DNV GL

About: DNV GL is a company organization based out in Sandvika, Norway. It is known for research contribution in the topics: Corrosion & Finite element method. The organization has 1929 authors who have published 2387 publications receiving 34644 citations.

Comparison of Levelized Cost of Energy of superconducting direct drive generators for a 10 MW offshore wind turbine

Abstract: A method for comparing the levelized cost of energy (LCoE) of different superconducting drive trains is introduced. The properties of a 10-MW MgB2 superconducting direct-drive generator and the cost break down of the nacelle components are presented and scaled up to a turbine with a rotor diameter of up to 280 m. The partial load efficiency of the generator is evaluated for a constant cooling power of 0, 50, and 100 kW, and the annual energy production is used to determine the impact on the LCoE.

A unified fuel spray breakup model for internal combustion engine applications

Abstract: A unified approach toward modeling fuel sprays for internal combustion engines is presented in this work. The fuel injection process has been divided in three subprocesses, namely, primary atomization, drop deformation and aerodynamic drag, and secondary atomization. Two different models have been used for the primary atomization, depending on whether a high-pressure swirl atomizer or a multihole nozzle is used. The drop deformation and secondary atomization have been modeled based on the physical properties of the system, independent of the way the droplets were created. The secondary atomization has been further divided into four breakup regimes, based on experimental observations reported in the literature. The model has been validated using a wide array of experimental conditions, ranging from gasoline to diesel sprays, in nonevaporating conditions. Overall, the model performs well, predicting correct trends for the spray characteristics, without the need for recalibration.

Accuracy and efficiency of germline variant calling pipelines for human genome data

Abstract: Advances in next-generation sequencing technology have enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data. However, there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using the Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show better accuracy in SNP and indel calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient execution speed, and therefore superior performance in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also suggests a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical applications.