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NASA NTRSDate not supplied

Aerosol Above Cloud Optical Depth in the Southeast Atlantic and Its Use in Continuity NASA Products

When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in aerosol optical depth (AOD) retrievals when clouds are underneath. To better constrain this quantity, we have measured aerosols above clouds over the course of 3 years in the southeast Atlantic as part of the ObseRvations of CLouds above Aerosols and their intEractionS (ORACLES). These directly measured AOD above clouds are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS. Here we present a summary of the above cloud aerosol optical depth measured during ORACLES, and that will be used for porting the MODACAERO algorithm to the continuity product. We use aerosol optical depth measured directly from sunlight attenuation using the Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and compare it to the active remote sensing, High Spectral Resolution Lidar-2 (HSRL-2). We present a combination of all 3 years of measurements during peak biomass burning season. We have observed average above cloud aerosol optical depth at 500 nm ranging from 0.28 to 0.37, with the maximum in August 2017. We emphasize the use of improved aerosol absorption and scattering models based on airborne measurements during ORACLES.

aerosol optical depth (AOD)↗

NASA NTRSDate not supplied

Use of Aerosol Observations From Aircraft in Satellite Remote Sensing and Modelling Comparisons

Aerosol extensive and intensive properties vary throughout the world and remain one of the largest uncertainties in determining future climate both because of difficulties in their measurement and their modelled behaviors. Here we highlight the contributions of airborne sampling by the sunphotometer 4STAR (Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) from two different parts of the world; Korean peninsula and region during the KORUS-AQ experiment (KORean-US Air Quality in May-June 2016) and over the South East Atlantic during ORACLES (ObseRvations of CLouds above Aerosols and their intEractionS in August-September-October 2016, 2017, and 2018) field campaign. The high temporal and spatial resolution, afforded by airborne sampling during KORUS-AQ, are used to determine the variability of aerosol intensive and extensive properties, which is a metric than can be compared in between multiple different observations and models, even over varying surfaces and spanning multiple different aerosol emission sources. We show the consistency over spatial scales of the AOD (Aerosol Optical Depth), and the aerosol intensive properties (Angstrom exponent - AE, fine mode fraction - FMF) observed by 4STAR, GOCI (Geostationary Ocean Color Imager Yonsei aerosol retrieval v2), MERRA-2 reanalysis (Modern-Era Retrospective Analysis for Research and Applications, v2), and from airborne in situ aerosol optical measurements by LARGE (NASA Langley Aerosol Research Group Experiment). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km and is dependent on the prevailing meteorological conditions. AE and FMF are found to be more spatially variable than AOD during all of KORUS-AQ, even when accounting for potential sampling biases. This may indicate that microphysical processes like aerosol particle formation, growth, and coagulation impact the dominant aerosol size at shorter scales than their combined effect on the aerosol optical depth by the aerosol emission, transport, and removal. Averaging between measurements and model, the distance at which the correlation to itself is reduced by 15% is 65 km for AOD, and 22.7 km for AE. Vertically resolved measurement from an aircraft enable the direct measurements of aerosols overlying clouds. When aerosol overlie bright clouds, their radiative impact on the incident light can be either positive or negative. This difference in radiative impact promotes difficulties in remote sensing aerosol properties, which have shown biases in AOD retrievals when clouds are underneath. The AOD above clouds measured by 4STAR during ORACLES are used to build and improve upon the current Near-Real-Time MODACAERO algorithm for above cloud AOD from MODIS, into a continuity product for NASA EOS/SNPP/JPSS.

Aerosol↗

NASA NTRS2024

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification

Two phase-optimized multisine peak factor minimization algorithms are presented and evaluated. The first algorithm minimizes peak factor by iteratively clipping the peaks of generated multisine signals. The second algorithm optimizes peak factor indirectly through minimization of an approximation of the infinity norm of the multisine. Algorithm performance was evaluated as a function of different signal properties, including the number of harmonics, harmonic spacing, and number of snow harmonics (extra harmonics included for further reduction of the peak factor). The two algorithms are compared against results obtained by minimizing peak factor directly using a simplex algorithm, which has been a common approach when designing phase-optimized multisines for system identification flight tests. Sample results show that the clipping and infinity norm algorithms produced multisine signals with comparable peak factors that were lower than that of the simplex algorithm. However, the clipping algorithm runs an order of magnitude faster than the other two algorithms, which also makes it practical to repeat the algorithm multiple times to achieve even lower peak factors.

system identification↗

NASA NTRSDate not supplied

Comparison of Multisine Peak Factor Minimization Algorithms for Aircraft System Identification(Presentation)

flight test↗

NASA NTRS1975

An algorithm for a single machine scheduling problem with sequence dependent setup times and scheduling windows

An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.

