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Magnetospheric diurnal electron concentration variation from Faraday polarization rotation and differential Doppler frequency measurements of lunar radar echoes
Recent results from the Apollo seismic network suggest that primitive differentiation occurred in the outer shell of the moon to a depth of approximately 300 km; and the central region of the moon is presently molten to a radius of between 200 and 300 km. If early melting to a depth of 300 to 400 km was a consequence of accretional energy, very short accretion times are required. The best model for the zone of original differentiation appears to be a crust 40 to 80 km thick, ranging in composition from anorthositic gabbro to gabbro; overlying an ultramafic cumulate (olivine-pyroxene) about 250 km thick. The best candidate for the molten core appears to be iron or iron sulphide. A new class of seismic signals has recently been identified that may correspond to shallow moonquakes. These are rare, but much more energetic than the more numerous, deep moonquakes.
Recent results from the Apollo Seismic Network suggest that primitive differentiation occurred in the outer shell of the moon to a depth of approximately 300 km and the central region of the moon is presently molten to a radius of between 200 and 300 km. If early melting to a depth of 300 to 400 km was a consequence of accretional energy, very short accretion times are required. It was shown that the best model for the zone of original differentiation is a crust 40 to 80 km thick, ranging in composition from anorthositic gabbro to gabbro, and overlying an ultramafic cumulate about 250 km thick. The best candidate for the molten core appears to be iron or iron sulphide. A new class of seismic signals recently were identified that may correspond to shallow moonquakes. These are rare, but much more energetic than the more numerous, deep moonquakes.
The development and first observations of the partial-reflection drifts experiment at Urbana, Illinois (40 N) are described. The winds data from the drifts experiment are compared with electron concentration data obtained by the differential-absorption technique to study the possible meteorological causes of the winter anomaly in the mesosphere at midlatitudes. winds data obtained by the meteor-radar experiment at Urbana are also compared with electron concentration data measured at Urban. A significant correlation is shown is both cases between southward winds and increasing electron concentration measured at the same location during winter. The possibility of stratospheric/mesospheric coupling is investigated by comparing satellite-measured 0.4 mbar geopotential data with mesospheric electron concentration data. No significant coupling was observed. The winds measured at Saskatoon, Saskatchewan (52 N) are compared with the electron concentrations measured at Urban, yielding constant fixed relationship, but significant correlations for short segments of the winter. A significant coherence is observed at discrete frequencies during segments of the winter.
A joint field experiment between NASA and NOAA was conducted during August to September 1987 to obtain in situ and remote measurements of key gases and aerosols from aircraft platforms during the formation of the ozone (O3) hole over Antarctica. The ER-2 (advanced U-2) and DC-8 aircraft from the NASA Ames Research Center were used in this field experiment. The NASA Langley Research Center's airborne differential absorption lidar (DIAL) system was operated from the DC-8 to obtain profiles of O3 and polar stratospheric clouds in the lower stratosphere during long-range flights over Antarctica from August 28 to September 29, 1987. The airborne DIAL system was configured to transmit simultaneously four laser wavelengths (301, 311, 622, and 1064 nm) above the DC-8 for DIAL measurements of O3 profiles between 11 to 20 km ASL (geometric altitude above sea level) and multiple wavelength aerosol backscatter measurements between 11 to 24 km ASL. A total of 13 DC-8 flights were made over Antarctica with 2 flights reaching the South Pole. Polar stratospheric clouds (PSC's) were detected in multiple thin layers in the 11 to 21 km ASL altitude range with each layer having a typical thickness of less than 1 km. Two types of PSC's were found based on aerosol backscattering ratios: predominantly water ice clouds (type 2) and clouds with scattering characteristics consistent with binary solid nitric acid/water clouds (type 1). Large-scale cross sections of O3 distributions were obtained. The data provides additional information about a potentially important transport mechanism that may influence the O3 budget inside the vortex. There is also some evidence that strong low pressure systems in the troposphere are associated with regions of lower stratospheric O3. This paper discusses the spatial and temporal variations of O3 inside and outside the polar vortex region during the development of the O3 hole and relates these data to other measurements obtained during this field experiment.
