[article-cris] Palvelut / Services

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  • Computational analysis of conjugate heat transfer in a 2D rectangular channel with mounted obstacles using lattice Boltzmann method
    (2025-01-13) Nejadseifi, Majid; Karimkashi Arani, Shervin; Tynjälä, Tero; Jalali, Payman
     A4 Artikkeli konferenssijulkaisussa
    The objective of this paper is to investigate the fluid flow and conjugate heat transfer in a 2D channel using lattice Boltzmann method (LBM). In this work, fluid flow and heat transfer are studied for the Reynolds numbers varying between 250 and 1000. The working fluid in the simulations is air with the Prandtl number of 0.72. At the Reynolds number of 600, the effect of different conductivity ratio (1, 10, 100, 400) between solid and fluid are investigated. Furthermore, at this Reynolds number, the distance between obstacles for the conductivity ratio of 10 is evaluated. The results show that any increase in Reynolds number leads to a heat transfer improvement. Moreover, increase in the conductivity ratio leads to an isothermal surface and enhanced heat transfer. The more the distance between the obstacles, the better the heat transfer rate. The results obtained from LBM are in good agreement with experimental and conventional computational fluid dynamics methods.
  • Semantic-Enhanced Digital Twin for Industrial Working Environments
    (2025) Yang, Chao; Guo, Qize; Yu, Hao; Chen, Yan; Taleb, Tarik; Tammi, Kari
     A4 Artikkeli konferenssijulkaisussa
    Real-time data from diverse Internet of Things (IoT) sensors (such as cameras, temperature, light, and air quality sensors) is essential for monitoring smart manufacturing environments. However, efficiently perceiving, integrating, and interpreting this data remains a challenge, as it involves dealing with heterogeneous data formats, ensuring data accuracy, and providing real-time analytics. This paper proposes a semantic-enhanced Digital Twin (DT) to address these complexities and aims to offer a comprehensive view of industrial working environments. The paper first presents a conceptual overview of the semantic-enhanced DT architecture, followed by a detailed description of the system architecture, encompassing edge, cloud, and interface modules. Additionally, the implementation of the entire system is presented. The results demonstrate the feasibility of the proposed DT, showing its potential for deployment in real-world scenarios.
  • LES-TFM modeling of hydrogen combustion in internal combustion engines
    (2024-09-02) Mahmoodi, Bayazid; Tamadonfar, Parsa; Bhattacharya, Atmadeep; Kaario, Ossi
     A4 Artikkeli konferenssijulkaisussa
    The integration of hydrogen (H2) as a fuel for internal combustion engines (ICEs) offers a promising avenue towards eco-friendly transportation. Despite having relatively simple combustion chemistry, H2 combustion in ICEs needs to overcome challenges like high flame speed, wide flammability limits, extremely thin flame thickness, and thermo-diffusive instabilities (TDI). The scale-resolving 3-dimensional (3D) computational fluid dynamics (CFD) simulation of H2 combustion at engine-relevant conditions is an accepted strategy for understanding these complex phenomena. Thus, to enhance the understanding of H2 combustion in ICEs, this study presents an advanced numerical investigation of H2 combustion within ICEs using Large Eddy Simulation (LES). The primary objective of this research is to investigate the crucial influence of TDI on the dynamics of H2 flames under elevated temperatures and pressure conditions pertinent to ICEs. The simulation is carried out using StarCCM+ on a simplified piston-cylinder configuration, which accurately captures the key operational parameters of ICEs. For Turbulence-Chemistry Interactions (TCI), a dynamic Thickened Flame Model (TFM) is used. The combustion model is combined with Adaptive Mesh Refinement (AMR) within the reaction zone, where reaction sources are calculated using a detailed chemistry model. In order to consider suppressed TDI caused by artificial flame thickening, a TDI efficiency model is integrated into the TFM. The results of the TFM model have been validated against the established Turbulent Flame Speed Closure (TFC) model predictions for lean fuel-air combustion conditions. Quantitative comparisons reveal that the TFM results when integrated with the TDI efficiency model, align closely with those of the TFC model. Additionally, the TFM effectively captures the dynamics of H2 flame propagation at high pressures.
