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At least 109 records · Page 6

An early solar dynamo prediction: Cycle 23 is approximately cycle 22

In this paper, we briefly review the 'dynamo' and 'geomagnetic precursor' methods of long-term solar activity forecasting. These methods depend upon the most basic aspect of dynamo theory to predict future activity, future magnetic field arises directly from the magnification of pre-existing magnetic field. We then generalize the dynamo technique, allowing the method to be used at any phase of the solar cycle, through the development of the 'Solar Dynamo Amplitude' (SODA) index. This index is sensitive to the magnetic flux trapped within the Sun's convection zone but insensitive to the phase of the solar cycle. Since magnetic fields inside the Sun can become buoyant, one may think of the acronym SODA as describing the amount of buoyant flux. Using the present value of the SODA index, we estimate that the next cycle's smoothed peak activity will be about 210 +/- 30 solar flux units for the 10.7 cm radio flux and a sunspot number of 170 +/- 25. This suggests that solar cycle #23 will be large, comparable to cycle #22. The estimated peak is expected to occur near 1999.7 +/- 1 year. Since the current approach is novel (using data prior to solar minimum), these estimates may improve when the upcoming solar minimum is reached.

Schatten, Kenneth H.

An Amplitude-Based Estimation Method for International Space Station (ISS) Leak Detection and Localization Using Acoustic Sensor Networks

The development of a robust and efficient leak detection and localization system within a space station environment presents a unique challenge. A plausible approach includes the implementation of an acoustic sensor network system that can successfully detect the presence of a leak and determine the location of the leak source. Traditional acoustic detection and localization schemes rely on the phase and amplitude information collected by the sensor array system. Furthermore, the acoustic source signals are assumed to be airborne and far-field. Likewise, there are similar applications in sonar. In solids, there are specialized methods for locating events that are used in geology and in acoustic emission testing that involve sensor arrays and depend on a discernable phase front to the received signal. These methods are ineffective if applied to a sensor detection system within the space station environment. In the case of acoustic signal location, there are significant baffling and structural impediments to the sound path and the source could be in the near-field of a sensor in this particular setting.

Tian, Jialin

Development of a capacitance measurement for pulsed magnetic fields

Capacitance measurements are crucial for probing the electrical properties of materials. In this study, we develop and implement a capacitance measurement technique optimized for pulsed magnetic fields. Our approach employs an auto-balancing bridge method, leveraging a high-bandwidth transimpedance amplifier to mitigate parasitic contributions from coaxial cables. This technique enables precise capacitance measurements in rapidly changing magnetic fields, as demonstrated in experiments on the magnetoelectric material NiCo 2 TeO 6 . The results reveal strong magnetoelectric coupling, including a pronounced hysteresis in capacitance that coincides with magnetization measurements and an enhanced energy dissipation peak at high sweep rates. Compared to traditional LCR meter measurements in DC fields, our method exhibits excellent agreement while providing additional insight into field-induced phase transitions. This work establishes a robust methodology for capacitance measurements under extreme conditions and opens new opportunities for studying multiferroic and correlated electron systems under high magnetic fields.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND

Reporting Recommended Patch Density from Vehicle Panel Vibration Convergence Studies using both DAF and TBL Fits of the Spatial Correlation Function

Using the patch method to represent the continuous spatial correlation function of a phased pressure field over a structural surface is an approximation. The approximation approaches the continuous function as patches become smaller. Plotting comparisons of the approximation vs the continuous function may provide insight revealing: (1) For what patch size/density should the approximation be very good? (2) What the approximation looks like when it begins to break down? (3) What the approximation looks like when the patch size is grossly too large. Following these observations with a convergence study using one FEM may allow us to see the importance of patch density. We may develop insights that help us to predict sufficient patch density to provide adequate convergence for the intended purpose frequency range of interest

Smith, Andrew M.

Zernike Wavefront Sensor Modeling Development for LOWFS on WFIRST-AFTA

WFIRST-AFTA design makes use of an existing 2.4m telescope for direct imaging of exoplanets. To maintain the high contrast needed for the coronagraph, wavefront error (WFE) of the optical system needs to be continuously sensed and controlled. Low Order Wavefront Sensing (LOWFS) uses the rejected starlight from an immediate focal plane to sense wavefront changes (mostly thermally induced low order WFE) by combining the LOWFS mask (a phase plate located at the small center region with reflective layer) with the starlight rejection masks, i.e. Hybrid Lyot Coronagraph (HLC)'s occulter or Shaped Pupil Coronagraph (SPC)'s field stop. Zernike wavefront sensor (ZWFS) measures phase via the phase-contrast method and is known to be photon noise optimal for measuring low order aberrations. Recently, ZWFS was selected as the baseline LOWFS technology on WFIST/AFTA for its good sensitivity, accuracy, and its easy integration with the starlight rejection mask. In this paper, we review the theory of ZWFS operation, describe the ZWFS algorithm development, and summarize various numerical sensitivity studies on the sensor performance. In the end, the predicted sensor performance on SPC and HLC configurations are presented.

