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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Requirements for Cataloging Hanford Geophysical Datasets

Environmental management activities at the Hanford Site produce extensive data about site conditions, contaminants, cleanup, and more. Managing and archiving that data requires a high degree of collaboration among site contractors and a high level of awareness by project managers and staff. Part of that effort is developing a Hanford Environmental Information and Data Index (HEIDI) to organize the data and maximize its value by making it findable and available for reuse. The objective is to catalog the disparate data sets collected to address the evolving needs of planning, executing, and documenting cleanup over several decades up to the present day, including links to active data sources when available. A properly implemented data catalog makes finding environmental datasets related to an area or theme a routine, reliable process, without requiring the searcher to have special knowledge that a data set exists and where it may be stored. In this project, a working group, including the U.S. Department of Energy, the Hanford Site contractors, and Pacific Northwest National Laboratory staff, identified needs and requirements for handling complex site data. Geophysical data was chosen as a test case because it can be large and complex and often involves multiple processing steps to extract the information incorporated into deliverables. The ability to document those steps was one of the requirements identified for the catalog. In addition to developing requirements, other activities included selecting a metadata schema and initial testing with the objective of determining whether the workflow and capabilities of selected data catalog software platforms were sufficient to implement and impose the identified requirements. This initial testing involved running the default catalog instance using the software platform of interest and altering the configuration to achieve each requirement, if possible. Where configuration alone was insufficient, the possibility of modifying the software by changing the code was examined, but not implemented. A follow-on task is planned to reprogram the code as necessary to implement requirements in a prototype catalog.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

CO 2 Sequestration and Enhanced Oil Recovery at Depleted Oil/Gas Reservoirs

This study presents a quantitative evaluation of the operational and technical risks of an active CO 2 -EOR project. A set of risk factor metrics is defined to post-process the Monte Carlo (MC) simulations for statistical analysis. The risk factors are expressed as measurable quantities that can be used to gain insight into project risk (e.g. environmental and economic risks) without the need to generate a rigorous consequence structure, which include (a) CO 2 injection rate, (b) net CO 2 injection rate, (c) cumulative CO 2 storage, (d) cumulative water injection, (e) oil production rate, (f) cumulative oil production, (g) cumulative CH 4 production, and (h) CO 2 breakthrough time. The Morrow reservoir at the Farnsworth Unit (FWU) site, Texas, is used as an example for studying the multi-scale statistical approach for CO 2 accounting and risk analysis. A set of geostatistical-based MC simulations of CO 2 -oil/gas-water flow and transport in the Morrow formation are conducted for evaluating the risk metrics. A response-surface-based economic model has been derived to calculate the CO 2 -EOR profitability for the FWU site with a current oil price, which suggests that approximately 31% of the 1000 realizations can be profitable. If government carbon-tax credits are available, or the oil price goes up or CO 2 capture and operating expenses reduce, more realizations would be profitable.

02 PETROLEUM↗
DOE OSTI2024

Bayesian stability and force modeling for uncertain machining processes

Accurately simulating machining operations requires knowledge of the cutting force model and system frequency response. However, this data is collected using specialized instruments in an ex-situ manner. Bayesian statistical methods instead learn the system parameters using cutting test data, but to date, these approaches have only considered milling stability. This paper presents a physics-based Bayesian framework which incorporates both spindle power and milling stability. Initial probabilistic descriptions of the system parameters are propagated through a set of physics functions to form probabilistic predictions about the milling process. The system parameters are then updated using automatically selected cutting tests to reduce parameter uncertainty and identify more productive cutting conditions, where spindle power measurements are used to learn the cutting force model. The framework is demonstrated through both numerical and experimental case studies. Results show that the approach accurately identifies both the system natural frequency and cutting force model.

