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Results for “variance estimation”

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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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Spatiotemporal filtering of MR-temperature artifacts arising from bowel motion during transurethral MR-HIFU

Purpose: Transurethral MR-HIFU is a minimally invasive image-guided treatment for localized prostate cancer that enables precise targeting of tissue within the gland. The treatment is performed within a clinical MRI to obtain real-time MR thermometry used as an active feedback to control the spatial heating pattern in the prostate and to monitor for potential damage to surrounding tissues. This requires that the MR thermometry measurements are an accurate representation of the true tissue temperature. The proton resonance frequency shift thermometry method used is sensitive to tissue motion and changes in the local magnetic susceptibility that can be caused by the motion of air bubbles in the rectum, which can impact the performance of transurethral MR-HIFU in these regions of the gland. Methods: A method is proposed for filtering of temperature artifacts based on the temporal variance of the temperature, using empirical and dynamic positional knowledge of the ultrasonic heating beam, and an estimation of the measurement noise. A two-step correction strategy is introduced which eliminates artifact-detected temperature variations while keeping the noise level low through spatial averaging. Results: The filter has been evaluated by postprocessing data from five human transurethral ultrasound treatments. The two-step correction process led to reduced final temperature standard deviation in the prostate and rectum areas where the artifact was located, without negatively affecting areas distal to the artifact. The performance of the filter was also found to be consistent across all six of the data sets evaluated. The evaluation of the detection criterion parameter M determined that a value of M = 3 achieves a conservative filter with minimal loss of spatial resolution during the process. Conclusions: The filter was able to remove most artifacts due to the presence of moving air bubbles in the rectum during transurethral MR-HIFU. A quantitative estimation of the filter capabilities shows a systematic improvement in the standard deviation of the corrected temperature maps in the rectum zone as well as in the entire acquired slice.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1982

Jovian winds from Voyager 2. I - Zonal mean circulation

Independent measurements of Jovian cloud motions confirm previously published results on the general structure of Jupiter's zonal mean circulation. The new results are based on Voyager 2 images and measurement techniques which are different from those used in previous studies. The latitudes of the zonal jets agree with previous results, but there are some differences in the measured speed of the jets which exceed uncertainty estimates. These differences may be due to differences in sampling strategies. The structure of the zonal mean meridional velocity profile has still not been clearly resolved: mean meridional velocities generally differ from zero by no more than their estimated uncertainty. An analysis of successive measurements of the same cloud targets shows that most of the variance of individual velocity measurements is due to true variability of the winds.

Limaye, S. S.↗
NASA NTRS1987

Statistical tests for closure of plate motion circuits

Two methods, one based on a chi-square test and the second on an F-ratio test, of testing for the plate motion circuit closures are described and evaluated. The chi-square test is used to evaluate goodness of fit, and it is assumed that the assigned errors are accurate estimates of the true errors in the data. The F-ratio test is used to compare variances of distributions, and it is assumed that the relative values of assigned error are accurate. The two methods are applied to the data of Minster and Jordan (1978) on the motion of the three plates that meet at the Galapagos Triple Junction, and the motion of the three plates that meet at the Indian Ocean Triple Junction. It is noted that the F-ratio plate circuit closure test is more useful than the chi-square test for identifying systematic misfits in data because the chi-square test overestimates the errors of plate motion data.

Gordon, Richard G.↗
NASA NTRS1988

An automatic frequency control loop using overlapping DFTs (Discrete Fourier Transforms)

An automatic frequency control (AFC) loop is introduced and analyzed in detail. The new scheme is a generalization of the well known Cross Product AFC loop that uses running overlapping discrete Fourier transforms (DFTs) to create a discriminator curve. Linear analysis is included and supported with computer simulations. The algorithm is tested in a low carrier to noise ratio (CNR) dynamic environment, and the probability of loss of lock is estimated via computer simulations. The algorithm discussed is a suboptimum tracking scheme with a larger frequency error variance compared to an optimum strategy, but offers simplicity of implementation and a very low operating threshold CNR. This technique can be applied during the carrier acquisition and re-acquisition process in the Advanced Receiver.

