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The final COS-B database was used to study the gamma ray emission in the latitude range 10 deg /b/ 20 deg in terms of a four component model. The emissivity spectrum of the local interstellar medium is derived and compared with that found in previous studies. The intensity-Compton component is determined from the fitting procedure and is in good agreement with theoretical values.
Computer oriented error analysis of electrical circuits and nonlinear system performance
Suitable formulation of finite-element model speeds computation. Improved method developed for analysis of stress and compatibility of components in multicomponent rotating assembly. In method, single finite-element mathematical model represents all of components. Made possible by use of gap elements to represent contact surfaces between components.
An improved method of analyzing multicomponent rotating assemblies for the determination of operating stresses and component compatibility has been developed. In this method, a single finite element model is developed which contains all of the separate components in the rotating assembly. This is made possible by using gap elements to simulate the contact surfaces between components. The MARC finite element computer program is then used to perform the analysis. This improved method is less time consuming and more reliable than the conventional method of analyzing such systems. Results are presented for two different stress-compatibility analyses of a six-component axial flow compressor rotor. The results for the previously used flexibility analysis method are compared with those for the improved analysis method. The stresses predicted by each method compare quite well with each other. The predictions of the component compatibility, as well as the magnitude of the forces at the contact surfaces, also compare well for these two analysis procedures. It is therefore recommended that the improved analysis method be used to determine the stress-compatibility characterstics of multicomponent rotating systems.
Here, in this paper, we present the use of deep neural networks to estimate physical parameters from complex optical emission spectra of the D β /H β transition. Specifically, we focus on estimating the radio frequency electric field vector of the lower hybrid wave and isotope ratio within the scrape-off-layer plasma of the WEST tokamak. Fitting the spectral data using a traditional non-linear least squares analysis requires many free parameters and is computationally expensive, rendering the data unusable for real-time control. By implementing relatively small neural networks, the physical parameters can be directly extracted from the spectral data with reasonable accuracy in a few milliseconds. The deep neural network prediction can serve as input for a reduced model using least-squares fitting or for real-time control. We show that deep neural networks can be an effective tool for analyzing complex multicomponent spectra, providing a speedup of more than 10 5 times compared to least residual analysis, with an accuracy of 0.5% for the isotope ratio, and 0.09 kV/cm and 0.38 kV/cm for the RF radial and poloidal electric field respectively.
Complex, multicomponent, solutions have often been studied solely through the lens of specific applications of interest. Yet advances to both simulation methodologies (enhanced sampling, etc.) and analysis techniques (network analysis algorithms and others), are creating a trove of data that reveal transcending characteristics across vast compositional phase space. This perspective discusses technical considerations of the reliable and accurate simulations of complex solutions, followed by the advances to analysis algorithms that elucidate coupling of different length and timescale behavior (hierarchical phenomena). The different manifestations of hierarchical phenomena are presented across an array of solution environments, emphasizing fundamental and ongoing science questions. With a more advanced molecular understanding in hand, a quintessential application (solvent extraction) is discussed, where significant opportunities exist to re-imagine the technical scope of an established technology.
Managing the flow of water, nutrients, and contaminants in watersheds is vital to addressing pressing issues related to water scarcity, access to clean drinking water, energy production, resilience to natural and anthropogenic perturbations, and ecological restoration. Decisions about the management of watersheds critically depend on the accuracy with which the flow of water and chemicals through the watershed can be predicted by computer models. Prediction uncertainty can be reduced by matching the model to data, which are collected in the field at great expense. The contribution of watershed characterization data to reducing uncertainty of relevant model predictions can be evaluated in a so-called data-worth analysis, which provides transparent, quantitative metrics about a data set’s value for the support of relevant watershed management objectives. To achieve this goal, we developed a software package that implements the data-worth analysis approach for use with state-of-the-art watershed models. The purpose of the proposed data-worth analysis is to help decision-makers allocate resources for watershed characterization such that the uncertainty in model predictions can be significantly reduced, which leads to better, more effective management decisions. At the same time, watershed characterization costs can be reduced. The specific technical objectives of this SBIR/STTR Phase II project were to develop a framework and associated software toolsets that implement the uncertainty quantification and data-worth analysis approach for use with state-of-the-art watershed models. This goal was achieved by (A) developing a user-friendly, robust software package that is accessible to a wide audience, including watershed managers, policy-makers, and public stakeholders; (B) by demonstrating application of the prototype on several use cases that are representative of complex watershed management challenges spanning a range of scales and that consider different open-source, DOE-based codes and other modeling platforms; and (C) by gathering information about the needs and requirements from potential users to help guide future developments, ensuring that the final product will be commercially viable. The developed software consists of a graphical user interface that guides the user through a sequence of analysis steps, supported by toolsets that leverage state-of-the-art computational simulation-optimization capabilities. A prototype of the software runs on multiple platforms (PC, Mac, multi-processor Linux environment), is linked to diverse watershed simulators (e.g., ECOSYS, TOUGH2, TOUGHREACT, Amanzi-ATS), performs multiple analysis tasks (predictive simulations, sensitivity analysis, uncertainty analysis, automatic parameter estimation, and data-worth analysis, multicomponent geothermometry), and is readily extensible to include external simulators and analysis tools. The software is being commercialized and will be continually updated to address user needs.
