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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Front-End Engineering Design (FEED) Study for a Carbon Capture Plant Retrofit to a Natural Gas-Fired Gas Turbine Combined Cycle Power Plant (2x2x1 Duct-Fired 758-MWe Facility with F Class Turbines)

A comprehensive front-end engineering design (FEED) study has been undertaken by Bechtel National Inc. (Bechtel) for locating a post-combustion capture and compression (PCC) unit at Panda’s Sherman natural gas–combined cycle (NGCC) power plant in Sherman, Texas. This is described in the unredacted FEED Study report (Attachment 1) with all supporting documents, numbering over 150. The Study Report is publicly available. Sizing of the PCC plant is based on treating an amount of flue gas equivalent to that produced when generating 420 MW, which is approximately 68% of the total flue gas emitted by the NGCC power plant operating at guarantee condition with duct burners off. A reduced power plant capacity factor was used for sizing the PCC plant because the gas turbines at the site often operate at reduced load due to the high penetration of renewable power in the ERCOT region. The cost of carbon capture is primarily driven by capital cost (and therefore is highly sensitive to capacity factor). Sizing the capture unit so that when used it is nearly always operating at full capacity is critical to the economic viability of the proposed investment.

03 NATURAL GAS↗
DOE OSTI2020

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Final Report)

The Phase 1 Final Technical Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. A key part of the Phase 1 work was the completion of a thermodynamic cycle analysis for the MHHE at the selected module size. The hybrid heat engine has been developed as a modular unit (sized in the range of 500kW – 60MW) that can be used with modular coal or biomass gasifiers, with distributed power generation systems, with large power plants comprised of multiple generating units, and with natural gas compression stations. The MHHE will provide cleaner, more efficient, and lower cost generation with better load following capabilities than existing competing technologies with a singular generating source such as solar farm, gas turbine, or combustion engine. The drivers of the MHHE technology are: benefits of modular power generation (reduced equipment cost, construction cost and implementation time, connection ready on delivery, flexible scalability, serviceability), fuel flexibility, lowest cost power generation and reduced emissions.A logical progression of work and a clear path forward toward meeting the FOA goals and objectives have been established. Namely, a preliminary market analysis and primary fuel identification was completed first and then the modularity of the system was defined. The benefits of the proposed hybrid and modular heat engine were described when applied to modular coal gasifiers, distributed power generators, and larger power plants. Based on the market analysis, modularity, and chosen primary fuel, a conceptual design and layout of the hybrid heat engine was developed, analyzed, and characterized. The technology gaps already identified have been reviewed and expanded upon, and a test plan to address these gaps through bench scale testing in Phase 2 has been developed. Cost estimate methodology and considerations in support of a potential Phase 2 project have been described.

08 HYDROGEN↗
DOE OSTI2022

Towards multi-fidelity deep learning of wind turbine wakes

We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

17 WIND ENERGY↗
DOE OSTI2022

Turbocharger system with turbine bypass circuit to reduce emissions during engine cold start

An engine system includes an engine having first and second exhaust manifolds each having outlet ports and a bypass runner. A first turbocharger is in fluid communication with the outlet port of the first manifold, and a second turbocharger in fluid communication with the outlet port of the second manifold. An exhaust aftertreatment device is in fluid communication with the first and second turbochargers. A turbocharger bypass circuit includes a valve assembly having an inlet side connected in fluid communication with the bypass runners of the first and second manifolds, an outlet side in fluid communication with the aftertreatment device, and a valve having an open position in which the inlet and outlet sides are in fluid communication and a closed position in which the inlet and outlet sides are not in fluid communication.