Aguirre, S.↗
NASA NTRS2021

A Phenomenon-Based Decomposition of Model-Based Estimates of Boreal Winter ENSO Variability

Climate models are now routinely being used to simulate and predict climate variability on time scales ranging from sub-seasonal to seasonal and longer. As such, there are now long histories of such simulations and predictions spanning multiple decades and multiple ensemble members, both of which are crucial for separating climate signal from climate noise. A key focus of such runs has been the El Niño-Southern Oscillation (ENSO), spurred by recent improvements in our ability to predict such events, though questions remain as to how well climate models do beyond simply always predicting the “canonical” atmospheric response to an ENSO event—something simple statistical models already do reasonably well. This is a critical issue that needs addressing, given the importance of event-to-event differences for predicting regional impacts of ENSO teleconnections, and the need to justify the expense of running sophisticated climate models. Unfortunately, current diagnostic tools are not well suited for quantifying the different sources of variability associated with specific phenomena such as ENSO. More generally, while much effort has focused on addressing model bias, less has been done to address errors in second moment statistics—an issue whose importance is gaining increased attention particularly as we build climate prediction systems capable of taking advantage of forecasts of opportunity—a capability that requires reliable estimates of forecast uncertainty. In this report, we outline a phenomenon-based statistical decomposition of climate variance(in essence a detailed variance budget)that is specifically tailored to address the above questions by separating the variability (both the signal and noise) into that tied to the long-term average impact of a particular phenomenon(the composite mean) and the event-to-event(E2E) variability about the composite mean. In addition, we provide related decompositions of the correlations that allow us to quantify how much of the agreement with observations (the skill) comes from the composite mean as opposed to from the E2Evariability. As an example, we present the results of such a decomposition for ENSO based on simulations with the GEOS atmospheric general circulation model (AGCM), with a focus on the monthly mean impacts over North America during boreal winter(December –March). Here we take advantage of existing GEOS AGCM simulations that were produced as companion simulations to MERRA-2for the period 1980-2016. Comparisons are made throughout with MERRA-2.

ENSO↗
DOE OSTI2018

Enhancing statistical moment calculations for stochastic Galerkin solutions with Monte Carlo techniques

Here, we provide a method for enhancing stochastic Galerkin moment calculations to the linear elliptic equation with random diffusivity using an ensemble of Monte Carlo solutions. This hybrid approach combines the accuracy of low-order stochastic Galerkin and the computational efficiency of Monte Carlo methods to provide statistical moment estimates which are significantly more accurate than performing each method individually. The hybrid approach involves computing a low-order stochastic Galerkin solution, after which Monte Carlo techniques are used to estimate the residual. We show that the combined stochastic Galerkin solution and residual is superior in both time and accuracy for a one-dimensional test problem and a more computational intensive two-dimensional linear elliptic problem for both the mean and variance quantities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Radiation Dose Modeling for Niowave’s Accelerator Driven Uranium Target Assembly 3

Molybdenum-99 is a high-value radionuclide commonly used for medical purposes within the United States. The National Nuclear Security Administration (NNSA) seeks to reliably produce the radioisotope 99 Mo without the use of highly enriched uranium. NNSA’s Office of Material Management and Minimization (M3) provides funding and government laboratory expertise to private companies to expedite the production process domestically and currently funds designs that use low-enriched uranium or other 99 Mo production pathways. Several production designs are being explored across the industry, including uranium fission and photonuclear conversion of 100 Mo targets. Niowave Inc. seeks to produce 99 Mo via a high-energy electron accelerator that strikes a lead-bismuth eutectic target that ultimately produces a consistent neutron flux. The neutron flux then interacts in a subcritical reactor core configuration to produce fission in low-enriched or natural uranium targets. These fissionable targets are then processed to extract 99 Mo. The purpose of this work is to estimate the neutron and photon dose response across Niowave’s proposed facility for worker safety during operation. Owing to the size of the proposed Niowave facility and necessary shielding, unbiased Monte Carlo radiation transport is impractical, and variance reduction methods are required. This work focuses on the weight window variance reduction method to produce high confidence dose response results within a Monte Carlo radiation transport code. Specifically, an adjoint-informed weight window methodology was created to improve the dose response estimates for accelerator-driven subcritical reactor designs. This adjoint-informed methodology was implemented for Niowave’s proposed design and improved dose results at far-field locations across the facility. Acceptable dose rate contours for the proposed facility were generated across the facility and are presented in this work.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. An inverse estimation of the backshell and heatshield surface aeroheating was performed, using the data from the MEDLI2 Instrumented Sensor Plugs, a network of thermocouples embedded within the thermal protection system across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of the surface heat rate, temperature, and heat load to uncertainties in thermocouple depth and material properties. In this paper, a variance decomposition method using Sobol indices was employed to understand the relative contributions of each uncertainty parameter. Performing this analysis using results from the inverse analysis tool FIAT_Opt was found to require incredibly high computation time, and thus machine learning models were trained and evaluated as a surrogate model for FIAT_Opt. This paper demonstrates that machine learning models can be an efficient, accurate alternative to state-of-the-art inverse analysis tools like FIAT_Opt, especially for computationally-expensive processes. Using these models, the sensitivity analysis showed that uncertainties in heat capacity and thermal conductivity were the main drivers for the overall uncertainty in peak reconstructed heating and heat load.

