Environmental technology - How are we doing/ques/
Environmental technology and extrapolation into future, noting government role
DATA RETRIEVAL↗
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Results for “Data Extrapolation”
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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A comparison of two transient methods of measuring thermal conductivity of particulate samples.
A comparison is made of the line source (LS) method and the differential line source (DLS) method of measuring thermal conductivity of particulate materials in vacuum. The DLS method requires more instrumentation in the measuring circuitry (an additional amplifier and a differentiating circuit), but since it does not require a stable temperature to initiate a test, it does not need a sample temperature control system. DLS tests can be taken as the temperature in the samples is rising from liquid nitrogen temperature to room temperature. This eliminates the practice of extrapolating thermal conductivity over this large temperature range. Also, the advantages of reduced test time, data reduction time, and small sample temperature rise enable the experimenter to take about 7-12 DLS tests in the time of 2 LS tests. Test data from the two methods agree very well.
Scott, R. W.↗
A model of the trapped electron population for solar minimum
A model is presented of the trapped electron environment of solar minimum conditions. Solar maximum models have been presented for the inner radiation zone (AE-5 1967), and for the outer radiation zone (AE-4 1967). The present solar minimum model consists of an inner zone model (AE-5 1975 Projected) with an epoch of 1975, and an outer zone model with an epoch of 1964. With only minor modifications this latter model is identical to the AE-4 1964 model presented previous. The model, however, has not previously been issued in computer form. AE-4 1964 is based upon satellite data, while the inner zone solar minimum model AE-5 1975 Projected consists entirely of extrapolations from AE-5 1967. While the two components of the solar minimum model have epochs 11 years part, it is assumed that any differences between the successive solar minima are smaller than the model error, and the complete model is associated with an epoch of 1975.
Teague, M. J.↗
Data compression
An approach to data compression is discussed in which the effect achieved by compression is evaluated by the closeness of the approach to the minimum possible volume. An attempt is made to systematize the known results on data compression. The review contains: description of methods of data compression based on statistical coding and information theory; application of methods of interpolation and extrapolation; a specific compression method (related to description of the histogram of a sample); some criteria of effectiveness and methods of service information representation; and discussion of models suggested for theoretical analysis.
Babkin, V. F.↗
Temperature distribution from radiosonde and satellite measurements
Since air temperatures derived from satellite radiance measurements often contain large errors, a method is suggested for combining radiosonde and satellite measurements to achieve better accuracy. In particular, when geostationary satellite radiances become available, it would be possible to use the satellite measurements to interpolate geographically between the radiosonde stations, and also to extrapolate in time from one radiosonde launch time until the next launch time. Simulated radiance data were used to test this method in a region of possible severe local storm development, and the accuracy obtained by using real scanning microwave spectrometer data was determined.
Fritz, S.↗
The National Transonic Facility - A research perspective
The capabilities of the National Transonic Facility (NTF) at NASA-Langley and its impact on aerodynamics investigations are surveyed. The fan-driven, closed-circuit transonic wind tunnel has an 8.2 sq ft slotted test section. Trials can be run from Mach 0.2-1.2, pressures of 1-8.9 atm, and temperatures of -320 to 150 F using nitrogen as the working gas. Instrumentation has been developed for monitoring force, pressure, attitude, deformation, temperature, skin friction, flow transition, and flow velocity as well as visualizing flows around the models. Pressures of 15-130 psi are available, as are Re up to 120 million at Mach 1. Correlations are being made with flight data from the Shuttle, 767, X-29A, and TACT aircraft for real-world extrapolations. The NTF, when combined with computational fluid dynamics techniques, will permit testing of aerodynamically sophisticated shapes while narrowing the design goals for each model during basic research in fluid mechanics, transport, and aerodynamic phenomena.