  • Consumer Sovereignty and the Ethics of Recognition
    (2024-06) Bhatnagar, Kushagra; Cayla, Julien; Dion, Delphine; Fuschillo, Gregorio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The rising prominence of consumer sovereignty, wherein businesses prioritize customers as kings, presents complex ethical dilemmas. This paper delves into the ethical implications of consumer sovereignty by examining the lack of recognition to which service workers are subjected in their interactions with customers. Applying the sensitizing lens of recognition theory, we investigate how the relational domination inherent in the service industry ultimately results in four main recognition gaps: visibility, status recognition, affective recognition, and capacity recognition gaps. These gaps considerably hinder an employee’s ability to experience workplace dignity. Our findings enrich the business ethics literature by providing a more holistic analysis of the ethical challenges raised by consumer sovereignty. We introduce recognition theory as a framework to address these concerns and offer recommendations for managers to better support their service employees in overcoming the absence of customer recognition.
  • Coddora : CO2-Based Occupancy Detection Model Trained via Domain Randomization
    (2024) Weber, Manuel; Banihashemi, Farzan; Stjelja, Davor; Mandl, Peter; Mayer, Ruben; Jacobsen, Hans Arno
     A4 Artikkeli konferenssijulkaisussa
    Information about human presence in indoor spaces is crucial for building energy optimization. While there has been a considerable amount of research on using neural networks to automatically detect occupancy from CO2 sensors, their application in practice is limited due to the scarcity of labeled training data. In this paper, we propose Coddora, an off-the-shelf deep learning model pretrained on data from randomized room simulations. Coddora enables quick adaptation to real-world rooms, requiring only minimal data collection. Our contribution includes two model variants for application via fine-tuning or zero-shot classifying, as well as the synthetic dataset providing data from simulations with 100,000 room models.
  • Validation of a Heat Pump System Model for Energy Recycling in Grocery Stores Through On-Site Energy Monitoring
    (2025-02) Söderholm, Niklas; Gröndahl, Mikko; Niemelä, Tuomo; Jokisalo, Juha; Kosonen, Risto; Ni, Long
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper presents a validated simulation model for heat pump-based energy recycling systems, with a focus on heat recovery applications in grocery stores. Heat is recovered through heat pumps from a subcritical CO2-based refrigeration system, with exhaust air heat recovery used on demand according to the heating demand. The model is validated through a case study on a Finnish hypermarket-sized grocery store, where the heat pump system has been operational since 2020. Multi-objective energy optimization is used to validate the model by estimating critical decision variable values and providing error estimates compared to the measured data. The calibrated energy system model has a maximum mean bias error, MBE, of ±5% and a 10–15% coefficient of variation of root mean squared error, CV(RMSE), for the heat pump-related energy balance. Energy optimizations indicate that the control algorithm of the investigated heat pump system can be enhanced to reduce district heating consumption by 12%. The study emphasizes the need for numerous input parameters tailored to a system-specific layout to accurately reproduce the heat pump system’s control algorithm. Compared to a typical transcritical CO2 booster system with heat recovery, the novel heat recovery system shows superior heat recovery potential and a high total COP for both heating and cooling.
  • Effect of Additives on Heat Hardened Inorganic Solid Foundry Binder
    (2025) Anwar, Nurul; Major-Gabryś, Katarzyna; Jalava, Kalle; Orkas, Juhani
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Renewed interest in inorganic binders for sand molding has also intensified research on different forms of it. In this study, solid inorganic sodium silicate binder was tested with different additives to see how these affected the silica mold quality. The five additives used were: glucose, sucrose, boric acid, aluminum oxide and iron(III)oxide powders. The mold quality was assessed through tests like bending strength, tensile strength, hot distortion, wear resistance, gas evolution and collapsibility tests. In addition, SEM imaging was done on some select mold fracture samples. In the end, a casting trial was carried out followed by a surface roughness and defects analysis. A reduction in mold strength was noticed with glucose and boric acid, while collapsibility was improved by glucose, sucrose and boric acid additives. Casting trials have shown the best surface finish to be obtained with sucrose additive. All the casts in general showed some penetration; however, repeat casts have proven that altering some casting parameters could result in casts with excellent surface finish using solid silicates.
