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A theory for flame spread above a solid fuel is presented. The special case is considered whereby the oxidation is an exothermic surface reaction. The spreading rate is predicted as a function of the thermochemical properties, fuel-bed thickness, and convective velocity. Also, the theory predicts temperature, mass fraction, and heat flux as a function of position.
For several years the Department of Defense has been sponsoring fuel accommodation investigations with gas turbine engine manufacturers and supporting organizations to quantify the effect of changes in fuel properties and characteristics on the operation and performance of military engine components and systems. Inasmuch as there are many differences in hardware between the operational engines in the military inventories, due to differences in design philosophy and requirements, efforts were initially expended to acquire fuel effects data from rigs simulating the hot sections of these different engines. Correlations were then sought using the data acquired to produce more general, generic relationships that could be applied to all military gas turbine engines regardless of their origin. Finally, models could be developed from these correlations that could predict the effect of fuel property changes on current and future engines. This presentation describes some of the work performed by Pratt and Whitney Aircraft, under Naval Air Propulsion Center sponsorship, to determine the effect of fuel properties on the hot section and fuel system of the Navy's TF30-P-414 gas turbine engine.
Monolithic fuel is a candidate fuel form that is being considered for the conversion of high-performance research reactors. This plate-type fuel consists of a high-density, U-Mo fuel in a monolithic form that is sandwiched between zirconium diffusion barriers and encapsulated in an aluminum cladding. To date, many plates were irradiated with a satisfactory irradiation performance, demonstrating that the conceptual design works. The program is now moving to the qualification phase, a predecessor to the timely conversion of the target reactors. To qualify this fuel system, the program must show that the fuel plates have predictable behavior, meet the safety standards, and perform well in reactors. The requirement of a satisfactory irradiation performance under normal operating conditions is primarily demonstrated by a successful testing. To demonstrate that the fuel system has a predictable behavior, the several key material properties should be quantified accurately since these properties are needed to estimate the thermal and mechanical behavior of the fuel system. Although, there is a large set of thermophysical property data available for unirradiated material, the property data for irradiated fuel is scarce. Since irradiation causes drastic effects in material, a significant change in material properties occurs. Consequently, using representative degradation models becomes essential for accurate performance assessments. This work examines thermal conductivity of U-10Mo, by evaluating recent experimental data from the literature and available theoretical models. The study has discovered inconsistencies in the literature data, revealing that previously developed theoretical models fail to predict the thermal conductivity of irradiated fuel.
MARMOT is a robust numerical tool for mesoscale modeling of fuel performance developed under the NEAMS Fuels technical area to predict the coevolution of microstructure and properties in fuel and cladding materials. MARMOT accomplishes this using the phase field method coupled with finite strain mechanics and heat conduction. MARMOT is based on the open source Multiphysics Object-Oriented Simulation Environment (MOOSE) and solves the coupled partial differential equations defining the physics using the finite element method. MARMOT is being developed in order to facilitate the development of improved materials models for fuel performance, but it is also being developed as a powerful tool in and of itself for the simulation of mesoscale fuel performance.
The purpose of this agreement between the National Renewable Energy Laboratory (NREL) and Gevo, under the DOE Small Business Vouchers Pilot, is to develop a predictive octane blending model for isobutanol and blendstocks for oxygenated blending (BOBs). Validated models of ethanol octane blending effects are currently used in refinery blending models. While it is known that isobutanol increases octane when blended into BOBs, the effect is non-linear, and dependent on hydrocarbon blendstock properties. Because no predictive model currently exists for isobutanol and BOBs, blenders must perform expensive and time-consuming tests to determine the octane effect on each batch of finished fuel. NREL will make basic measurements of research octane number (RON) and motor octane number (MON) of finished gasoline-isobutanol blends prepared in a broad range of BOBs. The key properties and detailed composition of BOBs utilized will be measured by NREL. Using these measured fuel properties, NREL will develop a predictive octane blending model for isobutanol in BOBs that can be used by terminals to further reduce barriers to market penetration of isobutanol.
The NASA-MSFC program to furnish a high-pressure LOX/hydrocarbon turbine drive combustor technology base for future booster engine programs is discussed. The program evaluated the LOX/RP-1 and LOX/methane preburner and gas generator performance and gas property prediction methods for fuel-rich and oxidizer-rich operating conditions. A small-scale hardware test program, which included 42 hot-fire tests of fuel-rich LOX/methane and LOX/RP-1 and oxidizer-rich LOX/methane, supplied information to reduce uncertainties in prediction techniques. The hot-fire results show that theoretical computer model features should be modified to support curves generated from experimental data.