Campbell, J. F.↗
Mass loss from carbon stars
The present consideration of the circumstellar envelopes around carbon stars on the basis of IRAS data indicates that the grains are composed of amorphous carbon rather than graphite, with an extrapolated outflowing material opacity that is large enough for the radiation pressure on the grains to drive the matter to infinity. At least 10 percent of the carbon stars have a mass loss rate that has changed by a factor of 2 during the past 1000 years. The stars that are losing large amounts of mass are generally long period variables. All these results are consistent with a two-step process of mass loss from most of the stars with substantial mass-outflow rates.
Jura, M.↗
Infrared observations and thermal models of the Beta Pictoris disk
Measurements of the flux from the central portion of the Beta Pic disk performed via photometry at 3.5-20 microns in 4- and 8-arcsec apertures are presented. The 10.1- and 20-micron measurements show an excess which is extended, with significant flux located in the annulus between 4- and 8-arcsec diameter centered on the star. Disk models are calculated to reproduce the ground-based IR data, IRAS fluxes, IRAS 60-micron scan profile widths, and optical scattered light distribution. The color temperature and angular scale of the disk are found to be inconsistent with emission from large ('blackbody') particles: most of the thermal emission originates from particles which are inefficient radiators, with sizes near 1 micron. A simple disk with a single power-law spatial distribution is inconsistent with the combined optical and IR data. The inner disk component represents a gross deficit of material relative to an inward extrapolation of the outer component.
Backman, D. E.↗
The Extreme Ultraviolet (EUV) Instrument for the MAVEN Mission
The Mars Atmosphere and Volatile Evolution Mission (MAVEN) will explore the variability in the planet's upper atmosphere and ionosphere that is dominated by interactions with the sun, specifically the high-energy photons in the soft X-ray and extreme ultraviolet wavelengths as well as interactions with the solar wind. Scientists will use MAVEN data to determine the current loss rate of volatile compounds from the Mars atmosphere, then extrapolate back in time in order to give historical estimations of state of the Mars atmosphere and climate, its ability to sustain liquid water, and the potential for the Martian habitability. The EUV instrument is critical in measuring the Space Weather driver of this atmospheric variability. It will directly observe a three EUV wavelength ranges and their variability due to solar flares (time scales of seconds to hours) as well as active region evolution (months), which will then act as proxies for a model to determine the entire 0.1-200 nm solar spectrum at all times during the MAVEN mission. These EUV measurements and models results will compliment the other instruments that will provide direct in-situ as well as remote sensing observations of the Martian atmospheric response to this solar driver. This presentation will be an introduction of this instrument and its science measurements and goals to the larger community, as well as a status report on its progress.
Chamberlin, Phillip C.↗
Characterization Strategy of a Limited Number of Radioactive Waste Effluent Tanks in View of Decommissioning a Large Number of Tanks - 20286
In order to organize the D and D of the former effluent treatment facility at the CEA Cadarache Center (France), the key data to gather through the facility characterization are the following:: volumes and masses, liquid/solid distribution and specific activity. By studying the treatment processes and the history of the facility, families of tanks have been defined and a reference tank chosen for investigation in each family. The intrusive investigations (video, residue thickness, dose rate..), the gamma spectrometry measurements carried out on site on the residue samples and radiochemical results collected in laboratories, led us to conclude that there was a relative radiological homogeneity (for specific activity and Am-241/Cs-137 ratio) within each tank. The next step consisted in extrapolating the specific activity evaluated for the reference tanks, to the other tanks of each family by using data such as geometry, dose rate or the function of the tanks within the process. Thus, a cartography of the 130 tanks of the facility is available. Finally, the conclusions drawn from the radiological homogeneity of the tanks and their affiliation to homogeneous, well-identified tank clusters lead to a simple and efficient operational approach for the D and D of the 130 tanks of the facility. Indeed, because of the residue characteristics (homogeneous activity and strong differentiation of Cs/Am ratio), a sample collected when opening the tank, and measured on site by gamma spectrometry will allow us to validate the operational design and the emptying techniques defined during the D and D study phase thanks to the characterization of a limited number of reference tanks. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Model Form Error Correction for a Black-Box Thermal Battery Heat Transfer Simulation
Thermal batteries are crucial for supplying power to high-consequence engineering applications such as rockets. Computational simulations have been developed to predict thermal battery behavior, but these simulations often suffer from modeling errors, including model form uncertainty. Addressing this uncertainty can be achieved by quantifying either the model discrepancy in the output or the model form error (MFE) in the governing equation. MFE is particularly valuable as it can be better extrapolated beyond observed outputs, which is essential for predictions involving changes in external system loading, system configuration and geometry, or output quantities. This paper employs a state estimation approach to estimate MFE using experimental data and then utilizes machine learning (ML) to model its relationship with state variables. A nonintrusive technique is used to estimate MFE in a black-box thermal battery heat transfer simulation. The trained machine learning model for MFE is then applied to correct simulation predictions under extrapolated initial conditions and battery configurations. In conclusion, the methodology's performance is evaluated using additional experimental data, demonstrating its effectiveness in improving prediction accuracy.