  • Polynomial eigenvalue decomposition for eigenvalues with unmajorised ground truth – Reconstructing analytic dinosaurs
    (2025-02-28) Schlecht, Sebastian J.; Weiss, Stephan
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    When estimated space-time covariance matrices from finite data, any intersections of ground truth eigenvalues will be obscured, and the exact eigenvalues become spectrally majorised with probability one. In this paper, we propose a novel method for accurately extracting the ground truth analytic eigenvalues from such estimated space-time covariance matrices. The approach operates in the discrete Fourier transform (DFT) domain and groups sufficiently eigenvalues over a frequency interval into segments that belong to analytic functions and then solves a permutation problem to align these segments. Utilising an inverse partial DFT and a linear assignment algorithm, the proposed EigenBone method retrieves analytic eigenvalues efficiently and accurately. Experimental results demonstrate the effectiveness of this approach in reconstructing eigenvalues from noisy estimates. Overall, the proposed method offers a robust solution for approximating analytic eigenvalues in scenarios where state-of-the-art methods may fail.
  • Wind, PV, and Hybrid Power Plant Operation in Competitive Nordic Electricity Market With High Profit Cannibalization
    (2025-01-01) Seppala, Simeon; Syri, Sanna
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This study presents a technoeconomic analysis of a hybrid wind-PV (photovoltaic) power plant (HPP) compared to onshore wind power plants (WPPs) and photovoltaic power plants (PVPPs) in the Nordic electricity market, focusing on locations in Finland and Sweden. Wind power capacity has recently increased significantly in the Nordics, increasing the profit cannibalization of wind power. Renewable energy subsidies have been phased out in Finland and Sweden, thus new wind and PV power value creation is formed from the power market. The PV power capacity has also encountered significant growth in the Nordics. However, the capacity is still relatively low, allowing more revenue for produced PV power compared to wind power. The lower PV power profit cannibalization has increased interest in HPPs instead of WPPs. This contribution studies the economic feasibility of wind and PV power in changing market conditions in the Nordic electricity market. The market operation is modeled with three different configurations including selling all the power into the day ahead spot market and baseload or pay-as-produced power purchase agreement (PPA). In addition, a battery energy storage system (BESS) investment is analyzed using the operating strategy of shifting production to more profitable spot price hours. This study shows that due to the profit cannibalization and high cost of capital, the power plants are currently not profitable in the Nordic electricity market except when the bidding area has high average spot prices. The worst profitability was with WPPs when exposed to the market shape risk and with PVPPs when pay-as-produced PPA was agreed upon due to the higher levelized cost of electricity. However, the PV power profit cannibalization is expected to increase in the future as more PVPPs operate in the Nordic power market. Thus, the PVPP shape risk may increase in the future as well.
  • Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network
    (2024-05-01) Sinjanakhom, Tantep; Chivapreecha, Sorawat
     A4 Artikkeli konferenssijulkaisussa
    This paper proposes a method to design and implement a zero-phase digital filter that can run in a real-time system. Generally, zero-phase filters are designed for non-causal systems only as the time-reversal operations are required. Thus, the typical usage of these filters is for offline applications. For this reason, we propose a real-time zero-phase digital filter that is designed based on a recurrent neural network model, particularly the gated recurrent units. The model learns to perform zero-phase filtering by using training data made from the filtered signals that are generated by using the conventionally designed zero-phase filter. The original digital filter used to create the dataset is an IIR filter performing forward-backward filtering. The best trained model yields the mean absolute loss values at approximately 0.001 and can process at least 30 times faster than real-time. Furthermore, the trained model was implemented as a 3-band zero-phase graphic equalizer to exhibit one of its applications.
