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It is pointed out that dealiasing of Doppler weather radar velocity measurements is identical to the problem of phase unwrapping encountered in radar and optical interferometry. A technique developed for phase unwrapping in interferometric synthetic aperture radar is described and successfully applied to weather radar velocity dealiasing.
A hybrid model for continuum phase-change problems is presented. The hybrid model accounts for flow in regions of concentrated mush, dilute mush, and single-phase liquid. Scale analysis shows that, in dilute mush, the Blake-Kozeny-Carman relation may lead to inaccuracy in the continuum formulation for certain values of the Rayleigh and Darcy numbers. The hybrid model uses arctangent switching functions to switch on the Darcy flow and variable viscosity terms depending on the local value of the fraction solid. Two-dimensional example calculations suggest that the hybrid model more accurately accounts for transport in the dilute mush region.
A unique numerical method has been developed to model the dynamic processes of bulk evaporation and condensation processes, associated with internal heat generation and natural convection under different gravity levels. The internal energy formulation, for the bulk liquid-vapor phase change problems in an encapsulated container, was employed. The equations, governing the conservation of mass, momentum and energy for both phases involved in phase change, were solved. The thermal performance of a multiphase uranium tetra-fluoride fuel element under zero gravity, micro-gravity and normal gravity conditions has been investigated. The modeling yielded results including the evolution of the bulk liquid-vapor phase change process, the evolution of the liquid-vapor interface, the formation and development of the liquid film covering the side wall surface, the temperature distribution and the convection flow field in the fuel element. The strong dependence of the thermal performance of such multiphase nuclear fuel cell on the gravity condition has been revealed. Under all three gravity conditions, 0-g, 10(exp -3)-g, and 1-g, the liquid film is formed and covers the entire side wall. The liquid film covering the side wall is more isothermalized at the wall surface, which can prevent the side wall from being over-heated. As the gravity increases, the liquid film is thinner, the temperature gradient is larger across the liquid film and smaller across the vapor phase. This investigation provides valuable information about the thermal performance of multi-phase nuclear fuel element for the potential space and ground applications.
Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.
Two-finite burns minimum fuel rendezvous problem using dual phase plane description, determining control law
Tension in Microgravity Modeled Microgravity poses many challenges to the designer of spacecraft tanks. Chief among these are the lack of phase separation and the need to supply vapor-free liquid or liquidfree vapor to the spacecraft processes that require fluid. One of the principal problems of phase separation is the creation of liquid jets. A jet can be created by liquid filling, settling of the fluid to one end of the tank, or even closing a valve to stop the liquid flow. Anyone who has seen a fountain knows that jets occur in normal gravity also. However, in normal gravity, the gravity controls and restricts the jet flow. In microgravity, with gravity largely absent, jets must be contained by surface tension forces. Recent NASA experiments in microgravity (Tank Pressure Control Experiment, TPCE, and Vented Tank Pressure Experiment, VTRE) resulted in a wealth of data about jet behavior in microgravity. VTRE was surprising in that, although it contained a complex geometry of baffles and vanes, the limit on liquid inflow was the emergence of a liquid jet from the top of the vane structure. Clearly understanding the restraint of liquid jets by surface tension is key to managing fluids in low gravity. To model this phenomenon, we need a numerical method that can track the fluid motion and the surface tension forces. The fluid motion is modeled with the Navier-Stokes equation formulated for low-speed incompressible flows. The quantities of velocity and pressure are placed on a staggered grid, with velocity being tracked at cell faces and pressure at cell centers. The free surface is tracked via the introduction of a color function that tracks liquid as 1/2 and gas as -1/2. A phase model developed by Jacqmin is used. This model converts the discrete surface tension force into a barrier function that peaks at the free surface and decays rapidly. Previous attempts at this formulation have been criticized for smearing the interface. However, by sharpening the phase function, double gridding the fluid function, and using a higher order solution for the fluid function, interface smearing is avoided. These equations can be rewritten as two coupled Poisson equations that also include the velocity. The method of solution is as follows: first, the phase equations are solved from this solution, a velocity field is generated, then a successive overrelaxation scheme is used to solve for a pressure field consistent with the velocity solution. After the code was implemented in axisymmetric form and verified by several test cases, the drop tower runs of Aydelott were modeled. The model handed the free-surface deformation quite nicely, even to the point of modeling geyser growth in the regime where the free surface was no longer restrained. A representative run is shown.
