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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model

With increased availability of computational resources, regional and global scale convection-permitting model (CPM, Δx ~ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under-resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid-scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month-long 4-km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low-precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo-top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection-permitting simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

SU-E-T-31: Alternative VMAT Technique Reduces Total Monitor Units for Lung SBRT

Purpose: To investigate an alternative approach to VMAT optimization for hypofractionation lung treatment which increases average aperture opening and results in lower total Monitor Units (MU) without significantly sacrificing plan quality. Methods: Benchmark Volumetric Modulated Arc Therapy (bVMAT) plans were generated for 10 lung Stereotactic Body radiotherapy (SBRT) cases using Eclipse Version 11.0.42 (Varian Medical Systems) without a maximum MU constraint. Prescriptions ranged from 40 to 54Gy in 3 to 5 fractions. AAA dose calculation and PRO fluence based optimization was utilized. Two comparison VMAT plans were generated for each case, one that forced an initial “open” mlc aperture conformal to the tumor as a starting condition (oVMAT) with similar optimization parameters and arc geometries, and one that repeated the bVMAT optimization but added a maximum MU constraint (muVMAT). All plans used two arcs with lengths between 168 to 230 degrees. PTV D 95% and Dmean, lung V20 Gy, chest wall V30 Gy, average aperture opening and MU's were compared. Statistical significance was evaluated using Wilcoxon signed rank test. Results: Average PTV D(95), PTV mean and lung V20Gy over all plans was 99.2 ± 1.7%, 103.3 ± 0.6% and 7.8 ± 2.4% respectively. The average chest wall V30Gy was 61 ± 61 cc and ranged between 0 to 166 cc. There were no significant differences between the three techniques for the dosimetric quantities. MUs were reduced by 11 ±11% (p<0.01) and 25 ± 5% (p<0.01) and the average aperture size was increased by 13.7 ± 14% (p=0.02) and 35.8 ± 10% (p<0.01) with muVMAT and oVMAT, respectively, compared to bVMAT. Conclusion: oVMAT and muVMAT techniques were both able to increase average aperture size and reduce total MU compared to the benchmark VMAT plan, but the magnitude of the changes observed for oVMAT was larger.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Entropy Splitting for High Order Numerical Simulation of Compressible Turbulence

A stable high-order numerical scheme for direct numerical simulation (DNS) of shock-free compressible turbulence is presented. The method is applicable to general geometries. It contains no upwinding, artificial dissipation, or filtering. Instead the method relies on the stabilizing mechanisms of an appropriate conditioning of the governing equations and the use of compatible spatial difference operators for the interior points (interior scheme) as well as the boundary points (boundary scheme). An entropy-splitting approach splits the inviscid flux derivatives into conservative and nonconservative portions. The spatial difference operators satisfy a summation-by-parts condition, leading to a stable scheme (combined interior and boundary schemes) for the initial boundary value problem using a generalized energy estimate. A Laplacian formulation of the viscous and heat conduction terms on the right hand side of the Navier–Stokes equations is used to ensure that any tendency to odd–even decoupling associated with central schemes can be countered by the fluid viscosity. The resulting methods are able to minimize the spurious high-frequency oscillations associated with pure central schemes, especially for long time integration applications such as DNS. For validation purposes, the methods are tested in a DNS of compressible turbulent plane channel flow at low values of friction Mach number, where reference turbulence data bases exist. It is demonstrated that the methods are robust in terms of grid resolution, and in good agreement with published channel data. Accurate turbulence statistics can be obtained with moderate grid sizes. Stability limits on the range of the splitting parameter are determined from numerical tests.

N.D. Sandham↗
DOE OSTI2015

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

Apache Spark↗
NASA NTRS2009

Kepler Data Validation and Follow-up Programs

The approach that the Kepler Mission uses to remove false positive events and to validate the discoveries consists of two parts; data validation (DPI) and follow up observations program (FOP). DV consists of several methods of examining the data from the spacecraft observations. First, to rule out statistical fluctuations in the data, accept only signals that show 3 or more transits and that have a total signal-to-noise ratio that exceeds 7 sigma. Second. to identify small stellar companions to the target star, we check for secondary eclipses and determine if the transit characteristics are appropriate for a planetary companion. Third, check for background binaries that are in the target aperture. Here we measure the movement of the image centroid before, during, and after the transit. If the target is producing the signal, a dimming wi11 move the image centroid in a known direction and magnitude. If the signal comes from a nearby star, the amplitude and direction of the motion wi11 be different, This test is expected to rule out the hundreds of binary signals expected from background stars. The precision of the measurement depends on the stellar fluxes and positions but can be better than 0.01 pixel; i.e., 0.04". Those candidates that pass these tests are examined using ground-based telescopes and radial velocity spectrometers. First medium precision RV is used to rule out any remaining stellar companions. Then high spatial resolution imaging is used to check for nearby stars that are in the aperture- (The Kepler apertures depend on magnitude but are of order 36 sq are sec in area.) If no stars are present that quid generate the observed signal, then the candidate goes to a large telescope such as Keck, HET, or Wi1lilam Herschel for high precision observations to get the planet mass or an upper limit to it, if there are some stars in the aperture, then the photometric observations are employed to look for the transit by cane of the confounding stars. If none are seen, then the candidate is sent to a large telescope such as Keck

