Mechanical defradation of Emplacement Drifts at Yucca Mountain- A Modeling Case Study. Part I: Nonlithophysal Rock

This paper outlines rock mechanics investigations associated with mechanical degradation of planned emplacement drifts at Yucca Mountain, which is the designated site for the proposed U.S. high-level nuclear waste repository. The factors leading to drift degradation include stresses from the overburden, stresses induced by the heat released from the emplaced waste, stresses due to seismically related ground motions, and time-dependent strength degradation. The welded tuff emplacement horizon consists of two groups of rock with distinct engineering properties: nonlithophysal units and lithophysal units, based on the relative proportion of lithophysal cavities. The term 'lithophysal' refers to hollow, bubble like cavities in volcanic rock that are surrounded by a porous rim formed by fine-grained alkali feldspar, quartz, and other minerals. Lithophysae are typically a few centimeters to a few decimeters in diameter. Part I of the paper concentrates on the generally hard, strong, and fractured nonlithophysal rock. The degradation behavior of the tunnels in the nonlithophysal rock is controlled by the occurrence of keyblocks. A statistically equivalent fracture model was generated based on extensive underground fracture mapping data from the Exploratory Studies Facility at Yucca Mountain. Three-dimensional distinct block analyses, generated with the fracture patterns randomly selected from the fracture model, were developed with the consideration of in situ, thermal, and seismic loads. In this study, field data, laboratory data, and numerical analyses are well integrated to provide a solution for the unique problem of modeling drift degradation

3D-Printed PLA-Bioglass Scaffolds with Controllable Calcium Release and MSC Adhesion for Bone Tissue Engineering

Large bone defects are commonly treated by replacement with auto- and allografts, which have substantial drawbacks including limited supply, donor site morbidity, and possible tissue rejection. This study aimed to improve bone defect treatment using a custom-made filament for tissue engineering scaffolds. The filament consists of biodegradable polylactide acid (PLA) and a varying amount (up to 20%) of osteoconductive S53P4 bioglass. By employing an innovative, additive manufacturing technique, scaffolds with optimized physico-mechanical and biological properties were produced. The scaffolds feature adjustable macro- and microporosity (200–2000 µm) with adaptable mechanical properties (83–135 MPa). Additionally, controllable calcium release kinetics (0–0.25 nMol/µL after 24 h), tunable mesenchymal stem cell (MSC) adhesion potential (after 24 h by a factor of 14), and proliferation (after 168 h by a factor of 18) were attained. Microgrooves resulting from the 3D-printing process on the surface act as a nucleus for cell aggregation, thus being a potential cell niche for spheroid formation or possible cell guidance. The scaffold design with its adjustable biomechanics and the bioglass with its antimicrobial properties are of particular importance for the preclinical translation of the results. This study comprehensibly demonstrates the potential of a 3D-printed bioglass composite scaffold for the treatment of critical-sized bone defects

An overview of DLR compound rotorcraft aerodynamics and aeroacoustics activities within the CleanSky2 NACOR Project

The challenge of increasing range and speed of a rotorcraft is encountered in the scope of the European CleanSky2 “Fast Rotorcraft” project by Airbus Helicopters with the compound helicopter design RACER (RapidAndCostEfficientRotorcraft) for which the box wing and the tail parts designs are respectively protected by patent. This paper presents the DLR contributions to the RACER development. This includes the aerodynamic design of the wing and tail section as well as an overall assessment of performance and noise. In a first step the aerodynamic properties of the configuration are evaluated both isolated and with consideration of the main rotor and lateral rotor interferences by the use of actuator discs. In the second step, the investigated possibilities to improve the configurations performance are described. These include airfoil design for improved high lift performance of the wing and tail section, an optimization of the box wing circulation distribution on the upper and lower wing. Additionally, the intersection fairings were improved and the efficiency of the trim flaps was evaluated. In this regard, it could be determined for which cases an isolated approach is appropriate and when the rotor interference should be considered. At the end the evaluation of the aero acoustics of the configuration is conducted. The applied configuration shows good aerodynamic characteristics with some further cruise and off design optimization potential

Resilience and Protection of Health Care and Research Laboratory Workers During the SARS-CoV-2 Pandemic: Analysis and Case Study From an Austrian High Security Laboratory

The SARS-CoV-2 pandemic has highlighted the interdependency of healthcare systems and research organizations on manufacturers and suppliers of personnel protective equipment (PPE) and the need for well-trained personnel who can react quickly to changing working conditions. Reports on challenges faced by research laboratory workers (RLWs) are rare in contrast to the lived experience of hospital health care workers. We report on experiences gained by RLWs (e.g., molecular scientists, pathologists, autopsy assistants) who significantly contributed to combating the pandemic under particularly challenging conditions due to increased workload, sickness and interrupted PPE supply chains. RLWs perform a broad spectrum of work with SARS-CoV-2 such as autopsies, establishment of virus cultures and infection models, development and verification of diagnostics, performance of virus inactivation assays to investigate various antiviral agents including vaccines and evaluation of decontamination technologies in high containment biological laboratories (HCBL). Performance of autopsies and laboratory work increased substantially during the pandemic and thus led to highly demanding working conditions with working shifts of more than eight hours working in PPE that stressed individual limits and also the ergonomic and safety limits of PPE. We provide detailed insights into the challenges of the stressful daily laboratory routine since the pandemic began, lessons learned, and suggest solutions for better safety based on a case study of a newly established HCBL (i.e., BSL-3 laboratory) designed for autopsies and research laboratory work. Reduced personal risk, increased resilience, and stress resistance can be achieved by improved PPE components, better training, redundant safety measures, inculcating a culture of safety, and excellent teamwor