Microgravity poses many challenges to the designer of spacecraft tanks. Chief among these are the lack of phase separation and the need to supply vapor-free liquid or liquidfree vapor to the spacecraft processes that require fluid. One of the principal problems of phase separation is the creation of liquid jets. A jet can be created by liquid filling, settling of the fluid to one end of the tank, or even closing a valve to stop the liquid flow. Anyone who has seen a fountain knows that jets occur in normal gravity also. However, in normal gravity, the gravity controls and restricts the jet flow. In microgravity, with gravity largely absent, surface tension forces must contain jets. To model this phenomenon, a numerical method that tracks the fluid motion and the surface tension forces is required. Jacqmin has developed a phase model that converts the discrete surface tension force into a barrier function that peaks at the free surface and decays rapidly away. Previous attempts at this formulation were criticized for smearing the interface. This can be overcome by sharpening the phase function, double gridding the fluid function, and using a higher order solution for the fluid function. The solution of this equation can be rewritten as two coupled Poisson equations that also include the velocity. After the code was implemented in axisymmetric form and verified by several test cases at the NASA Glenn Research Center, the drop tower runs of Aydelott were modeled. Work last year with a laminar model was found to overpredict Aydelott's results, except at the lowest Reynolds number conditions of 400. This year, a simple turbulence model was implemented by adding a turbulent viscosity based on the mixing-length hypothesis and empirical measurements of previous works. Predictions made after this change was implemented have been much closer to experimentally observed flow patterns and geyser heights. Two model runs is shown. The first, without any turbulence correction, breaks through the free surface and strikes the far end of the tank. In the second, the turbulence spreads the jet momentum over more of the free surface, enabling the surface tension forces to turn the jet back into the bulk liquid. The model geyser height with the second model is 1.1 cm. This is quite close to the 1.5-cm geyser height measured by Aydelott.
Fiber optic delivery systems promise to extend the application of holography to severe environments by simplifying test configurations and permitting the laser to be remotely placed in a more benign location. However, the introduction of optical fiber leads to phase stability problems. Environmental effects cause the pathlengths of the fibers to change randomly, preventing the formation of stationary interference patterns which are required for holography. An active phase control system has been designed and used with an all-fiber optical system to stabilize the phase difference between light emitted from two fibers, and to step the phase difference by 90 deg without applying any constraints on the placement of the fibers. The accuracy of the phase steps is shown to be better than 0.02 deg., and a stable phase difference can be maintained for 30 min. This system can be applied to both conventional and electro-optic holography, as well as to any system where the maintenance of an accurate phase difference between two coherent beams is required.
Fiber optic delivery systems promise to extend the application of holography to severe environments by simplifying test configurations and permitting the laser to be remotely placed in a more benign location. However, the introduction of optical fiber leads to phase stability problems. Environmental effects cause the pathlengths of the fibers to change randomly, preventing the formation of stationary interference patterns which are required for holography. An active phase control system has been designed and used with an all-fiber optical system to stabilize the phase difference between light emitted from two fibers, and to step the phase difference by 90 deg without applying any constraints on the placement of the fibers. The accuracy of the phase steps is shown to be better than 0.02 deg., and a stable phase difference can be maintained for 30 min. This system can be applied to both conventional and electro-optic holography, as well as to any system where the maintenance of an accurate phase difference between two coherent beams is required.
Microgravity poses many challenges to the designer of spacecraft tanks. Chief among these are the lack of phase separation and the need to supply vapor-free liquid or liquid-free vapor to the spacecraft processes that require fluid. One of the principal problems of phase separation is the creation of liquid jets. A jet can be created by liquid filling, settling of the fluid to one end of the tank, or even closing a valve to stop the liquid flow. Anyone who has seen a fountain knows that jets occur in normal gravity also. However, in normal gravity, the gravity controls and restricts the jet flow. In microgravity, with gravity largely absent, surface tension forces must be used to contain jets. To model this phenomenon, a numerical method that tracks the fluid motion and the surface tension forces is required. Jacqmin has developed a phase model that converts the discrete surface tension force into a barrier function that peaks at the free surface and decays rapidly away. Previous attempts at this formulation were criticized for smearing the interface. This can be overcome by sharpening the phase function, double gridding the fluid function, and using a higher-order solution for the fluid function. The solution of this equation can be rewritten as two coupled Poisson equations that also include the velocity.
