SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This manual explains the use of local sensitivity analysis, the global Morris OAT and Sobol’ methods, and a related data-worth analysis as implemented in iTOUGH2. In addition to input specification and output formats, it includes some examples to show how to interpret results.
Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.
Background: New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results: We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions: This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.
Electrical energy plays vital role in our socio-economic activity and therefore ensuring the reliability of the electric grid, from the generation, transmission and distribution level is critical. In order to maintain the power system parameter viz., frequency, voltage, etc., optimally, balancing of generation and consumption is very much essential. However, solar energy is infirm power by nature this is due to cloud cover / other local phenomena. Hence, Photovoltaic (PV) power generation brings a significant challenge to the grid operator due to the variability of the solar energy. The complexity of this challenge in terms of planning and dispatch ability of PV resources, aggravates with the high penetration of solar energy into the electric grid. In this setting, reliable solar radiation forecasting models based on accurate and quality input data become essential. In order to develop a suitable model for predicting solar radiation, quality historical / real time measurement is also needed. Under this study NIWE and NREL jointly developed / tested short-term solar forecasting frameworks using a smart persistence and physics-based smart persistence models for intra-hour forecasting of solar radiation (PSPI) and benchmarked 9 different data imputation techniques in 15 Solar Radiation Resource Assessment (SRRA) stations, located at different parts of India. During any measurement campaign, due to various technical reasons, we may miss few observations. However, the missing observation often reduce the performance of any forecasting model. Therefore, suitable data imputation method would assist us to obtain continuous observation of solar radiation. A station-by-station and method-by-method analysis was carried out to understand the performance of each model. Based on our analysis, among all the data imputation methods, the Kalman data imputation method is better for Indian Weather condition. In addition, Kalman StructTS, Linear, Stine and Arima methods yield slightly inferior accuracy compared to Kalman, but outperform the other methods. The extended solar radiation data are used by solar forecasting models to provide the prediction of solar radiation at 15 SRRA stations. As far as short term forecasting model is concerned, the PSPI model outperforms the Smart Persistence model. However, the forecast error is increases with the forecasting horizon.
Engine knock and misfire are barriers to pathways leading to high-efficiency Spark-Ignited (SI) Natural Gas (NG) engines. The general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by the low emission limits and the wide range of chemical reactivity in pipeline-quality natural gas. Depending on the region and the source of the natural gas, its reactivity, described by its Methane Number (MN), which is analogous to the Octane Number for liquid SI fuels, can span from 65 to 95. In order to realize diesel-like efficiencies, SI NG engines must be designed to operate at high Brake Mean Effective Pressures (BMEP), near or beyond knock limits, over a wide range of fuel reactivity. This requires a deep understanding of the combustion-engine interactions pertaining to flame propagation and End-Gas Autoignition (EGAI), i.e., the autoignition of the unburned gas (end gas) ahead of the flame front. However, EGAI, if controlled, provides an opportunity to increase SI NG engine efficiency by increasing the combustion rate and the total fraction of burned fuel, mitigating the effects of the slow flame speeds characteristic of natural gas fuels, which generally reduce BMEP and increase unburned hydrocarbon emissions. For this reason, to realize diesel-like efficiencies and ultra-low emissions on SI NG engines, this work proposes the study of the main parameters influencing the modeling and prediction of NG EGAI to allow for its control. In this work, a novel EGAI detection and onset determination method was developed to reliably quantify EGAI for data analysis and engine control. The new method allowed the prediction of EGAI on SI NG engines without the need to use engine- and operating-condition-dependent thresholds and reduced the error in quantifying the fraction of the total energy released by the EGAI event by up to 40%pts. One- and three-dimensional engine models were then developed to study the engine/fuel interactions that lead to NG EGAI and its performance benefits. These models, although having decent agreement with experimental data, showed the need to account for NOx chemistry when predicting NG EGAI due to a consistently later prediction of the EGAI onset (~1.65 crank-angle degrees) and thus, a new reduced chemical mechanism for real NG fuels was developed containing NOx chemistry. The new reduced mechanism improved the EGAI onset prediction agreement to within ±0.5 crank-angle degrees and decreased simulation time during combustion by nearly 50% when using the further reduced AREIS50NOx chemical mechanism. These models were then used to study the role of NG composition on EGAI, evaluate the engine/fuel interactions leading to NG EGAI, and perform engine optimization while leveraging EGAI to increase thermal efficiency. Piston design optimization combined with a Controlled EGAI (C-EGAI) combustion mode allowed a Heavy-Duty (HD) SI NG engine to operate at diesel-like efficiencies, i.e., Brake Thermal Efficiency (BTE) ≥44%. Experimental and modeling data analysis revealed that earlier and faster heat release increases combustion efficiency by an average of 1%pts, increases work transferred to the piston resulting in a decrease in exhaust losses by 50% depending on the engine operating condition while slightly increasing heat losses. Finally, the simulation results revealed an opportunity to further enhance the BTE (up to 50%) by enabling C-EGAI combustion at leaner conditions, λ=1.4-1.6.