Batteries↗
Learning genetic perturbation effects with variational causal inference
Advances in sequencing technologies have enhanced the understanding of gene regulation in cells. In particular, Perturb-seq has enabled high-resolution profiling of the transcriptomic response to genetic perturbations at the single-cell level. This understanding has implications in functional genomics and potentially for identifying therapeutic targets. Various computational models have been developed to predict perturbational effects. While deep learning models excel at interpolating observed perturbational data, they tend to overfit in the lack of enough data and may not generalize well to unseen perturbations. In contrast, mechanistic models, such as linear causal models based on gene regulatory networks, hold greater potential for extrapolation, as they encapsulate regulatory information that can predict responses to unseen perturbations. However, their application has been limited to small studies due to overly simplistic assumptions, making them less effective in handling noisy, large-scale single-cell data. We propose a hybrid approach that combines a mechanistic causal model with variational deep learning, termed Single Cell Causal Variational Autoencoder (SCCVAE). The mechanistic model employs a learned regulatory network to represent perturbational changes as shift interventions that propagate through the learned network. SCCVAE integrates this mechanistic causal model into a variational autoencoder, generating rich, comprehensive transcriptomic responses. Our results indicate that SCCVAE exhibits superior performance over current state-of-the-art baselines for extrapolating to predict unseen perturbational responses. Additionally, for the observed perturbations, the latent space learned by SCCVAE allows for the identification of functional perturbation modules and simulation of single-gene knockdown experiments of varying penetrance, presenting a robust tool for interpreting and interpolating perturbational responses at the single-cell level.
59 BASIC BIOLOGICAL SCIENCES↗
The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds
The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.
17 WIND ENERGY↗
Wing Shape Sensing from Measured Strain
A new two step theory is investigated for predicting the deflection and slope of an entire structure using strain measurements at discrete locations. In the first step, a measured strain is fitted using a piecewise least squares curve fitting method together with the cubic spline technique. These fitted strains are integrated twice to obtain deflection data along the fibers. In the second step, computed deflection along the fibers are combined with a finite element model of the structure in order to extrapolate the deflection and slope of the entire structure through the use of System Equivalent Reduction and Expansion Process. The theory is first validated on a computational model, a cantilevered rectangular wing. It is then applied to test data from a cantilevered swept wing model.
