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Results for “SELECTION RULES”

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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Symmetry and selection rules for wave-wave interactions

Selection rules for nonlinear three-wave interactions are derived on the basis of the symmetry of the background medium and the waves themselves. The process described bypasses the formal derivation of the coupling coefficient itself. The primary concern is with the effect of the polarization of the waves on the selection rules. Although consideration is limited to three-wave interactions, the method is easily generalized to more waves. Results are derived for these examples: three waves with parallel propagation vectors in an unmagnetized medium, and three waves propagating parallel, and then perpendicular, to a static magnetic field.

Harker, K. J.↗
NASA NTRS1990

A collision-selection rule for a particle simulation method suited to vector computers

A theory is developed for a selection rule governing collisions in a particle simulation of rarefied gas-dynamic flows. The selection rule leads to an algorithmic form highly compatible with fine grain parallel decomposition, allowing for efficient utilization of supercomputers having vector or massively parallel single instruction multiple data architectures. A comparison of shock-wave profiles obtained using both the selection rule and Bird's direct simulation Monte Carlo (DSMC) method show excellent agreement. The equation on which the selection rule is based is shown to be directly related to the time-counter procedure in the DSMC method. The results of several example simulations of representative rarefied flows are presented, for which the number of particles used ranged from 10 to the 6th to 10 to the 7th demonstrating the greatly improved computational efficiency of the method.

Baganoff, D.↗
NASA NTRS1971

Group theoretical selection rules for electron-impact spectroscopy.

Simple group theoretical principles are applied to the consideration of electron scattering off atoms and diatomic and polyatomic molecules. This approach is also used to estimate relative strengths of electron-impact-induced transitions of diatomic and polyatomic molecules.

Goddard, W. A., III↗
NASA NTRS1984

A rule for selecting analytical boundary conditions for the conservative quasi-one-dimensional nozzle flow equations

For the one-dimensional nozzle flow equations, the number of analytical boundary conditions that may be applied corresponds to the number of exterior characteristics reaching the boundary points. Since the slopes of the characteristics are the characteristic speeds u, u+c, and u-c, one concludes that two analytical boundary conditions are required at a subsonic inflow boundary and one at a subsonic outflow boundary. However, using this guideline alone can lead to nonunique solutions in subsonic cases. This paper presents a rule for selecting analytical boundary conditions for the dependent variables which will yield unique solutions if they exist.

Wornom, S. F.↗
NASA NTRS1997

Optical Characterization and 2,525 micron Lasing of Cr(2+):Cd(0.85)Mn(0.15)Te

Transition metal doped solids are of significant current interest for the development of tunable solid-state lasers for the near and mid-infrared (1-4 pm) spectral region. Applications of these lasers include basic research in atomic, molecular, and solid-state physics, optical communication, medicine, and environmental studies of the atmosphere. In transition metal based laser materials, absorption and emission of light arises from electronic transitions between crystal field split energy levels of 3d transition metal ions. The optical spectra generally exhibit broad bands due to the strong interaction between dopant and host (electron-phonon coupling). Broad emission bands offer the prospect of tunable laser activity over a wide wavelength range, e.g. the tuning range of Ti:Sapphire extends from 700-1100 run. The only current transition metal laser operating in the mid-infrared wavelength region (1.8-2.4 micro-m) is CO(2+):MgF2, but its performance is severely limited due to strong nonradiative decay at room temperature. Based on lifetime data, the quantum efficiency is estimated to be less than 3 deg/0 11,21. In general, the probability for non-radiative decay via multi-phonon relaxation increases with decreasing energy gap between ground and excited state. Therefore, efficient transition metal lasers beyond -1.6 micro-m are rare. Recently, tunable laser activity around 2.3 micro-m was observed from Cr doped ZnS and ZnSe. The new lasing center in these materials was identified as Cr(2+) occupying the tetrahedral Zn site. Tetrahedrally coordinated optical centers are rather unusual among transition metal lasers. Their potential usefulness, however, has been demonstrated by the recent development of near infrared laser materials such as Cr:forsterite and Cr:YAG, which are based on tetrahedrally coordinated Cr(4+) ions. According to the Laporte selection rule, electric-dipole transition within the optically active 3d-electron shells are parity forbidden. However, a static acentric electric crystal field or the coupling of asymmetric phonons can force electric-dipole transitions by the admixture of wave functions with opposite parity. Tetrahedral sites lack inversion symmetry which provides the odd-parity field necessary to relax the parity selection rule. Therefore, high absorption and emission cross sections are observed. An enhanced radiative emission rate is also expected to reduce the detrimental effect of non-radiative decay. Motivated by the initial results on Cr doped ZnS and ZnSe, we have started a comprehensive effort to study Cr(2+) doped II-VI semiconductors for solid-state laser applications. In this paper we present the optical properties and the demonstration of mid-infrared lasing from Cr doped Cd(0.85)Mn(0.15)Te.

