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DOE OSTI2021

Semi-Analytic Functions to Calculate the Deposition Coefficients for Ice Crystal Vapor Growth in Bin and Bulk Microphysical Models

Numerical cloud models require estimates of the vapor growth rate for ice crystals. Current bulk and bin microphysical parameterizations generally assume that vapor growth is diffusion limited, though some parameterizations include the influence of surface attachment kinetics through a constant deposition coefficient. A parameterization for variable deposition coefficients is provided herein. The parameterization is an explicit function of the ambient ice supersaturation and temperature, and an implicit function of crystal dimensions and pressure. The parameterization is valid for variable surface types including growth by dislocations and growth by step nucleation. Deposition coefficients are predicted for the two primary growth directions of crystals, allowing for the evolution of the primary habits. Comparisons with benchmark calculations of instantaneous mass growth indicate that the parameterization is accurate to within a relative error of 1%. Parcel model simulations using Lagrangian microphysics as a benchmark indicate that the bulk parameterization captures the evolution of mass mixing ratio and fall speed with typical relative errors of less than 10%, whereas the average axis lengths can have errors of up to 20%. The bin model produces greater accuracy with relative errors often less that 10%. The deposition coefficient parameterization can be used in any bulk and bin scheme, with low error, if an equivalent volume spherical radius is provided.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗

NASA NTRS1975

A wetlands inventory of the state of Nebraska using ERTS-1 imagery

The use of ERTS-1 imagery permitted a rapid, economic, and accurate inventory of wetlands in Nebraska that are ten acres or larger in size. Four categories of wetlands - Open Water, Subirrigated Meadows, Marshes, and Seasonally Flooded Basins - were delineated by using two seasons of imagery and an electronic image-enhancing system. Positive print enlargements of bands 5 and 7 at a scale of 1:250,000 (acquired in the spring) as well as band 7 (acquired in late summer) were used to delineate all categories. Electronic enhancement of band 6 (acquired in the fall) was used as an aid to further differentiate marshes. Accuracy estimates based on color infrared aerial photography as ground truth indicated, as an overall average, 85 percent correct identification.

Seevers, P. M.↗

NASA NTRS1976

Water vapor in the lower stratosphere measured from aircraft flight

Water vapor in the lower stratosphere was measured in situ by two aluminum oxide hygrometers mounted on the nose of an RB57 aircraft. Data were taken nearly continuously from January to May 1974 from an altitude of approximately 11 km to 19 km as the aircraft flew between 70 deg N and 50 deg S over the land areas in the Western Hemisphere. Pseudomeridional cross sections of water vapor and temperature are derived from the flight data and show mixing ratios predominantly between 2 and 4 micron gm/gm with an extreme range of 1 to 8 micron gm/gm. Measurement precision is estimated by comparing the simultaneously measured values from the two flight hygrometer systems. Accuracy is estimated to be about + or - 40 percent at 19 km. A height-averaged latitudinal cross section of water vapor shows symmetry of wet and dry zones.

Hilsenrath, E.↗

NASA NTRS1980

A proposed method for wind velocity measurement from space

An investigation was made of the feasibility of making wind velocity measurements from space by monitoring the apparent change in the refractive index of the atmosphere induced by motion of the air. The physical principle is the same as that resulting in the phase changes measured in the Fizeau experiment. It is proposed that this phase change could be measured using a three cornered arrangement of satellite borne source and reflectors, around which two laser beams propagate in opposite directions. It is shown that even though the velocity of the satellites is much larger than the wind velocity, factors such as change in satellite position and Doppler shifts can be taken into account in a reasonable manner and the Fizeau phase measured. This phase measurement yields an average wind velocity along the ray path through the atmosphere. The method requires neither high accuracy for satellite position or velocity, nor precise knowledge of the refractive index or its gradient in the atmosphere. However, the method intrinsically yields wind velocity integrated along the ray path; hence to obtain higher spatial resolution, inversion techniques are required.

Censor, D.↗

NASA NTRS1982

Analyzing the Broken Ridge area of the Indian Ocean using magnetic and gravity anomaly maps and geoid undulation and bathymetry data

A higher resolution anomaly map of the Broken Ridge area (2 degree dipole spacing) was produced and reduced to the pole using quiet time data for this area. The map was compared with equally scaled maps of gravity anomaly, geoid undulation, and bathymetry. The ESMAP results were compared with a NASA MAGSAT map derived by averaging data in two-degree bins. A survey simulation was developed to model the accuracy of MAGSAT anomaly maps as a function of satellite altitude, instrument noise level, external noise model, and crustal anomaly field model. A preliminary analysis of the geophysical structure of Broken Ridge is presented and unresolved questions are listed.

