Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “conditional probability distribution”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

State-space receptive fields of semicircular canal afferent neurons in the bullfrog

Receptive fields are commonly used to describe spatial characteristics of sensory neuron responses. They can be extended to characterize temporal or dynamical aspects by mapping neural responses in dynamical state spaces. The state-space receptive field of a neuron is the probability distribution of the dynamical state of the stimulus-generating system conditioned upon the occurrence of a spike. We have computed state-space receptive fields for semicircular canal afferent neurons in the bullfrog (Rana catesbeiana). We recorded spike times during broad-band Gaussian noise rotational velocity stimuli, computed the frequency distribution of head states at spike times, and normalized these to obtain conditional pdfs for the state. These state-space receptive fields quantify what the brain can deduce about the dynamical state of the head when a single spike arrives from the periphery. c2001 Elsevier Science B.V. All rights reserved.

Non-NASA Center↗
NASA NTRS1992

Integrated-Circuit Pseudorandom-Number Generator

Integrated circuit produces 8-bit pseudorandom numbers from specified probability distribution, at rate of 10 MHz. Use of Boolean logic, circuit implements pseudorandom-number-generating algorithm. Circuit includes eight 12-bit pseudorandom-number generators, outputs are uniformly distributed. 8-bit pseudorandom numbers satisfying specified nonuniform probability distribution are generated by processing uniformly distributed outputs of eight 12-bit pseudorandom-number generators through "pipeline" of D flip-flops, comparators, and memories implementing conditional probabilities on zeros and ones.

Steelman, James E.↗
NASA NTRS2014

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

autonomous↗
NASA NTRS2014

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

Trajectory optimization methods using MBH have become well developed during the past decade. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing RVs from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by Englander significantly improves MBH performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness, where efficiency is finding better solutions in less time, and robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive RWs originally developed in the field of statistical physics.

mission design↗
NASA NTRSDate not supplied

Warming Reduces the Efficacy of Wet Conditions to Moderate Extreme Heat and Atmospheric Aridity Across the Central Plains

In the literature, dry land surface conditions possess a well-established association with extreme heat and high atmospheric aridity, especially in regions that experience strong land-atmosphere feedbacks. However, few studies have examined whether global warming will impact the ability of wet conditions to constrain high temperatures and atmospheric aridity occurrences. Here we investigate how wet conditions, which we define using precipitation and soil moisture, constrain high temperature and high vapor pressure deficit during the warm season over the Central Plains in North America at different global warming levels (0-3 degrees K). Using a CMIP6 multi-model ensemble, we show that absolute changes in the maximum temperature distribution outweigh the historical cooling effect of high precipitation or soil moisture conditions, reducing the probability of non-extreme maximum temperatures under global warming. Strikingly, the conditional likelihood of non-extreme maximum temperatures falls from 97-98% at baseline conditions to 50-58% at 1 degree of global warming. Once 2 degrees of warming is reached, there is very little probability of constraining extreme maximum temperatures. On the other hand, high precipitation and high soil moisture are able to better retain the ability to constrain concurrent high vapor pressure deficit conditions – moving from 99% at baseline conditions to 81-91% at 1 degree of warming and 26-42% at 2 degrees of warming. With our results, we find that the extent of this retention is dependent on how individual models represent relative changes in the VPD distribution as temperatures warm. Overall, our results highlight the growing vulnerability of the Central North American region to warmer temperatures and drier atmospheric conditions, even during periods of high precipitation and soil moisture.

Extreme heat↗
NASA NTRS1979

Laser velocimetry measurements in a gas turbine research combustor

The effects of turbulence on the production of pollutant species in a gas-turbine research combustor are studied using laser diffraction velocimetry (LDV) techniques. Measurements that were made in the primary combustion zone include mean velocity, rms velocity fluctuations, velocity probability distributions, and autocorrelation functions. A unique combustor design provides relatively uniform flow conditions and independent control of drop size, equivalence ratio, inlet temperature, and combustor pressure. Parameters which characterize the nature of the spray combustion (i.e., whether single droplet or group combustion occurs), were determined from the LDV data. Turbulent diffusivity (eddy viscosity) reaches a value of 2930 sq cm/sec, corresponding to a convective integral length scale of 1.8 cm. The group combustion number, based on turbulent diffusivity, is measured to be 6.2

Driscoll, J. F.↗
NASA NTRS1999

Disturbance Dynamics in Transitional and Turbulent Flows

In order to expand the predictive capability of single-point turbulence closure models to account for the early-stage transition regime, a methodology for the formulation and calibration of model equations for the ensemble-averaged disturbance kinetic energy and energy dissipation rate is presented. First the decay of laminar disturbances and turbulence in mean shear-free flows is studied. In laminar flows, such disturbances are linear superpositions of modes governed by the Orr-Sommerfeld equation. In turbulent flows, disturbances are described through transport equations for representative mean quantities. The link between a description based on a deterministic evolution equation and a probability based mean transport equation is established. Because an uncertainty in initial conditions exists in the laminar as well as the turbulent regime, a probability distribution must be defined even in the laminar case. Using this probability distribution, it is shown that the exponential decay of the linear modes in the laminar regime can be related to a power law decay of both the (ensemble) mean disturbance kinetic energy and the dissipation rate. The evolution of these mean disturbance quantities is then described by transport equations similar to those for the corresponding turbulent decaying flow. Second, homogeneous shear flow, where disturbances can be described by rapid distortion theory (RDT), is studied. The relationship between RDT and linear stability theory is exploited in order to obtain a closed set of modeled equations. The linear disturbance equations are solved directly so that the numerical simulation yields a database from which the closure coefficients in the ensemble-averaged disturbance equations can be determined.