Plagens, Keith Michael↗
DOE OSTI2021

High Temperature Thermal Barrier Coating Evaluation of Yttrium Aluminum Garnet for Gas Turbine Applications

The Solution Precursor Spray Process (SPPS) process has been shown to overcome the durability and property challenges of applying yttrium aluminum garnet (YAG) coating for high temperature thermal barrier coating (TBC) applications (1200°C+) but only limited testing has been performed in representative application environments. In this study, YAG coatings were developed to optimize microstructure and tailor properties for combustion components in an industrial gas turbine. Thermal conductivity, erosion resistance and furnace cycling durability were used to validate YAG properties against the standard yttria-stabilized zirconia coating (YSZ). Graded and multi-layered microstructures were applied to enhance performance while maintaining durability. Further YAG process development was required to replicate coating properties and optimize deposition on more complex fuel injector nozzle and combustion liner components. Rig testing of both components was performed and compared against the baseline YSZ coating validating the high temperature capability and lower thermal conductivity of the SPPS YAG coating. The components in both tests were cycled for a minimum of ten cycles with the fuel injector test targeting a higher operating temperature while the combustor liner operated at standard conditions. A development engine test is planned to further validate performance in a gas turbine engine of this promising material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the importance of species immiscibility in mixing-layer dynamics at supercritical pressures

The mixing dynamics of injected propellants is a key factor in determining the ignition performance and combustion-instability response of rocket engines, internal combustion engines, and gas turbines. A key source of uncertainty in the prediction of phase-exchange dynamics is the immiscibility of the injected propellants at supercritical pressure conditions, which induces liquid/vapor phase separation and surface-tension dynamics. While experimental observations indicate the presence of liquid/vapor interfacial structures, this interfacial dynamics is typically neglected in numerical analyses. To address this issue, the objective of the present study is to systematically evaluate the importance of species immiscibility on the phase-exchange dynamics of cryogenic LOX/GH2 mixing layers at typical rocket engine injection conditions. This is accomplished by comparing simulations of (i) a recently developed interface-capturing Regularized-Interface Method (RIM) formulation, and (ii) the commonly employed Diffuse-Interface Method (DIM) formulation. Analysis shows that the interfacial dynamics significantly impact the atomization and mixing of the propellants in the near-injector region, which is not captured by the DIM formulation. The findings of this study extend to other immiscible injection systems, such as LOX/kerosene and hydrocarbon-fuel/air, thereby highlighting the importance of resolving species immiscibility in simulating high-pressure combustion engines.

diffused-interface method↗
DOE OSTI2020

Thermal power plant upgrade via a rotating detonation combustor and retrofitted turbine with optimized endwalls

Over the past decade, pressure gain combustion research has promised over 10 percentage-points of increase in power plant thermal efficiency. Alas, to realize such potential gain, one must effectively couple the turbine with the detonation combustor, whose exhaust conditions differ substantially from current state of the art gas turbines. This paper presents a modeling approach that enables a superior thermodynamic cycle with a rotating detonation combustor and a retrofitted gas turbine by including a diffuser downstream of the combustor and by contouring the turbine endwall, while preserving the airfoil geometry. We propose a multi-step optimization strategy, parametrizing the endwall geometry with a few control points, without altering the airfoil geometry, and with the stage turbine efficiency as objective function. In a first step the turbine performance is assessed with steady inlet conditions by solving the steady three-dimensional Reynolds-Averaged Navier-Stokes equations. In a second step, the inlet conditions are unsteady, as predicted from a detonation combustor and a diffuser, and three-dimensional full unsteady simulations are performed with an unsteady Reynolds-Averaged Navier-Stokes solver. By altering the vane endwall, the steady optimization yielded an efficiency increase of 12% relative to the baseline, while the unsteady optimization resulted in 21% increase compared to the datum turbine. Finally, a full engine analysis demonstrated the superiority of pressure gain combustion which included a realistic thermodynamic cycle of the combustor, the diffuser and the optimized turbine components.