Moore, J. E.↗

NASA NTRS1986

A real-time simulation evaluation of an advanced detection. Isolation and accommodation algorithm for sensor failures in turbine engines

An advanced sensor failure detection, isolation, and accommodation (ADIA) algorithm has been developed for use with an aircraft turbofan engine control system. In a previous paper the authors described the ADIA algorithm and its real-time implementation. Subsequent improvements made to the algorithm and implementation are discussed, and the results of an evaluation presented. The evaluation used a real-time, hybrid computer simulation of an F100 turbofan engine.

Merrill, W. C.↗

NASA NTRS1986

A real-time simulation evaluation of an advanced detection, isolation and accommodation algorithm for sensor failures in turbine engines

Merrill, W. C.↗

NASA NTRS1991

An implicit method for the nonlinear modelling and simulation of piezoceramic actuators displaying hysteresis

Experiments have shown that piezoceramic materials display a nonlinear relationship between the applied electric field and the actuation strain. This relationship also displays a substantial hysteresis upon reversal of the applied field. In this paper, piezoceramic actuator models are incorporated into the structural equations of motion to arrive at a set of nonlinear actively controlled structural equations of motion. A new implicit algorithm for determining the time history of the actively controlled structure is presented. The algorithm employs the trapezoidal rule for stepping the equations forward in time. The algorithm is compared to an explicit algorithm and is shown to provide greater numerical accuracy. In addition, the numerical stability and convergence characteristics are presented via example. The new algorithm displays convergence and stability properties that are comparable to the standard trapezoidal rule used for dynamic systems without hysteresis.

Leigh, Timothy D.↗

NASA NTRS1985

Automatic Blood Pressure Measurements During Exercise

Microprocessor circuits and a computer algorithm for automatically measuring blood pressure during ambulatory monitoring and exercise stress testing have been under development at SRI International. A system that records ECG, Korotkov sound, and arm cuff pressure for off-line calculation of blood pressure has been delivered to NASA, and an LSLE physiological monitoring system that performs the algorithm calculations in real-time is being constructed. The algorithm measures the time between the R-wave peaks and the corresponding Korotkov sound on-set (RK-interval). Since the curve of RK-interval versus cuff pressure during deflation is predictable and slowly varying, windows can be set around the curve to eliminate false Korotkov sound detections that result from noise. The slope of this curve, which will generally decrease during exercise, is the inverse of the systolic slope of the brachial artery pulse. In measurements taken during treadmill stress testing, the changes in slopes of subjects with coronary artery disease were markedly different from the changes in slopes of healthy subjects. Measurements of slope and O2 consumption were also made before and after ten days of bed rest during NASA/Ames Research Center bed rest studies. Typically, the maximum rate of O2 consumption during the post-bed rest test is less than the maximum rate during the pre-bed rest test. The post-bed rest slope changes differ from the pre-bed rest slope changes, and the differences are highly correlated with the drop in the maximum rate of O2 consumption. We speculate that the differences between pre- and post-bed rest slopes are due to a drop in heart contractility.

Weaver, Charles S.↗

NASA NTRS1993

Method and apparatus for fault tolerance

A method and apparatus for achieving fault tolerance in a computer system having at least a first central processing unit and a second central processing unit. The method comprises the steps of first executing a first algorithm in the first central processing unit on input which produces a first output as well as a certification trail. Next, executing a second algorithm in the second central processing unit on the input and on at least a portion of the certification trail which produces a second output. The second algorithm has a faster execution time than the first algorithm for a given input. Then, comparing the first and second outputs such that an error result is produced if the first and second outputs are not the same. The step of executing a first algorithm and the step of executing a second algorithm preferably takes place over essentially the same time period.

Masson, Gerald M.↗

NASA NTRS2001

Resource Selection Using Execution and Queue Wait Time Predictions

We developed techniques to predict application execution times for instance-based learning with an average error of 33% of average run time. We developed techniques to predict queue wait times that included a simulation of scheduling algorithms and execution time predictions. We implemented these techniques for the NAS Origin cluster.