H S Alpert↗
NASA NTRS1992

Variance of phase fluctuations of waves propagating through a random medium

As an electromagnetic wave propagates through a random scattering medium, such as a forest, its energy is attenuated and random phase fluctuations are induced. The magnitude of the random phase fluctuations induced is important in estimating how well a Synthetic Aperture Radar (SAR) can image objects within the scattering medium. The two-layer random medium model, consisting of a scattering layer between free space and ground, is used to calculate the variance of the phase fluctuations induced between a transmitter located above the random medium and a receiver located below the random medium. The scattering properties of the random medium are characterized by a correlation function of the random permittivity fluctuations. The effective permittivity of the random medium is first calculated using the strong fluctuation theory, which accounts for large permittivity fluctuations of the scatterers. The distorted Born approximation is used to calculate the first-order scattered field. A perturbation series for the phase of the received field in the Rytov approximation is then introduced and the variance of the phase fluctuations is also calculated assuming that the transmitter and receiver are in the paraxial limit of the random medium, which allows an analytic solution to be obtained. Results are compared using the paraxial approximation, scalar Green's function formulation, and dyadic Green's function formulation. The effects studied are the dependence of the variance of the phase fluctuations on receiver location in lossy and lossless regions, medium thickness, correlation length and fractional volume of scatterers, depolarization of the incident wave, ground layer permittivity, angle of incidence, and polarization.

Chu, Nelson C.↗
NASA NTRSDate not supplied

A Deep Neural Network for Achieving Spectrally Consistent and Seamless Infrared Radiance Measurements Across Geostationary Satellite Domains

The NASA Clouds and the Earth's Radiant Energy System (CERES) project provides the scientific community with observed top-of-atmosphere (TOA) shortwave and longwave fluxes for climate monitoring and climate model validation. To achieve this goal, CERES relies on TOA broadband fluxes derived from geostationary satellite (GEO) imagery to account for the diurnal flux variations between the CERES observation intervals. Consistent global flux derivation depends on accurate and consistent cloud retrievals. Scene-dependent spectral measurement inconsistency of the instruments that make up the contiguous ring of GEO observations (GEO-Ring), as well as limb darkening effects, can cause discontinuities in derived cloud properties and radiative fluxes at the boundaries of adjacent imager domains. Although the algorithms utilize radiative transfer models to account for instrument-band-dependent atmospheric correction and viewing zenith angle (VZA) dependency, small discontinuities may persist due to uncertainties inherent to the multiple imager-specific algorithms. Furthermore, while hyperspectral-instrument-based spectral band adjustment factors may effectively account for spectrally induced bias, they are less effective at reducing variance owed to the specific composition of the viewed scene, which is challenging to robustly characterize. As such, this article highlights the use of a deep neural network (DNN) to resolve spectral-and VZA-induced biases between GEO-Ring imagers. The DNN uses available infrared (IR) channels from the GEO instruments, along with viewing and solar illumination geometry, to estimate homogenized, VIIRS-like IR radiances for use in the GEO cloud algorithm. This approach is effective at mitigating scene-dependent spectral variance and VZA dependency, resulting in consistent radiance measurements across the GEO-Ring, thereby leading toward a more seamless global cloud assessment.

deep learning↗
DOE OSTI2017

Turbulent transport and mixing in transitional Rayleigh-Taylor unstable flow: A priori assessment of gradient-diffusion and similarity modeling

