Now showing items 121-140 of 311

    • Numerical investigation on the cage-to-cage wake effect: A case study of a 4 × 2 cage array 

      Sim, Jaesub; Cheng, Hui; Aarsæther, Karl Gunnar; Li, Lin; Ong, Muk Chen (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-12)
      Aquaculture has been the world’s fastest-growing food producing method and grown to become the second-largest export industry in Norway during the past 40 years. Usually, the high-value fish such as Atlantic Salmon (<i>Salmo salar</i>) is raised in a multi-cage fish farm, where the flow interactions between fish cages exist. In this study, the interactions between fish cages are implemented into the ...
    • Challenges Associated with Creeping Disasters in Disaster Risk Science and Practice: Considering Disaster Onset Dynamics 

      Staupe-Delgado, Reidar; Rubin, Olivier (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-01-12)
      In this article, we set out to reconcile a general conceptualization of disaster temporalities by drawing on the epitome example of a creeping disaster, namely famine. Our argument is driven by the recognition that slowly manifesting disaster impacts pose distinct challenges for decision makers and researchers while there is a tendency for the disaster literature to overlook the role of disaster ...
    • Exploiting more robust and efficacious deep learning techniques for modeling wind power with speed 

      Chen, Hao; Staupe-Delgado, Reidar (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-27)
      Abstract Sound analyses of the nonlinear relationship between wind speed and power generation are crucial for the advancement of wind energy optimization. As an emerging artificial intelligence technology, deep learning has received growing attention from energy researchers for its outstanding ability to provide complex mappings. However, deep neural networks involve complex configurations, ...
    • Drawing lessons from the COVID-19 pandemic: Seven obstacles to learning from public inquiries in the wake of the crisis 

      Eriksson, Kerstin; Staupe-Delgado, Reidar; Holst, Jørgen (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-09-13)
      On March 11, 2020, the World Health Organization declared the emerging COVID-19 threat a pandemic following the global spread of the virus. A year later, a number of governments are being handed the concluding reports of national public inquiries tasked with investigating responses, mishaps, and identifying lessons for the future. The present article aims to identify a set of learning obstacles that ...
    • Observed and unobserved heterogeneity in failure data analysis 

      Zaki, Rezgar; Barabadi, Abbas; Barabady, Javad; Qarahasanlou, Ali Nouri (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-08)
      In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to inaccurate ...
    • Performance Quantification of Icebreaker Operations in Ice Management by Numerical Simulations 

      Bjørnø, Jon; Skjetne, Roger; van den Berg, Marnix; Lu, Wenjun; Lubbad, Raed; Løset, Sveinung (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-15)
      The paper addresses the problem of quantifying and assessing the effectiveness of icebreaker operations in ice management (IM) using a high-fidelity simulator. The numerical model includes an accurate geometric representation of an icebreaker, a cylindrical protected structure, and a synthetic ice environment. A set of key performance indicators (KPIs) are defined and proposed to quantify the ...
    • An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic 

      Chen, Hao; Birkelund, Yngve (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-23)
      Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step ...
    • Evaluation of the Climate Forecast System Reanalysis data for hydrological model in the Arctic watershed Målselv 

      Bui, Minh Tuan; Lu, Jinmei; Nie, Linmei (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-04-13)
      The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The ...
    • A discipline without a name? Contrasting three fields dealing with hazards and disaster 

      Staupe-Delgado, Reidar; Abdel-Fattah, Dina; Pursiainen, Christer Henrik (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-29)
      A growing number of research fields have been striving for recognition as an academic discipline. Rather than argue that ‘our field’ should also be recognised as such, we stop to ask two fundamental questions. Our first question concerns whether and how disciplinary concerns would benefit research fields dealing with hazards and disasters. Second, we reflect on the implications of not having a broadly ...
    • Vanadium removal from mining ditch water using commercial iron products and ferric groundwater treatment residual-based materials 

      Zhang, Ruichi; Lu, Jinmei; Dopson, Mark; Leiviskä, Tiina (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-06)
      Removal of vanadium from liquid waste streams protects the environment from toxic vanadium species and promotes the recovery of the valuable metal. In this study, real mining ditch water was sampled from a closed vanadium mine (V–Fe–Ti oxide deposit, Finland) and used in sorption experiments at prevailing vanadium concentration (4.66–6.85 mg/L) and pH conditions (7.02–7.83). The high concentration ...
    • Impact of temperature on the leaching of sulphate, Co, Fe, Mn, Ni and Zn from the Ballangen tailings deposit, Norway: A laboratory column experiment 