42 ENGINEERING↗
DOE OSTI2021

Hydrothermal catalysis of waste greases into green gasoline, jet, and diesel biofuels in continuous flow supercritical water

Production of green gasoline, jet, and diesel biofuels from waste greases was achieved using a novel hydrothermal, continuous-flow catalytic process operating under supercritical water conditions, with recycled water the only added chemical. Thermally and chemically stable catalysts were explored to optimize yields of liquid biofuels and to minimize production of gases and acidic compounds. A 50:50 mixture of brown and yellow waste greases converted into 76.6 wt% liquid biocrude (BC); the remainder converted to water and gases. Less than 0.2% of the FS formed carbon char (mainly amongst catalyst particles). Various tubular reactors (Inconel, ® Hastelloy®, titanium, stainless) showed no interior defects, erosion, or mass loss after runs. Here, the titanium catalyst was fully recovered and regenerated back to its original potency. The BC was further refined into 28%, 48%, 20%, and 4 wt%, respectively for green gasoline, jet, diesel, and bunker. Biofuels were analyzed for compound class compositions and reaction mechanisms were proposed. The hundreds of identified fuel products (C3-C35) from processing oleic acid as a pure model compound were identified. The neat green gasoline and diesel biofuels along with a 50% green jet blend (with petroleum Jet A) were tested in appropriate spark ignition, turbine, and diesel engines at University of MN Engine Labs. Biofuels achieved 107.7%, 97.2%, and 101.3% engine power performance levels relative to petroleum fuels (91-Octane, Jet A, #2 Diesel) along with lower CO and pollutant emissions. Biofuels passed ASTM fuel specifications (D4814, D7566, D975) including mandated corrosion and low sulfur limits of all three biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Plasma assisted NH 3 /H 2 /air ignition in nanosecond discharges with non-equilibrium energy transfer

Ammonia (NH 3 ), with its high energy density and easiness to store and transport as a hydrogen carrier, has become a promising alternative green fuel. However, its adoption in power generation is hindered by challenges such as low burning velocity, slow low-temperature oxidation, high NO x emissions, and ignition difficulty. Here, this work computationally investigates the effects of non-equilibrium energy transfer by nanosecond discharges on NH 3 ignition and flame propagation in an NH 3 /H 2 /air flow at 700 K and 1 atm. The simulation results demonstrate that NH 3 /air mixtures require a large ignition energy due to their large critical ignition radius. It is shown that adding 30 % hydrogen significantly reduces the critical ignition radius and minimum ignition energy. Two-dimensional modeling further shows a non-monotonic dependence of ignition kernel volume on the applied voltage and reduced electric field. The optimum ignition enhancement occurs at 200 Td where the generation of electronically excited species and radicals including N 2 (B), O( 1 D) and OH becomes most efficient. Higher voltages divert electron energy toward ionization, which makes it less effective for NH 3 ignition. The study also identifies an optimal electrode gap size for a given pulse energy. Smaller gap sizes increase deposited energy density, raising temperature and radical concentrations. However, excessive reduction of the gap distance reduces flame propagation speed due to the flame stretch effect in rich mixtures with the effective Lewis number greater than unity. A nonlinear relationship between pulse repetition frequency and ignition kernel volume is observed in a nanosecond pulsed high frequency discharge (NPHFD). An optimal frequency range of 200 kHz to 2 MHz is found when two pulses are used. In addition, an optimal number of pulses exists for each pulse repetition frequency, with higher frequencies requiring more pulses to maximize the overlap region. These findings provide critical insights on developing controlled plasma discharge techniques for efficient NH 3 ignition in reactive flows within internal combustion engines and gas turbines.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Phase diagram to demarcate supercritical, transcritical, and continuous phase regimes for binary fluid equilibrium mixing relevant to combustion applications