Improving response time of indirect room-size calorimeters is still an outstanding problem in metabolic research. Accurate estimates of instantaneous rates of gaseous exchange require numerical differentiation of measured gaseousgas concentrations. We propose a new method to estimate the instantaneous gaseousgas exchange rates in indirect calorimetry. In contrast to the previously developed techniques, the method addresses the problem of differentiation of gaseous concentrations as an ill-posed problem. By applying the method of regularization, the problem of differentiation is converted into a well-posed problem resulting in smooth and consistent gaseous exchange rates. The validity of the method is tested on a large dataset of calorimeter experiments which included 313 human experiments along with 231 alcohol combustion experiments. It is demonstrated that the method is able to reliably differentiate between the “unphysiological” process of alcohol combustion and physiological variations produced by human metabolism. The method also allowed unraveling the previously unreported relative kinetics of O2 consumption and Respiratory Quotient (RQ) in humans. It was found that the kinetics of oxidative fuel selection lags behind the energy expenditure in humans exhibiting some sort of oxidative inertia. The time lag varies from 2-3 min up to 30 min, depending on particular individual. No such lag was found in alcohol combustion experiments. In addition to the relative kinetics of substrate oxidation, two statistical indexes reflecting variability of minute-by-minute RQ were estimated. The indexes were the RQ’s standard deviation and RQ’s first-order derivative. Both indexes showed statistically significant difference between human experiments and alcohol combustion experiments. We conclude that the proposed method can consistently extract physiologically-relevant information from noisy calorimetry data and the aforesaid information can provide additional insights into the mechanism of metabolic fuel selection in humans.
Improving response time of indirect room-size calorimeters is still an outstanding problem in metabolic research. Accurate estimates of instantaneous rates of gaseous exchange require numerical differentiation of measured gaseousgas concentrations. We propose a new method to estimate the instantaneous gaseousgas exchange rates in indirect calorimetry. In contrast to the previously developed techniques, the method addresses the problem of differentiation of gaseous concentrations as an ill-posed problem. By applying the method of regularization, the problem of differentiation is converted into a well-posed problem resulting in smooth and consistent gaseous exchange rates. The validity of the method is tested on a large dataset of calorimeter experiments which included 313 human experiments along with 231 alcohol combustion experiments. It is demonstrated that the method is able to reliably differentiate between the “unphysiological” process of alcohol combustion and physiological variations produced by human metabolism. The method also allowed unraveling the previously unreported relative kinetics of O2 consumption and Respiratory Quotient (RQ) in humans. It was found that the kinetics of oxidative fuel selection lags behind the energy expenditure in humans exhibiting some sort of oxidative inertia. The time lag varies from 2-3 min up to 30 min, depending on particular individual. No such lag was found in alcohol combustion experiments. In addition to the relative kinetics of substrate oxidation, two statistical indexes reflecting variability of minute-by-minute RQ were estimated. The indexes were the RQ’s standard deviation and RQ’s first-order derivative. Both indexes showed statistically significant difference between human experiments and alcohol combustion experiments. We conclude that the proposed method can consistently extract physiologically-relevant information from noisy calorimetry data and the aforesaid information can provide additional insights into the mechanism of metabolic fuel selection in humans.
This presentation covers motivation for the Experiments Performed. Secondly, it touches on upgrades to the Experimental Apparatus for Measurements. Thirdly, it presents quasi-Differential Experimental Methodology a) Pulse Shape Discrimination b) Validation Measurement of Carbon. The presentation concludes with Preliminary Results and Future Studies.
