Multiphysics analysis of heat pipe cooled microreactor core with adjusted heat sink temperature for thermal stress reduction using OpenFOAM coupled with neutronics and heat pipe code

Heat-pipe-cooled microreactors (HPRs) have advantages such as a compact design, easy transportation, and improved system reliability and stability. The core of an HPR consists of fuel rods and heat pipes in a monolith, which is a solid block structure containing many holes for the fuel rods and heat pipes. When designing the core of an HPR, high thermal stress and reactivity feedback owing to thermal expansion are important considerations. Therefore, a high-fidelity multiphysics analysis tool is required for accurately analyzing an HPR core. When performing a multiphysics analysis, it is necessary to couple the heat pipe thermal analysis code, thermal-structural analysis code, and neutronics code. To develop a multiphysics analysis tool, OpenFOAM, an open source Computational Fluid Dynamics (CFD) tool, and ANLHTP, a heat pipe thermal analysis code, were coupled. In this process, the structural analysis solver of OpenFOAM was verified, and its limitations were improved. To confirm the proper working of the code, the mini-core problem was analyzed using the OpenFOAM-ANLHTP coupled code. Next, to consider the reactivity feedback, coupling with PRAGMA, a GPU-based continuous-energy-Monte Carlo neutronics code was performed, and the multiphysics analysis capability of the OpenFOAM-ANLHTP-PRAGMA coupled code was confirmed through an analysis of the MegaPower reactor core. To reduce the temperature distribution within the monolith, the temperature distribution of the heat pipe sink was adjusted, and the reduced thermal stress of an HPR core was observed

Diagnostic potential of multimodal neuroimaging in posttraumatic stress disorder

<div><p>Despite accumulating evidence of physiological abnormalities related to posttraumatic stress disorder (PTSD), the current diagnostic criteria for PTSD still rely on clinical interviews. In this study, we investigated the diagnostic potential of multimodal neuroimaging for identifying posttraumatic symptom trajectory after trauma exposure. Thirty trauma-exposed individuals and 29 trauma-unexposed healthy individuals were followed up over a 5-year period. Three waves of assessments using multimodal neuroimaging, including structural magnetic resonance imaging (MRI) and diffusion-weighted MRI, were performed. Based on previous findings that the structural features of the fear circuitry-related brain regions may dynamically change during recovery from the trauma, we employed a machine learning approach to determine whether local, connectivity, and network features of brain regions of the fear circuitry including the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus could distinguish trauma-exposed individuals from trauma-unexposed individuals at each recovery stage. Significant improvement in PTSD symptoms was observed in 23%, 52%, and 88% of trauma-exposed individuals at 1.43, 2.68, and 3.91 years after the trauma, respectively. The structural features of the amygdala were found as major classifiers for discriminating trauma-exposed individuals from trauma-unexposed individuals at 1.43 years after the trauma, but these features were nearly normalized at later phases when most of the trauma-exposed individuals showed clinical improvement in PTSD symptoms. Additionally, the structural features of the OMPFC showed consistent predictive values throughout the recovery period. In conclusion, the current study provides a promising step forward in the development of a clinically applicable predictive model for diagnosing PTSD and predicting recovery from PTSD.</p></div

The relationships between candidate brain structural features and the group membership at each time point.

<p>The graph presents point-biserial correlation coefficients (<i>r</i>) between candidate features and the group membership at (A) time 1, (B) time 2, and (C) time 3 assessments. Error bars represent standard errors, which were calculated using 5,000 bootstraps. Asterisks in each graph indicate the first 10 brain structural features based on the rank of the absolute <i>r</i> values. Amy, amygdala; OMPFC, orbitofrontal and ventromedial prefrontal cortex; Hippo, hippocampus; Thal, thalamus.</p

Multimodal characteristics of the amygdala, OMPFC, hippocampus, insula, and thalamus assessed at each time point.

<p>A set of candidate brain structural features was derived from multimodal neuroimaging data analysis, which comprehensively characterized local, region-wise connectivity, pair-wise connectivity, and network features of the amygdala, orbitofrontal and ventromedial prefrontal cortex (OMPFC), hippocampus, insula, and thalamus.</p

Multimodal brain structural features and their contribution to the classification of the trauma-exposed group from the trauma-unexposed group at each time point.

<p>(A) Receiver operating characteristic curves of classification models at each time point are presented. Performance of each model for classifying the trauma-exposed group from the trauma-unexposed group as a function of the subset of candidate features was measured using the AUC. The model showing the best classification performance at each time point is plotted in orange color. (B) The best subset of multimodal features for classifying the trauma-exposed group from the trauma-unexposed group at each time point is presented. Classification performance measured using the AUC for individual features is plotted in radar graphs. AUC, area under a receiver operating characteristic curve; Amy, amygdala; OMPFC, orbitofrontal and ventromedial prefrontal cortex; Hippo, hippocampus; Thal, thalamus.</p

Demographic and clinical characteristics of participants.

<p>Demographic and clinical characteristics of participants.</p

Classification accuracy of individual brain structural features included in the best model for distinguishing trauma-exposed individuals from trauma-unexposed individuals at time 1, 2, and 3.

<p>Classification accuracy of individual brain structural features included in the best model for distinguishing trauma-exposed individuals from trauma-unexposed individuals at time 1, 2, and 3.</p