Laser-induced fluorescence (LIF) is a promising technique for laser radar applications. Laser radar using LIF has already been applied to algae blooms and oil slicks. Laser radar using LIF has great potential for remote chemical analysis because LIF spectra are extremely sensitive to chemical composition. However, most samples in the real world contain mixtures of fluorescing components, not merely individual components. Multicomponent analysis of laser radar returns from mixtures is often difficult because LIF spectra from solids and liquids are very broad and devoid of line structure. Therefore, algorithms for interpreting LIF spectra from laser radar returns must be able to analyze spectra that overlap in multicomponent systems. This paper analyzes the possibility of using factor analysis-rank annihilation (FARA) to analyze emission-time matrices (ETM) from laser radar returns instead of excitation-emission matrices (EEM). The authors here define ETM as matrices where the rows (or columns) are emission spectra at fixed times and the columns (or rows) are temporal profiles for fixed emission wavelengths. Laser radar usually uses pulsed lasers for ranging purposes, which are suitable for measuring temporal profiles. Laser radar targets are hard instead of diffuse; that is, a definite surface emits the fluorescence instead of an extended volume. A hard target would not broaden the temporal profiles as would a diffuse target. Both fluorescence lifetimes and emission spectra are sensitive to chemical composition. Therefore, temporal profiles can be used instead of excitation spectra in FARA analysis of laser radar returns. The resulting laser radar returns would be ETM instead of EEM.
Thermal Analyzer for Planetary Soil (TAPS) offers a specific implementation for the generic thermal analyzer/evolved-gas analyzer (TA/EGA) function included in the Mars Environmental Survey (MESUR) strawman payload; applications to asteroids and comets are also possible. The baseline TAPS is a single-sample differential scanning calorimeter (DSC), backed by a capacitive-polymer humidity sensor, with an integrated sampling mechanism. After placement on a planetary surface, TAPS acquires 10-50 mg of soil or sediment and heats the sample from ambient temperature to 1000-1300 K. During heating, DSC data are taken for the solid and evolved gases are swept past the water sensor. Through ground based data analysis, multicomponent DSC data are deconvolved and correlated with the water release profile to quantitatively determine the types and relative proportions of volatile-bearing minerals such as clays and other hydrates, carbonates, and nitrates. The rapid-response humidity sensors also achieve quantitative analysis of total water. After conclusion of soil-analysis operations, the humidity sensors become available for meteorology. The baseline design fits within a circular-cylindrical volume less than 1000 cm(sup 3), occupies 1.2 kg mass, and consumes about 2 Whr of power per analysis. Enhanced designs would acquire and analyze multiple samples and employ additional microchemical sensors for analysis of CO2, SO2, NO(x), and other gaseous species. Atmospheric pumps are also being considered as alternatives to pressurized purge gas.
Big-bang nucleosynthesis (BBN) is valuable as a means to constrain the physics of the early universe and it is the only probe of the radiation-dominated epoch. A fundamental assumption in BBN is that the nuclear velocity distributions obey Maxwell-Boltzmann (MB) statistics as they do in stars. Specifically, the BBN epoch is characterized by a dilute baryon plasma for which the velocity distribution of nuclei is mainly determined by the dominant Coulomb elastic scattering with mildly relativistic electrons. One must therefore deduce the momentum distribution for reacting nuclei from the multicomponent relativistic Boltzmann equation. However, the full multicomponent relativistic Boltzmann equation has only recently been analyzed and its solution has only been worked out in special cases. Moreover, a variety of schemes have been proposed that introduce nonthermal components into the BBN environment which can alter the thermal distribution of reacting nuclei. Here, we construct the relativistic Boltzmann equation in the context of BBN. We also derive a Langevin model and perform relativistic Monte-Carlo simulations which clarify the baryon distribution during BBN and can be used to analyze any relaxation from a nonthermal injection. Further, we show by these analyses that the thermalization process leads to a nuclear distribution function that remains very close to MB statistics even during the most relativistic environment relevant to BBN. Hence, the predictions of standard BBN remain unchanged.