Lazarewicz, A. R.↗

NASA NTRS1983

Experimental comparison of icing cloud instruments

Icing cloud instruments were tested in the spray cloud Icing Research Tunnel (IRT) in order to determine their relative accuracy and their limitations over a broad range of conditions. It was found that the average of the readings from each of the liquid water content (LWC) instruments tested agreed closely with each other and with the IRT calibration; but all have a data scatter (+ or - one standard deviation) of about + or - 20 percent. The effect of this + or - 20 percent uncertainty is probably acceptable in aero-penalty and deicer experiments. Existing laser spectrometers proved to be too inaccurate for LWC measurements. The error due to water runoff was the same for all ice accretion LWC instruments. Any given laser spectrometer proved to be highly repeatable in its indications of volume median drop size (DVM), LWC and drop size distribution. However, there was a significant disagreement between different spectrometers of the same model, even after careful standard calibration and data analysis. The scatter about the mean of the DVM data from five Axial Scattering Spectrometer Probes was + or - 20 percent (+ or - one standard deviation) and the average was 20 percent higher than the old IRT calibration. The + or - 20 percent uncertainty in DVM can cause an unacceptable variation in the drag coefficient of an airfoil with ice; however, the variation in a deicer performance test may be acceptable.

Olsen, W.↗

NASA NTRS1982

Preliminary results on the abradability of porous, sintered seal material

Preliminary results are presented for the case of titanium blade specimens, with bare tips (or covered with wear resistant, plasma sprayed materials) rubbing at 100 m/s against specimens of abradable nickel-chromium seal material moving toward the rotating blades at 0.0125 mm/s or at 0.025 mm/s. Using a two component dynamometer, the normal force of the rub interaction was measured and the shear component estimated. The elastoplastic properties of the seal material have been determined and those parameters as well as the rigidity of the rub tester system are considered in conjunction with those affecting the accuracy of the measurement of the forces arising at the blade-seal interface. The average and the 'local instantaneous' temperatures of the seal specimen and the temperature of the blade tip surface during rubbing are presented as functions of time. A seal densification factor is defined and its functional relationships with contact force components, temperature, wear ratio and blade tip abrading capability are indicated.

Wolak, J.↗

NASA NTRS1990

Finite-volume application of high order ENO schemes to multi-dimensional boundary-value problems

The finite volume approach in developing multi-dimensional, high-order accurate essentially non-oscillatory (ENO) schemes is considered. In particular, a two dimensional extension is proposed for the Euler equation of gas dynamics. This requires a spatial reconstruction operator that attains formal high order of accuracy in two dimensions by taking account of cross gradients. Given a set of cell averages in two spatial variables, polynomial interpolation of a two dimensional primitive function is employed in order to extract high-order pointwise values on cell interfaces. These points are appropriately chosen so that correspondingly high-order flux integrals are obtained through each interface by quadrature, at each point having calculated a flux contribution in an upwind fashion. The solution-in-the-small of Riemann's initial value problem (IVP) that is required for this pointwise flux computation is achieved using Roe's approximate Riemann solver. Issues to be considered in this two dimensional extension include the implementation of boundary conditions and application to general curvilinear coordinates. Results of numerical experiments are presented for qualitative and quantitative examination. These results contain the first successful application of ENO schemes to boundary value problems with solid walls.

Casper, Jay↗

NASA NTRS1990

A GCM parameterization for the shortwave radiative properties of water clouds

A new parameterization was developed for predicting the shortwave radiative properties of water clouds, suitable for inclusion in general circulation models (GCMs). The parameterization makes use of the simple relationships found by Slingo and Schrecker, giving the three input parameters required to calculate the cloud radiative properties (the optical depth, single scatter albedo and asymmetry parameter) in terms of the liquid water path and equivalent radius of the drop size distribution. The input parameters are then used to derive the cloud radiative properties, using standard two-stream equations for a single layer. The relationships were originally derived for fairly narrow spectral bands but it was found that it is possible to average the coefficients so as to use a much smaller number of bands, without sacrificing accuracy in calculating the cloud radiative properties. This makes the parameterization fast enough to be included in GCMs. The parameterization was programmed into the radiation scheme used in the U.K. Meteorological Office GCM. This scheme and the 24 band Slingo/Schrecker scheme were compared with each other and with observations, using a variety of published datasets. There is good agreement between the two schemes for both cloud albedo and absorption, even when only four spectral bands are employed in the GCM.