William, Borucki J.↗
DOE OSTI2024

Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases

Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.

CFD↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
NASA NTRS1987

Soil moisture estimation using GOES-VISSR infrared data - A case study with a simple statistical method

Five days of clear sky observations of Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudoindependent tests in which the test day is held out of the regression is 0.71. It is shown that a depletion coefficient of 0.92, when used to compute antecedent precipitation index (API), produces the best correlation between API and soil moisture as inferred from GOES thermal infrared data. By averaging daily predicted values over the 5-day rain-free case study period, 92 percent of the variance of the morning surface temperature change is explained by a simple multiple linear regression with all independent variables, or, alternatively, 85 percent of the observed variance in API is explained. It is concluded that this approach can distinguish at least four classes of soil wetness, but the necessity for measurement of surface advection may limit its usefulness in remote areas.

Wetzel, Peter J.↗
NASA NTRS1994

Implementing Software Safety in the NASA Environment

Until recently, NASA did not consider allowing computers total control of flight systems. Human operators, via hardware, have constituted the ultimate safety control. In an attempt to reduce costs, NASA has come to rely more and more heavily on computers and software to control space missions. (For example. software is now planned to control most of the operational functions of the International Space Station.) Thus the need for systematic software safety programs has become crucial for mission success. Concurrent engineering principles dictate that safety should be designed into software up front, not tested into the software after the fact. 'Cost of Quality' studies have statistics and metrics to prove the value of building quality and safety into the development cycle. Unfortunately, most software engineers are not familiar with designing for safety, and most safety engineers are not software experts. Software written to specifications which have not been safety analyzed is a major source of computer related accidents. Safer software is achieved step by step throughout the system and software life cycle. It is a process that includes requirements definition, hazard analyses, formal software inspections, safety analyses, testing, and maintenance. The greatest emphasis is placed on clearly and completely defining system and software requirements, including safety and reliability requirements. Unfortunately, development and review of requirements are the weakest link in the process. While some of the more academic methods, e.g. mathematical models, may help bring about safer software, this paper proposes the use of currently approved software methodologies, and sound software and assurance practices to show how, to a large degree, safety can be designed into software from the start. NASA's approach today is to first conduct a preliminary system hazard analysis (PHA) during the concept and planning phase of a project. This determines the overall hazard potential of the system to be built. Shortly thereafter, as the system requirements are being defined, the second iteration of hazard analyses takes place, the systems hazard analysis (SHA). During the systems requirements phase, decisions are made as to what functions of the system will be the responsibility of software. This is the most critical time to affect the safety of the software. From this point, software safety analyses as well as software engineering practices are the main focus for assuring safe software. While many of the steps proposed in this paper seem like just sound engineering practices, they are the best technical and most cost effective means to assure safe software within a safe system.

Wetherholt, Martha S.↗
NASA NTRS2008

Forecasting Lightning Threat using Cloud-Resolving Model Simulations

Two new approaches are proposed and developed for making time and space dependent, quantitative short-term forecasts of lightning threat, and a blend of these approaches is devised that capitalizes on the strengths of each. The new methods are distinctive in that they are based entirely on the ice-phase hydrometeor fields generated by regional cloud-resolving numerical simulations, such as those produced by the WRF model. These methods are justified by established observational evidence linking aspects of the precipitating ice hydrometeor fields to total flash rates. The methods are straightforward and easy to implement, and offer an effective near-term alternative to the incorporation of complex and costly cloud electrification schemes into numerical models. One method is based on upward fluxes of precipitating ice hydrometeors in the mixed phase region at the-15 C level, while the second method is based on the vertically integrated amounts of ice hydrometeors in each model grid column. Each method can be calibrated by comparing domain-wide statistics of the peak values of simulated flash rate proxy fields against domain-wide peak total lightning flash rate density data from observations. Tests show that the first method is able to capture much of the temporal variability of the lightning threat, while the second method does a better job of depicting the areal coverage of the threat. Our blended solution is designed to retain most of the temporal sensitivity of the first method, while adding the improved spatial coverage of the second. Exploratory tests for selected North Alabama cases show that, because WRF can distinguish the general character of most convective events, our methods show promise as a means of generating quantitatively realistic fields of lightning threat. However, because the models tend to have more difficulty in predicting the instantaneous placement of storms, forecasts of the detailed location of the lightning threat based on single simulations can be in error. Although these model shortcomings presently limit the precision of lightning threat forecasts from individual runs of current generation models,the techniques proposed herein should continue to be applicable as newer and more accurate physically-based model versions, physical parameterizations, initialization techniques and ensembles of forecasts become available.