In the course of studies on the iron-rich multicomponent system Fe-Mn-Cr-Mo-Nb-Al-Si-C, work was concentrated on pertinent quinary and six-component combinations namely Fe-Mn-Al-Si-C, Fe-Cr-Al-Si-C and Fe-Mn-Cr-Al-Si-C which had been elaborated at 65, 72, and 80 wt pct Fe. Manganese acts as a strong stabilizer for the cementite carbide. Chromium seems to stabilize the iron aluminide Fe2Al5 which forms in a considerable amount within an alloy of nominal composition Fe(65)Mn(15)Cr(12)Al(5)Si(2)C(1) (percent by weight). Although the Mn3AlC carbide is, like Fe3AlC, a perovskite carbide, manganese does not appear to favor the formation of the perovskite carbide. Because of the relatively low sintering temperature (700 C), for al large portion of the samples equilibria conditions are not always reached.
Powder X-ray diffraction (PXRD) has been employed extensively for the structural characterization of materials, quantitative analysis of multicomponent mixtures, phase identification, texture, and microstructural analysis. At the heart of all those lies the Rietveld method, which has revolutionized the use of powder diffraction for materials studies. Initially applied to minerals and inorganics, the Rietveld method has progressively been used for more complex materials, for instance, zeolites and pharmaceuticals. A major advance of the method came some 20 years ago, when the first protein structure was successfully refined. Because of the sheer complexity of macromolecules, several new approaches and algorithms had to be pioneered or adapted from macromolecular single-crystal diffraction experiments, thus constituting macromolecular PXRD a quite unique field of study. This review aims to provide necessary elements of theory and application of structure solution and refinement via the Rietveld method for macromolecular PXRD data. Here, practical explanations and highlighted case studies are also presented.
A comprehensive numerical analysis of multicomponent droplet vaporization at near critical conditions has been carried out. The model is based on the full time-dependent conservation equations and accommodates various important high-pressure phenomena. As an example, the case involving a two-component (n-pentane and n-octane) fuel droplet in nitrogen gas is studied. The influences of transient effects, surface regression, ambient gas solubility, and phase-equilibrium relations on vaporization mechanisms are examined in detail.
As a result of a multidisciplinary effort involving solid-state physics, quantum mechanics, and materials and surface science, the first version of a software package dedicated to the atomistic analysis of multicomponent systems was recently completed. Based on the BFS (Bozzolo, Ferrante, and Smith) method for the calculation of alloy and surface energetics, this package includes modules devoted to the analysis of many essential features that characterize any given alloy or surface system, including (1) surface structure analysis, (2) surface segregation, (3) surface alloying, (4) bulk crystalline material properties and atomic defect structures, and (5) thermal processes that allow us to perform phase diagram calculations. All the modules of this Alloy Design Workbench 1.0 (ADW 1.0) are designed to run in PC and workstation environments, and their operation and performance are substantially linked to the needs of the user and the specific application.
Perturbation method analysis of thermodynamic equilibrium properties of multicomponent plasma
Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Finally, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique
A fundamental analysis of multicomponent gas transport models was performed in application to the oxygen electrodes of solid oxide cells. It is common practice to neglect the effect of pressure gradients within oxygen electrodes, even though a net molar flux at the electrolyte surface implies that a pressure gradient must exist. The influence of both Darcy velocity and Knudsen flux are considered in the context of ordinary (Fickian) diffusion, the dusty gas model, and the binary friction model. Comparisons between the models and different sets of assumptions are made via parametric studies on operating load, oxygen partial pressure, microstructural properties, and electrode thickness. Results show that the pressure gradient will have a significant impact on the oxygen concentration distribution and therefore the concentration overpotential. In electrolysis mode, pressure increases up to 1 atm are predicted, indicating that pressure at the electrode/electrolyte interface could contribute to electrode delamination. Additionally, it is found that Darcy's law is insufficient for calculating the pressure distribution without accounting for the flux due to Knudsen diffusion. Additionally, it is found that for the range of properties typical of oxygen electrodes, there is a negligibly small difference between the dusty gas model and binary friction model from a practical standpoint.