The global wind industry has witnessed exciting developments in recent years. The future will be even brighter with further reductions in capital and operation and maintenance costs, which can be accomplished with improved turbine reliability, especially when turbines are installed offshore. One opportunity for the industry to improve wind turbine reliability is through the exploration of reliability engineering life data analysis based on readily available data or maintenance records collected at typical wind plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses fundamentals for wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.
A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable result in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates, in comparison to the measurement uncertainty. Range of true values depends on the physics of the experiment, while measurement uncertainty depends on the measurement method (properties of the detector not of the experiment). When data from one–of–a kind experiment are analyzed, only the measurement uncertainty is reported. It gives no information about the range, in which the true values of experiment would spread if the experiment was repeated. A frequently used approximation, that if a physical quantity is measured as a function of time, only measurement of this quantity, produces uncertainty is also in some real experiments fare to strong. Example: In reaction history time measurement uncertainty propagated to alpha dominated under certain conditions over the flux measurement uncertainty propagated to alpha. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable.
A Maia detector array has been utilized and installed on the CSIRO-MARC Nuclear Microprobe. Maia uses an annular array of 384 silicon detectors to provide a solid-angle of 1.3 sr to improve the collection of X-rays induced by Particle Induced X-ray Emission (PIXE) using a micron focussed MeV proton beam. It features event-by-event data acquisition with dead-time correction and pileup rejection and a total count-rate capacity above 10 M/s, versatile stage and beam scanning modes (coupled to the DAQ-36 system described previously) to cater for a variety of sample configuration and experimental needs, precise measurement of transit time and integration of beam fluence per pixel, and spectral deconvolution of event data in real-time using the Dynamic Analysis method to provide element images during data collection. In this work we demonstrate the system on thin sections made from geological samples extracted from active seafloor hydrothermal vents in the PACMANUS hydrothermal field in Papua New Guinea.
Computing a Voronoi or Delaunay tessellation from a set of points is a core part of the analysis of many simulated and measured datasets: N-body simulations, molecular dynamics codes, and LIDAR point clouds are just a few examples. Such computational geometry methods are common in data analysis and visualization; but as the scale of simulations and observations surpasses billions of particles, the existing serial and shared-memory algorithms no longer suffice. A distributed-memory scalable parallel algorithm is the only feasible approach. The primary contribution of this paper is a new parallel Delaunay and Voronoi tessellation algorithm that automatically determines which neighbor points need to be exchanged among the subdomains of a spatial decomposition. Other contributions include periodic and wall boundary conditions, comparison of our method using two popular serial libraries, and application to numerous science datasets.
Here we present a method for measurement analyses based on probabilistic deep neural networks that provide several advantages over conventional analyses with phenomenological models. These include predicting physical quantities directly from data, the rapid generation of statistically robust uncertainties, and the ability to bypass some parameters that may induce ambiguities and complications in data analysis. As deep learning methods make predictions through “black boxes,” the uncertainty quantification is typically challenging. We use a probabilistic framework that provides thorough uncertainty quantification and is straightforward to follow in practice. With the network architecture based on the Transformer, we demonstrate the current method for predicting nuclear resonance parameters from scattering data using the phenomenological R-matrix model.
