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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictates the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Method for radionuclide contaminatecontaminant mitigation

The method and system disclosed provides radionuclide contamination mitigation by applying an aqueous carrier solution comprising a cation to a surface bearing a radionuclide contaminant to cause the radionuclide contaminant to enter solution forming a laden solution, then contacting the laden solution with a sequestering agent to bind to the radionuclide contaminant to form a laden sequestering agent. The removal and sequestration of the radionuclide contaminant from the contaminated surface leads directly to a reduction in the amount of radiologically-impacted critical infrastructure and the environment. The method and system are able to be performed or utilized economically with materials quickly available in the event of a radiological dispersion event.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Optimization of the cerium-hydrogen redox flow cell

The Ce—H2 redox flow cell is optimized using commercially-available cell materials. Cell performance is found to be sensitive to the upper charge cutoff voltage, membrane boiling pretreatment, methanesulfonic-acid concentration, (+) electrode surface area and flow pattern, and operating temperature. Performance is relatively insensitive to membrane thickness, Cerium concentration, and all features of the (−) electrode including hydrogen flow. Cell performance appears to be limited by mass transport and kinetics in the cerium (+) electrode. Maximum discharge power of 895 mW cm−2 was observed at 60° C.; an energy efficiency of 90% was achieved at 50° C. The Ce—H2 cell is promising for energy storage assuming one can optimize Ce reaction kinetics and electrolyte.

Tucker, Michael C.↗
DOE OSTI2021

Design and Experimental Demonstration of an Additive Manufactured Smart Oxy-Methane Burner for High Pressure and Supercritical Combustion

Pressurized oxy combustion-based systems can improve efficiency by recovering latent heat of the steam in the Flue Gas and achieving 90% CO2 capture. In addition, the novel Directly Heated Supercritical Carbon Dioxide (DH-SCO2) power cycles can achieve high thermal efficiencies and provide nearly full carbon capture. Additionally, due to the reduction of flue gas at higher pressure, smaller system size and capital cost reductions are also possible. Recent thermodynamic analysis of the DH-SCO2 cycle performed by the UTEP research team shows that combustion conditions in the vicinity of 300 bar pressure and 1000-1400 K temperature allow for relatively high system efficiencies while operating within the limit of available combustor materials. However, the realization of a directly heated supercritical power cycle requires combustion systems to operate in supercritical conditions and at temperature far below the blowout limit of conventional flames (above 1500 K). The thermodynamic properties along with the combustion properties and kinetics are unexplored at such conditions. Additionally, the interaction of the supercritical environment with energy components is also unknown. High-pressure combustion tests are performed using a smart burner at some intermediate pressure ranges (<20 bar) to help minimize these knowledge gaps. The knowledge obtained from the high-pressure test will assist in understanding the combustion chamber pressurization mechanism, ignition and flame behavior at the elevated pressure. The obtained data will act as a systematic first step in testing at higher pressures of 100 and 300 bar pressures.The primary purpose of this Dissertation is to demonstrate the operability of a low NOx smart burner for oxy-methane combustion at high pressure (< 20 bar) and scalable up to supercritical conditions. A shear co-axial smart burner is designed with a real-time temperature monitoring capability to understand the burner face interaction at high-pressure conditions. In addition, the burner has four independent injection ports to allow the independent injection of fuel and dilution gases in the combustor. The maximum operating capability of the burner is 575 kWth. The burner was fabricated using a Laser Powder Bed Fusion process with Nickel Alloy 718. A powder removal technology was invented comprising ultrasonic vibration, liquid nitrogen exposure and media blasting to remove powders from internal channels of the burner. The burner operability tests are performed in the high-pressure combustor and the swirl combustor. The high-pressure combustor was used to investigate the burner operability and thermal soak back at different pressurized conditions. The experimental tests in high-pressure combustor up to 275 kWth input resulted in 16.5 bar chamber pressure and 198°C thermal soaks back to the burner. The burner was capable of providing the required thermal input within a 3% deviation range. In addition, soot formation occurred at high-pressure tests. The swirl combustor was used to observe the flame stability of the burner. Flame lift-off was observed for jet velocities above 450 m/s. Additionally, lift-off decreased for low co-flow velocities. CO2 dilution experiments showed increased flame instabilities for all conditions above 50% dilution ratios. At lower thermal inputs, partial flame blow-offs occurred for dilution ratios above 50%. All conditions significantly reduced the flame temperature and increased the flame lift-off height. Finally, a 2nd generation AM smart burner was designed using the knowledge from the 1st generation burner experiments. The 2nd generation burner incorporated two sets of swirlers with 0.9 swirl no. A cooling system was also designed for long-duration tests at higher pressures. The thermal input and division of the burner's power are kept the same as the 1st generation burner. The burner is to be fabricated using nickel-alloy 718 for high-pressure handling capability. The design can sustain at high-pressure conditions up to 100 bar.