42 ENGINEERING↗
DOE OSTI2017

A Study Of Geological Disposal Facility (GDF) Ageing Phenomena In Bentonite - 17605

Since the 1950's, the UK's nuclear activity in energy, defence and medicine has led to the accumulation of a large amount of higher activity radioactive waste (HAW). Most of nuclear waste arises in a wide variety of physical and chemical forms with different radioisotopes and loadings. Due to complexity in HAW, it is imperative that there be a rigorous management system that ensures population and environmental safety over extended periods. The concept of Deep Geological Disposal of waste in a stable geological formation is, nowadays, an internationally accepted solution as the surest and the most viable long term method for management. Any future Geological Disposal Facility (GDF) will comprise, among other artificial and natural engineered barriers, a barrier immediately around the waste containers compounded by a sealing material. The long-term stability of a GDF will be determined by the design and construction of the engineered barriers, and the mechanical, hydraulic and geochemical interactions that may happen between the different engineered components as a result of combined processes such as the heat produced by radioactive decay and the ion water composition of the surrounding host rock. Bentonite clay has been selected as the most promising buffer and backfill material in many disposal programmes because of its swelling capacity, low permeability, plasticity and cation exchange capacity. In the nuclear scope, bentonite testing at different scale-lengths (laboratory and field scale) has been performed and implemented in order to examine the thermo-hydro-mechanical (THM) and thermo-hydro-geochemical (THG) processes that may happen in the engineered barriers and geological settings associated with a GDF. Two major European field scale tests have been performed to better understand bentonite performance and behaviour immediately after HAW emplacements. The Full-Scale Engineered Barriers Experiment (FEBEX) at the Grimsel Test Sit (Switzerland) and the Alternative Buffer Material (ABM) Project at the Aespoe Hard Rock Laboratory (Sweden). The Febex Experiment was a pilot project designed for 'field research' of HAW repository hosted in crystalline rock where surrogate HAW canisters were placed horizontally in drifts, surrounded by compacted blocks of bentonite, sealed by a concrete plug and maintaining a constant canister surface temperature of 100 deg. C. After five years of operation, a partial dismantling was carried out in 2002. Heater processes occurring in the surrounding bentonite. The dismantling of the heater no. 2 (after 18 years of operation) started in late 2014 and is planned to be finished at the end of 2016 (termed FEBEX-DP). The FEBEX Project utilised bentonite from the Cortijo de Archidona deposit (Almeria, Spain). This 'Febex' bentonite is sourced from calcium-magnesium montmorillonite with good properties in relation to swelling capacity, permeability, adsorption capacity, and thermal conductivity. The ABM Project is a large-scale programme that has been running since 2006. The programme is designed to study a suite of possible buffer materials and correlate the physical and chemical properties of eleven different types of bentonites to their fundamental mineralogical properties. The ABM test consists in three 'packages' containing a central heater tube surrounded by different pre-compacted bentonite blocks and instrumentation. Each 'package' was installed in separate boreholes. The target temperature for the surface of the heated tubes in contact with the bentonite was 130 deg. C. Both FEBEX and ABM test the bentonite was saturated with water percolating from surrounding host rock (FEBEX and ABM) and also via artificial supply systems (ABM), being the water itself acting as a long term corrosion agent. For both experiments and emerging interest is in the interaction of bentonite with metallic components, under geological disposal conditions are not considered to be thermodynamically stable and hence arising corrosion products could interact with the buffer. These interactions depend on various factors such as the redox potential, temperature, pH, solute concentration and buffer material characteristics. During the operational phases of the FEBEX and ABM and after its 'closure' a significant volume of air was found to be retained in the bentonite. Under these conditions a period of initial aerobic corrosion by the air in the bentonite barrier would have occurred followed by establishment of anaerobic corrosion would be the dominant process ongoing. The anaerobic corrosion began when the bentonite became water saturated. Under these conditions, the water acted as the major oxidant. The present project focuses on the characterization of samples arising from the FEBEX and ABM Projects as well as performing additional independent experiments designed to complement these prior studies. Characterization will focus mainly on corrosion related alteration combined with thermal effects. Through detailed sample characterization, the intention is to achieve a greater knowledge regarding the changes to the bentonite under the experimental conditions to which it was exposed, and plausible alterations in its THM and THG properties occurring during the experiment due to blended effects of temperature,water content, engineering structural components and solutes are also to be measured. The fulfilment of a large-scale 'in-situ' test is intricate, time-consuming and with an abundance of external variables that are not always well controlled. For this reason, the University of Bristol is performing a series of well constrained time-resolved laboratory tests which bentonite is being subjected to different possible scenarios that may happen whilst in a HAW repository. In virtue of this experiment, the UoB aims to understand the illitisation and cementation process and their effects on bentonite safety properties. (authors)

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

Three-dimensional representation of late-arriving photons for detecting inhomogeneities in diffuse optical tomography

