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Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.
Paraffin Nanocomposites for Heat Management of Lithium-Ion Batteries: A Computational Investigation
(2016)
Lithium-ion (Li-ion) batteries are currently considered as vital components for advances in mobile technologies such as those in communications and transport. Nonetheless, Li-ion batteries suffer from temperature rises which sometimes lead to operational damages or may even cause fire. An appropriate solution to control the temperature changes during the operation of Li-ion batteries is to embed batteries inside a paraffin matrix to absorb and dissipate heat. In the present work, we aimed to investigate the possibility of making paraffin nanocomposites for better heat management of a Li-ion battery pack. To fulfill this aim, heat generation during a battery charging/discharging cycles was simulated using Newman’s well established electrochemical pseudo-2D model. We couple this model to a 3D heat transfer model to predict the temperature evolution during the battery operation. In the later model, we considered different paraffin nanocomposites structures made by the addition of graphene, carbon nanotubes, and fullerene by assuming the same thermal conductivity for all fillers. This way, our results mainly correlate with the geometry of the fillers. Our results assess the degree of enhancement in heat dissipation of Li-ion batteries through the use of paraffin nanocomposites. Our results may be used as a guide for experimental set-ups to improve the heat management of Li-ion batteries.
The release of the large language model-based chatbot ChatGPT 3.5 in November 2022 has brought considerable attention to the subject of artificial intelligence, not only to the public. From the perspective of higher education, ChatGPT challenges various learning and assessment formats as it significantly reduces the effectiveness of their learning and assessment functionalities. In particular, ChatGPT might be applied to formats that require learners to generate text, such as bachelor theses or student research papers. Accordingly, the research question arises to what extent writing of bachelor theses is still a valid learning and assessment format. Correspondingly, in this exploratory study, the first author was asked to write his bachelor’s thesis exploiting ChatGPT. For tracing the impact of ChatGPT methodically, an autoethnographic approach was used. First, all considerations on the potential use of ChatGPT were documented in logs, and second, all ChatGPT chats were logged. Both logs and chat histories were analyzed and are presented along with the recommendations for students regarding the use of ChatGPT suggested by a common framework. In conclusion, ChatGPT is beneficial for thesis writing during various activities, such as brainstorming, structuring, and text revision. However, there are limitations that arise, e.g., in referencing. Thus, ChatGPT requires continuous validation of the outcomes generated and thus fosters learning. Currently, ChatGPT is valued as a beneficial tool in thesis writing. However, writing a conclusive thesis still requires the learner’s meaningful engagement. Accordingly, writing a thesis is still a valid learning and assessment format. With further releases of ChatGPT, an increase in capabilities is to be expected, and the research question needs to be reevaluated from time to time.
A technique for using object-oriented technologies to write structural analysis software has been developed. The structural design information of an individual building is stored in an object-oriented database. A global database provides general design values as material data and safety factors. A class library for load elements has been evolved to model the transfer of loads in a building. This class library is the basis for the development of further classes for other structural elements such as beams, columns or slabs. A software has been developed to monitor the forces transferred from one structural member to another in a building for load cases and combinations according to Eurocode 1. The results of the analysis are stored in the projects database from which a structural design report may be generated. The software was developed under Microsoft Visual C++. The Microsoft Foundation Class Library (MFC) was used to program the Graphical User Interface (GUI). Object Linking and Embedding (OLE) technology is useful to include any type of OLE server objects for example texts written with a word processor or CAD drawings in the structural design report. The Object-Oriented Database Management System (OODBMS) ObjectStore provides services to store the large amount of objects.
Object-Oriented Damage Information Modeling Concepts and Implementation for Bridge Inspection
(2022)
Bridges are designed to last for more than 50 years and consume up to 50% of their life-cycle costs during their operation phase. Several inspections and assessment actions are executed during this period. Bridge and damage information must be gathered, digitized, and exchanged between different stakeholders. Currently, the inspection and assessment practices rely on paper-based data collection and exchange, which is time-consuming and error-prone, and leads to loss of information. Storing and exchanging damage and building information in a digital format may lower costs and errors during inspection and assessment and support future needs, for example, immediate simulations regarding performance assessment, automated maintenance planning, and mixed reality inspections. This study focused on the concept for modeling damage information to support bridge reviews and structural analysis. Starting from the definition of multiple use cases and related requirements, the data model for damage information is defined independently from the subsequent implementation. In the next step, the implementation via an established standard is explained. Functional tests aim to identify problems in the concept and implementation. To show the capability of the final model, two example use cases are illustrated: the inspection review of the entire bridge and a finite-element analysis of a single component. Main results are the definition of necessary damage data, an object-oriented damage model, which supports multiple use cases, and the implementation of the model in a standard. Furthermore, the tests have shown that the standard is suitable to deliver damage information; however, several software programs lack proper implementation of the standard.
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems.