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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Two-dimensional data were obtained in Mach range of from 0.40 to 0.94 and Reynolds Number range of (3.4 - 4.2) X 10 Degrees. Results indicate that thickness ratio is dominating shape parameter at high Mach numbers and that aerodynamic advantages are attainable by using thinnest possible sections. Effects of jet boundaries, Reynolds Number, and Data presented are free from jet-boundary and humidity effects.
The first binary neutron star merger has already been detected in gravitational waves. The signal was accompanied by an electromagnetic counterpart including a kilonova component powered by the decay of radioactive nuclei, as well as a short γ-ray burst. In order to understand the radioactively powered signal, it is necessary to simulate the outflows and their nucleosynthesis from the post-merger disk. Simulating the disk and predicting the composition of the outflows requires general relativistic magnetohydrodynamical (GRMHD) simulations that include a realistic, finite-temperature equation of state (EOS) and self-consistently calculating the impact of neutrinos. In this work, we detail the implementation of a finite-temperature EOS and the treatment of neutrinos in the GRMHD code HARM3D+NUC, based on HARM3D. We include formal tests of both the finite-temperature EOS and the neutrino-leakage scheme. We further test the code by showing that, given conditions similar to those of published remnant disks following neutron star mergers, it reproduces both recombination of free nucleons to a neutron-rich composition and excitation of a thermal wind.
Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.
This ECAR calculates the fluence and temperature of the AGC-1 specimens as they change elevation through the course of the experiment. The specimen elevation varies as the specimen stack shrinks due to irradiation and load induced creep. The compressed specimen stacks (S-1 thru S-6) have a graphite pushrod that applies a gas cylinder load. The top of the pushrod position is measured and recorded in the NGNP Data Management and Analysis System (NDMAS). The bottom of each of these compressed stacks is supported by the lower specimen holder which also shrinks as a result of irradiation and load. The specimen holder post-irradiation length was recorded during post-irradiation examination (PIE). Assuming the specimen holder shrinkage is linear with respect to the fluence received, the position of the top of the specimen holder can be calculated with respect to reactor integrated power. Assuming that individual specimen shrinkage is proportional to the fluence received, and that the shrinkage behavior is similar in all the specimens, the individual specimen position can be calculated.
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ABSTRACT We perform 3D hydrodynamical simulations to study recombination and ionization during the common envelope (CE) phase of binary evolution, and develop techniques to track the ionic transitions in time and space. We simulate the interaction of a $2\, \mathrm{M_\odot }$ red giant branch primary and a $1\, \mathrm{M_\odot }$ companion modelled as a particle. We compare a run employing a tabulated equation of state (EOS) that accounts for ionization and recombination, with a run employing an ideal gas EOS. During the first half of the simulations, ∼15 per cent more mass is unbound in the tabulated EOS run due to the release of recombination energy, but by simulation end the difference has become negligible. We explain this as being a consequence of (i) the tabulated EOS run experiences a shallower inspiral and hence smaller orbital energy release at late times because recombination energy release expands the envelope and reduces drag, and (ii) collision and mixing between expanding envelope gas, ejecta and circumstellar ambient gas assists in unbinding the envelope, but does so less efficiently in the tabulated EOS run where some of the energy transferred to bound envelope gas is used for ionization. The rate of mass unbinding is approximately constant in the last half of the simulations and the orbital separation steadily decreases at late times. A simple linear extrapolation predicts a CE phase duration of ${\sim}2\, {\rm yr}$, after which the envelope would be unbound.
This report is a compilation of thermodynamic functions of 50 elements in their standard reference state. The functions are C(sub p)(sup 0), (H(sup 0)(T) - H(sup 0)(0)), S(sup 0)(T), and -(G(sup 0)(T) - H(sup 0)(O)) for the elements Ag, Al, Ar, B, Ba, Be, Br2, C, Ca, Cd, Cl2, Co, Cr, Cs, Cu, F2, Fe, Ge, H2, He, Hg, I2, K, Kr, Li, Mg, Mn, Mo, N2, Na, Nb, Ne, Ni, O2, P, Pb, Rb, S, Si, Sn, Sr, Ta, Th, Ti, U, V, W, Xe, Zn, and Zr. Deuterium D2 and electron gas e(sup -) are also included. The data are tabulated as functions of temperature as well as given in the form of least-squares coefficients for two functional forms for C(sub p)(sup 0) with integration constants for enthalpy and entropy. One functional form for C(sub p)(sup 0) is a fourth-order polynomial and the other has two additional terms, one with T(exp -1) and the other with T(exp -2). The gases Ar, D2, e(sup -), H2, He, Kr, N2, Ne, O2, and Xe are tabulated for temperatures from 100 to 20,000 K. The remaining gases Cl2 and F2 are tabulated from 100 to 6000 K and 1000 to 6000 K. The second functional form for C(sub p)(sup 0) has an additional interval from 6000 to 20,000 K for the gases tabulated to 20,000 K. The fits are constrained so that the match at the common temperature endpoints. The temperature ranges for the condensed species vary with range of the data, phase changes, and shapes of the C(sub p)(sup 0) curves.
