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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling Ti–6Al–4V using crystal plasticity, calibrated with multi-scale experiments, to understand the effect of the orientation and morphology of the α and β phases on time dependent cyclic loading

Classically, crystal plasticity modeling has used a range of constitutive equations, in which the incorporation of additional physics-based relationships typically results in additional model parameters. These additional parameters need to be reliably calibrated, which often necessitates the use of a range of experimental data acquired at multiple length scales. In this work, a crystal plasticity based finite element (CPFE) model for a dual-phase Titanium alloy, Ti–6Al–4V, is developed. The α and β phases of the microstructure are explicitly modeled. The model is calibrated using a systematic optimization routine and experimental data that consist of macroscopic stress-strain curves coupled with lattice strains on different crystallographic planes for the two phases. These experimental data were obtained from in situ high energy X-ray diffraction experiments for multiple material pedigrees, with varying crystallographic orientation distribution and β volume fractions. Depending on the thermomechanical-processing route and the heat treatment used to manufacture the alloy, Ti–6Al–4V can exist in a wide number of microstructural forms, which often results in the α and β phases either having well aligned slip systems (following the Burgers orientation relationship (BOR)) or possessing no alignment of the slip systems across the interphase boundary (not following the BOR). In this study, the fully-calibrated CPFE model is used to gain a comprehensive understanding of the deformation behavior of Ti–6Al–4V, specifically, the effect of microstructures that follow the BOR (or not) on time-dependent cyclic loading (including the effects of dwell hold times).

36 MATERIALS SCIENCE↗
NASA NTRS1980

Progress report on the Block 1 VLBI correlator implementation

The Block 1 VLBI Processor Subsystem (VPS) of the Very Long Baseline Interferometry (VLBI) System for the determination of station clock-sync parameters, UT1 and polar motion, and station platform parameters and for use in spacecraft tracking is discussed. The Block 1 VPS performs three basic functions: it receives the 500 kbit/sec video data over the GCF wideband data lines from each of the three DSN 64 m antennas; it cross correlates data; and it performs several postcorrelation data reduction operations on the correlated data including calibration, editing, and parameter estimation.

Peterson, J. C.↗
NASA NTRS2014

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Clevleand, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSLthe first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Ice Crystal Icing↗
NASA NTRS2014

Validation Ice Crystal Icing Engine Test in the Propulsion Systems Laboratory at NASA Glenn Research Center

The Propulsion Systems Laboratory (PSL) is an existing altitude simulation jet engine test facility located at NASA Glenn Research Center in Cleveland, OH. It was modified in 2012 with the integration of an ice crystal cloud generation system. This paper documents the inaugural ice crystal cloud test in PSL--the first ever full scale, high altitude ice crystal cloud turbofan engine test to be conducted in a ground based facility. The test article was a Lycoming ALF502-R5 high bypass turbofan engine, serial number LF01. The objectives of the test were to validate the PSL ice crystal cloud calibration and engine testing methodologies by demonstrating the capability to calibrate and duplicate known flight test events that occurred on the same LF01 engine and to generate engine data to support fundamental and computational research to investigate and better understand the physics of ice crystal icing in a turbofan engine environment while duplicating known revenue service events and conducting test points while varying facility and engine parameters. During PSL calibration testing it was discovered than heated probes installed through tunnel sidewalls experienced ice buildup aft of their location due to ice crystals impinging upon them, melting and running back. Filtered city water was used in the cloud generation nozzle system to provide ice crystal nucleation sites. This resulted in mineralization forming on flow path hardware that led to a chronic degradation of performance during the month long test. Lacking internal flow path cameras, the response of thermocouples along the flow path was interpreted as ice building up. Using this interpretation, a strong correlation between total water content (TWC) and a weaker correlation between median volumetric diameter (MVD) of the ice crystal cloud and the rate of ice buildup along the instrumented flow path was identified. For this test article the engine anti-ice system was required to be turned on before ice crystal icing would occur. The ice crystal icing event, an uncommanded reduction in thrust, was able to be turned on and off by manipulating cloud TWC. A flight test point where no ice crystal icing event occurred was also duplicated in PSL. Physics based computational tools were successfully used to predict tunnel settings to induce ice buildup along the low pressure compression system flow path for several test points at incrementally lower altitudes, demonstrating that development of ice crystal icing scaling laws is potentially feasible. Analysis of PSL test data showed that uncommanded reduction in thrust occurs during ice crystal cloud on operation prior to fan speed reduction. This supports previous findings that the reduction of thrust for this test article is due to ice buildup leading to a restricted airflow from either physical or aerodynamic blockage in the engine core flow path.