Data from a 1152×760×1280 direct numerical simulation of a Rayleigh-Taylor mixing layer modeled after a small-Atwood-number water-channel experiment is used to investigate the validity of gradient diffusion and similarity closures a priori. The budgets of the mean flow, turbulent kinetic energy, turbulent kinetic energy dissipation rate, heavy-fluid mass fraction variance, and heavy-fluid mass fraction variance dissipation rate transport equations across the mixing layer were previously analyzed at different evolution times to identify the most important transport and mixing mechanisms. Here a methodology is introduced to systematically estimate model coefficients as a function of time in the closures of the dynamically significant terms in the transport equations by minimizing the L2 norm of the difference between the model and correlations constructed using the simulation data. It is shown that gradient-diffusion and similarity closures used for the turbulent kinetic energy K, turbulent kinetic energy dissipation rate ε, heavy-fluid mass fraction variance S, and heavy-fluid mass fraction variance dissipation rate χ equations capture the shape of the exact, unclosed profiles well over the nonlinear and turbulent evolution regimes. Using order-of-magnitude estimates for the terms in the exact transport equations and their closure models, it is shown that several of the standard closures for the turbulent production and dissipation (destruction) must be modified to include Reynolds-number scalings appropriate for Rayleigh-Taylor flow at small to intermediate Reynolds numbers. The late-time, large Reynolds number coefficients are determined to be different from those used in shear flow applications and largely adopted in two-equation Reynolds-averaged Navier-Stokes (RANS) models of Rayleigh-Taylor turbulent mixing. In addition, it is shown that the predictions of the Boussinesq model for the Reynolds stress agree better with the data when additional buoyancy-related terms are included. It is shown that an unsteady RANS paradigm is needed to predict the transitional flow dynamics from early evolution times, analogous to the small Reynolds number modifications in RANS models of wall-bounded flows in which the production-to-dissipation ratio is far from equilibrium. Although the present study is specific to one particular flow and one set of initial conditions, the methodology could be applied to calibrations of other Rayleigh-Taylor flows with different initial conditions (which may give different results during the early-time, transitional flow stages, and perhaps asymptotic stage). Finally, the implications of these findings for developing high-fidelity eddy viscosity-based turbulent transport and mixing models of Rayleigh-Taylor turbulence are discussed.

42 ENGINEERING↗
DOE OSTI2024

Profiling the molecular destruction rates of temperature and humidity as well as the turbulent kinetic energy dissipation in the convective boundary layer

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵ m ≃ 7 x 10 -4 g 2 kg −2 s −1 for mixing ratio and ϵ θ ≃ 1.6 x 10 -3 K 2 s −1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃ 10 -2 m 2 s −3 . We also demonstrate that the vertical wind ACF coefficient k w ∝ $\overline{w'^{2}}$ and the TKE dissipation ϵ ∝ ($\overline{w'^{2}}$) 3/2 . For the molecular destruction rates, we show that ϵ m ∝ $\overline{m'^{2}}$ ($\overline{w'^{2}}$) 1/2 and ϵ θ ∝ $\overline{θ'^{2}}$ ($\overline{w'^{2}}$) 1/2 . These equations can be used for parameterizations of ϵ, ϵ m , and ϵ θ . All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

dissipation rate↗
DOE OSTI2022

Vision-based inspection of prefabricated components using camera poses: Addressing inherent limitations of image-based 3D reconstruction

Modular construction can lead to additional cost overruns and delays when a defect is found on the construction site and is not easily repairable. Researchers have developed various methods that use image-based 3D reconstruction for quality assessment, but they have inherent limitations, such as inconsistency and dealing with surfaces with reflectivity and limited visual features. Therefore, this paper presents a vision-based quality assessment method using cameras for prefabricated components by addressing these limitations. Specifically, this paper proposes a novel quality inspection method with sub-millimeter accuracy using cameras focused on leveraging camera poses (as opposed to 3D point clouds that are often not consistent in quality) from the image-based 3D reconstruction. The 3D point estimation by computing triangulation was used for achieving accurate measurement. The proposed method is validated using six different variances and two case studies – an aluminum pipe with a reflective surface and a fabricated concrete column. Furthermore, the results demonstrate the accuracy and effectiveness of the proposed method.

42 ENGINEERING↗
DOE OSTI2014

Statistical estimation of the performance of a fast-neutron multiplicity system for nuclear material accountancy

Statistical analyses have been performed to develop bounding estimates of the expected performance of a conceptual fast-neutron multiplicity system (FNMS) for assaying plutonium. The conceptual FNMS design includes 32 cubic liquid scintillator detectors, measuring 7.62 cm per side, configured into 4 stacked rings of 8 detectors each. Expected response characteristics for the individual FNMS detectors, as well as the response characteristics of the entire FNMS, were determined using Monte Carlo simulations based on prior validation experiments. The results from these simulations were then used to estimate the Pu assay capabilities of the FNMS in terms of counting time, assay mass, and assay mass variance, using assay mass variance as a figure of merit. The analysis results are compared against a commonly used thermal-neutron coincidence counter. The advantages of using a fast-neutron counting system versus a thermal-neutron counting system are significant. Most notably, the time required to perform an assay to an equivalent assay mass variance is greatly reduced with a fast-neutron system, by more than an order of magnitude compared with that of the thermal-neutron system, due to the reduced probability of random summing with the fast system. The improved FNMS performance is especially relevant for assays involving Pu masses of 10 g or more.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

Extragalactic foreground contamination in temperature-based CMB lens reconstruction