This report is a compilation of thermodynamic functions of 50 elements in their reference state. The functions are: C(sup 0, sub p), {H(T)-H(sup 0)(0)}, S(sup 0)(T), and - {G(sup 0)(T) - H(sup 0)(0)} for the elements Ag, Al, Ar, B, Ba, Be, Br2, C, Ca, Cd, Cl2, Co, Cr, Cs, Cu, F2, Fe, Ge, H2, He, Hg, I2, K, Kr, Li, Mg, Mn, Mo, N2, Na, Nb, Ne, Ni, O2, P, Pb, Rb, S, Si, Sn, Sr, Th, Th, Ti, U, V, W, Xe, Zn, and Zr. Deuterium D, and electron gas e(sup -) are also included. The data are tabulated as functions of temperature as well as given in the form of least-squares coefficients for two functional forms for C(sup 0, sub p) with integration constants for enthalpy and entropy. One functional form for C(sup 0, sub p) is a fourth-order polynomial and the other has two additional terms, one with T(sup -1) and the other with T(sup -2). The gases Ar, D2, e(sup -), H2, He, Kr, N2, Ne, O2, and Xe are tabulated for temperatures from 100 to 20 000 K. The remaining gases Cl2 and F2 are tabulated from 100 to 6000 K. The polynomial functional form for C(sup 0, sub p) for all these gases is split into two temperature intervals of 200 to 1000 K and 1000 to 6000 K. The second functional form for (sup 0, sub p) has an additional interval from 6000 to 20 000 K for the gases tabulated to 20 000 K. The fits are constrained so that the properties match at the common temperature endpoints. The temperature ranges for the condensed species vary with range of the data, phase changes, and shapes of the C(sup 0, sub p) curves.
Soot production (including formation and oxidation) is studied in the transient, high-pressure and turbulent n-dodecane Spray A flames from the Engine Combustion Network (ECN) using computational fluid dynamics (CFD) simulations. A two-equation soot-in-flamelet modeling approach is applied within the framework of the Unsteady Flamelet Progress Variable (UFPV) model and results are validated against experimental data. Equations for soot mass fraction and soot number density derived in the mixture fraction space are solved in the context of detailed flamelet calculations. Source terms for the different steps in the soot chemistry are tabulated and incorporated in the flamelet manifold. For the reference condition, the modeling approach based on the tabulated flamelet manifold reduces the computational cost of a CFD calculation by approximately 40 times compared to a non-tabulated well-mixed (WM) modeling approach. The soot-in-flamelet approach is then extended to study the effect of ambient oxygen concentration, ambient mixture composition and ambient temperature on soot production. Results show that the modeling approach is able to capture the experimental trends for the soot volume fraction (SVF) with good quantitative agreement, especially in the soot ramp-up region.
Tabulated chemistry models, including the flamelet/progress variable approach, have been successfully used for a variety of turbulent flame simulations. The progress variable describes the progress of reactions in a system and parameterizes a lookup table of thermochemical variables. This approach reduces the cost of simulations, transporting only one scalar (progress variable) instead of the many species mass fractions required for detailed chemistry. Originally developed for low Mach number flame simulations, recent works have focused on extensions of this approach to compressible flames, supersonic combustion, and detonations, with applications such as scramjet combustors and rotating detonation engines. Unlike low Mach simulations, compressible flow simulations require solving the energy transport equation, which is coupled to the equation of state. This leads to additional modeling challenges regarding the thermodynamics and its impact on the chemistry. The validity of modeling assumptions, for example the relationship between energy and temperature, also varies with the combustion regime. The present work provides a detailed assessment of the existing strategies for chemistry tabulation for compressible/supersonic combustion, including detonations. A priori analysis indicates that approximations which are reasonable for weakly compressible flames may break down for shock-induced combustion. Furthermore, the analysis identifies specific assumptions and approximations that do not hold for detonations, emphasizing that care must be taken when applying tabulated chemistry models outside their intended combustion regimes.