A method for rapid detection of absorbing inhomogeneity in a strongly scattering medium having the properties of a biological tissue before the image reconstruction is described based on the principles of diffuse optical tomography. The method is based on preliminary processing of a three-dimensional surface obtained from the set of time-resolved data in the Cartesian coordinate system, followed by its conformal transformation into two surfaces in the cylindrical coordinate system. A specific feature of the method is the use of late-arriving photons, scattered and diffusely transmitted through an optically turbid object. (optical tomography)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Deep Learning Emulation of Subgrid-Scale Processes in Turbulent Shear Flows

Deep neural networks (DNNs) are developed from a data set obtained from the dynamic Smagorinsky model to emulate the subgrid-scale (SGS) viscosity (ν sgs ) and diffusivity (κ sgs ) for turbulent stratified shear flows encountered in the oceans and the atmosphere. These DNNs predict ν sgs and κ sgs from velocities, strain rates, and density gradients such that the evolution of the kinetic energy budget and density variance budget terms is similar to the corresponding values obtained from the original dynamic Smagorinsky model. These DNNs also compute ν sgs and κ sgs ~2–4 times quicker than the dynamic Smagorinsky model resulting in a ~2–2.5 times acceleration of the entire simulation. This study demonstrates the feasibility of deep learning in emulating the subgrid-scale (SGS) phenomenon in geophysical flows accurately in a cost-effective manner. In a broader perspective, deep learning-based surrogate models can present a promising alternative to the traditional parameterizations of the subgrid-scale processes in climate models.

58 GEOSCIENCES↗
DOE OSTI2018

High-Temperature Sapphire Pressure Sensors for Harsh Environments

The project aimed to develop and characterize an optical pressure sensor capable of withstanding high temperatures, up to 1000 °C. Survivability in harsh environments was enabled using sapphire for both the substrate and the optical fibers, therefore mitigating thermal expansion mismatch. Such material cannot be machined through conventional microfabrication techniques, and unique additive and subtractive methods had to be studied and implemented. Subtractive methods included laser-machining the sapphire substrate using a picosecond laser machining tool. At first, the parameters of importance were theoretically and experimentally evaluated such that the fluence, the pulse spacing, and the number of passes were found to strongly influence the laser ablation process. A simulation tool was designed to predict the final pattern when inputting a set of parameters and a profile. This model was complemented with laser ablation material physics models and Bayesian uncertainty analysis for one dimensional and three dimensional ablation processes. An additive process called thermocompression bonding was studied in order to seal the diaphragm substrate to the back cavity. At first, bonding strength using this technique was examined using the chevron-notch method that allow to determine the fracture toughness of this bond. This parameter is independent of the geometry used and therefore can be extended to other geometries used in actual sensor bonding. A theoretical and experimental study of internal damage caused by the laser micromachining of the sapphire material was carried out. The research identified enhanced strength and fracture toughness along laser ablated surfaces. This was determined using bend bar specimens and Vicker’s microindentations. Softening of the plasticity behavior (as indicated by nanoindentation experiments) was also investigated using finite deformation plasticity modeling via a contact mechanics finite element simulation. Good correlations between experiments and the model were achieved to determine material parameters that can be used in future MEMS based sensor design and modeling to minimize sensor failure loads. The enhanced strength and fracture toughness was determined to be due to compressive residual stress near laser ablated surfaces. This was confirmed through x-ray spectra taken along different crystal structure planes in combination and Bayesian uncertainty analysis. The Bayesian analysis inferred the entire residual strain tensor (six components) and the residual stress tensor. The results illustrated compressive in-plane stress was the most likely cause of enhanced strength and toughness after laser machining. Due to major equipment failures, a parallel route for fabricating the sensor was taken, using commercially available sapphire removal method (subtractive) and ceramic adhesive bonding (additive). The fully-packaged sensor utilized laser micromachining for the sapphire substrate fabrication and was comprised only of high-temperature-compatible materials. Calibration tests of the sensor in both room-temperature and high-temperature plane-wave tubes showed good linearity and consistent frequency response, in the tested frequency range. The sensor also showed good thermal stability in tests up to 300 °C, proving the fiber-optic lever and its packaging process to be a promising approach for high-temperature measurements. The calibration from 23 °C to 300 °C gave a raw sensitivity of 3.9 × 10 -7 V/Pa and a normalized sensitivity of 9.8 × 10 -8 Pa -1 which were consistent within the temperature range. The sensor had a resonance at 16.3 kHz and a flat band (±3 dB) up to 10.3 kHz. The noise floor of the sensor at 1 kHz was 316 nV/Hz 1/2 , resulting in a MDP of 0.8 Pa. Previously, diaphragm delamination due to CTE mismatch was found at 500 °C, causing packaging failure. Unfortunately high temperature validation in hot jet environment could not be made in time. In the future, it is suggested that the two sapphire substrates be bonded by thermal compression bonding with an intermediate platinum layer instead of using an alumina-based adhesive, and therefore achieve higher temperature capacity up to 1000 °C.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2019