      Lu, Jinmei; Walder, Ingar; Leiviskä, Tiina (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-10-29)
      Temperature is an important factor affecting the leaching of contaminants from waste deposits, especially in the Nordic region where temperature change is more drastic than other areas. In this study, the impact of temperature variation in the leaching of sulphate, Co, Fe, Mn, Ni and Zn from the Ballangen tailings deposit, northern Norway, was investigated using a column leaching experiment. ...
    • International collaboration for meeting the challenges of huge and cascading disasters 

      Nilsen, Aud Solveig; Stakkeland, Linda Marie (Conference object; Konferansebidrag, 2021-09)
      Some crisis have an international impact. How is it possible to enhance collaboration between nations in huge and cascading disasters? We will present Barents Rescue (BR) as an inspiration for our Student Barents Rescue (SBR) and thereafter show examples from a student pilot with an international scenario.
    • How and why develop scenarios for training students to use their knowledge in practice? 

      Nilsen, Aud Solveig (Conference object; Konferansebidrag, 2021-09)
      To be able to handle crisis and risks there is a need for different skills. We will focus on learning by combining theory and practice. Since NEEDS is a disaster management community, scenarios in line with this theme are wanted.<p> <p>This colloquium can be an experience transfer between educators that work with scenario-based training and want to develop this further. Drawing on different ...
    • Preliminary Hazard Analysis for UAV-assisted Bridge Inspection 

      Ayele, Yonas Zewdu; Ashrafi, Behrooz (Journal article; Tidsskriftartikkel; Peer reviewed, 2020)
      Unmanned aerial vehicle (UAV) technology has found its way into a number of civilian applications in the last 20 years, predominantly due to lower costs and tangible scientific improvements. In its application to structural bridge inspection, UAVs provide two main functions. The first, being the most common, detects damage through visual sensors. The 2D imagery data can be used to quickly ...
    • Recoverability modeling of power distribution systems using accelerated life models: Case of power cut due to extreme weather events in Norway 

      Rød, Bjarte; Barabadi, Abbas; Naseri, Masoud (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-07-09)
      Today’s societies rely on electric power distribution systems. Recent weather events have illustrated that the loss of such service can lead to severe consequences for societies and stakeholders. Hence, to reduce the impact of such extreme events on infrastructure systems and limit the associated losses, it is crucial to design infrastructure that can bounce back and recover rapidly after disruptions ...
    • Lumped, constrained cable modeling with explicit state-space formulation using an elastic version of Baumgarte stabilization 

      Skjong, Stian; Reite, Karl-Johan; Aarsæther, Karl Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-29)
      This paper presents a modeling approach for efficient simulation of slender structures, such as wires, cables and ropes. Lumped structural elements are connected using constraints. These are solved explicitly, using an elastic version of Baumgarte stabilization. This avoids singularities in the matrix inversions. The resulting explicit state-space formulation filters the higher order dynamics and ...
    • Experimental and simulated evaluations of airborne contaminant exposure in a room with a modified localized laminar airflow system 

      Cheng, Zhu; Aganovic, Amar; Cao, Guangyu; Bu, Zhongming (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-15)
      The traditional mixing ventilation is not an energy effective approach to remove indoor air pollutants, maintain breath zone air quality, and control the airborne transmission. This study investigated the potential of a localized laminar airflow ventilation system to alleviate human exposure to pollutants. Breathing thermal manikins with sitting posture and supine posture were used to simulate the ...
    • An Slow Motion Detection Algorithm using High Order Statistic Approach 

      Yuan, Fuqing; Lu, Jinmei (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-16)
      Motion detection is vital for consumer electronics and the Internet of things (IOT). For a scenario where the motion is slow and gentle, the resolution of the motion sensor is critical for the detection, while the algorithm development is another critical issue to differentiate the motion signal from noise measurement. This paper investigates the feasibility of using higher order statistics ...
    • Quantifying the effects of watershed subdivision scale and spatial density of weather inputs on hydrological simulations in a Norwegian Arctic watershed 

      Bui, Minh Tuan; Lu, Jinmei; Nie, Linmei (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-12-01)
      The effects of watershed subdivisions on hydrological simulations have not been evaluated in Arctic conditions yet. This study applied the Soil and Water Assessment Tool and the threshold drainage area (TDA) technique to evaluate the impacts of watershed subdivision on hydrological simulations at a 5,913-km<sup>2 </sup>Arctic watershed, Målselv. The watershed was discretized according to four TDA ...
    • Data-augmented sequential deep learning for wind power forecasting 

      Chen, Hao; Birkelund, Yngve; Qixia, Zhang (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-15)
      Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and ...