Simulation of thermomechanical fatigue of ductile cast iron and lifetime calculation

In this paper, both standard and constrained thermomechanical fatigue (TMF) tests were conducted on a high silicon ductile cast iron (DCI). The standard TMF tests were conducted with independent control of mechanical strain, out-of-phase (OP) and in-phase (IP) strain, and temperature in the range from 300 to 800\ub0C. The constrained TMF tests were conducted with various constraint ratios of 100%, 70%, 60% and 50% at the temperature ranges of 160 to 600\ub0C and 160 to 700\ub0C. Based on a material model as calibrated with low-cycle fatigue (LCF) data of DCI, finite element analyses (FEA) of the above TMF tests were carried out with Abaqus. A damage mechanism-based lifetime model was integrated into a C++ API code to post-process the Abaqus output results. Simulation predictions show good agreement with experiments for stress-strain responses and lifetime under different TMF conditions.Peer reviewed: YesNRC publication: Ye

Simulation of thermomechanical fatigue of ductile cast iron and lifetime calculation

In this paper, both standard and constrained thermomechanical fatigue (TMF) tests were conducted on a high silicon ductile cast iron (DCI). The standard TMF tests were conducted with independent control of mechanical strain, out-of-phase (OP) and in-phase (IP) strain, and temperature in the range from 300 to 800\ub0C. The constrained TMF tests were conducted with various constraint ratios of 100%, 70%, 60% and 50% at the temperature ranges of 160 to 600\ub0C and 160 to 700\ub0C. Based on a material model as calibrated with low-cycle fatigue (LCF) data of DCI, finite element analyses (FEA) of the above TMF tests were carried out with Abaqus. A damage mechanism-based lifetime model was integrated into a C++ API code to post-process the Abaqus output results. Simulation predictions show good agreement with experiments for stress-strain responses and lifetime under different TMF conditions.Peer reviewed: YesNRC publication: Ye

Thermomechanical fatigue of ductile cast iron and its life prediction

Thermomechanical fatigue (TMF) behaviors of ductile cast iron (DCI) were investigated under out-of-phase (OP), in-phase (IP), and constrained strain-control conditions with temperature hold in various temperature ranges: 573 K to 1073 K, 723 K to 1073 K, and 433 K to 873 K (300 \ub0C to 800 \ub0C, 450 \ub0C to 800 \ub0C, and 160 \ub0C to 600 \ub0C). The integrated creep-fatigue theory (ICFT) model was incorporated into the finite element method to simulate the hysteresis behavior and predict the TMF life of DCI under those test conditions. With the consideration of four deformation/damage mechanisms: (i) plasticity-induced fatigue, (ii) intergranular embrittlement, (iii) creep, and (iv) oxidation, as revealed from the previous study on low cycle fatigue of the material, the model delineates the contributions of these physical mechanisms in the asymmetrical hysteresis behavior and the damage accumulation process leading to final TMF failure. This study shows that the ICFT model can simulate the stress\u2013strain response and life of DCI under complex TMF loading profiles (OP and IP, and constrained with temperature hold).Peer reviewed: YesNRC publication: Ye

Thermomechanical fatigue of ductile cast iron and its life prediction

Thermomechanical fatigue (TMF) behaviors of ductile cast iron (DCI) were investigated under out-of-phase (OP), in-phase (IP), and constrained strain-control conditions with temperature hold in various temperature ranges: 573 K to 1073 K, 723 K to 1073 K, and 433 K to 873 K (300 \ub0C to 800 \ub0C, 450 \ub0C to 800 \ub0C, and 160 \ub0C to 600 \ub0C). The integrated creep-fatigue theory (ICFT) model was incorporated into the finite element method to simulate the hysteresis behavior and predict the TMF life of DCI under those test conditions. With the consideration of four deformation/damage mechanisms: (i) plasticity-induced fatigue, (ii) intergranular embrittlement, (iii) creep, and (iv) oxidation, as revealed from the previous study on low cycle fatigue of the material, the model delineates the contributions of these physical mechanisms in the asymmetrical hysteresis behavior and the damage accumulation process leading to final TMF failure. This study shows that the ICFT model can simulate the stress\u2013strain response and life of DCI under complex TMF loading profiles (OP and IP, and constrained with temperature hold).Peer reviewed: YesNRC publication: Ye