Here, a robust methodology to develop phase diagrams of binary fluid mixtures at fixed thermo dynamic conditions (pressure, temperature, and mole fraction) as well as of two initially separated fluids undergoing mixing near critical conditions are presented for fluids and con ditions relevant to rockets, gas turbines, and diesel engine applications. Phase equilibria of mixtures is first examined to provide insight into the continuous-phase mixing behavior (including but not limited to supercritical behavior), and to develop a broadly applicable phase-diagram for binary fluid mixtures at fixed conditions. Next, adiabatic mixing theory and reduced Helmholtz equations of state are used to predict the thermodynamic conditions required to attain continuous-phase binary fluid mixing near critical conditions. Then, a 3D surface diagram (P,T fuel ,T amb ) separating single and two-phase regions is constructed by varying the ambient pressure and the initial temperatures of the two fluids. Polynomial fits of the 3D surfaces for 10 different binary mixtures are tabulated for nitrogen-alkane and methane-oxygen blends relevant to air-breathing and propellant based engines, respectively.

42 ENGINEERING↗
DOE OSTI2020

Progress and Prospective of Nitrogen-Based Alternative Fuels

Alternative fuels are essential to enable the transition to a sustainable and environmentally friendly energy supply. Synthetic fuels derived from renewable energies can act as energy storage media, thus mitigating the effects of fossil fuels on environment and health. Their economic viability, environmental impact, and compatibility with current infrastructure and technologies are fuel and power source specific. Nitrogen-based fuels pose one possible synthetic fuel pathway. In this paper, we discuss the progress and current research on utilization of nitrogen-based fuels in power applications, covering the complete fuel cycle. We cover the production, distribution, and storage of nitrogen-based fuels. We assess much of the existing literature on the reactions involved in the ammonia to nitrogen atom pathway in nitrogen-based fuel combustion. Furthermore, we discuss nitrogen-based fuel applications ranging from combustion engines to gas turbines, as well as their exploitation by suggested end-uses. Thereby, we evaluate the potential opportunities and challenges of expanding the role of nitrogen-based molecules in the energy sector, outlining their use as energy carriers in relevant fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Chemical influence of ammonia on suppressing soot formation pathways

There is interest in mixing ammonia (NH3) with hydrocarbons, as this decreases overall CO2 emissions from the fuel mixture when burned. Co-firing NH3 with hydrocarbons like methane (CH4) also enables stable combustion over a larger set of operating conditions in engines and gas turbines than firing only NH3 permits. When NH3 is co-fired with hydrocarbons, the influence of NH3 on soot formation chemistry becomes relevant. In this work, soot concentrations in laminar nonpremixed co-flow flames of NH3/CH4 and N2/CH4 were analyzed using color-ratio pyrometry. The concentrations of combustion species (CH4, CO2, H2O, O2, C2H2, C6H6, HCN) across the different flames were also determined using on-line electron-impact mass spectrometry. The flames were also interrogated with 2D CFD simulations, which employ a detailed chemical mechanism tailored to capturing fuel-nitrogen chemistry. Addition of NH3 to CH4 flames was found to decrease soot concentrations more significantly than N2-addition (Fig. 1). The NH3/CH4 flames were found to have higher flame temperatures than the N2/CH4 flames, implying the suppression of soot afforded by NH3 is due to a chemical influence of NH3, as opposed to thermal or dilution effects. Based on experimental and simulated soot volume fractions and species concentrations, soot formation pathways affected by NH3 are postulated.

