1,720,954 research outputs found
To accelerate the battery simulation process for crash and impact tests using machine learning
No full textAn investigation into the prediction of thermal runaway in lithium-ion batteries subjected
to abusive mechanical loading is comparable to a crash or impact test and a crucial safety
measure that can prevent catastrophic events. Using an explicit crash simulation method, it
is possible to simulate the indentation test model and predict the thermal runaway. However,
it is important to note that this approach is associated with a significant time investment.
State-of-the-art technologies that involve machine learning for prediction of thermal runaway
can be utilised for faster predictions.
The research conducted in this thesis focuses on a cell model that simulates an indentation
test and a machine learning model that predicts the thermal runaway. This model successfully
captures the behavior of an internal short circuit and is verified using experimental data from
existing literature. Additionally, a workflow is generated in Altair Hyperstudy to produce
data essential for training the machine learning model using an automated process. This data
created is based on the design variables of the indenter (impacting body). This facilitates
comprehension of potential deformation, damage and related patterns. The machine learning
model is created using the Altair Physics AI tool and subsequently trained by the provided
dataset. Data, as foundational resource is used to train the machine learning model. These
datasets must be available in adequate quantities and of high quality for neural network training
to discover the relationship between input and output. The outcomes of this Machine
learning model yield an adequate degree of accuracy. The accuracy of these results is highly
dependent on the model, quality of data derived from this dataset and the Hyperparameters
used for the model training. Insights of the predicted results by ML model are of great use
for Design consideration and validation of lithium-ion batteries. The outcomes of conducting
the indentation test simulation on the cell jellyroll model provides insights on the potential
time in which a thermal runaway will occur, which could potentially lead to the occurrence of
a fire or explosion. Moreover, the development of a comprehensive scaled model incorporating
relevant data holds significant implications for future battery regulations.Outgoin
To accelerate the battery simulation process for crash and impact tests using machine learning
Get PDFAn investigation into the prediction of thermal runaway in lithium-ion batteries subjected
to abusive mechanical loading is comparable to a crash or impact test and a crucial safety
measure that can prevent catastrophic events. Using an explicit crash simulation method, it
is possible to simulate the indentation test model and predict the thermal runaway. However,
it is important to note that this approach is associated with a significant time investment.
State-of-the-art technologies that involve machine learning for prediction of thermal runaway
can be utilised for faster predictions.
The research conducted in this thesis focuses on a cell model that simulates an indentation
test and a machine learning model that predicts the thermal runaway. This model successfully
captures the behavior of an internal short circuit and is verified using experimental data from
existing literature. Additionally, a workflow is generated in Altair Hyperstudy to produce
data essential for training the machine learning model using an automated process. This data
created is based on the design variables of the indenter (impacting body). This facilitates
comprehension of potential deformation, damage and related patterns. The machine learning
model is created using the Altair Physics AI tool and subsequently trained by the provided
dataset. Data, as foundational resource is used to train the machine learning model. These
datasets must be available in adequate quantities and of high quality for neural network training
to discover the relationship between input and output. The outcomes of this Machine
learning model yield an adequate degree of accuracy. The accuracy of these results is highly
dependent on the model, quality of data derived from this dataset and the Hyperparameters
used for the model training. Insights of the predicted results by ML model are of great use
for Design consideration and validation of lithium-ion batteries. The outcomes of conducting
the indentation test simulation on the cell jellyroll model provides insights on the potential
time in which a thermal runaway will occur, which could potentially lead to the occurrence of
a fire or explosion. Moreover, the development of a comprehensive scaled model incorporating
relevant data holds significant implications for future battery regulations.Outgoin
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