Monitoring for Additive Manufacturing Technologies: Report on Progress, Achievements, and Limitations of Monitoring Techniques

The Transformational Challenge Reactor (TCR) program is leveraging additive manufacturing (AM) technologies to produce multiple nuclear components to be assembled into a fully functional microreactor core. AM was selected as the main manufacturing technology of choice for TCR because it has the potential to disrupt the nuclear industry on two fronts: (1) it enables the manufacturing of very complex geometries with optimized and tailored material properties for the intended use of the component, opening up new options for reactor designs, and (2) it allows for a better understanding of the manufacturing process through real-time in situ monitoring, data analytics, and artificial intelligence that can lead to a streamlined qualification and certification process. Within TCR, the development and deployment of a digital platform aims at addressing the latter opportunity. As part of this effort, the collection of pedigree data sets is vital, should these data be generated before, during, or after the manufacturing process. This report focuses on the collection of manufacturing process data using systems instrumentation and monitoring. It presents an inventory of the various monitoring modalities installed on the manufacturing systems by the machine manufacturer or specifically designed by our team to fill information gaps. It also provides details on other relevant data streams available and leverageable to evaluate the manufacturing process on all classes of AM systems investigated during the first year of this program. Through examples, the report gives an overview of the data collected and presents a path forward for the digital platform.

42 ENGINEERING↗
DOE OSTI2023

Another Set of Python Tools for Visualizing and Manipulating Small-Angle Neutron Scattering Data: Descriptions and Examples

The GP-SANS, Bio-SANS and EQ-SANS instruments at ORNL utilize drtsans for data reduction. drtsans is built on Python, and it can be run using python scripts and Jupyter notebooks. The flexibility afforded by Python makes it possible to incorporate additional actions into the scripts used for data reduction, such as analysis and visualization. Here, a new set of tools for visualizing and manipulating SANS data that can be incorporated into the data reduction scripts for the ORNL SANS instruments, or employed during post–processing, is presented that expands the capabilities of the two previously-released tool sets.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

THE GRISM LENS-AMPLIFIED SURVEY FROM SPACE (GLASS). V. EXTENT AND SPATIAL DISTRIBUTION OF STAR FORMATION IN z ∼ 0.5 CLUSTER GALAXIES

We present the first study of the spatial distribution of star formation in z ∼ 0.5 cluster galaxies. The analysis is based on data taken with the Wide Field Camera 3 as part of the Grism Lens-Amplified Survey from Space (GLASS). We illustrate the methodology by focusing on two clusters (MACS 0717.5+3745 and MACS 1423.8+2404) with different morphologies (one relaxed and one merging) and use foreground and background galaxies as a field control sample. The cluster+field sample consists of 42 galaxies with stellar masses in the range 10{sup 8}–10{sup 11} M{sub ⊙} and star formation rates in the range 1–20 M{sub ⊙} yr{sup −1}. Both in clusters and in the field, Hα is more extended than the rest-frame UV continuum in 60% of the cases, consistent with diffuse star formation and inside-out growth. In ∼20% of the cases, the Hα emission appears more extended in cluster galaxies than in the field, pointing perhaps to ionized gas being stripped and/or star formation being enhanced at large radii. The peak of the Hα emission and that of the continuum are offset by less than 1 kpc. We investigate trends with the hot gas density as traced by the X-ray emission, and with the surface mass density as inferred from gravitational lens models, and find no conclusive results. The diversity of morphologies and sizes observed in Hα illustrates the complexity of the environmental processes that regulate star formation. Upcoming analysis of the full GLASS data set will increase our sample size by almost an order of magnitude, verifying and strengthening the inference from this initial data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Adaptive sampling for accelerating neutron diffraction-based strain mapping *