Understanding irradiation creep of nuclear fuel cladding is important to properly size the initial fuel-cladding gap and understand when pellet-cladding contact is expected to occur due to a combination of fuel swelling and cladding creep-down. Irradiation creep also plays a role in relaxing stresses that develop in-pile. Silicon carbide fiber–reinforced silicon carbide matrix (SiC/SiC) composites are the leading long-term accident-tolerant fuel cladding concept for light-water reactors (LWRs). Although some limited data are available regarding irradiation creep of the individual constituents (fibers, matrix), data regarding irradiation creep of SiC/SiC composites are currently insufficient. Additional data regarding irradiation creep compliance and the rupture lifetime (combination of creep and slow crack growth) are needed to understand material limitations. This work describes the design and development of two irradiation vehicles that are being pursued for testing SiC/SiC concepts in the High Flux Isotope Reactor (HFIR). The first is a passive experiment, referred to as the PRECISE experiment, that leverages the constant coolant pressure of HFIR to compress a metallic bellows and provide a well-characterized load to drive creep in a SiC/SiC dog bone specimen. The total creep strain would be quantified post-irradiation by measuring dimensional changes of the specimen length as well as local dimensional changes within the gauge region. Non-stressed specimens would also be irradiated under the same conditions to provide an indication of dimensional changes due to radiation-induced swelling in the absence of creep. A second, more complex experiment, referred to as the INSITE experiment, is being designed in parallel that would use pneumatics to pressurize a metal bellows and linear variable differential transformers (LVDTs) to measure the specimen displacement in situ during irradiation. Such an experiment would provide significantly more data regarding the evolution of the creep compliance as a function of dose and applied stress within a single experiment but would require significantly more development time and cost to execute. The primary concern with the INSITE experiment is the accuracy, reliability, and expected lifetime of the LVDTs during irradiation at elevated temperatures. Efforts are being made to adjust the experiment design and operating procedure to limit LVDT temperatures and mitigate or otherwise compensate for uncertainties due to factors such as temperature fluctuations, creep in the surrounding structural materials, and drift of the LVDTs. This work describes the experiment designs, thermal and structural analysis that were performed to ensure that the desired temperature and stress conditions can be achieved, some initial sensitivity analyses to predict the evolution of the radiation-induced specimen displacements, and potential sources of uncertainty in the measurements. Out-of-pile testing is being performed in parallel to confirm that the test trains achieve the expected stress states in the specimens and do not result in prohibitive stress concentrators (e.g., in the grip regions) that might risk pre-mature failure. The PRECISE experiments are proceeding toward fabrication and assembly with HFIR insertion planned during fiscal year 2026. The INSITE experiment is progressing toward out-of-pile demonstrations, which will provide more conclusive evidence regarding the feasibility of executing these tests in HFIR or whether alternative displacement monitoring techniques may need to be considered.
The proton-proton elastic differential cross section dσ/dt has been measured by the TOTEM experiment at √ s = 2.76 TeV energy with β* = 11 m beam optics. The Roman Pots were inserted to 13 times the transverse beam size from the beam, which allowed to measure the differential cross-section of elastic scattering in a range of the squared four-momentum transfer (|t|) from 0.36 GeV 2 to 0.74 GeV 2 . The differential cross-section can be described with an exponential in the |t|-range between 0.36 and 0.54 GeV 2 , followed by a diffractive minimum (dip) at |t dip | = (0.61 ± 0.03) GeV 2 and a subsequent maximum (bump). The ratio of the dσ/dt at the bump and at the dip is 1.7±0.2. When compared to the proton–antiproton measurement of the D0 experiment at √ s = 1.96 TeV, a significant difference can be observed. Under the condition that the effects due to the energy difference between TOTEM and D0 can be neglected, the result provides evidence for a colourless C-odd three-gluon bound state exchange in the t-channel of the proton-proton and proton–antiproton elastic scattering.
Microalgae biofilms have been demonstrated to recover nutrients from wastewater and serve as biomass feedstock for bioproducts. However, there is a need to develop a platform to quantitatively describe microalgae biofilm production, which can provide guidance and insights for improving biomass areal productivity and nutrient uptake efficiency. Here, this paper proposes a unified experimental and theoretical framework to investigate algae biofilm growth on a rotating algae biofilm reactor (RABR). Experimental laboratory setups are used to conduct controlled experiments on testing environmental and operational factors for RABRs. We propose a differential–integral equation‐based mathematical model for microalgae biofilm cultivation guided by laboratory experimental findings. The predictive mathematical model development is coordinated with laboratory experiments of biofilm areal productivity associated with ammonia and inorganic phosphorus uptake by RABRs. The unified experimental and theoretical tool is used to investigate the effects of RABR rotating velocity, duty cycle (DC), and light intensity on algae biofilm growth, areal productivity, nutrient uptake efficiency, and energy efficiency in wastewater treatment. Our framework indicates that maintaining a reasonable light intensity range improves biomass areal productivity and nutrient uptake efficiency. Our framework also indicates that faster RABR rotation benefits biomass areal productivity. However, maximizing the nutrient uptake efficiency requires a reasonably low RABR rotating speed. Energy efficiency is strongly correlated with RABR rotating speed and DC.