Grosch, Chester E.↗
NASA NTRS1972

Evaluation of Skylab IB sensitivity to on-pad winds with turbulence

Computer simulation was performed to estimate displacements and bending moments experienced by the SKYLAB 1B vehicle on the launch pad due to atmospheric winds. The vehicle was assumed to be a beam-like structure represented by a finite number of generalized coordinates. Wind flow across the vehicle was treated as a nonhomogeneous, stationary random process. Response computations were performed by the assumption of simple strip theory and application of generalized harmonic analysis. Displacement and bending moment statistics were obtained for six vehicle propellant loading conditions and four representative reference wind profile and turbulence levels. Means, variances and probability distributions are presented graphically for each case. A separate analysis was performed to indicate the influence of wind gradient variations on vehicle response statistics.

Coffin, T.↗
NASA NTRSDate not supplied

Effects of Natural Variability on the Use of Standard Deviation to Represent Measurement Uncertainties in Atmospheric Composition Studies

Measurement uncertainty is defined as a “non-negative parameter characterizing the dispersion of the quantity values being attributed to a measurand”. It is most common that the uncertainties of GAW hourly measurements, such as greenhouse gas measurements, are reported as standard deviations derived from individual sampling at a higher time resolution (e.g., 1 min). In contrast, the uncertainties of GAW measurements of reactive gases and aerosol properties are reported in percentiles covering the same probability. A quick look at hourly CO 2 data from Cape Grim, Australia yielded some interesting findings: the hourly standard deviation is, on average, more than a factor of 10 higher for the measurements under non-background conditions (over 50% observations), while the difference in average CO 2 amount fraction was less than 2 ppm. The dramatic contrast cannot be explained by the difference in measurement uncertainties, but can largely be attributed the natural variability, or episodic ambient CO 2 variation reflecting changes in meteorological conditions or emissions. These initial findings motivated a more in-depth analysis of the ground-based measurements of trace gases and aerosol properties. This analysis will be using continuous 1 min ground site observations to construct time averaged statistical indicators to evaluate whether the standard deviation is adequate to represent the dispersion, especially under marked influence by natural variability. The suitability of this representation can be determined by examining the difference between the standard deviation and percentiles encompassing the same probability. We will examine time intervals of 1 hour, 3 hours, and 24 hours, with the latter time intervals chosen to match those commonly used in model assessments. We will also investigate how natural variability can alter the probability distribution of the measurands and how adequate the quadrature propagation of uncertainties is under these conditions. The results will include several trace gases (e.g., CO 2 , CO, O 3 , and NO 2 ) with a range of measurement techniques (e.g., PANDORA, in situ), atmospheric lifetimes, and aerosol properties (e.g. scattering coefficient). The findings from this analysis should provide some useful feedback on the best practices for uncertainty reporting.

Measurement Uncertainty↗
DOE OSTI2024

Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required

As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.

human error probability, human performance, time e↗
DOE OSTI2026

GPM IMERG V07B and V06B: Evaluation Using Ground-Based Radar Observations and Application in Global Mesoscale Convective System Tracking

This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.

Zhang, Sihan↗
NASA NTRS1980

Assessment of risk due to the use of carbon fiber composites in commercial and general aviation

The development of a national risk profile for the total annual aircraft losses due to carbon fiber composite (CFC) usage through 1993 is discussed. The profile was developed using separate simulation methods for commercial and general aviation aircraft. A Monte Carlo method which was used to assess the risk in commercial aircraft is described. The method projects the potential usage of CFC through 1993, investigates the incidence of commercial aircraft fires, models the potential release and dispersion of carbon fibers from a fire, and estimates potential economic losses due to CFC damaging electronic equipment. The simulation model for the general aviation aircraft is described. The model emphasizes variations in facility locations and release conditions, estimates distribution of CFC released in general aviation aircraft accidents, and tabulates the failure probabilities and aggregate economic losses in the accidents.