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.All SEQC2 participants freely donated their time, reagents, and computing resources for the completion and analysis of this project. Part of this work was carried out with the support of the Intramural Research Program of the National Institutes of Health (to Mehdi Pirooznia), National Institute of Environmental Health Sciences (to Pierre Bushel), and National Library of Medicine (to Danielle Thierry-Mieg, Jean Thierry-Mieg, and Chunlin Xiao). Leming Shi and Yuanting Zheng were supported by the National Key R&D Project of China (2018YFE0201600), the National Natural Science Foundation of China (31720103909), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). Donald J. Johann, Jr. acknowledges the support by FDA BAA grant HHSF223201510172C. Timothy Mercer and Ira Deveson were supported by the National Health and Medical Research Council (NHMRC) of Australia grants APP1108254, APP1114016, and APP1173594 and Cancer Institute NSW Early Career Fellowship 2018/ECF013. This research has also been, in part, financially supported by the MEYS of the CR under the project CEITEC 2020 (LQ1601), by MH CR, grant No. (NV19-03-00091). Part of this work was carried out with the support of research infrastructure EATRIS-CZ, ID number LM2015064, funded by MEYS CR. Boris Tichy and Nikola Tom were supported by research infrastructure EATRIS-CZ, ID number LM2018133 funded by MEYS CR and MEYS CR project CEITEC 2020 (LQ1601).S

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

none74siBackground Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.noneJones, Wendell; Gong, Binsheng; Novoradovskaya, Natalia; Li, Dan; Kusko, Rebecca; Richmond, Todd A.; Johann, Donald J.; Bisgin, Halil; Sahraeian, Sayed Mohammad Ebrahim; Bushel, Pierre R.; Pirooznia, Mehdi; Wilkins, Katherine; Chierici, Marco; Bao, Wenjun; Basehore, Lee Scott; Lucas, Anne Bergstrom; Burgess, Daniel; Butler, Daniel J.; Cawley, Simon; Chang, Chia-Jung; Chen, Guangchun; Chen, Tao; Chen, Yun-Ching; Craig, Daniel J.; del Pozo, Angela; Foox, Jonathan; Francescatto, Margherita; Fu, Yutao; Furlanello, Cesare; Giorda, Kristina; Grist, Kira P.; Guan, Meijian; Hao, Yingyi; Happe, Scott; Hariani, Gunjan; Haseley, Nathan; Jasper, Jeff; Jurman, Giuseppe; Kreil, David Philip; Łabaj, Paweł; Lai, Kevin; Li, Jianying; Li, Quan-Zhen; Li, Yulong; Li, Zhiguang; Liu, Zhichao; López, Mario Solís; Miclaus, Kelci; Miller, Raymond; Mittal, Vinay K.; Mohiyuddin, Marghoob; Pabón-Peña, Carlos; Parsons, Barbara L.; Qiu, Fujun; Scherer, Andreas; Shi, Tieliu; Stiegelmeyer, Suzy; Suo, Chen; Tom, Nikola; Wang, Dong; Wen, Zhining; Wu, Leihong; Xiao, Wenzhong; Xu, Chang; Yu, Ying; Zhang, Jiyang; Zhang, Yifan; Zhang, Zhihong; Zheng, Yuanting; Mason, Christopher E.; Willey, James C.; Tong, Weida; Shi, Leming; Xu, JoshuaJones, Wendell; Gong, Binsheng; Novoradovskaya, Natalia; Li, Dan; Kusko, Rebecca; Richmond, Todd A.; Johann, Donald J.; Bisgin, Halil; Sahraeian, Sayed Mohammad Ebrahim; Bushel, Pierre R.; Pirooznia, Mehdi; Wilkins, Katherine; Chierici, Marco; Bao, Wenjun; Basehore, Lee Scott; Lucas, Anne Bergstrom; Burgess, Daniel; Butler, Daniel J.; Cawley, Simon; Chang, Chia-Jung; Chen, Guangchun; Chen, Tao; Chen, Yun-Ching; Craig, Daniel J.; del Pozo, Angela; Foox, Jonathan; Francescatto, Margherita; Fu, Yutao; Furlanello, Cesare; Giorda, Kristina; Grist, Kira P.; Guan, Meijian; Hao, Yingyi; Happe, Scott; Hariani, Gunjan; Haseley, Nathan; Jasper, Jeff; Jurman, Giuseppe; Kreil, David Philip; Łabaj, Paweł; Lai, Kevin; Li, Jianying; Li, Quan-Zhen; Li, Yulong; Li, Zhiguang; Liu, Zhichao; López, Mario Solís; Miclaus, Kelci; Miller, Raymond; Mittal, Vinay K.; Mohiyuddin, Marghoob; Pabón-Peña, Carlos; Parsons, Barbara L.; Qiu, Fujun; Scherer, Andreas; Shi, Tieliu; Stiegelmeyer, Suzy; Suo, Chen; Tom, Nikola; Wang, Dong; Wen, Zhining; Wu, Leihong; Xiao, Wenzhong; Xu, Chang; Yu, Ying; Zhang, Jiyang; Zhang, Yifan; Zhang, Zhihong; Zheng, Yuanting; Mason, Christopher E.; Willey, James C.; Tong, Weida; Shi, Leming; Xu, Joshu