Abstract Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically, we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time measure-infer-predict workflow on additively manufactured steel parts—demonstrating that we can get an accurate strain estimate even with 30%–40% of the typical number of measurements—leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Concurrent probing of electron-lattice dephasing induced by photoexcitation in 1 T -TaSeTe using ultrafast electron diffraction

It has been technically challenging to concurrently probe the electrons and the lattices in materials during nonequilibrium processes, allowing their correlations to be determined. Here, in a single set of ultrafast electron diffraction patterns taken on the charge-density-wave (CDW) material 1T-TaSeTe, we discover a temporal shift in the diffraction intensity measurements as a function of scattering angle. With the help of dynamic models and theoretical calculations, we show that the ultrafast electrons probe both the valence-electron and lattice dynamic processes, resulting in the temporal shift measurements. Our results demonstrate unambiguously that the CDW is not merely a result of the periodic lattice deformation ever present in 1T-TaSeTe but has significant electronic origin. This method demonstrates an approach for studying many quantum effects that arise from electron-lattice dephasing in molecules and crystals for next-generation devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Final Report: Vapor Transport Deposition for Thin Film III-V Photovoltaics

Silicon, the dominant photovoltaic (PV) technology, is reaching its fundamental performance limits as a single absorber/junction technology. Higher efficiency devices are needed to reduce cost further because the balance of systems account for about two-thirds of the overall cost of the solar electricity. III-V semiconductors such as GaAs are used to make the highest-efficiency photovoltaic devices, but the costs of manufacture are much too high for non-concentrated terrestrial applications. The cost of III-V’s is driven by two factors: (1) metal-organic chemical vapor deposition (MOCVD), the dominant growth technology, employs expensive, toxic and pyrophoric gas-phase precursors, and (2) the growth substrates conventionally required for high-performance devices are monocrystalline III-V wafers. The primary goal of this project was to show that close-spaced vapor transport (CSVT), using water vapor as a transport agent, is a scalable deposition technology for growing low-cost epitaxial III-V photovoltaic devices. The secondary goal was to integrate those devices on Si substrates for high-efficiency tandem applications using interface nanopatterning to address the lattice mismatch. In the first task, we developed a CSVT process that used only safe solid-source powder precursors to grow epitaxial GaAs with controlled n and p doping and mobilities/lifetimes similar to that obtainable via MOCVD. Using photoelectrochemical characterization, we showed that the best material had near unity internal quantum efficiency for carrier collection and minority carrier diffusions lengths in of ~ 8 μm, suitable for PV devices with >25% efficiency. In the second task we developed the first pn junction photovoltaics using CSVT and showed unpassivated structures with open circuit photovoltages > 915 mV and internal quantum efficiencies >0.9. We also characterized morphological and electrical defects and identified routes to reduce those defects. In task three we grew epitaxial ternary GaAsxP 1-x and In 0.5 Ga 0.5 P alloys, with composition set by the ratio of GaAs/GaP or InP/GaP mixed as the source powder. GaAs 0.3 P 0.7 has the appropriate bandgap to serve as a top cell on Si and In 0.5 Ga 0.5 P is near the composition used as a surface passivation layer on GaAs pn junction photovoltaics. In the final task we demonstrated III-V selective area epitaxy using CSVT as a first step toward the growth of III-V micro- or nanostructures for an integrated tandem solar cell on Si. We also found that direct epitaxial growth on Si appears to be impossible in the current H 2 O-CSVT reactor design, likely due to the formation of SiO x . This work sets the stage for targeted development of an improved CSVT process and for the scale up of the proof-of-concept work from a research to manufacturing-relevant platform. Replacing H 2 O as a transport agent with HCl would provide the ability to deposit directly on Si by avoiding oxide formation and to allow for the deposition of Al-containing alloys that would otherwise oxidize. Improved engineering design and implementation of an in-line multi-station CSVT would allow for direct deposition of device structures in a single system.