Islam, Md Nawshad Arslan↗
DOE OSTI2019

Development of an Assessment Method for Radiation Exposure from NORM in Construction Materials - 19032

Once radiological impact is usually considered to be related to nuclear energy accidents and atomic bombs, most of the research on radiation protection has been focused on artificial radionuclides and radioactive waste. Less attention had been given to radiation exposure due to the presence of naturally occurring radioactive materials in raw materials usually considered as not radioactive and used in manufacturing of many building materials. At national level, radiological data from building materials is not available as the control of building materials regarding the content of naturally occurring radionuclides is addressed for the first time in the EU Directive 2013/59/EURATOM. At international level the research developed is largely incomplete: data on the activity concentration measurements of Ra-226, Th-232 and K-40 in building materials used in the construction industry in the EU is non-statistically representative. The data collected are not enough to have an idea of the European situation, but they confirm high concentration of Ra-226, Th-232 and K-40, in some cases. Moreover, most of the research studies carried out in the EU are based on the assessment of the activity concentration index, which was derived for practical monitoring purposes to identify whether a dose criterion is met; to be used only as a screening tool for identifying materials which might be of concern. The dose criterion considers only effective dose due to γ-radiation, but population at home is also exposed to Rn-222 (and Rn-220) from soil, water and building material itself, sometimes to β radiation from superficial materials, and even to Cs-137 from some types of wood. The indoors exposure can be actually much higher than the value assessed with the dose criterion method. Also, in most cases toxic and radioactive components are inseparably. Therefore, all technical questions related to the toxic part of these materials should be addressed: an integrated approach is needed to assess the impacts of using these materials (new materials, recyclates and by-products). Until recently, radiation from natural sources seemed both unremarkable and unalterable - a background phenomenon. It is now known that doses from natural sources and in particular, decay products of radon can be remarkably high within houses in some areas. Therefore, concerning exposure to natural radiation sources, special provisions have been laid down in the EU Directive 1996/28 EURATOM. Still, the radiological evaluation of building materials was not mandatory in this Directive. The most recent EU Directive 2013/59/EURATOM brings some changes concerning the exposure to natural occurring radioactive materials but the previous screening method was kept. An effective dose of 1 mSv/year was set, as a basis for the exposure to radionuclides of natural origin (except for radon). This work seeks to develop an integrated radiological and chemical-mineralogical assessment methodology for realistic exposure scenarios concerning different types of raw materials, by-products and recyclates and used as building materials. The methodology includes the characterization of selected materials accounting for radiological exposure - calculation of effective dose; toxicity assessment - determination of chemical composition and leaching tests, study of changes in the microstructure and consequently in the radon emanation coefficient. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Radiation protection at the SwissFEL free electron laser facility: Beam stopping devices - Paper 53

The new large-scale facility SwissFEL (Swiss Free Electron Laser) is currently under construction at the Paul Scherrer Institute. The facility is situated underground in the ground floor of a two-floor building, partly integrated in a recreation area. This results into new challenges for radiation protection related tasks, in particular for the design of beam-stopping devices. The design of each beam-stopping device has to ensure that the dose rate during operation outside of the accelerator vault is in compliance with the legal limits defined by the Swiss radiation protection ordinance. In this work, the final design of the dumps is presented with the description of the underlying operational and legal constraints. Reuse of activated material as concrete, lead and steel is included in the main design criteria. As a result, four beam stopping devices consist for more than 90 % of material already available at PSI. In addition, routine operation and maintenance scenarios are discussed from a radiation protection point of view. (authors)

42 ENGINEERING↗
DOE OSTI2022

Phase-field simulations to inform nuclear fuel performance modeling

Software tools to simulate nuclear fuel performance at the engineering scale, such as Idaho National Laboratory (INL)’s BISON code, are increasingly relied upon in regulatory and economic decision-making. However, accurate results from these tools depends on the availability of materials parameters that are used as input. In recent years, atomistic and mesoscale simulation methods have emerged as a cost-effective, expedient means to obtain such input parameters. Phase-field simulations using INL’s Marmot application have been used to obtain microstructure-level parameters and to improve material models for fuel performance modeling using BISON. In this talk, recent examples of this process are given, including applications in UO2, U3Si2, and UZr fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Accelerating the discovery of battery electrode materials through data mining and deep learning models

The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.

25 ENERGY STORAGE↗