Turbofan Engine↗
DOE OSTI2025

A review of thermo-hydro-mechanical modeling of coupled processes in fractured rock: From continuum to discontinuum perspective

Coupled thermo-hydro-mechanical (THM) processes in fractured rock are playing a crucial role in geoscience and geoengineering applications. Diverse and conceptually distinct approaches have emerged over the past decades in both continuum and discontinuum perspectives leading to significant progress in their comprehending and modeling. This review paper offers an integrated perspective on existing modeling methodologies providing guidance for model selection based on the initial and boundary conditions. By comparing various models, one can better assess the uncertainties in predictions, particularly those related to the conceptual models. The review explores how these methodologies have significantly enhanced the fundamental understanding of how fractures respond to fluid injection and production, and improved predictive capabilities pertaining to coupled processes within fractured systems. It emphasizes the importance of utilizing advanced computational technologies and thoroughly considering fundamental theories and principles established through past experimental evidence and practical experience. The selection and calibration of model parameters should be based on typical ranges and applied to the specific conditions of applications. The challenges arising from inherent heterogeneity and uncertainties, nonlinear THM coupled processes, scale dependence, and computational limitations in representing field scale fractures are discussed. Realizing potential advances on computational capacity calls for methodical conceptualization, mathematical modeling, selection of numerical solution strategies, implementation, and calibration to foster simulation outcomes that intricately reflect the nuanced complexities of geological phenomena. Future research efforts should focus on innovative approaches to tackle the hurdles and advance the state-of-the-art in this critical field of study.

Coupling scheme↗
NASA NTRS1984

Planetary Data Definition

Planetary data include all of those data which have resulted from measurements made by the instruments carried aboard planetary exploration spacecraft, and (for our purposes) exclude observations of Moon and Earth. The working, planetary data base is envisioned to contain not only these data, but also a wide range of supporting measurements such as calibration files, navigation parameters, spacecraft engineering states, and the various Earth-based and laboratory measurements which provide the planetary research scientist with historical and comparative data. No convention exists across the disciplines of the planetary community for defining or naming the various levels through which data pass in the progression from a sensed impulse at the spacecraft to a reduced, calibrated, and/or analyzed element in a planetary data set. Terms such as EDR (experiment data record), RDR (reduced data record), and SEDR (supplementary experiment data record) imply different meanings depending on the data set under consideration. The development of standard terminology for the general levels of planetary data is necessary.

Source record↗
DOE OSTI2022

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

58 GEOSCIENCES↗
DOE OSTI2024

Predicting biomass comminution: Physical experiment, population balance model, and deep learning

An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design.

09 BIOMASS FUELS↗
DOE OSTI2020

A Novel Relative Permeability Model for Gas and Water Flow in Hydrate-Bearing Sediments With Laboratory and Field-Scale Application