McCaul, Eugene W., Jr.↗
NASA NTRS2014

Profiling Sea Ice with a Multiple Altimeter Beam Experimental Lidar (MABEL)

The sole instrument on the upcoming ICESat-2 altimetry mission is a micropulse lidar that measures the time-of-flight of individual photons from laser pulses transmitted at 532 nm. Prior to launch, MABEL serves as an airborne implementation for testing and development. In this paper, we provide a first examination of MABEL data acquired on two flights over sea ice in April 2012: one north of the Arctic coast of Greenland, and the other in the East Greenland Sea.We investigate the phenomenology of photon distributions in the sea ice returns. An approach to locate the surface and estimate its elevation in the distributions is described, and its achievable precision assessed. Retrieved surface elevations over relatively flat leads in the ice cover suggest that precisions of several centimeters are attainable. Restricting the width of the elevation window used in the surface analysis can mitigate potential biases in the elevation estimates due to subsurface returns at 532 nm. Comparisons of nearly coincident elevation profiles from MABEL with those acquired by an analog lidar show good agreement.Discrimination of ice and open water, a crucial step in the determination of sea ice free board and the estimation of ice thickness, is facilitated by contrasts in the observed signal background photon statistics. Future flight lines will sample a broader range of seasonal ice conditions for further evaluation of the year-round profiling capabilities and limitations of the MABEL instrument.

Lidar↗
DOE OSTI2023

Wave-function-based emulation for nucleon-nucleon scattering in momentum space

Emulators for low-energy nuclear physics can provide fast and accurate predictions of bound-state and scattering observables for applications that require repeated calculations with different parameters, such as Bayesian uncertainty quantification. In this paper, we extend a scattering emulator based on the Kohn variational principle (KVP) to momentum space (including coupled channels) with arbitrary boundary conditions, which enable the mitigation of spurious singularities known as Kohn anomalies. We test it on a modern chiral nucleon-nucleon (N N) interaction, including emulation of the coupled channels. We provide comparisons between a Lippmann-Schwinger equation emulator and our KVP momentum-space emulator for a representative set of neutron-proton (n p) scattering observables, and also introduce a quasi-spline-based approach for the KVP-based emulator. Furthermore, our findings show that while there are some trade-offs between accuracy and speed, all three emulators perform well. Self-contained Jupyter notebooks that generate the results and figures in this paper are publicly available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1989

Raman intensity as a probe of concentration near a crystal growing in solution

The feasibility of using Raman spectral scattering signals for measurements of concentration profiles near a crystal interface during growth or dissolution is discussed. With KH2PO4 (KDP) as a test material, optical multichannel analyzer (OMA) detection of a solute Raman vibrational band provided direct quantification of solute concentration with band intensity. The intersection of incident laser and Raman collection optics provided 3-D selective point measurements of the solution concentration field. Unlike many other techniques, the Raman band intensity is not sensitive to the typical temperature variations. Precision calibration of Raman intensity versus KDP concentration with less than 1 pct standard deviation error levels was demonstrated. A fiber optic, which sampled incident laser intensity and coupled it to the OMA, provided a fully synchronized monitor of fluctuations in laser power to correlate with observed Raman signals. With 1 W of laser power at the sample, good data statistics required eight repeated data collections at approximately 2.5 min collection. The accumulated time represents the concentration measurement time at one spatial location. Photomicroscopy documented a 30 micrometer diameter by 200 micrometer of laser Raman scattering region in the solution near the crystal surface. The laser beam was able to approach up to 25 micrometer from the crystal surface. However, a crystal surface reflected intensity contribution was weakly detectable. Nucleated microcrystals were seen in the crystal-growing solution. These microcrystals convect right up to the crystal surface and indicate no quiet diffusion region under normal gravity conditions. Translation of the solution cell with respect to the optics caused systematic intensity errors.

