Browsing by Author "Rao, N.R."
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Item Finite element modeling and experimental validation of rectangular pin buckle arrestors for offshore pipelines(2020) Rao, N.R.; Kaliveeran, V.Finite element modeling was performed, and experiments were conducted on pipeline models made of stainless steel of grade SS304. Present research work focuses on the improvement in structural properties of offshore pipelines stiffened with rectangular pin buckle arrestors by varying length and placing them at different locations along the length of pipeline. The optimum length of buckle arrestors was identified from finite element analysis and pipeline models were fabricated for conducting buckling experiments. Bending experiments were conducted on the pipeline models to determine flexural capacity of the pipeline models. Finite element analysis results showed good agreement with experimental results. 2020, 2020 Taylor & Francis Group, LLC.Item Finite element modeling and experimental validation of rectangular pin buckle arrestors for offshore pipelines(Taylor and Francis Ltd., 2022) Rao, N.R.; Kaliveeran, V.Finite element modeling was performed, and experiments were conducted on pipeline models made of stainless steel of grade SS304. Present research work focuses on the improvement in structural properties of offshore pipelines stiffened with rectangular pin buckle arrestors by varying length and placing them at different locations along the length of pipeline. The optimum length of buckle arrestors was identified from finite element analysis and pipeline models were fabricated for conducting buckling experiments. Bending experiments were conducted on the pipeline models to determine flexural capacity of the pipeline models. Finite element analysis results showed good agreement with experimental results. © 2020 Taylor & Francis Group, LLC.Item The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics(Association for Computational Linguistics (ACL), 2021) Gehrmann, S.; Adewumi, T.; Aggarwal, K.; Ammanamanchi, P.S.; Anuoluwapo, A.; Bosselut, A.; Chandu, K.R.; Clinciu, M.; Das, D.; Dhole, K.D.; Du, W.; Durmus, E.; DuÅ¡ek, O.; Emezue, C.; Gangal, V.; Gârbacea, C.; Hashimoto, T.; Hou, Y.; Jernite, Y.; Jhamtani, H.; Ji, Y.; Jolly, S.; Kale, M.; Kumar, D.; Ladhak, F.; Madaan, A.; Maddela, M.; Mahajan, K.; Mahamood, S.; Majumder, B.P.; Martins, P.H.; McMillan-Major, A.; Mille, S.; van Miltenburg, E.; Nadeem, M.; Narayan, S.; Nikolaev, V.; Niyongabo, R.A.; Osei, S.; Parikh, A.; Perez-Beltrachini, L.; Rao, N.R.; Raunak, V.; Rodriguez, J.D.; Santhanam, S.; Sedoc, J.; Sellam, T.; Shaikh, S.; Shimorina, A.; Sobrevilla Cabezudo, M.A.S.; Strobelt, H.; Subramani, N.; Xu, W.; Yang, D.; Yerukola, A.; Zhou, J.We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate. © 2021 Association for Computational Linguistics