Fiksel, J.↗
NASA NTRS1984

Development of a global model for atmospheric backscatter at CO2 wavelengths

The effect of aerosol microphysical processes on the backscatter from an aerosol plume undergoing long-range atmospheric transport was studied. A numerical model which examines the effects of coagulation and sedimentation on an aerosol size distribution is under development and the initial results for a single homogeneous layer were obtained. Use was made of the SAGE/SAM II data set to study the global variation of aerosol concentration and, hence, to predict the variation of Beta sub CO2. Computer programs were written to determine the mean, median, and the probability distribution of the measured aerosol extinction as a function of altitude, latitude and geographical conditions. The first data sets analyzed in this way are under study. Data was used to study aerosol behavior over the U.S.A. and the Pacific Ocean.

Kent, G. S.↗
NASA NTRS2012

A Deterministic Annealing Approach to Clustering AIRS Data

We will examine the validity of means and standard deviations as a basis for climate data products. We will explore the conditions under which these two simple statistics are inadequate summaries of the underlying empirical probability distributions by contrasting them with a nonparametric, method called Deterministic Annealing technique

clustering↗
NASA NTRS2016

Analysis of Terrestrial Interference Protection from UAS CNPC Satellite Transmitters

Unmanned aircraft (UA) are projected to have a major impact on future aviation. Larger UA operating at altitudes above 3000 feet will require at least occasional access to non-segregated, that is, controlled airspace. In order for unmanned aircraft to be integrated into the airspace and operate with other commercial aircraft, a very reliable command and control (a. k. a. control and non-payload communications, (CNPC)) link is required. For operations covering large distances or over remote locations, a beyond-line-of-sight (BLOS) CNPC link implemented through a satellite will almost always be required. Protected aviation spectrum (aeronautical mobile satellite (route) service, or AMS(R)S) would normally be used for such a safety-critical link, however studies have shown that currently available aviation safety satellite spectrum is inadequate to support the projected BLOS CNPC link bandwidth requirements. To address this inadequacy, the 2015 World Radio communication Conference studied the possible use of the Fixed Satellite Service (FSS) to provide CNPC, including possible allocations in Ku-Band and Ka-Band, under Agenda Item (AI) 1.5. Although UA CNPC satellite links in these bands were shown to meet operational availability and continuity requirements, a serious complication exists in that there are also terrestrial service allocations in these bands, in particular, Fixed Service (FS) point-to-point and point-to-multipoint microwave digital links. During the WRC-15 study cycle, much opposition to AI 1.5 was generated based on fears that UA CNPC satellite transmitters in these bands would impose unacceptable levels of interference to the FS receivers. NASA analyzed the possible interference from the UA transmitters based on probable UA transmission and FS receiver characteristics, and UA traffic distributions and densities to determine conditions under which UA could operate without imposing unacceptable interference levels to the FS. Ultimately, UA power flux density transmission limits were proposed as a way to insure protection of FS receivers and further studies were prepared on the various proposals. This paper presents the results of these studies and discusses possible implications on future UA BLOS operations.

aircraft communications↗
NASA NTRS1980

Evaluation of pulsed RFI effects on digital satellite repeaters

This paper presents an analytical approach for assessing the effect of pulsed RFI on the error probability of a coherent phase-shift keyed signal through a nonlinear satellite repeater. The RFI is assumed to affect the uplink channel and to consist of CW pulses with random power levels and arriving randomly in time with a Poisson distribution. A model to approximate the effect of intermodulation products is introduced and the error probability conditioned on the output of the satellite repeater is computed. The classical moment technique is then used as an efficient method of averaging the conditional error probability over the numerous random parameters associated with the uplink signal.

Huang, T. C.↗
NASA NTRS1993

Global/local methods for probabilistic structural analysis

A probabilistic global/local method is proposed to reduce the computational requirements of probabilistic structural analysis. A coarser global model is used for most of the computations with a local more refined model used only at key probabilistic conditions. The global model is used to establish the cumulative distribution function (cdf) and the Most Probable Point (MPP). The local model then uses the predicted MPP to adjust the cdf value. The global/local method is used within the advanced mean value probabilistic algorithm. The local model can be more refined with respect to the g1obal model in terms of finer mesh, smaller time step, tighter tolerances, etc. and can be used with linear or nonlinear models. The basis for this approach is described in terms of the correlation between the global and local models which can be estimated from the global and local MPPs. A numerical example is presented using the NESSUS probabilistic structural analysis program with the finite element method used for the structural modeling. The results clearly indicate a significant computer savings with minimal loss in accuracy.

Millwater, H. R.↗
NASA NTRS1983

Range reference atmosphere models

A description is given of the methods used to establish the statistical parameters and models for wind and various thermodynamic quantities at an altitude of 0-70 km for nine geographical locations. It is noted that wind is modeled as a vector quantity using the bivariate normal probability function. With the five parameters of the bivariate normal distribution, the distribution for wind speed is derived as a generalized Rayleigh distribution. In addition, the frequency of wind direction is derived, and the conditional distribution of wind speed given the wind direction is derived. It is pointed out that these and other wind models are consistent with the rigorous mathematical properties of the bivariate normal probability theory. The thermodynamic quantities are consistent with the hydrostatic equation and the equation of state for the mean values. With these methods, many statistical relationships can be derived.

Smith, O. E.↗