Drying of porous media is governed by a combination of evaporation and movement of the liquid phase within the porous structure. Contact angle hysteresis induced by surface roughness is shown to influence multi-phase flows, such as contact line motion of droplet, phase distribution during drainage and coffee ring formed after droplet drying in constant contact radius mode. However, the influence of contact angle hysteresis on liquid drying in porous media is still an unanswered question. Lattice Boltzmann model (LBM) is an advanced numerical approach increasingly used to study phase change problems including drying. In this paper, based on a geometric formulation scheme to prescribe contact angle, we implement a contact angle hysteresis model within the framework of a two-phase pseudopotential LBM. The capability and accuracy of prescribing and automatically measuring contact angles over a large range are tested and validated by simulating droplets sitting on flat and curved surfaces. Afterward, the proposed contact angle hysteresis model is validated by modeling droplet drying on flat and curved surfaces. Then, drying of two connected capillary tubes is studied, considering the influence of different contact angle hysteresis ranges on drying dynamics. Finally, the model is applied to study drying of a dual-porosity porous medium, where phase distribution and drying rate are compared with and without contact angle hysteresis. The proposed model is shown to be capable of dealing with different contact angle hysteresis ranges accurately and of capturing the physical mechanisms during drying in different porous media including flat and curved geometries.
The Voyager radio science experiments at Neptune required the creation of a ground data system array that includes a Deep Space Network complex, the Parkes Radio Observatory, and the Usuda deep space tracking station. The performance requirements were based on experience with the previous Voyager encounters, as well as the scientific goals at Neptune. The requirements were stricter than those of the Uranus encounter because of the need to avoid the phase-stability problems experienced during that encounter and because the spacecraft flyby was faster and closer to the planet than previous encounters. The primary requirement on the instrument was to recover the phase and amplitude of the S- and X-band (2.3 and 8.4 GHz) signals under the dynamic conditions encountered during the occultations. The primary receiver type for the measurements was open loop with high phase-noise and frequency stability performance. The receiver filter bandwidth was predetermined based on the spacecraft's trajectory and frequency uncertainties.
An enthalpy transforming scheme is proposed to convert the energy equation into a nonlinear equation with the enthalpy, E, being the single dependent variable. The existing control-volume finite-difference approach is modified so it can be applied to the numerical performance of Stefan problems. The model is tested by applying it to a three-dimensional freezing problem. The numerical results are in agreement with those existing in the literature. The model and its algorithm are further applied to a three-dimensional moving heat source problem showing that the methodology is capable of handling complicated phase-change problems with fixed grids.
Abstract Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.
A focus-diverse phase-retrieval algorithm has been shown to perform adequately for the purpose of image-based wavefront sensing when (1) broadband light (typically spanning the visible spectrum) is used in forming the images by use of an optical system under test and (2) the assumption of monochromaticity is applied to the broadband image data. Heretofore, it had been assumed that in order to obtain adequate performance, it is necessary to use narrowband or monochromatic light. Some background information, including definitions of terms and a brief description of pertinent aspects of image-based phase retrieval, is prerequisite to a meaningful summary of the present development. Phase retrieval is a general term used in optics to denote estimation of optical imperfections or aberrations of an optical system under test. The term image-based wavefront sensing refers to a general class of algorithms that recover optical phase information, and phase-retrieval algorithms constitute a subset of this class. In phase retrieval, one utilizes the measured response of the optical system under test to produce a phase estimate. The optical response of the system is defined as the image of a point-source object, which could be a star or a laboratory point source. The phase-retrieval problem is characterized as image-based in the sense that a charge-coupled-device camera, preferably of scientific imaging quality, is used to collect image data where the optical system would normally form an image. In a variant of phase retrieval, denoted phase-diverse phase retrieval [which can include focus-diverse phase retrieval (in which various defocus planes are used)], an additional known aberration (or an equivalent diversity function) is superimposed as an aid in estimating unknown aberrations by use of an image-based wavefront-sensing algorithm. Image-based phase-retrieval differs from such other wavefront-sensing methods, such as interferometry, shearing interferometry, curvature wavefront sensing, and Shack-Hartmann sensing, all of which entail disadvantages in comparison with image-based methods. The main disadvantages of these non-image based methods are complexity of test equipment and the need for a wavefront reference.
Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many algorithms are proposed, evaluated and applied to different areas. Many of these algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these algorithms, the phase neural operator (PhaseNO) shows promising results on regional data sets comparing to existing algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on four different local data sets and compare the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. By manually reviewing the newly detected events, we find that the PhaseNO model outperforms the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO’s behaviours when only using one station, as well as gradually increasing the number of stations in the seismic network to better understand its behaviour. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.
Recovering a signal from its Fourier magnitude is referred to as phase retrieval, which occurs in different fields of engineering and applied physics. This paper gives a new characterization of the phase retrieval problem. Particularly useful is the analysis revealing that the common gradient-based regularization does not restrict the set of solutions to a smaller set. Specifically focusing on binary signals, we show that a box relaxation is equivalent to the binary constraint for Fourier-types of phase retrieval. Here, we further prove that binary signals can be recovered uniquely up to trivial ambiguities under certain conditions. Finally, we use the characterization theorem to develop an efficient denoising algorithm.
Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.