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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2022

Novel Proppant Logging Technique for Infill Drilling of Unconventional Shale Wells

Summary During the development of an unconventional play, wells are drilled and completed in batches, and depending on the development plans, current and expected energy market trends, as well as other developmental considerations, new wells are drilled and hydraulically fractured later near existing producing laterals. This creates challenges in terms of optimizing resource recovery and reducing interwell communication. A novel approach is proposed that utilizes systematic composite sampling and analysis of drilling mud returns to look for and quantitatively identify sand particles. The workflow involves cleaning, drying, and segregation of samples into sizes of interest to us (size distribution of pumped proppant in offset parent wells). These samples are imaged at a very high resolution and analyzed for grains using characteristic optical imaging properties to classify proppant sand particles using computer vision algorithms. Further analysis, such as elemental compositional analysis, is used to validate the results from the imaging workflow. We present a case study from the Permian Basin, where a new child well was used as a test case to prove this technology at the Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin. We introduce new proppant parameters that help identify sustained proppant zones vs. localized propped fractures. We have used additional diagnostics and data collected at the test site to validate observations from the proppant log and have successfully interpreted significantly propped vs. unpropped zones. A key finding from this test has been the significant proppant transport distances observed away from parent wells. Observable proppant was found at a lateral distance of approximately 425 m for one set of parent wells and more than 915 m for another set of parent wells. While a major limitation of this technique is the sampling rate, given adequate sampling, the proposed technology represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling infill wells. It provides us with unique opportunities to better understand the current state of the reservoir being targeted, including zones that are likely highly drained relative to others, and how the planned hydraulic fracturing of child wells can be improved.

Energy & Fuels↗
DOE OSTI2022

Demonstration of Novel Monitoring Techniques for a North Dakota Carbon Capture and Storage Project

Conference paper presented at 16th International Conference on Greenhouse Gas Control Technologies (GHGT-16), Lyon, France, October 23–27, 2022. We present an overview and initial results of the novel and sustainable monitoring techniques developed by the Energy & Environmental Research Center (EERC) and its commercial partners associated with the Red Trail Energy (RTE) carbon capture and storage (CCS) project. RTE is an investor-owned 64-million-gallon dry mill ethanol production plant in Richardton, North Dakota, USA. Currently in operation as of June 2022, the RTE facility will capture 180,000 tonnes of carbon dioxide (CO 2 ) annually (>99% CO 2 ) that is otherwise emitted during the ethanol production process and inject it into the Broom Creek Formation, a deep saline aquifer, for permanent on-site geologic storage.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential \varphi φ . In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

Kong, Kyoungchul↗
DOE OSTI2024

Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Predicting Solar Plant Generation with Machine Learning Techniques

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.

14 SOLAR ENERGY↗
DOE OSTI2020

Development of Innovative Measurement Techniques for Fission Product Transport Quantification

This document provides the progress of a research project at Texas A&M University (TAMU), led by Idaho National Laboratory (INL), in support of the Versatile Test Reactor (VTR) program and work scope area of gas-cooled fast reactors. The VTR TAMU INL project started in October 2018. The document includes the main objectives of the project, scaling analysis applied to the experimental facilities, experimental methodology including instrumentation and techniques applied, and up-to-date results from the experimental and numerical studies.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Stronger field-emission science via coupling novel nanoscale imaging techniques

We implemented a vacuum field emission electron microscope (FEM) using the electron optics of a low-energy /photoemission electron microscope (LEEM/PEEM). Historically, there have been other FEM hardware platforms, and the distinctive feature of our method is that it integrates with the LEEM/PEEM and associated techniques, enabling a powerful multi-capability toolset for studying fundamental materials properties underpinning field emission (FE) and vacuum arc initiation. Typically, LEEM is used to image surface structure, which influences both work function and electric field distribution near a surface, while PEEM is used to map photoelectric work function across a surface. Our FEM adds the capability for spatially-correlated coincident-site measurements of FE currents to go-along with structure and work function. LEEM, PEEM, and our FEM implementation achieve nanoscale spatial resolution relevant for materials studies in nanoscience/engineering. Our approach requires a straightforward calibration of the electron optics to enable focused FEM imaging under intentional electric field variation. We demonstrate the FEM approach by imaging field emitter arrays relevant for vacuum nanoelectronics. We demonstrate submicron spatial resolution and dynamic measurement of FE versus applied electric field. We anticipate this capability will enable fundamental structure-function studies of FE and arc initiation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Development of Monitoring Techniques for Binderjet Additive Manufacturing of Silicon Carbide Structures

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear components to be assembled into a fully functional microreactor core. Compared with traditional manufacturing technologies, AM technologies allow (1) observation of the manufacturing process at a much higher resolution in real-time using in situ monitoring technologies to capture the sensor signature that scientifically describes each event occurring over time and space and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on binder jetting in situ process monitoring and associated data analytics results, as well as sample placement and tracking for the subsequent chemical vapor infiltration (CVI) process. Examples are provided to illustrate the progress. Elements of the Digital Thread and data management are discussed in the main document, and an extensive supplemental material section is provided detailing the Digital Platform, as well as its implementation and components. In conclusion the path forward for the next fiscal year is discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Development of Monitoring Techniques for Laser Powder Bed Additive Manufacturing of Metal Structures (Progress Report)