We discuss the effect of unresolved point source contamination on estimates of the CMB lensing potential, from components such as the thermal Sunyaev-Zel'dovich effect, radio point sources, and the Cosmic Infrared Background. We classify the possible trispectra associated with such source populations, and construct estimators for the amplitude and scale-dependence of several of the major trispectra. We show how to propagate analytical models for these source trispectra to biases for lensing. We also construct a ''source-hardened'' lensing estimator which experiences significantly smaller biases when exposed to unresolved point sources than the standard quadratic lensing estimator. We demonstrate these ideas in practice using the sky simulations of Sehgal et al., for cosmic-variance limited experiments designed to mimic ACT, SPT, and Planck. We find that for radio sources and SZ the bias is significantly reduced, but for CIB it is essentially unchanged. However, by using the high-frequency, all-sky CIB measurements from Planck and Herschel it may be possible to suppress this contribution.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Molecular counting by photobleaching in protein complexes with many subunits: best practices and application to the cellulose synthesis complex

The constituents of large, multisubunit protein complexes dictate their functions in cells, but determining their precise molecular makeup in vivo is challenging. One example of such a complex is the cellulose synthesis complex (CSC), which in plants synthesizes cellulose, the most abundant biopolymer on Earth. In growing plant cells, CSCs exist in the plasma membrane as six-lobed rosettes that contain at least three different cellulose synthase (CESA) isoforms, but the number and stoichiometry of CESAs in each CSC are unknown. To begin to address this question, we performed quantitative photobleaching of GFP-tagged AtCESA3-containing particles in living Arabidopsis thaliana cells using variable-angle epifluorescence microscopy and developed a set of information-based step detection procedures to estimate the number of GFP molecules in each particle. The step detection algorithms account for changes in signal variance due to changing numbers of fluorophores, and the subsequent analysis avoids common problems associated with fitting multiple Gaussian functions to binned histogram data. The analysis indicates that at least 10 GFP-AtCESA3 molecules can exist in each particle. In conclusion, these procedures can be applied to photobleaching data for any protein complex with large numbers of fluorescently tagged subunits, providing a new analytical tool with which to probe complex composition and stoichiometry.

09 BIOMASS FUELS↗
DOE OSTI2018

Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements

An inverse error variance weighting of the anomalies of three terrestrial evaporation (ET) products from the WACMOS-ET project based on FLUXNET sites is presented. The three ET models were run daily and at a resolution of 25 km for 2002–2007, and based on common input data when possible. The local weights, derived based on the variance of the difference between the tower ET anomalies and the modelled ET anomalies, were made dynamic by estimating them using a 61-day running window centred on each day. These were then extrapolated from the tower locations to the global landscape by regressing them on the main model inputs and derived ET using a neural network. Over the stations, the weighted scheme usefully decreased the random error component, and the weighted ET correlated better with the tower data than a simple average. The global extrapolation produced weights displaying strong seasonal and geographical patterns, which translated into spatiotemporal differences between the ET weighted and simple average ET products. However, the uncertainty of the weights after the extrapolation remained large. Out-sample prediction tests showed that the tower data set, mostly located at temperate regions, had limitations with respect to the representation of different biome and climate conditions. Therefore, even if the local weighting was successful, the extrapolation to a global scale remains problematic, showing a limited added value over the simple average. Overall, this study suggests that merging tower observations and ET products at the timescales and spatial scales of this study is complicated by the tower spatial representativeness, the products' coarse spatial resolution, the nature of the error in both towers and gridded data sets, and how all these factors affect the weights extrapolation from the tower locations to the global landscape.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Flight-Determined Stability and Control Derivatives for an Executive Jet Transport

A modified maximum likelihood estimation (MMLE) technique which included a provision for including a priori information about unknown parameters was used to determine the aerodynamic derivatives of the Lockheed JetStar airplane. Two hundred sixty-five maneuvers were performed with the JetStar airplane, which was modified to include direct lift controls, to obtain lateral-directional and longitudinal derivatives. Data were obtained at altitudes of 3048 meters, 6096 meters, and 9144 meters (10,000 feet, 20,000 feet, and 30,000 feet) and over an angle of attack range from approximately 3 deg to 13 deg and a Mach number range from 0.25 to 0.75. Side force generators were installed and tested in 87 maneuvers to determine their effectiveness and their effect on the other derivatives. Lateral-directional data for four flight conditions were analyzed without using a priori information to assess the effect of this feature on the results. The MMLE method generally gave consistent (repeatable) estimates of the derivatives, with the exception of the rolling moment due to yaw rate, which showed large variances.

Smith, H. J.↗