While regulators, the scientific community, and MSR developers still lack access to literature data on the thermal properties of clean fuel salts, even less information is available on the properties of fuel salts containing impurities. It is essential to understand, benchmark, and predict crucial data on the changes in thermal properties of fuel salt systems due to impurities arising from moisture, corrosion, and reactor operation (i.e., fission products). This research focuses on two actinide fuel salts (1) to investigate a worst-case scenario buildup of actinide fission product in a NaCl-UCl3 eutectic fuel salt and (2) to investigate NaCl-PuCl3 eutectic salt after 1000 hours of operation in a natural circulation flow loop flow to determine if corrosion or atmospheric (moisture/oxygen) products are present. For the first salt, a conservative assumption or worst-case scenario, for fission product buildup in a fuel salt was investigated by adding PuCl3 to eutectic 67 mol% NaCl – 33 mol% UCl3 salt resulting in a ternary salt having a composition of 61 mol% NaCl – 30 mol% UCl3 – 9mol% PuCl3. Addition of PuCl3 to eutectic NaCl-UCl3 resulted in a ternary salt that had a higher melting temperature than either the NaCl-PuCl3 or NaCl-UCl3 binary eutectic mixture. Addition of PuCl3 also resulted in an increase in density which was expected. The second salt was extracted from a micro loop. The composition of the fuel (primary) salt prior to flow loop operations was determined to be 64 mol% NaCl – 36 mol% PuCl3, however, the post-flow loop salt showed increased levels of MgCl2 and NaCl changing the salt composition to 10 mol% MgCl2 – 63mol% NaCl – 26mol% PuCl3) indicating the primary salt interacted with the rinse salt. Analysis of the post flow loop salt detected low concentrations of Al, Ni, Co, Nb, and Zr, most likely corrosion products from the flow loop material of construction. Contamination of the fuel salt (with the rinse salt NaCl-MgCl2) decreased the density by approximately 10% and reduced the onset of melting temperature by 50 °C, from 451 °C to approximately 400 °C. Results from the fission product simulated salt (61 mol% NaCl – 30 mol% UCl3 – 9mol% PuCl3) and the corrosion product salt (10 mol% MgCl2 – 63mol% NaCl – 26mol% PuCl3) will be included in two separate manuscripts for submission to peer-reviewed journals.
Abstract This work presents an investigation into the ability of recurrent neural networks (RNNs) to provide long term predictions of time series data generated by coal fired power plants. While there are numerous studies which have used artificial neural networks (ANNs) to predict coal plant parameters, to the authors’ knowledge these have almost entirely been restricted to predicting values at the next time step, and not farther into the future. Using a novel neuro-evolution strategy called Evolutionary eXploration of Augmenting Memory Models (EXAMM), we evolved RNNs with advanced memory cells to predict per-minute plant parameters and per-hour boiler parameters up to 8 hours into the future. These data sets were challenging prediction tasks as they involve spiking behavior in the parameters being predicted. While the evolved RNNs were able to successfully predict the spikes in the hourly data they did not perform very well in accurately predicting their severity. The per-minute data proved even more challenging as medium range predictions miscalculated the beginning and ending of spikes, and longer range predictions reverted to long term trends and ignored the spikes entirely. We hope this initial study will motivate further study into this highly challenging prediction problem. The use of fuel properties data generated by a new Coal Tracker Optimization (CTO) program was also investigated and this work shows that their use improved predictive ability of the evolved RNNs.
