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Deterministic and Probabilistic Deep Learning in Predicting Reactor Physics of a Source-Driven Subcritical System
No full textSpatial, spectral, and modal effects prominent in subcritical regime led to challenges in evaluating integral physics parameters of source-driven subcritical systems. These effects cannot be natively accounted by standard experimental techniques based on Point Reactor Kinetics (PRK) primarily developed for critical systems. Deviation from PRK assumptions led to non-ideal response such as dependence to detector position of evaluated reactivity coefficient k-effective, presence of multiple-Alpha modes, and increasing reactivity coefficient k-effective bias as system becomes deeply subcritical. Capability to determine reactivity coefficient k-effective and other kinetics and subcritical parameters from system observables is paramount for ensuring nuclear safety by maintaining reactivity margins in Subcritical Assemblies (SCA), and safe approach to critical state in research reactors.
This dissertation introduces a data-driven methodology based on Deep Learning (DL) for predicting the reactor physics parameters of an SCA by mapping from directly measurable properties like core arrangement, reaction rates, and detector response. Deterministic and Probabilistic deep neural networks were configured through supervised learning approach using simulation data from physics-based neutronics calculations covering both stationary state in source-equilibrium, and in dynamic state in Pulsed Neutron Source experiment. Optimized hyperparameters, architecture priors, preprocessing technique, and input data modality were assessed by inference on a withheld Test set. Test metrics showed accurate DL predictions with coefficient for determination greater than or equal to 0.99 for reactivity coefficient k-effective, lambda effective, orbital angular momentum of the unpaired nucleon, ks, Alpha, and theoretical intensity-independent effective similarity parameter that surpassed baseline performance derived from statistical and criticality safety considerations with superior accuracy and scalability than traditional Machine Learning approaches.
Compared to standard reactivity measurement techniques, such as Amplified Source, Area-ratio, and Slope-fit methods, DL consistently provided accurate predictions well within plus-minus1$ of true values regardless of source location and subcriticality, eliminating the need for auxiliary corrective measures. Other physics parameters with no equivalent experimental techniques were also accurately predicted. This capability is unique from neutronics evaluations as measurable quantities are leveraged as predictors. These advantages extended to probabilistic DL capable of modelling aleatoric and epistemic uncertainties thus providing confidence bounds.
Overall, the novel application of DL in subcritical physics evaluation shows promise in an operational setting, addressing current analytical challenges which can enhance the safety and performance of SCAs, and emerging subcritical nuclear systems for waste transmutation
Empirical Visualization of Fish Schooling Dynamics via Particle Image Velocimetry (PIV) and Force Testing
Get PDFThe realm of fish schooling motions and their underlying mechanics have been extensively explored through myriad of experiments and numerical simulations. Despite successful applications of numerical simulations in recreating diverse schooling patterns and arrangements, physical experiments predominantly remain confined to bi-body or mono-body systems, barring tests involving real fish. With numerical simulations enabling multi-body investigations, it becomes crucial to amplify the scope of physical experiments to affirm the precision of multi-body numerical models. Our study elucidates the conception and fabrication of an autonomous robotic fish, utilizing soft robotics to emulate tail locomotion. The goal is to build on a pre-existing robotic model OpenFish pioneered at Delft University of Technology, Netherlands, with the intent to scrutinize its hydrodynamic efficiency via Particle Image Velocimetry (PIV) and drag testing within an aquatic tank. Our experimental approach will allow future researchers the capabilities to assess the efficiencies of different schooling configurations and their potential practical implementation within a swarm of robotic fish. Our study seeks to yield results of two such modified fish named FISHR positioned in straight-line formation within a water tank that simulates an opposing current, imitating the fish's aquatic motion
Dementia-Friendly Communities: The Role of Neighborhood Built Environments in Community-Dwelling Older Adults' Cognitive Health, Physical Activity, and Healthy Aging in Place
No full textThe rapid growth of aging population has brought numerous public health challenges about older persons living with dementia (PLwD). Although there has been growing interests worldwide in dementia-friendly community (DFC) and aging in place, few studies focused on the role of neighborhood built environments (NBE). This dissertation study, targeting community-dwelling older adults with and without dementia, aims to: 1) systematically synthesize the existing studies on relationships between NBE and cognition/dementia; 2) investigate COVID-19 impact on physical activity of people with memory problems (PwMP) and whether the impact varies by the diversity of neighborhood destinations within walking distance (NDIWD); and 3) learn experts��� perceptions on the importance of NBE in supporting aging in place for PLwD and other salient topics around DFC in the U.S. Three independent studies were conducted to address these three study aims.
The systematic review paper identified six NBE domains that have been linked to cognition or dementia risk but the findings on specific NBE features were mixed and sometimes contradictory across studies. The findings of this review called for more standardized and long-term measurement of NBE attributes and further investigation of the underlying mechanisms through which NBE affects cognition.
The second study, used retrospective caregiver-report survey data focusing on PwMP living in Texas communities to compare their changes in recreational walking and moderate-to-strenuous exercise (MSE) before and since COVID-19 and to what extent the diversity of NDIWD can explain the changes. The study results indicated that living in neighborhoods with high diversity of NDIWD tended to show less decrease in MSE than living in low diversity of NDIWD.
The third study, used an U.S.-wide expert survey data, identified 40 NBE characteristics that achieved consensus regarding their importance in supporting aging in place for PLwD. Experts commonly recognized the current low priority of implementing DFC in the U.S. and identified a number of barriers to implementing DFC in the U.S. Ten planning and design principles for DFC were formulated based on the findings of this study.
Overall, this research highlighted the importance of integrating NBE strategies in the development of DFC and called for more consideration of older adults especially PLwD in the urban planning and design process. The empirical evidence of this research can contribute to the literature and provide guidance for policy-makers, urban planners, designers, and professionals in health care and aging services working towards making the communities more dementia-friendly
Quantum Emitters and Their Applications in Cavity QED
Get PDFConfinement of light can increase the probability of coherent light-matter interaction up to a single quantum level. Here, we employ 1D photonic crystal cavity that allows very high Q/Vmode and high transmission at Zero-phonon line of a color center. We experimental measure 24,000 Q-factor in quadratic based tapering photonic crystal cavity design in silicon nitride (Si3N4) platform. We report the fabrication steps to precisely deposit the small nanodiamonds (size ��� 10 nm) on substrate. Germanium vacancy (GeV) center in a nanodiamond located on top of cavity site can allow us to achieve such Cavity QED (cQED) system in strong coupling regime.
Scientists have shown a great interest in metal halide perovskite nanocrystals because of their remarkable opto-electronic properties and excellent quantum yield, making them highly useful in various fields of science. However, the dark exciton state and its characteristics in such nanocrystals have yet to be fully understood, mainly due to the low emission compared to that of the bright exciton state. In this thesis, we report investigation of dark exciton state in CsPbX3 and FAPbBr3 strongly quantum confinement nanocrystals by direct measurement of intense emission from the long-lived dark exciton state. Furthermore, we have conducted a systematic study on the size dependency of the dark exciton state, highlighting the significance of quantum confinement in determining its properties and potential future applications.
Lastly, we present an integrated system of Quantum dots(QDs) and Si3N4 micro-ring cavity in which we observe enhancement of the spontaneous emission from QDs. We report Purcell enhancement of light-matter interaction in QDs-Si3N4 micro-ring cQED system at room temperature and at low temperature ��� 11K. This results promises building high quality on-chip photon source for wide range of applications in sciences
Hemp Disease Diagnosis Form
Get PDFRequest form for plant disease going to Texas High Plains Plant Disease Diagnostic La