The rapid growth of methods emerging in the past decade for synthesis of “designer” catalysts—ranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered bimetallic surfaces, to single site and pair site catalysts—has opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data of catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Finally, emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed.
Purpose: To verify IMRT and Rapid Arc (RA) plan's agreement with treatment delivery for radiation therapy using an on-board EPID compared to MapCHECK2 and ArcCHECK. Methods: On-board EPID (Exact arm) is commissioned using manufacturer's Preconfigured Data Input Package (PDIP). Portal dose calculation engine for comparison with measurement was commissioned with PDIP. The absolute dosimetric calibration of the EPID is performed using 10×10cm field size at 105cm source to image detector distance with 90.7 CU in the integrated mode. The dark and flood field of EPID were also calibrated in the integrated mode for all energies and dose rates. The output dose-response data from the manufacturer was verified. The improved gamma test method was used for EPID data analysis. Gamma test were used for MapCHECK2 (or ArcCHECK). The sensitivity of the method was tested using collimator rotation at 3 degree and 5 degree. Student T-test was used to distinguish the difference among the groups. Results: IMRT: The average of Gamma passing rate using EPID and MapCHECK2 is 98.9±1.6% and 98.7±2.0% (p = 0.342). There is no statistical difference between two groups.RA: The average of Gamma passing rate using EPID and ArcCHECK is 97.2±2.3% and 93.2±4.1%. (p = 0.002). EPID significantly improved the RapidARC plan passing rate compared to the ArcCHECK. 5% dose change typically causes between 3 – 55% change in Gamma. Conclusion: Using the EPID, we were able to perform QA verification on IMRT plans comparable to MapCHECK2 (p=0.342) and superior to that of ArcCHECK (p=0.002) using Gamma 3%, 3mm criteria. The sensitivity of EPID was found to be comparable to that of the MapCHECK2 and ArcCHECK. EPID dosimetry is faster (4.3 mins vs. 10 mins) due to easier setup and integrated data processing.
The objectives and purpose of this research has been to produce laboratory-based experimental and numerical analyses to provide a physics-based understanding of shear stimulation phenomena (hydroshearing) and its evolution during stimulation. Water was flowed along fractures in hot and stressed fractured rock, to promote slip. The controlled laboratory experiments provide a high resolution/high quality data resource for evaluation of analysis methods developed by DOE to assess EGS “behavior” during this stimulation process. Segments of the experimental program will provide data sets for model input parameters, i.e., material properties, and other segments of the experimental program will represent small scale physical models of an EGS system, which may be modeled. The coupled lab/analysis project has been a study of the response of a fracture in hot, water-saturated fractured rock to shear stress experiencing fluid flow. Under this condition, the fracture experiences a combination of potential pore pressure changes and fracture surface cooling, resulting in slip along the fracture. The laboratory work provides a means to assess the role of “hydroshearing” on permeability enhancement in reservoir stimulation. Using the laboratory experiments and results to define boundary and input/output conditions of pore pressure, thermal stress, fracture shear deformation and fluid flow, and models were developed and simulations completed by the University of Oklahoma team. The analysis methods are ones used on field scale problems. The sophisticated numerical models developed contain parameters present in the field. The analysis results provide insight into the role of fracture slip on permeability enhancement-“hydroshear” is to be obtained. The work will provide valuable input data to evaluate stimulation models, thus helping design effective EGS.
: Conventional rock mechanics testing techniques typically involve the loading of samples and measurement of displacements or strains on the outer boundary of the specimen surface. Neutron diffraction based strain measurement techniques represent a unique and powerful tool for measuring the strain within geological materials under load. The structural variability and non-uniform crystallinity of geological materials, however, create many complexities in the intensity patterns that must be analyzed to quantify strains within the material. The attenuating and scattering properties of the pressure cell housing the sample further add difficulties to the data analysis. This paper describes the methods and processes used to process neutron scattering data for strain measurement in geological materials. It is intended to provide a primer for those in the rock mechanics community that are interested in utilizing this technique along with additional discussion of neutron diffraction experimental factors that may affect data quality.