10 SYNTHETIC FUELS↗
DOE OSTI2021

Role of Inlet Boundary Conditions on Fuel-Air Mixing at Supercritical Conditions

Advanced engine design and alternative fuels present the possibility of fuel injection at purely supercritical conditions in diesel engines and gas turbines. The complex interactions that govern this phenomenon still need significant research, particularly the boundary conditions for fuel injection are critical for accurate simulation. However, the flow inside the injector itself is often omitted to reduce the computational efforts, and thus, velocity, mass flux, or total pressure is specified at the injector exit (or domain inlet), often with simplified velocity profiles and turbulence levels. This simplified inlet boundary treatment has minimal effects on results for conventional fuel injection conditions, however, the validity of this approach at supercritical conditions has not been assessed. Comprehensive real-gas and binary fluid mixing models have been implemented for computational fluid dynamics (CFD) analysis of fuel-air mixing at supercritical conditions. The model is verified using prior CFD results from the literature. The model is used to investigate the effects of the shape of axial velocity and mass fraction profiles at the inlet boundary with the goal to improve the comparison of predictions to experimental data. Results show that the boundary conditions have a significant effect on the predictions, and none of the cases match precisely with experimental data. Furthermore, the study reveals that the physical location of the inlet boundary might be difficult to infer correctly from the experiments and highlights the need for high-quality, repeatable measurements at supercritical conditions to support the development of relevant high-fidelity models for fuel-air mixing.

02 PETROLEUM↗
DOE OSTI2024

Machine Learning Enhanced Development of Functionally Graded Materials Enabled by Directed Energy Deposition

The ability to functionally grade materials provides designers with a new dimension of design flexibility that can be leveraged to improve functionality, reduce cost, or improve efficiency in a wide range of applications. This program was specifically focused on FGMs for hot and harsh gas path environments. These environments are common for the hot sections of jet engines and gas turbines, where parts undergo high temperature and mechanical loads in a corrosive environment. Expensive high γ' strengthened Ni superalloys such as René 41 (R41) and René 80 (R80) are generally used exclusively for a whole part, although only a section of the part demands such superalloys. To minimize cost, low/no γ' strengthened Ni superalloys such as Inconel 718 (INC718) could be used at less-demanding sections of the part could be welded to the high γ' strengthened Ni superalloys. However, the welding typically is a failure site due to the low durability at the welding interface. Functionally grading provides a welding alternative that can allow cost reduction without sacrificing mechanical performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Develop a Simulation Framework for Understanding Physico-Chemical Processes and Optimization of CHZ' Plastic Thermolyze Technology (CRADA Final Report)

Thermolyzer(TM) technology is a third-generation, multi-reactor, oxygen-free, low pressure, slow pyrolysis process. It has proven to be remarkably versatile in successfully processing all types of hydrocarbon waste. These include all seven types of plastics, tires, auto shredder residue, carpet, electronic waste, and composites. Working with the IACMI, Thermolyzer(TM) technology has recovered the glass and carbon fibers from wind turbine blades for reuse into new applications. Its primary output is a synthesis gas that is rich in H 2 , CH 4 , and small amounts of C 2-4 aliphatic hydrocarbons. Other components are CO and CO 2 . Three unique features set Thermolyzer(TM) apart from other pyrolysis systems: the syngas is clean enough to run in Siemens or Solar gas turbines or IC engines without fouling, clean, salable, byproducts are produced, and CO 2 emissions are lower than natural gas power plants. Research results have been obtained from an operating 7 ton/day facility. A 44 ton/day plant has successfully operated as noted above. The process works because it makes use of the recoverable embodied energy in the feedstock. Surprisingly, a pound of some plastics has the same recoverable energy content as a pound of gasoline. Thus, it is imperative to develop a process that economically converts scrap plastics (including ocean plastics) into energy and thereby conserve the non-renewable fossil fuels for future generations. The Thermolyzer(TM) technology can also be used to create liquid fuels like gasoline and diesel from hydrocarbon wastes. Because of the high hydrogen content of the synthesis gas, hydrogen can be recovered more inexpensively than the current solar or wind energy being used to electrolyze water. That hydrogen can be used for fuel cell powered vehicles or converted into ammonia for agriculture or as a hydrogen storage medium. Extension of this technology to other wastes such as tires, auto shredder residue or wood wastes would expand the circular economy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

MACHINE LEARNING BASED CHEMICAL EXPLOSIVE MODE ANALYSIS (ML-CEMA)