Advanced multitude of experiments ranging from the liquid fuel to combustion are conducted on Co-OPTIMA fuels to aid the characterization of the fuels. The series of targeted experiments characterized Co-OPTIMA fuel spray atomization, flame topology, flame speed, autoignition, volatility, viscosity, soot/coking, and compatibility. The fuels are selected and prioritized based on input from national lab members. The research characterized and predicted biomass-based, low greenhouse gas fuels and blends combustion, autoignition, and physical properties of mixtures of identified compounds at engine-relevant conditions, in particular those properties that blend non-linearly. The main challenge of Co-OPTIMA is the evaluation of a variety of biofuels and blends in all the reaction conditions that might be encountered in new high-efficiency engines. Despite the improved high-throughput experimental techniques, it seems unlikely that all of the performance metrics could be measured for all relevant petroleum derived, bio-derived molecules and mixtures, and reaction conditions. A series of targeted experiments ranging from the liquid fuel to the combustion process is required, and to extract the maximum information from each experiment. These targeted experiments evaluated how a specific fuel will perform in an engine. The series of targeted experiments are as follows: (a) Spray Atomization, Vaporization and Droplet Formation (b) Combustion Flame and Local Fuel/Air Image-Based Measurements (c) Laminar Flame Speed Measurements (d) Autoignition and Soot Measurements (e) Synchrotron Coupled Fundamental Autoignition Experiments (f) Fuel Coking and Hot Surface Deposit g) Fuel Volatility Measurements (h) Viscosity Measurements (i) Seal Flexible Fuel Compatibility These experimental processes provide an essential pathway for the prediction of fuel behaviors in engines and systematic process for fuel down select.
Manufacturing of tristructural isotropic (TRISO) particles involves the deposition of pyrolytic carbon (PyC) and silicon carbide (SiC) layers using the fluidized bed chemical vapor deposition (CVD) process. The CVD process is known to generate polycrystalline layers with crystallographic textures, which imparts anisotropic thermophysical properties to the layers. Past studies have shown the risk for particle failure increases with an increase in anisotropy. The limit beyond which the anisotropy of PyC layers becomes unacceptable due to failure risk has been identified as a high-priority knowledge gap. This work presents a first systematic study on the effects of anisotropic thermal and mechanical properties on TRISO fuel performance. This computational study, performed using the fuel performance code BISON, investigates how the anisotropy in elasticity and thermal properties affect the stresses, temperature, and failure of a TRISO particle. The influence of other factors, such as operating temperature and particle geometry on the anisotropy effects, also has been analyzed. The studies utilize the recently published anisotropic elasticity and thermal behavior models for TRISO PyC and SiC layers implemented using tensors with full anisotropic capability. The spherical TRISO particles with anisotropic properties were found to have greater maximum tensile stress and significantly higher failure probability than the spherical particles with isotropic properties. In conclusion, the fuel performance predicted using these recently developed models was found to be comparable with the performance obtained using the historical models.
Mixing controlled compression ignition or diesel engines are highly efficient and are likely to continue to be the primary means for movement of goods for many years to come. Low-carbon biofuels have the potential to significantly reduce the carbon footprint of diesel combustion and could potentially have advantageous properties for combustion such as high cetane number and reduced engine out particle emissions. In this study, we developed a list of potential biomass-derived diesel blendstocks. An online database of properties and characteristics of these bioblendstocks was populated with data. Fuel properties were determined by measurement, model prediction, or literature review. Screening criteria were developed to determine if a bioblendstock met the basic requirements for handling in the diesel distribution system and use as a blend with conventional diesel. Criteria included cetane number =40, flashpoint =52 degrees C, and boiling point or final boiling point <338 degrees C. Blendstocks needed to be soluble in diesel fuel, have a toxicity no worse than conventional diesel, and not be corrosive. Additionally, cloud point or freezing point below 0 degrees C was required. This screening produced a list of 27 potential bioblendstocks. Of these candidates, 13 were available commercially or could be synthesized by biofuels production researchers and included 11 nominally pure components and two mixtures. These 13 candidates were then subjected to further study based on how they impact fuel properties upon blending. Blend properties included cetane number, lubricity, conductivity, oxidation stability and viscosity. Results indicate that all thirteen candidates can meet the basic requirements for diesel fuel blending and are likely to reduce particle emissions from diesel combustion.
We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.