In a producing gas hydrate reservoir the effective porosity available for fluid flow constantly changes with dissociation of gas hydrate. Therefore, accurate prediction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) that were developed for conventional oil and gas reservoirs would require empirical parameters to be calibrated at various S h over its range of variation, but such calibrations are precluded because of lack of experimental relative permeability data. This study proposes a new relative permeability model for gas hydrate-bearing media that is a function of maximum capillary pressure, capillary entry pressure, pore size distribution index, residual saturations, hydrate saturation, and four other constants. The three novel features of the proposed model are: (i) requires fitting its six empirical parameters only once using experimental data from any single S h , and the same set of empirical parameters predict relative permeability at all S h , (ii) includes the effect of capillarity, and (iii) includes the effect of pore-size distribution. From practical standpoint, the model can be used to simulate multiphase flow in gas hydrate-bearing sediments where the proposed relative permeability can account for the evolving hydrate saturation. The proposed model is implemented in a numerical simulator and the wall time required to perform simulations using the proposed model is shown to be similar to the time it takes to run same simulations with the B-C model. The proposed model is a step forward towards achieving the goal of physically accurate modeling of multiphase flow for gas hydrate-bearing sediments that accounts for the effect of gas hydrate saturation change on relative permeability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. Here, we present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum $C_ℓ$ and the redshift distribution $n(z)$ of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of $C_ℓ$ and $n(z)$ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of $Ω_m$, $σ_8$, and intrinsic alignment amplitude $A_{IA}$, defined as $S_8D_{IA}=σ_8(Ω_m/0.3)^{0.5}D_{IA}$, where $D_{IA}=1-0.11(A_{IA}-1)$. We find $S_8D_{IA}=0.8954_{-0.039}^{+0.054}$, where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2014

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin, USA

The Soil Moisture and Ocean Salinity (SMOS) satellite mission is routinely providing global multi-angular observations of brightness temperature (TB) at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture (SM). To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multi-angular and multi-polarization top of atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity (VIC) LSM coupled with the Community Microwave Emission Modelling platform (CMEM) for simulating SMOS TB observations over the Upper Mississippi basin, USA. For a period of 2 years (2010-2011), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin averaged bias of 30K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After CDF matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30K to less than 5K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.

SMOS↗
NASA NTRS2018

In-Flight Calibration of Hitomi Soft X-Ray Spectrometer (3) Effective Area

We present the result of the in-flight calibration of the effective area of the Soft X-ray Spectrometer (SXS) on board the Hitomi X-ray satellite using an observation of the Crab nebula. We corrected for artifacts when observing high count rate sources with the X-ray microcalorimeter. We then constructed a spectrum in the 0.5–20 keV band, which we modeled with a single power-law continuum attenuated by interstellar extinction. We evaluated the systematic uncertainty of the spectral parameters by various calibration items. In the 2–12 keV band, the SXS result is consistent with the literature values in flux (2.20 ± 0.08 × 10(exp −8) erg s(exp-1) cm(exp −2) with a 1 σ statistical uncertainty) but is softer in the power-law index (2.19 ± 0.11). The discrepancy is attributable to the systematic uncertainty of about +6%/−7% and +2%/−5% respectively for the flux and the power-law index. The softer spectrum is affected primarily by the systematic uncertainty of the Dewar gate valve transmission and the event screening.

Tsujimoto, Masahiro↗
DOE OSTI2024

Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under conditions of high ice-shelf basal melt

Abstract. Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctic Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near-complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the response of the Lambert–Amery system (hereafter “the Amery sector”) to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at a medium resolution (4–20 km mesh) using the Model for Prediction Across Scales (MPAS)-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble and then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low- and high-greenhouse-gas-emission scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to emulate multivariate sea-level contribution time series data from the MALI simulations. We then use these emulators to propagate uncertainty from model input parameters to predictions of glacier mass loss through 2300, demonstrating that the calibrated posterior distributions have both greater mass loss and reduced variance compared to the uncalibrated prior distributions. Parametric uncertainty is large enough through about 2130 that the two projections under different emission scenarios are indistinguishable from one another. However, after rapid ocean warming in the first half of the 22nd century, the projections become statistically distinct within decades. Overall, this study demonstrates an efficient Bayesian calibration and uncertainty propagation workflow for ice-sheet model projections and identifies the potential for large sea-level rise contributions from the Amery sector of the Antarctic Ice Sheet after 2100 under high-greenhouse-gas-emission scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Characterizing hole trap production due to proton irradiation in germanium cross-strip detectors