My Uncle Willie circa 1600 wrote “What’s in a name; a rose by any other name would smell as sweet.” I fear in this case we have a somewhat similar problem in that we may be using the same word but are not using the same definition; specifically, the word Unresolved. The simplest physics definition as it applies to neutron resonances, is the energy point where we can no longer see/measure ALL – let me repeat that – ALL - of the individual resonances. That seems simple and clear, but the question is: how to represent resonances beyond this point in order to accurately reproduce the effects we have seen in measurements and expect/need to reproduce in our applications. We know there are more, unseen resonances, otherwise we wouldn’t say Unresolved. The ENDF approach is well defined in ENDF-102 and simple: for ENDF data the only way to represent Unresolved data is by using a theoretical model to define the distribution of resonances, including those that are too narrow to measure (i.e., are unresolved). It is important to note that in ENDF this is the one and only Unresolved model, e.g., there is no provision in ENDF to accurately define individually ALL resonances above the Resolved energy range – by ALL here I mean both those that we can measure and those that we cannot individually measure, but that theory and integral measurements tells us are present. An alternative approach, which would appear to be equally valid, would be to include the latest measured data as tabulated energy expendent data extending upwards in energy above the Resolved energy range. In this approach the evaluation would not include an ENDF style Unresolved energy range; it would only include a Resolved resonance region, followed by tabulated higher energy points, representing the resonances that could be measured beyond the Resolved range. But an important point to note: By listing these resonances above the resolved energy one admits that at least some resonances in this energy range are missing as Unresolved; i.e., they are too narrow or overlapping to measure. The purpose of this paper is to illustrate that the later approach, while done with good intentions, and appearing to be valid/adequate in plots, does not meet the need of our engineering applications. Why? As we will see below, of these two possible approaches, only the ENDF use of a model to statistically include the missing, i.e., unresolved, resonances, can meet our engineering needs to reproduce the integral effects we have measured and understand. Only with this statistical model can we predict and include in our calculated results the important effects of temperature (Doppler broadening), and energy integrals (self-shielding). Below I will first present results using two ENDF/B-VIII.1 evaluations, U235 and U238, that use the correct ENDF-102 definition of an Unresolved resonance region, using a statistical model to include the effects of resonances that theory predicts are present, but are too narrow to measure. These two evaluations reproduce the expected temperature (Doppler) and energy integral (self-shielding) effects that we expect. Next I will present results using one ENDF/B-VIII.1 evaluation, 26-Fe-56, that does not use an ENDF-102 Unresolved resonance region; instead above its Resolved energy range it lists many tabulated energy points, that look like measured data, but by definition, since they are included above the ENDF Resolved energy range there are missing Unresolved resonances, i.e., there are missing the resonances that are too narrow to resolve, i.e., are unresolved. My conclusion, and I hope yours, is that the below figures illustrate that this approach does not reproduce the temperature and energy integrals that we expect and need to accurately calculate results for our fission reactor calculations. As such this approach should not be used in ENDF formatted evaluations.
Normal- and oblique-shock flow parameters for air in thermochemical equilibrium are tabulated as a function of shock angle for altitudes ranging from 15.24 km to 91.44 km in increments of 7.62 km at selected hypersonic speeds. Post-shock parameters tabulated include flow-deflection angle, velocity, Mach number, compressibility factor, isentropic exponent, viscosity, Reynolds number, entropy difference, and static pressure, temperature, density, and enthalpy ratios across the shock. A procedure is presented for obtaining oblique-shock flow properties in equilibrium air on surfaces at various angles of attack, sweep, and dihedral by use of the two-dimensional tabulations. Plots of the flow parameters against flow-deflection angle are presented at altitudes of 30.48, 60.96, and 91.44 km for various stream velocities.
Two major topics are treated: methods to measure and evaluate impurity effects in silicon and comprehensive tabulations of data derived during the study. Discussions of deep level spectroscopy, detailed dark I-V measurements, recombination lifetime determination, scanned laser photo-response, conventional solar cell I-V techniques, and descriptions of silicon chemical analysis are presented and discussed. The tabulated data include lists of impurity segregation coefficients, ingot impurity analyses and estimated concentrations, typical deep level impurity spectra, photoconductive and open circuit decay lifetimes for individual metal-doped ingots, and a complete tabulation of the cell I-V characteristics of nearly 200 ingots.