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to fabricate nuclear grade components to be assembled into a fully functional microreactor core. Compared to traditional manufacturing technologies, additive manufacturing technologies allow (1) observation of the manufacturing process at a much higher resolution in real time using in situ monitoring technologies to capture the sensor signature that scientifically describes each event occurring over time and space, and (2) validation of the manufacturing process quality using domain-informed data analytics techniques as a potential qualification and certification methodology for the final component. This report provides an update on the program work on laser powder bed fusion in-situ process monitoring and associated data analytics results. Examples are provided to illustrate the progress. Elements of the Digital Thread and data management are discussed in the main document, and an extensive supplemental material section is provided detailing the Digital Platform, as well as its implementation and components. In conclusion the path forward for the next fiscal year is discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

SWIFT - Shaped-Waveform Interrogation of Fluids Technique

Researchers at Los Alamos have developed the Shaped-Waveform Interrogation of Fluids Technique (SWIFT), a technology enabling unprecedented process control capability. This technology provides important composition, flow rate, and pipe thickness measurements in real-time, at an extremely low cost and maintenance cycle. By utilizing a specialized shaped-waveform, as well as state-of-the-art signal processing, this information can be gathered without any frequency-domain signal processing, allowing for simplified electronics compared to nearly any other meter on the market. Since this technology is in expensive and non-invasive, with the capability of measuring liquids in both flow pipes and sealed containers, it is perfect for several industrial applications in the oil and gas, beverage, chemical, and pharmaceutical spaces. We are seeking a commercialization partner to license the technology or fund us through a Cooperative Research and Development Agreement (CRADA) to a further develop a usable product for commercial purposes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3$\sigma$ Sensitivity

This thesis describes an analysis developed for the MicroBooNE experiment to investigate an anomalous excess of electron-like events observed in the MiniBooNE detector. The hypothesis investigated here is that the MiniBooNE anomaly represents appearance of electron neutrinos. Using an amalgam of novel Deep Learning and standard algorithmic techniques this analysis reconstructs and identifies a highly pure sample of charged current quasi-elastic muon neutrino and electron neutrino interactions. This thesis describes the steps in the analysis chain and provides data-to-simulation comparisons for each step that establish confidence in the final prediction. When interpreted in the context of a $\nu e$ appearance like model, this analysis predicts a 3.2$\sigma$ sensitivity to exclude a standard model fluctuation which would appear as a MiniBooNE like anomaly using $7\times10^{20}$ protons on target of MicroBooNE Data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales

Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Enhancing Resilience of Urban Systems Against Climate-Induced Floods Using Advanced Data-Driven and Computing Techniques: A Driver-Pressure-State-Impact-Response (DPSIR) Framework

Focal Area: This white paper proposes advanced data-driven and computing techniques to improve the predictability of extreme climate events (precipitation) and focuses on comprehending their impact dynamics to enhance resilience of urban systems. Science Challenge: In urban systems, climate change has presented significant challenges related to accurate spatiotemporal prediction of extreme climate events (heavy precipitation) and their impacts, i.e. flooding. The occurrence of extreme climate events and their compounding repercussions on the built environment make urban systems increasingly more vulnerable. The interactions between extreme climate events, urban systems, and the corresponding impacts are irregular, non-linear, and dynamic, making it difficult to standardize resilience. As it stands, the management of complex urban systems is affected by uncertainties related to the nature of short- and long-term consequences caused by extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing Heat Transfer in sCO 2 Cycle Coolers Utilizing the Wilson Plot Technique

Effective design of coolers can decrease the capital expense of supercritical carbon dioxide (sCO 2 ) power plants. Improved datasets reporting heat transfer from the CO 2 in the cycle cooler allow design engineers to calculate the required length of heat exchanger streams more accurately, thus reducing cooler material and heat exchanger cost. This study considered a shell-and-tube heat exchanger with non-condensing CO 2 on the tube side and water flow on the shell side. The average Nusselt number on the tube side was measured employing the Wilson plot technique for a flow at a Reynolds number, Re, equal to 1×10 5 and for a heat flux to mass flux ratio, Q"/G, ranging from 50 to 350 J/kg. The results indicate that the Nusselt number, Nu, increases as the CO 2 pressure decreases towards the critical point. The results also indicate that Nu increases to maximum at Q"/G = 215 J/kg and then decreases with further increases in Q"/G. The difference in Nu measured between the horizontal and vertical heat exchanger with downward flowing CO 2 is less than the experimental error.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Solid State Ionics: Materials Development by Multiscale Modeling and Advanced Manufacturing Techniques

Solid-state ionic materials are an important enabling technology for energy conversion and storage. Solid-state batteries would be a safer and higher energy density alternative to commercially available lithium ion batteries (LIB), however their implementation requires ion conduction in solids at room temperature to occur on the same level as the current generation of liquid electrolytes. Microstructural modifications have been demonstrated to play a major role on ion transport through the control of grain boundary interfaces, which traditionally serve as “blocking” layers. Ultimately, these materials will be fabricated in thin films form as electrolytes in order to minimize ohmic losses in electrochemical devices. This work uses advanced manufacturing techniques in combination with theoretical modeling to implement a science-based approach in the deposition of thin films ion conductors with controlled microstructures used in ceramic energy conversion and storage devices.

25 ENERGY STORAGE↗