14 SOLAR ENERGY↗
DOE OSTI2019

Distributed optimal power flow processes for unbalanced radial distribution networks

Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control on an unbalanced network. One embodiment includes a node controller including a distributed power control application; a plurality of node operating parameters describing the operating parameter of a node in an unbalanced network; wherein the processor is configured by the distributed power control application to: send node operating parameters to nodes in the set of at least one node; receive operating parameters from the nodes in the set of at least one node; calculate a plurality of updated node operating parameters using an iterative process to determine updated node operating parameters using the node operating parameters that describe the operating parameters of the node, and the operating parameters of the set of at least one node, where each iteration in the iterative process involves evaluation of a subproblem; and adjust node operating parameters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Laboratory Enrichment of Radioactive Assemblages and Estimation of Thorium and Uranium Radioactivity in Fractions Separated from Placer Sands in Southeast Bangladesh

The present study reports the likely first attempt of separating radioactive minerals for estimation of activity concentration in the beach placer sands of Bangladesh. Several sand samples from heavy mineral deposits located at the south-eastern coastal belt of Bangladesh were processed to physically upgrade their radioactivity concentrations using plant and laboratory equipment. Following some modified flow procedure, individual fractions were separated and investigated using gamma-ray spectrometry and powder-XRD analysis. The radioactivity measurements indicated contributions of the thorium and uranium radioactive series and of {sup 40}K. The maximum values of {sup 232}Th and {sup 238}U, estimated from the radioactivity of {sup 208}Tl and {sup 234}Th in secular equilibrium, were found to be 152,000 and 63,300 Bq/kg, respectively. The fraction of the moderately conductive part in electric separation contained thorium predominantly, while that of the non-conductive part was found to be uranium rich. The present arrangement of the pilot plant cascade and the fine tuning of setting parameters were found to be effective and economic separation process of the radioactive minerals from placer sands in Bangladesh. Probable radiological impacts and extraction potentiality of such radioactive materials are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Selection Criteria for Sustainable Fuels for High-Efficiency Spark-Ignition Engines with Examination of their Storage Stability, Impact on Engine Knock, and Fine Particle Emissions

It is possible to significantly improve the efficiency of spark-ignition engines given fuels with improved autoignition, evaporative cooling, and particle emission properties. At the same time, a vast range of different fuel chemistries are accessible from biomass - leading to questions about how fuel chemistries outside the range available from petroleum and ethanol can impact engine operation. This presentation will briefly describe the factors leading to poor efficiency in current SI engines, and the technologies available for improving efficiency. Improved fuel properties that enable high efficiency engine designs to be pursued aggressively will be reviewed, including octane index and sensitivity. A screening process based on fuel properties was applied to a large set of proposed biomass-derived gasoline blendstocks, and the properties of the best blendstocks were evaluated. Some of these fuels exhibit poor stability towards oxidation in the liquid phase, and storage stability studies for alkyl furans and cyclopentanone will be presented in brief. The importance of fuel heat of vaporization for direct injection engines, along with new research on measurement of this parameter, will be presented including an SI engine study of the impact of heat of vaporization on octane index and engine knock. Fuel effects on fine particle emissions and how our understanding breaks down for oxygenates will be discussed. Engine combustion experiments, droplet evaporation simulations, and heat of vaporization measurements conducted to better understand how oxygenates affect particle emissions will be described. This research defines a process that can be used to evaluate fuels for other types of combustion such as diesel, gasoline compression ignition, or strategies with mixed modes.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

A Sensitivity-informed, Process Model Abstraction Implementation in GoldSim - 19276

System-level Performance Assessment (PA) models rely on specific process-level models for some inputs, such as infiltration. For probabilistic PA, the value of the process model output depends on the values of input parameters that also vary in the PA model. The simplest conceptual solution is to draw values of the input parameters from their distributions, run the process level model at these values, and then use the output where it is needed in the PA model. However, this solution is unsatisfactory for multiple reasons: (1) the PA Model should be set up such that new runs can be made and distributions can change without having to go back and run the process-level model; (2) calling the process model from the system model is possible, but technical difficulties remain in coupling the two; and (3) there is no uncertainty in the process-level model results incorporated into the PA model. Therefore, it is desirable to develop a method that maps the values of PA Model inputs to process level model outputs, in real time within the PA Model set up and using as simple of a map as possible without losing key features of the relationship. This has been termed 'model abstraction' because information about process model runs is 'abstracted' to be used with the PA model. A simple 'nearest neighbor' abstraction approach is presented to overcome these problems, in which a sensitivity-informed distance metric is used to map a system-level model realization to the most appropriate process-level model realization. (authors)

97 MATHEMATICS AND COMPUTING↗