  • Performance of direct air capture process in honeycomb channel configuration: A CFD study
    (2025-01-14) Nejadseifi, Majid; Karimkashi Arani, Shervin; Tynjälä, Tero; Jalali, Payman
     A4 Artikkeli konferenssijulkaisussa
    This study presents a kinetic reaction modeling method for direct air capture (DAC) process of CO2 adsorption using computational fluid dynamics (CFD). Here, CO2 is adsorbed by amine coated air-surface contact area. The Langmuir model is employed to represent the kinetics of CO2 adsorption. Despite neglecting the diffusive phase of the adsorption, which is dominant only in the later stages of adsorption, the surface reaction model gives a satisfactory representation of the adsorption for a major part of the process. Honeycomb reactors with coated adsorbent may yield a better control of reaction rate and pressure drop compared to commonly used packed bed adsorption columns. Their enhanced performance in distributing the flow homogeneously between and within channels creates unique features for the reactor. In this study, we have analyzed mechanical and electrical energy demand for adsorbing CO2 per unit mass of adsorbed CO2 as a function of air flow rate. Adsorption performance of honeycomb structure is anticipated to significantly improve in comparison to the packed beds.
  • Collaborative contract and contract theory
    (2024-08-06) Nysten-Haarala, Soili; Hurmerinta-Haanpää, Anna; Nuottila, Jouko; Kaave, Piia
     A3 Kirjan tai muun kokoomateoksen osa
    Theory of contract law has difficulties in covering collaborative contracts, because it focuses on adversarial relationships, complete contracts and disputes in courts. Whereas, economic contract theories are not litigation-oriented but focus mostly on explaining how governance structures are chosen and how to safeguard from opportunistic behaviour. Relational contract theory, which sees contract as a relationship, is often applied for collaborative contracts. However, traditional old-fashioned contract law is based on freedom of contract, which allows contracting parties to create their own contracts and build their own framework for collaboration within their private autonomy. These practices of private governance can be called contracting. In this chapter, we argue that the freedom of contract could be used more effectively, were path-dependency and traditional mindset not constraining contract designers. One of the path-dependent constraints is that contracts are still too often written for potential disputes in courts, even when they could be written for the contracting parties to coordinate their collaboration. Practice-oriented and multidisciplinary proactive contracting approaches contracts from ex ante as tools for collaboration between contracting parties. Proactive contracting also approaches contract as a lifecycle process. Like relational contract theory, the proactive contract approach contributes to designing framework for collaboration in business and maintaining the relationship. To develop to a recognized framework theory, proactive contracting needs more interdisciplinary empirical research.
  • Smart piezoelectric composite : impact of piezoelectric ceramic microparticles embedded in heat-treated 7075-T651 aluminium alloy
    (2025-02) Ferreira, Pedro M.; Caçador, David; Machado, Miguel A.; Carvalho, Marta S.; Vilaça, Pedro; Sorger, Gonçalo; Farias, Francisco Werley Cipriano; Figueiredo, Arthur Ribeiro; Vidal, Catarina
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Significant advances have been made in material synthesis in the last two decades, with a focus on polymers, ceramics, metals, and smart materials. Piezoelectric-based smart materials generate an electric voltage in response to loads, enabling distributed monitoring in critical structural parts. Friction stir processing (FSP) is a versatile approach that can enhance material performance in various engineering fields. The primary objective of the current research is to examine the sensorial properties of heat-treated AA7075-T651 aluminium plates that have been included with Lead Zirconate Titanate (PZT) and Barium Titanate (BT) particles via FSP. This study includes a comparative analysis of sensitivities with AA5083-H111 self-sensing material, metallographic and physicochemical characterization, and an assessment of the mechanical properties impacted by the incorporation of piezoelectric particles. The sensitivity of AA7075-PZT was found to be significantly higher than that of AA7075-BT. AA7075-PZT achieved a maximum sensitivity of 15.27 × 10−4 μV/MPa while AA7075-BT had a sensitivity of only 7.28 × 10−4 μV/MPa, which is 52% lower. Microhardness and uniaxial tensile tests demonstrated that the presence of particles has an influence on both mechanical strength and electrical conductivity of aluminium components, as opposed to those that do not have particles. The complete investigation intends to give significant insights into the performance and prospective uses of these innovative smart materials, therefore advancing materials science and engineering. Graphical abstract: (Figure presented.)