Wilkinson, R. Allen↗
NASA NTRS1981

A stochastic-dynamical approach to the study of the natural variability of the climate

A method, suggested by Leith (1975), which employed stochastic-dynamic forecasts obtained from a general circulation model in such a way as to satisfy the definition of climatic noise, was used to validate assumptions accounting for the effects of external influences in estimating the climatic noise. Two assumptions were investigated: (1) that the weather fluctuations can be represented as a Markov process, and (2) that changing external conditions do not influence the atmosphere's statistical properties on short time scales. The general circulation model's simulation of the daily weather fluctuations was generated by performing integrations with prescribed climatological boundary conditions for random initial atmospheric states, with resulting dynamical forecasts providing an ensemble of simulated data for the autoregressive modeling of weather fluctuations. To estimate the climatic noise from the observational data (consisting of hourly values of sea level pressure and surface temperature at 54 U.S. stations for the month of January for the years 1949-1975) use of the short time-scale assumption is made. The simulated and observed data were found not to be consistent with either white noise or a Markov process of weather fluctuations. Good agreement was found between the results of the hypothetical testing of the simulated and the observed surface temperatures; and only partial support was found for the short time-scale assumption, i.e., for sea level pressure.

Straus, D. M.↗
DOE OSTI2013

Large-scale Manufacturing of Nanoparticulate-based Lubrication Additives for Improved Energy Efficiency and Reduced Emissions

