Einspieluntersuchungen von Faserverstärkte Metallmatrix-Kompositmaterialien für fusionsrelevante thermomechanische Bedingungen

The aim of thermonuclear fusion research is to confine a hot deuterium-tritium (D-T) plasma long enough so that fusion reactions between hydrogen isotope ions occur, leading to a commercial power generation. The successful operation of fusion devices depends on the development of plasma facing components (PFCs) which can withstand the surface heat loads of up to 20 MW/m2 under quasi-stationary conditions. Copper alloys have been considered as a structural material for the heat sink substrate of a PFC due to their excellent thermal conductivity. However, insufficient high temperature strength and large thermal expansion set the limitations to structural applications. Fiber-reinforced metal matrix composites (FRMMCs) can be a candidate for a structural material for the future PFCs due to the excellent high temperature strength. Since the FRMMCs of the PFCs are exposed to thermal and mechanical loads, the resulting stress fields in mesoscopic level is highly heterogeneous and often exceed the yield limit of the matrix. The shakedown limit was investigated as the safety criterion of the FRMMCs considering the fusion-relevant thermomechanical loads. In principle, it is possible to determine the macro- and mesoscopic stress states by means of finite element method (FEM), in which the real FRMMC architecture is modeled by direct meshing. Surely, this is not a practical approach since it requires a high computational cost. In this case, shakedown analysis can be an appropriate tool to estimate structural safety. The shakedown theorems were formulated by several researchers. Further, these could be combined with FEM and the large-scale nonlinear optimization program and applied to complex system. In this work, the shakedown formulation was extended to three-dimensional models. The developed computational algorithm was verified by comparing with literature results. The shakedown limits were determined for both lamina and laminate of FRMMC composite system. The results showed that shakedown limits were dependent on geometrical factor (fiber architecture and fiber volume fraction), loading direction, thermal loading, and hardening effect. They were discussed based on the maximum value and the distribution of von Mises stress. The stress and temperature loading paths of FRMMC components were determined in the fusion-relevant loading. The thermomechanical loading paths obtained were compared with the shakedown limits. The results showed that the loading paths in the real operation situation were only partly covered by the area of shakedown limit. It was interpreted that the FRMMC layers may undergo low cycle fatigue.Das Ziel der thermonuklearen Fusionsforschung ist es, ein heißes Deuterium-Tritium (D-T) Plasma lange genug einzuschließen, so dass Fusionsreaktionen zwischen Wasserstoffisotopen stattfinden, so dass eine kommerzielle Elektrizitätserzeugung ermöglicht wird. Der erfolgreiche Betrieb von Fusionsanlagen hängt von der Entwicklung plasmabelasteter Komponenten (PFCs) ab, die einer Wärmelast von bis zu 20 MW/m² auf ihrer Oberfläche unter quasistationären Bedingungen standhalten können. Als Strukturmaterial für die Wärmesenkenträger einer PFC werden Kupferlegierungen wegen ihrer exzellenten thermischen Leitfähigkeit in Betracht gezogen. Ungenügende Hochtemperaturfestigkeit und starke Wärmeausdehnung setzen jedoch Grenzen in der Strukturanwendung. Faserverstärkte Metallmatrix-Kompositmaterialien (FRMMCs) können wegen ihrer hervorragenden Hochtemperaturfestigkeit als Strukturmaterialien für künftige PFCs in Frage kommen. Da die FRMMCs der PFCs mit ihrer heterogenen Mikrostruktur thermischen und mechanischen Lasten ausgesetzt sind, sind die resultierenden Spannungsfelder auf mesoskopischer Ebene stark heterogen und überschreiten oft die Fließgrenze der Matrix. In dieser Arbeit wurden die Einspielgrenzen als Sicherheitskriterien der FRMMCs unter Berücksichtigung fusionsrelevanter thermomechanischer Lasten untersucht. Es ist prinzipiell möglich die makroskopischen und mesoskopischen Spannungszustände mit der Finite-Elemente-Methode (FEM) zu ermitteln, wenn der tatsächliche FRMMC-Aufbau durch direkte Vernetzung modelliert ist. Das ist natürlich keine praktische Näherung, da sie hohe Rechnerleistung erfordert. Alternativ kann eine Einspielanalyse ein geeignetes Werkzeug zur Abschätzung der strukturellen Sicherheit sein. Die Einspieltheoreme wurden von mehreren Forschern formuliert. Ferner können sie mit FEM und großskaligen nichtlinearen Optimierungsprogrammen kombiniert und auf komplexe Systeme angewandt werden. In dieser Arbeit wurde die Einspielformulierung auf dreidimensionale Modelle erweitert. Der entwickelte Rechenalgorithmus wurde durch den Vergleich mit Literaturergebnissen überprüft. Die Einspielgrenzen wurden sowohl für Einzelschichten als auch für Laminate von FRMMC-Kompositsystemen ermittelt. Die Ergebnisse zeigten, dass die Einspielgrenzen von geometrischen Faktoren (Faseraufbau und Faservolumenanteil), Belastungsrichtung, thermischer Last und Aufhärtungseffekten abhängen. Sie wurden unter Berücksichtigung der maximalen von-Mises-Spannungen und ihrer Verteilungen interpretiert. Spannungs- und Temperaturlastkurven der FRMMC-Komponenten wurden für fusionsrelevante Bedingungen bestimmt. Die gewonnenen thermomechanischen Lastkurven wurden mit den Einspielgrenzen verglichen. Die Lastkurven decken im realen Betrieb nur teilweise den Bereich der Einspielgrenzen ab. Dies lässt sich mit plastischer zyklischer Ermüdung der FRMMC-Schichten interpretieren

