Progress on the ultrasonic testing and laser thermography techniques for NDT of tokamak plasma-facing components

During manufacturing and operation, different kinds of defects, e.g., delamination or surface cracks, may be generated in the plasma-facing components (PFCs) of a Tokamak device. To ensure the safety of the PFCs, various kinds of nondestructive testing (NDT) techniques are needed for different defect and failure mode. This paper gives a review of the recently developed ultrasonic testing (UT) and laser thermography methods for inspection of the delamination and surface cracks in PFCs. For monoblock W/Cu PFCs of divertor, the bonding quality at both W-Cu and Cu-CuCrZr interfaces was qualified by using UT with a focus probe during manufacturing. A noncontact, coupling-free and flexible ultrasonic scanning testing system with use of an electromagnetic acoustic transducer and a robotic inspection manipulator was introduced then for the in-vessel inspection of delamination defect in first wall (FW). A laser infrared thermography testing method is highlighted for the on-line inspection of delamination defect in FW through the vacuum vessel window of the Tokamak reactor. Finally, a new laser spot thermography method using laser spot array source was described for the online inspection of the surface cracks in FW

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code

Nf1 functions in sleep homeostasis neurons for circadian rhythms and memory

The sleep/wake cycle is thought to be controlled by two main processes: a circadian clock that regulates timing of arousal and a sleep homeostat that regulates duration of arousal according to sleep need. It is unknown though how these two processes work together: whether they work in parallel or functionally interact with each other. This gap in conceptual understanding is due in part to the dearth of information about the nature of the sleep homeostat as well as the neuroanatomy responsible for transmitting time-of-day information from the circadian clock network to the rest of the brain.In unpublished data, my lab has shown that a cluster of neurons in the antennal mechanosensory and motor center (AMMC) functions in both processes. In this thesis I describe the contribution that expression of the Neurofibromatosis 1 (Nf1) gene within these neurons makes to the sleep/wake cycle. By utilizing various Gal4>RNAi combinations, I found that Nf1 is required AMMC neurons for aversive associative memory and required in a GABAergic subset of these neurons for circadian locomotor rhythmicity. In genetic epistasis experiments I also found that deficits in both behaviors can be rescued by suppressing Insulin Receptor-Ras signaling. Collectively, my results illustrate a specific molecular pathway for the circadian and memory functions controlled by Nf1 and indicate that sleep need, circadian timing, and memory are linked by neurons in the AMMC

Nf1 functions in sleep homeostasis neurons for circadian rhythms and memory

The sleep/wake cycle is thought to be controlled by two main processes: a circadian clock that regulates timing of arousal and a sleep homeostat that regulates duration of arousal according to sleep need. It is unknown though how these two processes work together: whether they work in parallel or functionally interact with each other. This gap in conceptual understanding is due in part to the dearth of information about the nature of the sleep homeostat as well as the neuroanatomy responsible for transmitting time-of-day information from the circadian clock network to the rest of the brain.In unpublished data, my lab has shown that a cluster of neurons in the antennal mechanosensory and motor center (AMMC) functions in both processes. In this thesis I describe the contribution that expression of the Neurofibromatosis 1 (Nf1) gene within these neurons makes to the sleep/wake cycle. By utilizing various Gal4>RNAi combinations, I found that Nf1 is required AMMC neurons for aversive associative memory and required in a GABAergic subset of these neurons for circadian locomotor rhythmicity. In genetic epistasis experiments I also found that deficits in both behaviors can be rescued by suppressing Insulin Receptor-Ras signaling. Collectively, my results illustrate a specific molecular pathway for the circadian and memory functions controlled by Nf1 and indicate that sleep need, circadian timing, and memory are linked by neurons in the AMMC

Porous carbon material production from microwave-assisted pyrolysis of peanut shell

Abstract Due to the complex porous structure, biochar usually has good adsorption capacity. Therefore, compared with direct combustion, conversion of peanut shell into biochar by pyrolysis is considered to be an environmentally friendly and efficient method for agricultural solid waste disposal. In this study, biochar production from microwave-assisted pyrolysis of peanut shell was detailed. The yields, surface topographies, and pore structures (pore size distribution and micropore volume) of biochars prepared at different pyrolysis temperatures (700, 750, 800, 850, 900, and 950 °C), microwave powers (350, 400, 450, 500, and 550 W), and residence times (0.5, 1.0, 1.5, 2.0, and 3.0 h) were elaborated. The results showed that the biochar yield gradually decreased and finally stabilized to around 30% while the specific surface area improved within the range of 4.68–67.29 m2/g when the pyrolysis temperature, microwave power, or residence time increased. Biochar with micropore was first obtained at pyrolysis temperature of 800 °C, microwave power of 500 W, and residence time of 2.0 h. This study further proposed quantitative relationships between the pore structures of peanut shell based biochars and experimental conditions (pyrolysis temperature, microwave power and residence time). The results presented in this study can provide guidance for the reuse of peanut shell and the production of porous biochar. The peanut shell biochar prepared in this study can be used in soil remediation, air purification, liquid purification and other fields for its porous structural characteristics

Inputs to the sleep homeostat originate outside the brain.