  • Health-demand ventilation control strategy in northern Chinese homes : how much ventilation do we need to protect occupants’ health
    (2025) Wang, Zhigang; Hou, Jing; Fu, Jinming; Tian, Zhe; Feng, Shuyuan; Kosonen, Risto; Sun, Yuexia
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Introduction: Indoor air quality in dwellings has particular importance regarding occupants’ health. Ventilation is an important means to improve indoor air quality and guarantee occupants’ health. Methods: We utilized CO2 produced by occupants to measure air exchange rate (i.e., the volumetric out-to-indoor airflow rate divided by building volume, h−1) in Chinese homes, which were then linked to health outcomes like asthma and sick building syndrome symptom. Finally, we proposed a “health-demand” ventilation control strategy according to the relationship between air exchange rate and health outcomes. Results and Discussion: Each 0.1 h−1 increase in air exchange rate at night was associated with adjusted odds ratios of 0.97 (Confidence Interval (CI): 0.94–1.00) for rhinitis among children and 0.95 (95% CI: 0.91–0.98) for mucosal sick building symptom among adults. Finally, we proposed a “health-demand” ventilation control strategy according to the relationship between air exchange rate and health outcomes. Air exchange rate of 2.5 h−1 and 6.5 h−1 was suggested to deal with sick building syndrome symptoms among adults and rhinitis symptoms among children, respectively.
  • Potential of explanations in enhancing trust – What can we learn from autonomous vehicles to foster the development of trustworthy autonomous vessels?
    (2025-05-01) Ranjan, Rohit; Kulkarni, Ketki; Musharraf, Mashrura
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    The development of autonomous vessels presents a complex socio-technical challenge where AI and humans must coexist and cooperate. A crucial aspect of successfully deploying these systems is ensuring trust in the AI-powered autonomy. Our research aims to explore the potential of explanations in enhancing trust and its correlated metrics (such as preference, understanding, anxiety) in autonomous vessels. While the investigation of explainability and its role in increasing end-user trust is still at an elementary level for autonomous vessels, it has already been identified as a key requirement for successful adoption of self-driving cars and highly automated vehicles in general. We conducted a systematic literature review to investigate how the impact of explainability on trust and its correlated metrics has been studied in the domain of autonomous vehicles. We examined the diverse experimental setups employed to assess trust-building, exploring instruments, explanation modes, types, timings, and additional human factors influencing trust. The study scrutinizes prevalent data collection methods and commonly used questionnaires for measuring trust levels following explanations and examines the characteristics and theories integral to effective explanations for trust development. Review results indicate that explanations generally have a positive impact on trust and its correlated metrics preference, although this impact is not statistically significant in all cases. The effect of explanations on correlated metrics understanding was found to be statistically significant in all cases. For correlated metrics anxiety, a decrease was observed with the presence of explanations in most cases, even though this decrease wasn't always statistically significant. This study discusses how lessons learned from autonomous vehicles can be applied in the context of autonomous vessels, with the aim of fostering the development of trustworthy autonomous vessels.
  • The Discomfort of Things! Tidying-up and Decluttering in Consumers' Homes
    (2024-05-23) Gollnhofer, Johanna Franziska; Bhatnagar, Kushagra; Manke, Birte
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Most relatively affluent consumers are fighting a losing battle with material disorder in their homes. No matter how hard they try to rein it in, material disorder always comes out on top. We argue that part of the continued obduracy of material disorder is because of its messy understanding. We clarify material disorder’s muddled conceptual boundaries by theorizing from an ethnographic investigation of consumers who recently dealt with material disorder through decluttering their homes. Leveraging twin analytical lenses that we label the possessive materialist and post-materialist lenses, we surface two distinct yet inter-dependent forms of disorder (disorder-as-untidiness and disorder-as-clutteredness) that together plague consumers’ homes. We contribute a pluralized understanding of material disorder, that is, disorders not disorder. We also offer novel insight into agentic struggles between consumers and home possessions over material dis/orders.