Understanding turbulence in the divertor leg of tokamaks is essential to predicting the heat deposition profile on the divertor plate. This in turn is important for evaluating advanced divertor configurations, such as the super-X divertor. Within the divertor region, the private flux region is of interest because it is relatively unaffected by turbulence extending from the outboard midplane, so instabilities in this region could have a particularly pronounced effect on transport. These instabilities are modeled using the Arbitrary Topology Equation Reader (ArbiTER) eigenvalue code. Eigenmodes are examined further by comparing physics models to determine the fundamental mechanisms behind their formation, and quantifying the effect of individual terms. This analysis is conducted on both conventional and super-X divertors to compare these effects. Here, the resulting analysis reveals the presence of a geodesic curvature driven instability that is significantly more pronounced in the super-X configuration.
High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.
The advent of the Gaia era has led to potentially revolutionary understanding of dark matter (DM) dynamics in our galaxy, which has important consequences for direct detection (DD) experiments. In this paper, we study how the empirical DM velocity distribution inferred from Gaia-sausage, a dominant substructure in the solar neighborhood, affects the interpretation of DD data. We survey different classes of operators in the nonrelativistic effective field theory that could arise from several relativistic benchmark models and emphasize that the Gaia velocity distribution could modify both the total number of events as well as the shape of the differential recoil spectra, the two primary observables in DD experiments. Employing the Euclideanized signal method, we investigate the effects of the Gaia distribution on reconstructing DM model parameters and identifying the correct DM model given a positive signal at future DD experiments. We find that for light DM with mass ~10 GeV, the Gaia distribution poses an additional challenge for characterizing DM interactions with ordinary matter, which may be addressed by combining complementary DD experiments with different targets and lowering the detection threshold. Meanwhile, for heavy DM with mass above ~30 GeV , depending on the type of DM model, there could be a (moderate) improvement in the sensitivity at future DD experiments.
We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.
Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO 2 plume predictions near, and far away, from the monitoring wells.
Conventional Bayesian optimization methods implicitly assume that the data and model being optimized are “perfect.” This assumption leads to inaccurate posterior probability distribution functions (PDFs) when applied to “imperfect” data or models. The new Bayesian optimization framework presented in this report provides a way to parameterize the effect of imperfections usually encountered in a prior PDF of generalized data or a model on the posterior PDF. The effects of imperfections are parameterized by a set of constraints imposed on the posterior expectation values of deviations between the data and the model and on their covariance matrix elements. A particular set of values for these constraints conveys an evaluator’s best estimate of the effect of imperfections on the corresponding posterior expectation values. When a prior PDF of generalized data is assumed to be normal, an expression for a posterior PDF satisfying an arbitrary set of constraints is derived analytically for linear models. An analogous iterative algorithm is given for nonlinear models. The corresponding posterior PDF should be used to estimate any posterior expectation values in the presence of imperfections parameterized by that set of constraints. A posterior PDF of a conventional Bayesian optimization method is recovered analytically when all evaluator-specified constraints are set to zero (i.e., in the absence of any imperfections). The analytical expressions derived in this report for normal PDFs and linear models were verified numerically by a Metropolis–Hastings Monte Carlo method. The methods presented herein could be applied to any kind of data or models, including differential cross-section data or integral benchmark experiments.
Conservative thermal transient calculations and simulations show that a loss of vacuum condition would lead to a small (~6 °C) temperature differential across the Jemima Plates. Jemima plates would experience very low thermal stresses, well below the temperature differences of concern (>40 °C).