Purpose: The standard TomoTherapy 2D scanning system requires manual analysis using spreadsheets or similar software. Manual analysis is time consuming and does not allow efficient use of the medical physicist or timely results that may indicate issues during the acquisition of data. The purpose of this study is to determine if an in-house solution can provide timely analysis of TomoTherapy scan data. Methods: A custom MATLAB program utilizing a streamlined graphical interface was developed to interpret the native scanning data acquired by the TomoTherapy Electrometer Measurement System (TEMS). The gold beam data provided by the manufacturer was used as a baseline for the acquired data. Two separate sets of scan data were used including longitudinal, transverse, and percent depth dose (PDD). The scan data were compared visually in an overlay and analytically for each beam width using the full width half max (FWHM) for longitudinal profiles and difference inside the field core for the transverse profiles as recommended by TG-148. A plot of the PDD percent error at each point along the curve was generated to show agreement. The time to analysis was measured on a laptop running an Intel i5 processor with 8GB of RAM. Results: The software program required on average less than 5 seconds to load and analyze the results. No conversion or reformatting of the data was required. This allowed real-time visualization of the comparison to the gold beam data as well as the parameters recommended by TG-148. The average percent difference was 0.3% for the longitudinal profiles and 0.1% for the transverse profiles. All PDD data was less than 1% different beyond dmax. Conclusion: We have developed a tool to analyze TomoTherapy scan data in real-time. This allows an efficient method to perform beam commissioning, annual QA, and adjust the beam parameters if necessary.
This report, the second in the series, analyzes historical benefits and impacts of all state RPS policies, in aggregate, employing a consistent and well-vetted set of methods and data sets. The analysis focuses on three specific benefits: greenhouse gas emissions, air pollution, and water use. It also analyzes three other impacts: gross job additions, wholesale electricity market price suppression, and natural gas price suppression. These are an important subset, but by no means a comprehensive set, of all possible effects associated with RPS policies. These benefits and impacts are also subject to many uncertainties, which are described and, to the extent possible, quantified within the report.
Disposal of low-and intermediate-level activated waste generated at nuclear power plants is being planned or carried out in many countries. The radioactivity concentrations and/or total quantities of long-lived, difficult-to-measure nuclides (DTM nuclides), such as C-14, Ni-63, Nb-94, α emitting nuclides etc., are often restricted by the safety case for a final repository as determined by each country's safety regulations, and these concentrations or amounts are required to be known and declared. With respect to waste contaminated by contact with process water, the Scaling Factor method (SF method), which is empirically based on sampling and analysis data, has been applied as an important method for determining concentrations of DTM nuclides. This method was standardized by the International Organization for Standardization (ISO) and published in 2007 as ISO21238 'Scaling factor method to determine the radioactivity of low and intermediate-level radioactive waste packages generated at nuclear power plants' [1]. However, for activated metal waste with comparatively high concentrations of radioactivity, such as may be found in reactor control rods and internal structures, direct sampling and radiochemical analysis methods to evaluate the DTM nuclides are limited by access to the material and potentially high personnel radiation exposure. In this case, theoretical calculation methods in combination with empirical methods based on remote radiation surveys need to be used to best advantage for determining the disposal inventory of DTM nuclides while minimizing exposure to radiation workers. Pursuant to this objective a standard for the theoretical evaluation of the radioactivity concentration of DTM nuclides in activated waste, is in process through ISO TC85/SC5 (ISO Technical Committee 85: Nuclear energy, nuclear technologies, and radiological protection; Subcommittee 5: Nuclear fuel cycle). The project team for this ISO standard was formed in 2011 and is composed of experts from 11 countries. The project team has been conducting technical discussions on theoretical methods for determining concentrations of radioactivity, and has developed the draft International Standard of ISO16966 'Theoretical activation calculation method to evaluate the radioactivity of activated waste generated at nuclear reactors' [2]. This paper describes the international standardization process developed by the ISO project team, and outlines the following two theoretical activity evaluation methods:? Point method? Range method. (authors)
The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.