Calculations were performed to assess how uncertainties in molten salt property values impact the results of behavior models of molten salt reactor systems under steady-state conditions. The thermophysical and thermochemical properties of salt mixtures being considered for use in molten salt reactors are not yet well characterized and methods used to measure property values to molten salts are not yet well established. The limited property data for salts of interest that are available in the literature are inconsistent and unreliable. Many methods used to measure property values remain developmental in the sense that not all variables affecting measured values have been identified or controlled during the measurement and not all aspects of the measurements affecting data quality are calibrated. A prior report described the repeatability of measurements made at Argonne and the reproducibility of measurements made with the same salts at several institutions is being evaluated. There is significant uncertainty in measured property values due to instrumental limitations and the effects of contaminants in the salts used to make the measurements that impacts predictions of salt behavior in a reactor. Most salts of interest are multicomponent eutectic or near-eutectic mixtures and both uncertainty in the salt composition and uncertainty in measured values. Furthermore, the salt composition evolves over time due to the uptake of impurities from the atmosphere and corrosion of containment vessels. More significantly, fission products will accumulate during operation that change the salt compositions and property values. MSR developers need to understand how this uncertainty in the basic property data of the fuel will impact modeling predictions for normal reactor operation as well was in transient scenarios that will impact the safety basis. The calculations and analyses in this report address the impact of uncertainty in measured property values under steady-state conditions through a generalized parametric study. The pooled uncertainty from contributions by a number of factors were considered and quantified, including varying composition due to impurities and fission products, imprecision in the measured values, and uncontrolled aspects of the measurement. In this way, we can begin to gain an understanding of the magnitude of the impact uncertainty will have in modeling MSR systems.
This proposed work has the potential to rewrite the way the nuclear industry investigates new fuel and nuclear material designs. The current rubric of nuclear material design has myriad steps in the process, and while certain physics are modeled accurately, each step must be connected in order to obtain an entire description of the process. At present, neutronic, thermal, microstructural, fission product chemistry and migration, and radiation defect analysis (hereafter referred to together as “combined analysis”) are performed, albeit separately. There is no existing method which combines these physics in an attempt to understand the natural interactions between these phenomena. Consequently, the timeline for design, fabrication, experiment, validation, and licensing can take years. A disruptive approach is required to accelerate the development of new technology. This proposed undertaking creates a validated computational framework, generating a new microscopic-to-macroscopic methodology yielding thermal property predictions for nuclear fuels and materials at an engineering spatial scale.
To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.
In this work, we have been developing a three-step conversion of biomass-derived syngas to methanol to dimethyl-ether (DME) to non-aromatic hydrocarbons for use as high-octane gasoline and sustainable aviation fuel. This process produces branched alkanes from DME using a Cu/BEA catalyst and is a promising alternative to other syngas conversion processes such as Fischer-Tropsch to linear alkanes and traditional ZSM-5 catalyzed methanol to aromatic gasoline. In this short article we describe some advances in our understanding related to separations and purification via the use of more detailed experimental speciation in an updated process model involving multiple phase equilibrium-based separation steps. Primary modeled reactor outlet constituents (and weight %) are: C3 and lighter hydrocarbon gases (11.1%), C4s (54.5%), H 2 (1.2%), CO 2 (2.9%), water (5.0%), unreacted DME (16.5%), methanol (2.3%), and C5+ hydrocarbons (6.4%). DME (the primary reactant) and H 2 recycle and reuse are important for the overall process efficiency, and the recycle of C4s is important to increase the C5+ yield via reactivation and homologation. Thus H 2 , C4s, and DME are targeted for recycle, while methanol and water need to be removed from the product to conform with fuel specifications. Model predictions from Aspen Plus using the NRTL-RK property method indicate a fuel composition with C5+ content of 97.1 wt%, with minor constituents: 2.4 wt% C4s, 0.3 wt% methanol, 0.1 wt% DME, 0.03 wt% water, and 0.01 wt% C3s. These ranges of minor components conform with fuel quality requirements, and the modeled product is amenable for unconstrained blending to boost gasoline octane ratings.
Conclusions and recommendations are presented for an analysis of the total energy situation; the effect of the energy problem on air transportation; and hydrogen fuel for aircraft. Properties and production costs of fuels, future prediction for energy and transportation, and economic aspects of hydrogen production are appended.
The performances of Navy Jet Mix and MIL-F-5624A (JP-3) fuels were compared in J33, J47, and NACA experimental annular combustion chambers. Combustion efficiencies, altitude operational limits, and carbon-forming tendencies wer determined and discussed. The results indicate that Jet Mix fuel can be utilized satisfactorily in a number of current turbojet engines over the normal operating range. Small differences in combustion efficiences and altitude operational limits existed between the two fuels but these differences depended on the particular combustor and simulated altitude and rotor speed condition. Excessive carbon deposition is not predicted for Jet Mix fuel, although this property may be marginal.