Davis, V. R.↗
NASA NTRS1992

Machine Learning Techniques in Optimal Design

Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution to the problem, is then obtained by solving in parallel each of the sub-problems in the set and computing the one with the minimum cost. In addition to speeding up the optimization process, our use of learning methods also relieves the expert from the burden of identifying rules that exactly pinpoint optimal candidate sub-problems. In real engineering tasks it is usually too costly to the engineers to derive such rules. Therefore, this paper also contributes to a further step towards the solution of the knowledge acquisition bottleneck [Feigenbaum, 1977] which has somewhat impaired the construction of rulebased expert systems.

Cerbone, Giuseppe↗
NASA NTRS1989

Simulation Of Combat With An Expert System

Proposed expert system predicts outcomes of combat situations. Called "COBRA", combat outcome based on rules for attrition, system selects rules for mathematical modeling of losses and discrete events in combat according to previous experiences. Used with another software module known as the "Game". Game/COBRA software system, consisting of Game and COBRA modules, provides for both quantitative aspects and qualitative aspects in simulations of battles. COBRA intended for simulation of large-scale military exercises, concepts embodied in it have much broader applicability. In industrial research, knowledge-based system enables qualitative as well as quantitative simulations.

Provenzano, J. P.↗
NASA NTRS1992

Spaceborne construction and operations planning - Decision rules for selecting EVA, telerobot, and combined work-systems

An approach is presented for selecting an appropriate work-system for performing construction and operations tasks by humans and telerobots. The decision to use extravehicular activity (EVA) performed by astronauts, extravehicular robotics (EVR), or a combination of EVA and EVR is determined by the ratio of the marginal costs of EVA, EVR, and IVA. The approach proposed here is useful for examining cost trade-offs between tasks and performing trade studies of task improvement techniques (human or telerobotic).

Smith, Jeffrey H.↗
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS2021

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗
NASA NTRS1980

Two-photon excitation cross-section in light and intermediate atoms

The method of explicit summation over the intermediate states is used along with LS coupling to derive an expression for two-photon absorption cross section in light and intermediate atoms in terms of integrals over radial wave functions. Two selection rules, one exact and one approximate, are also derived. In evaluating the radial integrals, for low-lying levels, the Hartree-Fock wave functions, and for high-lying levels, hydrogenic wave functions obtained by the quantum defect method are used. A relationship between the cross section and the oscillator strengths is derived. Cross sections due to selected transitions in nitrogen, oxygen, and chlorine are given. The expression for the cross section is useful in calculating the two-photon absorption in light and intermediate atoms.

Omidvar, K.↗
NASA NTRS1971

Long-range interaction between metastable helium and ground state helium.

Results of analysis for the long-range interaction between ground state helium and triplet metastable helium, which shows that the long-range coefficient is the same for both gerade and ungerade states. Those terms which lead to the second term on the right hand side of a developed equation vanish through spin selection rules.

Victor, G. A.↗