Zernike phase contrast

Self-supervised physics-informed generative networks for phase retrieval from a single X-ray hologram

X-ray phase contrast imaging significantly improves the visualization of structures with weak or uniform absorption, broadening its applications across a wide range of scientific disciplines. Propagation-based phase contrast is particularly suitable for time- or dose-critical in vivo/in situ/operando (tomography) experiments because it requires only a single intensity measurement. However, the phase information of the wave field is lost during the measurement and must be recovered. Conventional algebraic and iterative methods often rely on specific approximations or boundary conditions that may not be met by many samples or experimental setups. In addition, they require manual tuning of reconstruction parameters by experts, making them less adaptable for complex or variable conditions. Here we present a self-learning approach for solving the inverse problem of phase retrieval in the near-field regime of Fresnel theory using a single intensity measurement (hologram). A physics-informed generative adversarial network is employed to reconstruct both the phase and absorbance of the unpropagated wave field in the sample plane from a single hologram. Unlike most state-of-the-art deep learning approaches for phase retrieval, our approach does not require paired, unpaired, or simulated training data. This significantly broadens the applicability of our approach, as acquiring or generating suitable training data remains a major challenge due to the wide variability in sample types and experimental configurations. The algorithm demonstrates robust and consistent performance across diverse imaging conditions and sample types, delivering quantitative, high-quality reconstructions for both simulated data and experimental datasets acquired at beamline P05 at PETRA III (DESY, Hamburg), operated by Helmholtz-Zentrum Hereon. Furthermore, it enables the simultaneous retrieval of both phase and absorption information.

36 MATERIALS SCIENCE

Lidar measurements of cloud extinction coefficient distribution and its forward scattering phase function according to multiply scattered lidar returns

The study of the inversion algorithm for the single scatter lidar equation, for quantitative determination of cloud (or aerosol) optical properties, has received much attention over the last thirty years. Some of the difficulties associated with the solution of this equation are not yet solved. One problem is that a single scatter lidar equation has two unknowns. Because of this, the determination of the far-end boundary value, in the case of Klett's algorithm, is a problem if the atmosphere is optically inhomogeneous. Another difficulty concerns multiple scattering. There is a large error in the extinction distribution solution, in many cases, if only the single scattering component is considered, while neglecting the multiple scattering component. However, the use of multiple scattering in the remote sensing of aerosol or cloud optical properties is promising. In our early study, an inversion method for simultaneous determination of the cloud (or aerosol) Extinction Coefficient Distribution (ECD) and its Forward Scattering Phase Function (FSPF) was proposed according to multiply scattered lidar returns with two fields of view for the receiver. The method is based on a parameterized multiple scatter lidar equation. This paper is devoted to further numerical tests and an experimental study of lidar measurements of cloud ECD and FSPF using this method.

Qiu, Jinhuan

SULI Intern Final Report: Computationally Investigating Hydrogen Thermo-Diffusion in Yttrium Hydride Using Multiscale Methods