We present an investigation into the effects of high-energy proton damage on charge trapping in germanium cross-strip detectors with the goal of accomplishing three important measurements. First, we calibrated and characterized the spectral resolution of a spare COSI-balloon detector in order to determine the effects of intrinsic trapping, finding that electron trapping due to impurities dominates over hole trapping in the undamaged detector. Second, we performed two rounds of proton irradiation of the detector in order to quantify, for the first time, the rate at which charge traps are produced by proton irradiation. We find that the product of the hole trap density and cross-sectional area, [nσ] h , follows a linear relationship with the proton fluence, F p , with a slope of (5.4 ± 0.4) x 10 -11 cm/p + . Third, by utilizing our measurements of physical trapping parameters, we performed calibrations which corrected for the effects of trapping and mitigated degradation to the spectral resolution of the detector.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Optical See-Through Head Mounted Display Direct Linear Transformation Calibration Robustness in the Presence of User Alignment Noise

Augmented Reality (AR) is a technique by which computer generated signals synthesize impressions that are made to coexist with the surrounding real world as perceived by the user. Human smell, taste, touch and hearing can all be augmented, but most commonly AR refers to the human vision being overlaid with information otherwise not readily available to the user. A correct calibration is important on an application level, ensuring that e.g. data labels are presented at correct locations, but also on a system level to enable display techniques such as stereoscopy to function properly [SOURCE]. Thus, vital to AR, calibration methodology is an important research area. While great achievements already have been made, there are some properties in current calibration methods for augmenting vision which do not translate from its traditional use in automated cameras calibration to its use with a human operator. This paper uses a Monte Carlo simulation of a standard direct linear transformation camera calibration to investigate how user introduced head orientation noise affects the parameter estimation during a calibration procedure of an optical see-through head mounted display.

Axholt, Magnus↗
NASA NTRS1990

Long-Term Trends in the Concentrations of SF6, CHClF2, and COF2 in the Lower Stratosphere from Analysis of High-Resolution Infrared Solar Occultation Spectra

Long-term trends in the concentrations of SF6, CHClF2 (CFC-22), and COF2 in the lower stratosphere have been derived from analysis of ca. 1980 and more recent infrared solar occultation spectra recorded near 32 deg N latitude at approx. 0.02/ cm resolution. Consistent sets of line parameters and spectral calibration methods have been used in the retrievals to minimize systematic error effects. Quoted error limits are 1 sigma estimated precisions. The SF6 and CHClF2 results are based on spectra recorded by balloon-borne interferometers in March 1981 and June 1988 and a comparison of these results with the Atmospheric Trace Molecule Spectroscopy (ATMOS) Experiment/Spacelab 3 measurements obtained in May 1985 near 30 deg N latitude. In the 13-18 km altitude range the mean measured SF6 mixing ratio in parts per trillion by volume (pptv) increased from 1.17 +/- 0.21 in March 1981 to 2.02 +/- 0.20 pptv in June 1988, and the CHClF2 mixing ratio below 15 km altitude increased from 51 +/- 8 pptv in March 1981 to 102 +/- 10 pptv in June 1988. The CHClF2 retrievals used new empirical CHClF2 line parameters derived from 0.03/cm resolution laboratory spectra recorded at six temperatures between 203 and 293 K; the derived mixing ratios are approx. 30% higher than obtained with earlier sets of line parameters, thereby removing a large discrepancy noted previously between IR and in situ measurements of CHClF2. Assuming an exponential growth model for fitting the trends, SF6 and CHClF2 mean increase rates of 7.4% +/- 1.9% and 9.4% +/- 1.3% /year, are obtained, respectively, which correspond to cumulative increases by factors of approx. 1.7 and -2.0 in the concentrations of these gases over the 7.2-year measurement period. Analysis of spectra recorded in October 1979 and April 1989 yields COF2 volume mixing ratios that are respectively 0.44 +/- 0.17 and 1.21 +/- 0.24 times the ATMOS/Spacelab 3 values, from which an average COF2 increase rate of 10.3 +/- 1.8%/ year over this time period has been estimated. The present results are compared with previously reported observations and trends and with one-dimensional model calculations. The model calculated trends are in reasonably good agreement with the observations.

Rinsland, C. P.↗