Drift Degradation Analysis

Degradation of underground openings as a function of time is a natural and expected occurrence for any subsurface excavation. Over time, changes occur to both the stress condition and the strength of the rock mass due to several interacting factors. Once the factors contributing to degradation are characterized, the effects of drift degradation can typically be mitigated through appropriate design and maintenance of the ground support system. However, for the emplacement drifts of the geologic repository at Yucca Mountain, it is necessary to characterize drift degradation over a 10,000-year period, which is well beyond the functional period of the ground support system. This document provides an analysis of the amount of drift degradation anticipated in repository emplacement drifts for discrete events and time increments extending throughout the 10,000-year regulatory period for postclosure performance. This revision of the drift degradation analysis was developed to support the license application and fulfill specific agreement items between the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE). The earlier versions of ''Drift Degradation Analysis'' (BSC 2001 [DIRS 156304]) relied primarily on the DRKBA numerical code, which provides for a probabilistic key-block assessment based on realistic fracture patterns determined from field mapping in the Exploratory Studies Facility (ESF) at Yucca Mountain. A key block is defined as a critical block in the surrounding rock mass of an excavation, which is removable and oriented in an unsafe manner such that it is likely to move into an opening unless support is provided. However, the use of the DRKBA code to determine potential rockfall data at the repository horizon during the postclosure period has several limitations: (1) The DRKBA code cannot explicitly apply dynamic loads due to seismic ground motion. (2) The DRKBA code cannot explicitly apply loads due to thermal stress. (3) The DRKBA code, which determines structurally controlled key-block failure, is not applicable for stress-controlled failure in the lithophysal units. To address these limitations, additional numerical codes have been included that can explicitly apply seismic and thermal loads, providing significant improvements to the analysis of drift degradation and extending the validity of drift degradation models

Método para la detección automatizada de defectos en la producción de láminas de acero alfajor mediante visión artificial y aprendizaje profundo

ilustraciones, graficasAnomaly detection is of great importance in the production of steel plates, in order to guarantee that the products are defect-free. Various deep-learning approaches for defect-detection in steel surfaces have emerged in the recent years, however, they are mainly limited to plain steel surfaces. Furthermore, deep-learning-based anomaly detection is still a challenging task if not enough training samples are available, which is often the case in real world scenarios. As for patterned steel plates, the availability anomalous samples is low, as productions are optimized to minimize the occurrence of defects. Hence, the main purpose of this work is the determination of a suitable deep learning-based method for the detection of surface anomalies in patterned steel plates. Several methods were trained and compared in terms of segmentation ability and classification accuracy. On the one hand, a convolutional neural network pretrained on artificial defects was adapted to images from a different production line, of which only anomaly-free data was available for training. On the other hand, an autoencoder was trained in a semi-supervised fashion to reconstruct anomaly-free images, in order to identify defective regions by measuring the reconstruction error. Moreover, an analysis of the frequency spectrum for images of patterned steel plates under the application of discrete fourier transform is provided. It was found out that a reconstructing autoencoder trained with a structural similarity loss provided the most accurate localizations of surface anomalies.La detección de anomalías es de gran importancia en la producción de placas de acero para garantizar que los productos no tengan defectos. En los últimos años han surgido varios métodos de aprendizaje profundo para la detección de defectos en superficies de acero limitándose principalmente a superficies de acero planas. Además, la detección de anomalías basada en el aprendizaje profundo sigue siendo una tarea difícil si no se dispone de suficientes muestras de entrenamiento, lo que suele ocurrir en escenarios del mundo real. En cuanto a las placas de acero texturizadas, como las láminas alfajor, la disponibilidad de muestras anómalas es baja, ya que las producciones están optimizadas para minimizar la aparición de defectos. Por lo tanto, el objetivo principal de este trabajo es la determinación de un método adecuado basado en el aprendizaje profundo, para la detección de anomalías superficiales en placas de acero texturizadas. Se entrenaron varios modelos, los que se compararon en términos de capacidad de segmentación y precisión de clasificación. Por un lado, se adaptó una red neuronal convolucional pre-entrenada en defectos artificiales a imágenes procedentes de una línea de producción diferente, de la que solo se disponía de datos libres de anomalías para su entrenamiento. Por otro lado, se entrenó un autocodificador de forma semi-supervisada para reconstruir imágenes libres de anomalías, con el fin de identificar las regiones defectuosas midiendo el error de reconstrucción. Además, se realiza un análisis del espectro de frecuencias para las imágenes de placas de acero texturizadas bajo la aplicación de la transformada discreta de Fourier. Se descubrió que un autocodificador de reconstrucción entrenado con una función de pérdida que mide la similitud estructural, proporciona las localizaciones más precisas de las anomalías superficiales. (Texto tomado de la fuente)MaestríaMaestría en Ingeniería - Ingeniería MecánicaAutomation, Control and Mechatronic