This project was funded under the Department of Energy (DOE) Lab Call on Nanomanufacturing for Energy Efficiency and was directed toward the development of novel boron-based nanocolloidal lubrication additives for improving the friction and wear performance of machine components in a wide range of industrial and transportation applications. Argonne's research team concentrated on the scientific and technical aspects of the project, using a range of state-of-the art analytical and tribological test facilities. Argonne has extensive past experience and expertise in working with boron-based solid and liquid lubrication additives, and has intellectual property ownership of several. There were two industrial collaborators in this project: Ashland Oil (represented by its Valvoline subsidiary) and Primet Precision Materials, Inc. (a leading nanomaterials company). There was also a sub-contract with the University of Arkansas. The major objectives of the project were to develop novel boron-based nanocolloidal lubrication additives and to optimize and verify their performance under boundary-lubricated sliding conditions. The project also tackled problems related to colloidal dispersion, larger-scale manufacturing and blending of nano-additives with base carrier oils. Other important issues dealt with in the project were determination of the optimum size and concentration of the particles and compatibility with various base fluids and/or additives. Boron-based particulate additives considered in this project included boric acid (H 3 BO 3 ), hexagonal boron nitride (h-BN), boron oxide, and borax. As part of this project, we also explored a hybrid MoS 2 + boric acid formulation approach for more effective lubrication and reported the results. The major motivation behind this work was to reduce energy losses related to friction and wear in a wide spectrum of mechanical systems and thereby reduce our dependence on imported oil. Growing concern over greenhouse gas emissions was also a major reason. The transportation sector alone consumes about 13 million barrels of crude oil per day (nearly 60% of which is imported) and is responsible for about 30% of the CO 2 emission. When we consider manufacturing and other energy-intensive industrial processes, the amount of petroleum being consumed due to friction and wear reaches more than 20 million barrels per day (from official energy statistics, U.S. Energy Information Administration). Frequent remanufacturing and/or replacement of worn parts due to friction-, wear-, and scuffing-related degradations also consume significant amounts of energy and give rise to additional CO 2 emission. Overall, the total annual cost of friction- and wear-related energy and material losses is estimated to be rather significant (i.e., as much as 5% of the gross national products of highly industrialized nations). It is projected that more than half of the total friction- and wear-related energy losses can be recovered by developing and implementing advanced friction and wear control technologies. In transportation vehicles alone, 10% to 15% of the fuel energy is spent to overcome friction. If we can cut down the friction- and wear-related energy losses by half, then we can potentially save up to 1.5 million barrels of petroleum per day. Also, less friction and wear would mean less energy consumption as well as less carbon emissions and hazardous byproducts being generated and released to the environment. New and more robust anti-friction and -wear control technologies may thus have a significant positive impact on improving the efficiency and environmental cleanliness of the current legacy fleet and future transportation systems. Effective control of friction in other industrial sectors such as manufacturing, power generation, mining and oil exploration, and agricultural and earthmoving machinery may bring more energy savings. Therefore, this project was timely and responsive to the energy and environmental objectives of DOE and our nation. In this project, most of the boron-based materials with known and potential anti-friction and -wear properties have been manufactured as colloidal additives and tested for their effectiveness in controlling friction and wear. Unlike other anti-friction and -wear additives, which consist of zinc, molybdenum, sulfur, phosphorus, and even chlorine, lubricious boron compounds considered in this project are made of boron, oxygen, nitrogen, and hydrogen, which are more environmentally benign. Among others, boric acid is a natural mineral (known in mineralogy as "sassolite"). Based on our earlier exploratory research, it was found to offer the best overall prospect in terms of performance improvements, environmental friendliness, and ease of manufacturing and, hence, cost effectiveness. Hexagonal boron nitride and borax also offered good prospects for improving the tribological properties of lubricated sliding surfaces. Boron oxide particles were found to be rather hard and somewhat abrasive and, hence, were not considered beyond the initial screening studies. In our bench-top tribological evaluation, we also demonstrated that those additives which worked well with engine oils could work equally well with very common gear oils. When added at appropriate concentrations, such gear oils were found to provide significant resistance to micropitting and scuffing failures in bench-top tribological test systems. Their traction coefficients were also reduced substantially and their scuffing limits were improved considerably. Such impressive tribological behavior of boron-based additives may have been due to their high chemical affinities to interact with sliding contact surfaces and to form slick and protective boundary films. Indeed, our surface studies have confirmed that most of the boron-based nanoparticulate additives prepared in our project possess a strong tendency to form a boron-rich boundary film on sliding contact surfaces. It is believed that the formation of such slick and highly durable boundary films is perhaps one of the fundamental reasons for their superior anti-friction, -wear, and -scuffing performance. Boron-based additives developed under this project have shown potential to reduce or replace the uses of environmentally unsafe sulfur- and phosphorus-bearing anti-wear and friction additives, such as zinc dialkyl dithiophosphate (ZDDP) and molybdenum dialkyl dithiocarbamate (MoDTC), in current lubricating oils. Because ZDDP and MoDTC were suspected of adversely impacting the performance of after-treatment catalysts in current engines, the Environmental Protection Agency (EPA) and other regulatory agencies are demanding that the concentrations of these catalysts in current oils be curtailed drastically. The boron-based nano-additives developed in this project may help reduce the use of ZDDP and MoDTC additives and, hence, help ease the poisoning effects on after-treatment catalysts. When used as lubricity additives, these boron additives can chemically interact with sliding or contacting surfaces and form a protective and slick boundary film, which can, in turn, help reduce friction and wear and increase resistance to scuffing. In the cases of traditional anti-friction and -wear additives mentioned, such protective boundary films result from phosphorus, sulfur, and other elements in the additive package, and again they have been under increased scrutiny in recent years, mainly because of their adverse effects on after-treatment devices. Overall, the boron-based nano-additive technology of this project was shown to hold promise for a broad range of industrial and transportation applications where lower friction and higher resistance to wear and scuffing are needed. Due to more stringent operating conditions of modern machinery, rolling, rotating, and sliding components have been failing to meet the projected lifetimes, mainly because of failures related to mechanical wear, corrosion, and scuffing. The novel boron-based additive technology developed under this project may help such machine components to function reliably by cutting down the friction and wear losses and by increasing resistance to scuffing.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. Additionally, this paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Multifidelity validation of digital surrogates using variable-density turbulent mixing models

In this study, ensembles of experimental data are presented and utilized to compare and validate two models used in the simulation of variable-density (Atwood = 0.22), compressible turbulent mixing. Though models of this kind (Reynolds averaged NavierStokes and large-eddy simulations) have been validated extensively with more canonical flows in previous studies, here the present approach offers novelty in the complexity of the geometry, the ensemble-based validation, and the uniformity of the computational framework on which the models are tested. Moreover, all experimental and computational tasks were completed by the authors which has led to a tightly coupled experimental configuration with its “digital twin.” The experimental divergent-shock-tube facility and its data acquisition methods are described and replicated in simulation space. A 2D Euler model which neglects the turbulent mixing at the interface is optimized to experimental data using a Gaussian process. This model then serves as the basis for both the 2D RANS and 3D LES studies that make comparisons to the mixing-layer data from the experiment. A relatively simple RANS model is shown to produce good agreement with experimental data only at late flow development times. The LES ensembles generally show good agreement with experimental data but display sensitivity to the characterization of initial conditions. Resolution-dependent behavior is also observed for certain higher-order statistics of interest. Overall, the LES model successfully captures the effects of divergent geometry, compressibility, and combined nonlinear instabilities inherent to the problem. The successful prediction of mixing width and its growth rate highlight the existence of three distinct regimes in the development of the instability, each with similarities to previously studied instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

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