The software consists of a Machine Learning based Chemical Explosive Mode Analysis (ML-CEMA) tool for advanced computational flame diagnostics. CEMA, originally based on eigen-analysis of the local thermochemical system, is capable of identifying reaction fronts and limit phenomena such as auto-ignition and extinction in practical combustion systems, such as internal combustion engines and gas turbine combustors. However, the original CEMA is computationally expensive for large reaction mechanisms that are typically needed to describe fuel chemistry of practical large-hydrocarbon fuels. This novel ML-CEMA tool employs a ML technique to accelerate the eigen-analysis of the basic CEMA approach by orders of magnitude, thus making it suitable for practical fuels. In ML-CEMA,zero-dimensional (0D) reactors and one-dimensional (1D) premixed flames are first used to generate a large number of data points for neural network based ML training. The trained ML model is then used to perform CEMA prediction. This ML-CEMA tool has been demonstrated in canonical 0D and 1D configurations as well as highly-transient three-dimensional spray flames exhibiting multi-mode turbulent combustion, showing promising results. ML-CEMA, as a standalone tool, can be used for computationally-efficient diagnostics of massive datasets generated from both experiments and simulations. For example, based on spatially resolved measurements of a small set of reactive scalars(such as temperature, hydroxyl radical and formaldehyde), ML-CEMA can effectively identify flame fronts and rare events. ML-CEMA also provides a robust online or offline flame feature detection tool. When used for on-the-fly simulations, ML-CEMA further enables zone-adaptive combustion modeling, in which the predicted eigenvalue is used as a robust mode indicator for judicious assignment of locally-valid combustion models. This ML-CEMA based zone-adaptive model can lead to substantial computational cost savings when used for large-scale simulations of multi-mode combustion systems. Third Party Code Web Page to Download Code Web Page Location of Third Party License

Xu, Chao↗
DOE OSTI2020

A statistical model to predict ignition probability

Ignition capability is a critical design constraint for aeronautical gas turbines. However the current trend toward overall lean burn is detrimental to the engine ignition and relight and the ignition system must be adapted to ensure a fast and reliable light-round in all circumstances. As ignition is a stochastic phenomenon, the optimization of an ignition system requires to build ignition probability maps, which is difficult and costly with either experiment or numerical simulation as both require many tests. Here, this work proposes a model to predict the ignition probability map, knowing only flow statistics in non-reacting conditions, i.e., with only one test. The originality of the model is to construct statistics of the flame kernel trajectory, which are then combined with local flow indicators to evaluate the ignition probability at the considered sparking location. Application to a swirled burner operated in premixed, non-premixed and spray combustion modes illustrates the model concepts and demonstrates its ability to recover the experimental ignition map with good accuracy.

42 ENGINEERING↗
DOE OSTI2023

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part II: The Effect of Roughness on the Performance of a High-Pressure Turbine Blade

Abstract Turbine blades generally present surface roughness introduced in the manufacturing process or caused by in-service degradation, which can have a significant impact on aero-thermal performance. A better understanding of the fundamental physical mechanisms arising from the interaction between the roughness and the turbine flow at engine-relevant conditions can provide insights for the design of blades with improved efficiency and longer operational life. To this end, a high-fidelity numerical framework combining a well-validated solver for direct numerical simulation and a second-order accurate immersed boundary method is employed to predict roughness-induced aero-thermal effects on an LS89 high-pressure turbine (HPT) blade at engine-relevant conditions. Different amplitudes and distributions of surface roughness are investigated and a reference smooth-blade simulation under the same flow conditions is conducted for comparison. Roughness of increasing amplitude progressively shifts the blade suction side boundary layer transition upstream, producing larger values of the turbulent kinetic energy and higher total wake losses. The on-surface data-capturing capabilities of the numerical framework provide direct measurements of the heat flux and the skin friction coefficient, hence offering quantitative information between the surface topology and engineering-relevant performance parameters. This work may provide a benchmark for future numerical studies of turbomachinery flows with roughness.

Engineering↗