EXOT: Exit-aware Object Tracker for Safe Robotic Manipulation of Moving Object

Current robotic hand manipulation narrowly operates with objects in predictable positions in limited environments. Thus, when the location of the target object deviates severely from the expected location, a robot sometimes responds in an unexpected way, especially when it operates with a human. For safe robot operation, we propose the EXit-aware Object Tracker (EXOT) on a robot hand camera that recognizes an object's absence during manipulation. The robot decides whether to proceed by examining the tracker's bounding box output containing the target object. We adopt an out-of-distribution classifier for more accurate object recognition since trackers can mistrack a background as a target object. To the best of our knowledge, our method is the first approach of applying an out-of-distribution classification technique to a tracker output. We evaluate our method on the first-person video benchmark dataset, TREK-150, and on the custom dataset, RMOT-223, that we collect from the UR5e robot. Then we test our tracker on the UR5e robot in real-time with a conveyor-belt sushi task, to examine the tracker's ability to track target dishes and to determine the exit status. Our tracker shows 38% higher exit-aware performance than a baseline method. The dataset and the code will be released at https://github.com/hskAlena/EXOT.Comment: 2023 IEEE International Conference on Robotics and Automation (ICRA

Axial strain dependence of all-fiber acousto-optic tunable filters

We report the axial strain dependence of two types of all-fiber acousto-optic tunable filters based on flexural and torsional acoustic waves. Experimental observation of the resonant wavelength shift under applied axial strain could be explained by theoretical consideration of the combination of acoustic and optical effects. We discuss the possibility of suppressing the strain effect in the filters, or conversely, the possibility of using the strain dependence for wavelength tuning or strain sensors

A hybrid decision support model to discover informative knowledge in diagnosing acute appendicitis

BACKGROUND: The aim of this study is to develop a simple and reliable hybrid decision support model by combining statistical analysis and decision tree algorithms to ensure high accuracy of early diagnosis in patients with suspected acute appendicitis and to identify useful decision rules. METHODS: We enrolled 326 patients who attended an emergency medical center complaining mainly of acute abdominal pain. Statistical analysis approaches were used as a feature selection process in the design of decision support models, including the Chi-square test, Fisher's exact test, the Mann-Whitney U-test (p < 0.01), and Wald forward logistic regression (entry and removal criteria of 0.01 and 0.05, or 0.05 and 0.10, respectively). The final decision support models were constructed using the C5.0 decision tree algorithm of Clementine 12.0 after pre-processing. RESULTS: Of 55 variables, two subsets were found to be indispensable for early diagnostic knowledge discovery in acute appendicitis. The two subsets were as follows: (1) lymphocytes, urine glucose, total bilirubin, total amylase, chloride, red blood cell, neutrophils, eosinophils, white blood cell, complaints, basophils, glucose, monocytes, activated partial thromboplastin time, urine ketone, and direct bilirubin in the univariate analysis-based model; and (2) neutrophils, complaints, total bilirubin, urine glucose, and lipase in the multivariate analysis-based model. The experimental results showed that the model with univariate analysis (80.2%, 82.4%, 78.3%, 76.8%, 83.5%, and 80.3%) outperformed models using multivariate analysis (71.6%, 69.3%, 73.7%, 69.7%, 73.3%, and 71.5% with entry and removal criteria of 0.01 and 0.05; 73.5%, 66.0%, 80.0%, 74.3%, 72.9%, and 73.0% with entry and removal criteria of 0.05 and 0.10) in terms of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under ROC curve, during a 10-fold cross validation. A statistically significant difference was detected in the pairwise comparison of ROC curves (p < 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1). The larger induced decision model was more effective for identifying acute appendicitis in patients with acute abdominal pain, whereas the smaller induced decision tree was less accurate with the test data. CONCLUSIONS: The decision model developed in this study can be applied as an aid in the initial decision making of clinicians to increase vigilance in cases of suspected acute appendicitis

Pulse shape discrimination in an organic scintillation phoswich detector using machine learning techniques

We developed machine learning algorithms for distinguishing scintillation signals from a plastic-liquid coupled detector known as a phoswich. The challenge lies in discriminating signals from organic scintillators with similar shapes and short decay times. Using a single-readout phoswich detector, we successfully identified $\gamma$ radiation signals from two scintillating components. Our Boosted Decision Tree algorithm demonstrated a maximum discrimination power of 3.02 $\pm$ 0.85 standard deviation in the 950 keV region, providing an efficient solution for self-shielding and enhancing radiation detection capabilities.Comment: 11pages, 7 figure

Experimental Study of the Injection System for CO2 Geologic Storage Demonstration

AbstractThe worldwide issue of greenhouse gas reduction has recently drawn great attention to carbon capture and storage (CCS). Almost CCS studies have been focused in the capture technology of carbon dioxide and the geological investigation for underground storage. The study of mechanical injection system for carbon dioxide has not implemented nearly. We are intended to develop a ground system for underground injection of carbon dioxide. In this study, we made lab-scale underground injection system and implemented injection simulation test experimentally. The 10,000 ton/year pilot plant for geological storage of carbon dioxide will be designed on the base of these test results. Major components of the lab-scale underground injection system include a pressure pump and an in-line heater to bring liquid carbon dioxide into its supercritical state. Test results assure that this system readily achieves the designed injection pressure and temperature, showing satisfactory control performance