (for example, supporting twin models). Indeed, by applying machine learning to an ongoing project, schedules could be optimized to sequence tasks and hit target deadlines, and divergences from blueprints could be caught closer to real time and corrected using a variety of predetermined potential scenarios."[1]

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