Smith, Warren↗

NASA NTRS1995

Linear and nonlinear trending and prediction for AVHRR time series data

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Smid, J.↗

NASA NTRS2002

Synthesizing Monitors for Safety Properties

The problem of testing a linear temporal logic (LTL) formula on a finite execution trace of events, generated by an executing program, occurs naturally in time analysis of software. An algorithm which takes a past time LTL formula and generates an efficient dynamic programming algorithm is presented. The generated algorithm tests whether the formula is satisfied by a finite trace of events given as input and runs in linear time, its constant depending on the size of the LTL formula. The memory needed is constant, also depending on the size of the formula. Further optimizations of the algorithm are suggested. Past time operators suitable for writing succinct specifications axe introduced and shown definitionally equivalent to the standard operators. This work is part of the PathExplorer project, the objective of which it is to construct a flexible framework for monitoring and analyzing program executions.

Havelund, Klaus↗

NASA NTRS1995

Building a better leapfrog

In stellar dynamical computer simulations, as well as other types of simulations using particles, time step size is often held constant in order to guarantee a high degree of energy conservation. In many applications, allowing the time step size to change in time can offer a great saving in computational cost, but variable-size time steps usually imply a substantial degradation in energy conservation. We present a meta-algorithm' for choosing time steps in such a way as to guarantee time symmetry in any integration scheme, thus allowing vastly improved energy conservation for orbital calculations with variable time steps. We apply the algorithm to the familiar leapfrog scheme, and generalize to higher order integration schemes, showing how the stability properties of the fixed-step leapfrog scheme can be extended to higher order, variable-step integrators such as the Hermite method. We illustrate the remarkable properties of these time-symmetric integrators for the case of a highly eccentric elliptical Kepler orbit and discuss applications to more complex problems.

Hut, Piet↗

NASA NTRS1995

Algorithm Helps Monitor Engine Operation

Real-Time Failure Control (RTFC) algorithm part of automated monitoring-and-shutdown system being developed to ensure safety and prevent major damage to equipment during ground tests of main engine of space shuttle. Includes redundant sensors, controller voting logic circuits, automatic safe-limit logic circuits, and conditional-decision logic circuits, all monitored by human technicians. Basic principles of system also applicable to stationary powerplants and other complex machinery systems.

Eckerling, Sherry J.↗

NASA NTRS1992

Combining constraint satisfaction and local improvement algorithms to construct anaesthetists' rotas

A system is described which was built to compile weekly rotas for the anaesthetists in a large hospital. The rota compilation problem is an optimization problem (the number of tasks which cannot be assigned to an anaesthetist must be minimized) and was formulated as a constraint satisfaction problem (CSP). The forward checking algorithm is used to find a feasible rota, but because of the size of the problem, it cannot find an optimal (or even a good enough) solution in an acceptable time. Instead, an algorithm was devised which makes local improvements to a feasible solution. The algorithm makes use of the constraints as expressed in the CSP to ensure that feasibility is maintained, and produces very good rotas which are being used by the hospital involved in the project. It is argued that formulation as a constraint satisfaction problem may be a good approach to solving discrete optimization problems, even if the resulting CSP is too large to be solved exactly in an acceptable time. A CSP algorithm may be able to produce a feasible solution which can then be improved, giving a good, if not provably optimal, solution.

Smith, Barbara M.↗

NASA NTRS1980

Parameter identification of linear discrete stochastic systems with time delays

An identification algorithm that uses the maximum likelihood technique to identify the unknown time delays, plant parameters, and noise covariances of linear discrete stochastic systems is presented. Cases of additive white noise and colored measurement noises are considered. The likelihood function is evaluated using either a minimum-variance (Kalman) filter or a minimal-order observer. The Kalman filter is used in the identification algorithm to provide minimum-variance estimates. The minimal-order observer is a lower-dimensional and computationally simpler filter, and is advantageous especially for systems with long delays. It provides a less optimal solution to the minimum-mean-square state estimation problem. The colored-noise observer algorithm has the disadvantage of having to compute an extra error covariance matrix of lower order.

Wong, E. C.↗

NASA NTRS1971

Information retrieval for nonstationary data records

A review and a critical discussion are made on the existing methods for analysis of nonstationary time series, and a new algorithm for splitting nonstationary time series, is applied to the analysis of sunspot data.

Su, M. Y.↗