Slingo, A.↗

NASA NTRS1989

Tropical rain mapping radar on the Space Station

The conceptual design for a tropical rain mapping radar for flight on the manned Space Station is discussed. In this design the radar utilizes a narrow, dual-frequency (9.7 GHz and 24.1 GHz) beam, electronically scanned antenna to achieve high spatial (4 km) and vertical (250 m) resolutions and a relatively large (800 km) cross-track swath. An adaptive scan strategy will be used for better utilization of radar energy and dwell time. Such a system can detect precipitation at rates of up to 100 mm/hr with accuracies of roughly 15 percent. With the proposed space-time sampling strategy, the monthly averaged rainfall rate can be estimated to within 8 percent, which is essential for many climatological studies.

Im, Eastwood↗

NASA NTRS1946

Comparison of Measured and Calculated Stresses in Built-up Beams

Web stresses and flange stresses were measured in three built-up beams: one of constant depth with flanges of constant cross-section, one linearly tapered in depth with flanges of constant cross section, and one linearly tapered in depth with tapered flanges. The measured stresses were compared with the calculated stresses obtained by the methods outlined in order to determine the degree of accuracy that may be expected from the stress analysis formulas. These comparisons indicated that the average measured stresses for all points in the central section of the beams did not exceed the average calculated stresses by more than 5 percent. It also indicated that the difference between average measured flange stresses and average calculated flange stresses on the net area and a fully effective web did not exceed 6.1 percent.

Levin, L Ross↗

NASA NTRS2005

Estimating moisture transport over oceans using space-based observations

The moisture transport integrated over the depth of the atmosphere (0) is estimated over oceans using satellite data. The transport is the product of the precipitable water and an equivalent velocity (ue), which, by definition, is the depth-averaged wind velocity weighted by humidity. An artificial neural network is employed to construct a relation between the surface wind velocity measured by the spaceborne scatterometer and coincident ue derived using humidity and wind profiles measured by rawinsondes and produced by reanalysis of operational numerical weather prediction (NWP). On the basis of this relation, 0 fields are produced over global tropical and subtropical oceans (40_N- 40_S) at 0.25_ latitude-longitude and twice daily resolutions from August 1999 to December 2003 using surface wind vector from QuikSCAT and precipitable water from the Tropical Rain Measuring Mission. The derived ue were found to capture the major temporal variability when compared with radiosonde measurements. The average error over global oceans, when compared with NWP data, was comparable with the instrument accuracy specification of space-based scatterometers. The global distribution exhibits the known characteristics of, and reveals more detailed variability than in, previous data.

Artificial Neural Network↗

NASA NTRS2017

Validation of a Solid Rocket Motor Internal Environment Model

In a prior effort, a thermal/fluid model of the interior of Penn State University's laboratory-scale Insulation Test Motor (ITM) was constructed to predict both the convective and radiative heat transfer to the interior walls of the ITM with a minimum of empiricism. These predictions were then compared to values of total and radiative heat flux measured in a previous series of ITM test firings to assess the capabilities and shortcomings of the chosen modeling approach. Though the calculated fluxes reasonably agreed with those measured during testing, this exercise revealed means of improving the fidelity of the model to, in the case of the thermal radiation, enable direct comparison of the measured and calculated fluxes and, for the total heat flux, compute a value indicative of the average measured condition. By replacing the P1-Approximation with the discrete ordinates (DO) model for the solution of the gray radiative transfer equation, the radiation intensity field in the optically thin region near the radiometer is accurately estimated, allowing the thermal radiation flux to be calculated on the heat-flux sensor itself, which was then compared directly to the measured values. Though the fully coupling the wall thermal response with the flow model was not attempted due to the excessive computational time required, a separate wall thermal response model was used to better estimate the average temperature of the graphite surfaces upstream of the heat flux gauges and improve the accuracy of both the total and radiative heat flux computations. The success of this modeling approach increases confidence in the ability of state-of-the-art thermal and fluid modeling to accurately predict SRM internal environments, offers corrections to older methods, and supplies a tool for further studies of the dynamics of SRM interiors.

Martin, Heath T.↗

NASA NTRS2017

Validation of a Solid Rocket Motor Internal Environment Model

Martin, Heath T.↗

DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗

DOE OSTI2021

Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km 2 . The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km 2 , the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

DTVGM↗