The renaissance of nuclear energy has arrived, heralding an age of abundant inexpensive clean energy, and renewed space exploration. In nuclear-powered spacecraft and microreactors, safety and size are of utmost importance. Yttrium Hydride (YHx) is being researched for its utility as a neutron moderator in nuclear reactors; the hydrogen in YHx slows down neutrons, enabling a continuous nuclear reaction in the reactor. This has the benefit of allowing reactors to be more safe, compact, and efficient. The goal of this effort is to computationally predict the coefficient of temperature-dependent hydrogen diffusion within YHx, the Soret coefficient. This parameter is essential for determining the safe operating modes of YHx moderators. Zirconium Hydride (ZrHx) is used in the Training, Research, Isotopes, General Atomics (TRIGA) reactor, is the reference material for these calculations. In this work, nanoscale atomic modeling in the Vienna Ab initio Simulation Package (VASP) is combined with the mesoscale finite element phase-field module in the Multiphysics Object-Oriented Simulation Environment (MOOSE); this culminates in a new multiscale computational method to simulate Soret diffusion of hydrogen in YHx. This data is useful for predicting experimental outcomes. This workflow involves convergence testing followed by static, Nudged Elastic Band (NEB), Quasi-Harmonic Approximation (QHA), and Molecular Dynamics (MD) calculations - linked with phase field simulation. NEB simulates hydrogen migration, while QHA and MD predict temperature-dependent properties. The static calculations align with literature, and preliminary NEB and QHA calculations yield accurate results. Once the atomic calculations are complete, we will incorporate Electron Backscatter Diffraction (EBSD) images and VASP-generated parameters into the phase field module to simulate intra- and intergranular transport of hydrogen in ZrHx and YHx. Future research will extend our approach to fuel-moderator materials systems such as Uranium-Yttrium Hydride (U-YHx). This work contributes to the development of advanced nuclear energy solutions for space travel.

36 - MATERIALS SCIENCE

Phase Calibration of Antenna Arrays Aimed at Spacecraft

A document describes a method of calibrating phase differences among ground antennas in an array so that the maximum-intensity direction of the far-field interference pattern of the array coincides with the direction for aiming the antennas to enable radio communication with a distant spacecraft. The method pertains to an array typically comprising between two and four 34-m (or similar size) antennas. The antennas are first calibrated pair-wise to maximize the uplink power received at a different spacecraft that is close enough for communication via a single ground antenna. In the calibration procedure, the phase of the signal transmitted by one of the antennas is ramped through a complete cycle, thereby causing the interference pattern to sweep over this closer spacecraft and guaranteeing that, at some point during the sweep, this spacecraft is illuminated at maximum intensity. The varying received uplink power is measured by a receiver in the closer spacecraft and the measurement data are transmitted to a ground station to enable determination of the optimum phase adjustment for the direction to the closer spacecraft. This adjustment is then translated to the look direction of the distant spacecraft, which could not be reached effectively using only one antenna.

Vilnrotter, Victor

Holographic matched filter for full-field in-line signal processing of optical-fiber sensor outputs

A holographic matched filter is used to measure the changes in the output of a dual-mode fiber undergoing axial strain. The hologram is formed by interfering collimated light from a single-mode reference fiber and an unstrained dual-mode sensor fiber on a small piece of holographic material. When the hologram is illuminated by the strained sensor fiber, the cross-correlation field comparing the dual-mode output and its previous state, as recorded in the hologram, is collected, focused into a pick-up fiber, and sent to a photodiode. This arrangement allows for a compact, in-line method for full-field processing of the strain-induced changes in phase and amplitude in the sensor fiber. Other uses of such fiber-based correlators are suggested.

Indebetouw, Guy

Analysis of a Lunar Base Electrostatic Radiation Shield Concept

Space weather can be defined as the total ensemble of radiation in space, as well as on the surface of moons and asteroids. It consists of electromagnetic, charged-particle, and neutral particle radiation. The fundamental goal behind this NIAC Phase I research is to investigate methods of generating a static electric-field potential phi(x, y, z) in the volume above and around a "safe" or protected area on the lunar surface so that trajectories of harmful charged particle radiation are modified (deflected or reflected), thus creating a shadow over that region. Since the charged particles are not neutralized but merely redirected, there will be areas outside of the shadowed protected region that will have a higher flux concentration of radiation. One of the fundamental limitations of the static electric (electrostatic)-field approach to radiation shielding is that complete shadowing is accomplished only by complete reflection, which can only occur for shield voltages greater than or equal to the kinetic energy (in electron volts) of the incoming charged particles. Just as habitats on Earth are protected from severe weather events and conditions, such as extreme temperatures, high winds, and UV radiation, using multiple methods of shielding protection from severe space weather will undoubtedly require multiple strategies. The electrostatic shield concept may be one of many methods employed to protect astronaut habitats on the lunar surface from some of the harmful effects of space weather.

Buhler, Charles R.