The need to sleep is sensed and discharged in a poorly understood process that is homeostatically controlled over time. In flies, different contributions to this process have been attributed to peripheral ppk and central brain neurons, with the former serving as hypothetical inputs to the sleep homeostat and the latter reportedly serving as the homeostat itself. Here we re-evaluate these distinctions in light of new findings using female flies. First, activating neurons targeted by published ppk and brain drivers elicits similar phenotypes - namely sleep deprivation followed by rebound sleep. Second, inhibiting activity or synaptic output with one type of driver suppresses sleep homeostasis induced using the other type of driver. Third, drivers previously used to implicate central neurons in sleep homeostasis unexpectedly also label ppk neurons. Fourth, activating only this subset of co-labeled neurons is sufficient to elicit sleep homeostasis. Thus, many published contributions of central neurons to sleep homeostasis can be explained by previously unrecognized expression of brain drivers in peripheral ppk neurons, most likely those in the legs that promote walking. Lastly, we show that activation of certain non-ppk neurons can also induce sleep homeostasis. Notably, axons of these as well as ppk neurons terminate in the same ventral brain region, suggesting that a previously undefined neural circuit element of a sleep homeostat may lie nearby.SIGNIFICANCE STATEMENT:The biological need(s) that sleep fulfills are unknown, but they are reflected by an animals ability to compensate for prior sleep loss in a process called sleep homeostasis. Researchers have searched for the neural circuitry that comprises the sleep homeostat so that the information it conveys can shed light on the nature of sleep need. Here we demonstrate that neurons originating outside of the brain are responsible for phenotypes previously attributed to the proposed central brain sleep homeostat in flies. Our results support a revised neural circuit model for sensing and discharging sleep need in which peripheral inputs connect to a sleep homeostat through previously unrecognized neural circuit elements in the ventral brain

Trans-level multi-scale simulation of porous catalytic systems: Bridging reaction kinetics and reactor performance

Multi-scale porous structures inside and/or between the catalyst pellets or particles are found in many chemical processes, where strong coupling of reaction and transport results in complex apparent reaction kinetics influ-ential to the reactor performance. Traditional continuum-based porous media models and simulation methods can hardly describe such structures and their scale effects faithfully. A trans-level multi-scale discrete compu-tational framework is hence proposed to address this complexity and implemented for an olefin catalytic cracking (OCC) process. The apparent reaction kinetics at the REV (representative elementary volume) scale is obtained by hard-sphere pseudo-particle modeling (HS-PPM), and coupled with computational fluid dynamics / discrete element method (CFD-DEM) for the reactor-level hydrodynamics via a one-dimensional (1D) finite difference scheme for particle-level diffusion. The mesoscales of the REVs and the flow networks between the particles are thus covered by the framework, which are previously described by simple average quantities in the continuum methods. The reactant conversion rate and target product selectivity obtained agree well with experimental results, while a continuum approach may give significantly different and unreasonable results. The multi-scale method is, therefore, demonstrated to be necessary and effective for bridging the intrinsic reaction kinetics with the performance of porous catalytic reactors

Trans-level multi-scale simulation of porous catalytic systems: Bridging reaction kinetics and reactor performance

Multi-scale porous structures inside and/or between the catalyst pellets or particles are found in many chemical processes, where strong coupling of reaction and transport results in complex apparent reaction kinetics influ-ential to the reactor performance. Traditional continuum-based porous media models and simulation methods can hardly describe such structures and their scale effects faithfully. A trans-level multi-scale discrete compu-tational framework is hence proposed to address this complexity and implemented for an olefin catalytic cracking (OCC) process. The apparent reaction kinetics at the REV (representative elementary volume) scale is obtained by hard-sphere pseudo-particle modeling (HS-PPM), and coupled with computational fluid dynamics / discrete element method (CFD-DEM) for the reactor-level hydrodynamics via a one-dimensional (1D) finite difference scheme for particle-level diffusion. The mesoscales of the REVs and the flow networks between the particles are thus covered by the framework, which are previously described by simple average quantities in the continuum methods. The reactant conversion rate and target product selectivity obtained agree well with experimental results, while a continuum approach may give significantly different and unreasonable results. The multi-scale method is, therefore, demonstrated to be necessary and effective for bridging the intrinsic reaction kinetics with the performance of porous catalytic reactors

Effects of UAV flight height on biomass estimation of desert shrub communities

Accurate estimation of desert vegetation biomass is crucial for monitoring changes in carbon stocks and productivity status. Unmanned aerial vehicle (UAV) remote sensing allows large-scale biomass surveys at the individual or patch scale. However, since desert shrubs are short and sparse, the UAV-based techniques do not always accurately capture biomass-related indicators at any flight height. This study investigated the effects of flight height on above-ground biomass (AGB) estimation using UAV images of typical shrub communities (Reaumuria soongarica) captured at different heights (i.e., 30 m, 50 m, 70 m, 90 m, 110 m, 130 m, and 150 m) in desert-grassland ecosystems. Several structural indicators associated with shrub allometric growth were extracted for AGB modeling, including canopy area (horizontal properties), canopy height (vertical properties), and canopy volume. Results revealed that the values of canopy height and volume decreased with increasing flight height, which made the poor performance of AGB models based on these indicators worse. For example, the variance explained (VE) of the models based on the mean canopy height decreased from about 62% to -137%, while the root mean square error (RMSE) increased from about 39 g to 92 g. In contrast, the canopy area was less affected by flight height, maintaining stable AGB models with VE around 72% and RMSE at 33 g. Adjusting the coefficients of linear models based on canopy height and volume with flight height significantly improved their predictive performance, with VE between 54% and 77% and RMSE between 30 g and 43 g for the optimized models based on mean canopy height. Furthermore, a higher flight height (e.g., 90–110 m) could be chosen to enhance operational efficiency while ensuring the accuracy of biomass observation. Our study offers valuable insights and guidance for vegetation surveys and research in desert-grassland ecosystems