  • Review of energy efficiency and technological advancements in data center power systems
    (2024-11-15) Khosravi, Ali; Sandoval, Oscar R.; Taslimi, Melika Sadat; Sahrakorpi, Tiia; Amorim, Gessica; Garcia-Pabon, Juan Jose
     A2 Katsausartikkeli tieteellisessä aikakauslehdessä
    In order to support a wide range of applications and services, data centers are crucial components of contemporary computer infrastructure. Nevertheless, the rapid expansion and functioning of these entities have resulted in notable energy usage and ecological consequences. Power usage effectiveness, dependability, and operating costs are all impacted by electricity supply systems, which are crucial to the operating and energy efficiency of data centers. This review article offers a thorough summary of the state of the art in data center power supply systems research, covering case studies, best practices, developing technologies, and potential directions for future investigation. The study begins with a survey of energy-efficient data centers and an outline of power usage effectiveness design concerns. The categorization of electricity supply systems is discussed through various integration strategies with renewable sources of electricity generation. This includes identifying scenarios where data centers operate in countries with a 100% fossil fuel electricity matrix, strategies for implementing clean energy purchase contracts, and investments in both on-site and off-site renewable energy projects. The review of the literature addresses current research on data center power systems, emphasizing significant discoveries and patterns in the field while pointing out gaps and restrictions. The assessment also looks at new developments in energy storage, power management, and renewable energy integration. The research, which draws from case studies of effective energy supply systems in data centers, offers useful suggestions and best practices for planning, executing, and overseeing data center power systems. The paper ends with recommendations for further study and application.
  • Intelligent shipping : integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor
    (2025-12) Zhao, Liang; Xu, Mengqiao; Liu, Lei; Bai, Yong; Zhang, Mingyang; Yan, Ran
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model. Based on historical maritime big data, the framework offers customized routes for companies with specific routing preferences. The autonomous maneuvering model then evaluates the safety and reliability of the routes by considering ship motion characteristics and ocean hydrodynamics. We validate its effectiveness on the world’s longest Green and Digital Shipping Corridor between Singapore and Rotterdam. Results demonstrate that our model can provide customized route design for companies and enhance safety for shipping. The framework could serve as a fundamental structure to build a fully digitalized platform for route customization and evaluation for global shipping, optimizing operational decision-making and safety assurance.
  • Dataset of design factors and corresponding properties for sustainable design of cellulose lightweight materials
    (2025-05) Zhu, Yeling; Talebjedi, Behnam; Zhang, Weijia; Tang, Zirui; Jiang, Feng; Tu, Qingshi
     Short survey
  • A systems-theoretic approach using association rule mining and predictive Bayesian trend analysis to identify patterns in maritime accident causes
    (2025-06) Bairami-Khankandi, Shahrokh; Bolbot, Victor; BahooToroody, Ahmad; Goerlandt, Floris
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Accident investigations are commonly conducted to improve safety in ship design and operations. Given the lack of comprehensive approaches to understand causal factors of maritime accidents considering systems-theoretic views on accident causation, this paper presents a novel approach using information from accident investigation reports to this effect. The proposed approach combines key elements of the Causal Analysis based on Systems Theory method, Association Rule Mining and predictive Bayesian trend analysis to gain deeper understanding of patterns and trends in accident causal factors. This new approach goes beyond the state of the art by offering insights on accident causal patterns and trends at the system level, which can be used by maritime authorities and industries to enhance maritime safety by understanding co-occurring accident causes. Additionally, the approach is applied to 30 years of Canadian shipping accident reports from the Transportation Safety Board, producing new knowledge about accident causes across different commercial vessel types and accident categories. The results highlight accident causes in interactions between shipping management and vessels, and between ship crews and bridge equipment. Differences between passenger and cargo vessels, and between onboard fires and navigational accidents are observed. Discussions on results, limitations, and future research directions conclude the article.