system equivalent reduction and expansion process↗
Injection data analysis using material balance time for CO 2 storage capacity estimation in deep closed saline aquifers
Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO 2 . Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO 2 ) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred through adapting the concept of the material balance time. Material balance time is an approximate superposition time function developed to interpret production data from oil and gas wells operating at variable pressure/rate conditions during the boundary-dominated flow period. Using material balance techniques, the ultimate storage capacity is then estimated through linear extrapolation of the average pressure trend to the maximum allowable pressure the formation can withstand. The average pressure is not available in practice, but is can be obtained from the injection data. Two approaches are presented in this study to calculate the average pressure; namely the rigorous and the approximate approaches. Unlike the rigorous approach, the approximate approach does not require a prior knowledge of some reservoir properties (e.g. relative permeability, absolute permeability, formation porosity and thickness) to calculate the average pressure. To investigate its potential and reliability in analyzing CO 2 injection data, the proposed technique is applied to four synthetic cases representing different well operating conditions. Results indicate that the approximate approach consistently overestimates the actual (simulated) storage capacity as compared to the rigorous approach. The agreement - between the inferred and the simulated reservoir pore volume, and between the analytical and numerical estimates of storage capacity - validates the potential application of the technique to CO 2 storage in closed saline aquifers. The technique is further substantiated through application to a field data set utilized from a commercial-scale geological storage (CGS) project. Finally, field data interpretation shows that the proposed technique can be utilized to identify the degree of hydraulic continuity and reservoir compartmentalization within a target formation by interpreting the corresponding pressure and rate responses.
02 PETROLEUM↗
Collection and analysis of remotely sensed data from the Rhode River Estuary Watershed
NASA chose the watershed of Rhode River, a small sub-estuary of the Bay, as a representative test area for intensive studies of remote sensing, the results of which could be extrapolated to other estuarine watersheds around the Bay. A broad program of ecological research was already underway within the watershed, conducted by the Smithsonian Institution's Chesapeake Bay Center for Environmental Studies (CBCES) and cooperating universities. This research program offered a unique opportunity to explore potential applications for remote sensing techniques. This led to a joint NASA-CBCES project with two basic objectives: to evaluate remote sensing data for the interpretation of ecological parameters, and to provide essential data for ongoing research at the CBCES. A third objective, dependent upon realization of the first two, was to extrapolate photointerpretive expertise gained at the Rhode River watershed to other portions of the Chesapeake Bay.
Jenkins, D. W.↗
New methods to improve the vertical extrapolation of near-surface offshore wind speeds
Accurate characterization of the offshore wind resource has been hindered by a sparsity of wind speed observations that span offshore wind turbine rotor-swept heights. Although public availability of floating lidar data is increasing, most offshore wind speed observations continue to come from buoy-based and satellite-based near-surface measurements. The aim of this study is to develop and validate novel vertical extrapolation methods that can accurately estimate wind speed time series across rotor-swept heights using these near-surface measurements. We contrast the conventional logarithmic profile against three novel approaches: a logarithmic profile with a long-term stability correction, a single-column model, and a machine-learning model. These models are developed and validated using 1 year of observations from two floating lidars deployed in US Atlantic offshore wind energy areas. We find that the machine-learning model significantly outperforms all other models across all stability regimes, seasons, and times of day. Machine-learning model performance is considerably improved by including the air–sea temperature difference, which provides some accounting for offshore atmospheric stability. Finally, we find no degradation in machine-learning model performance when tested 83 km from its training location, suggesting promising future applications in extrapolating 10 m wind speeds from spatially resolved satellite-based wind atlases.
17 WIND ENERGY↗
Automated image processing of LANDSAT 2 digital data for watershed runoff prediction
The U.S. Soil Conservation Service (SCS) model for watershed runoff prediction uses soil and land cover information as its major drivers. Kern County Water Agency is implementing the SCS model to predict runoff for 10,400 sq cm of mountainous watershed in Kern County, California. The Remote Sensing Unit, University of California, Santa Barbara, was commissioned by KCWA to conduct a 230 sq cm feasibility study in the Lake Isabella, California region to evaluate remote sensing methodologies which could be ultimately extrapolated to the entire 10,400 sq cm Kern County watershed. Digital results indicate that digital image processing of Landsat 2 data will provide usable land cover required by KCWA for input to the SCS runoff model.
Sasso, R. R.↗