Reductive quantum phase estimation

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and molecular physics and quantum phase estimation (QPE) in quantum computing. We demonstrate that these canonical examples are instances of a larger class of phase estimation protocols, which we call reductive quantum phase estimation (RQPE) circuits. Here, we present an explicit algorithm that allows one to create an RQPE circuit. This circuit distinguishes an arbitrary set of phases with a smaller number of qubits and unitary applications, thereby solving a general class of quantum hypothesis testing to which RI and QPE belong. We further demonstrate a tradeoff between measurement precision and phase distinguishability, which allows one to tune the circuit to be optimal for a specific application. Published by the American Physical Society 2024

Papadopoulos, Nicholas J. C. (ORCID:00000002635700

Multispacecraft observations of microscale fluctuations in the solar wind

Data from the Explorer 33 and 35 satellites were used to study Alfvenic fluctuations (i.e., fluctuations with a high correlation between perturbation in velocity and magnetic field) in the solar wind. It was found that the average minimum variance method does not give a good estimate of the phase front direction of these fluctuations. There is a distinct class of Alfvenic fluctuations with phase planes nearly parallel to the mean field B sub 0. The characteristic length of the phase fronts of the fluctuations is probably significantly greater than 50 R-E. It is concluded that since tangential discontinuities and possibly other types of static structures appear in Alfvenic fluctuations, these fluctuations are not solely Alfven waves.

Denskat, K. U.

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations

Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design.

copolymers

Solution of mixed convection heat transfer from isothermal in-line fins

Transient and steady state combined natural and forced convective flows over two in-line finite thickness fins (louvers) in a vertical channel are numerically solved using two methods. The first method of solution is based on the 'Simple Arbitrary Lagrangian Eulerian' (SALE) technique which incorporates mainly two computational phases: (1) a Lagrangian phase in which the velocity field is updated by the effects of all forces, and (2) an Eulerian phase that executes all advective fluxes of mass, momentum and energy. The second method of solution uses the finite element code entitled FIDAP. In the first part, comparison of the results by FIDAP, SALE, and available experimental work were done and discussed for steady state forced convection over louvered fins. Good agreements were deduced between the three sets of results especially for the flow over a single fin. In the second part and in the absence of experimental literature, the numerical predictions were extended to the transient transports and to the opposing flow where pressure drop is reversed. Results are presented and discussed for heat transfer and pressure drop in assisting and opposing mixed convection flows.

Khalilollahi, Amir

A theoretical investigation of the sound radiation fields associated with a Bellmouth inlet

Analytical results are obtained by numerical integration of a cylindrically symmetric integral representation of the external solutions of the Helmholtz equation. The accuracy of this method is checked by comparisons of computed results with 'exact' solutions generated by the point source method. In all cases, the average error for the amplitude and phase of the points calculated in the field is found to be less than ten percent. Theoretical studies which model experiments run for the NASA Langely Bellmouth inlet configuration are presented and comparisons are made with the experimental results. In all cases very good agreement is obtained between the experimental and theoretically calculated values.

Meyer, W. L.

SAIL Field Campaign X-Band Precipitation Radar Seasonal Surface Hydrometeor Phase Classification Value-Added Product Report

The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign generated an unprecedented, multisensory record of cold-season precipitation in the Upper Colorado River Basin (UCRB). To complement the Surface Quantitative Precipitation Estimation (SQUIRE) snow water equivalent (SWE) product derived from the Colorado State University (CSU) X-band radar, a hydrometeor phase classification data set was developed to systematically identify phase of the near-surface precipitation. The algorithm uses Corrected Moments in Antenna Coordinates (CMAC) fields with semi-supervised classification (SSC) and fuzzy-logic hydrometeor classification (FHC) methods and maps hydrometeor classes to a compact set of phase classes suitable for Quantitative Precipitation Estimation (QPE) and hydrologic applications. We used SQUIRE grid projections to mitigate beam-blockage issues by selecting the near-surface gates for each beam as well as to make it directly usable for the QPE products.

54 ENVIRONMENTAL SCIENCES

Graph neural networks for mechanical property prediction of 2D fiber composites

This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Element (RVE) size limit and, consequently, are notably anisotropic. Hence, training only the isotropic limit properties may not be acceptable. Another aspect is the need to normalize elastic stiffness values for ML, especially when high elastic contrast ratios are encountered between composite phases or in the material set. We introduce a normalization technique based on the mean-field method (MFM) to handle such high contrast ratios and train for the entire stiffness tensor. We show that the proposed GNN approaches exhibit high accuracy and efficiency compared to traditional methods and convolutional neural networks, utilizing unstructured graphs constructed from microstructure topology. Our model successfully predicts the stiffness tensor, peak strength under bulk damage, and brittle fracture initiation strength across diverse microstructure configurations while maintaining high accuracy even for extreme material contrasts and volume fractions. We also present a method to improve prediction accuracy for small dataset sizes using Voronoi partitioning.

Brittle strength