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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗
DOE OSTI2026

Background-Oriented Schlieren Velocimetry of Helium Coolant Flow in Additively Manufactured Channels

High-pressure helium gas cooling is an attractive solution for thermal management of the fusion blanket first wall, as this coolant is chemically and neutronically inert and separable from hydrogenic species. However, due to the low thermal mass of helium, geometric optimization of these channels is required to provide sufficient cooling at manageable flow rates and pumping burdens. Increasingly, analysis and optimization of these coolant channels rely on computational fluid dynamics (CFD) simulations, and these require relevant experimental data for turbulence model validation. Toward this end, a high-pressure helium gas flow visualization system has been employed to image the flow of helium in flow channels with one-sided heating, mimicking the blanket first wall environment. Flow of helium at 4 MPa pressure and flow rates up to 68 g/s (Reynolds number 57 000) is supplied to rectangular channel test sections, with uniform heating applied to the bottom wall of the channel at heat fluxes varied between roughly 50 and 130 kW/m2. A high-speed camera is used to image index of refraction (IOR) gradients in the fluid via background-oriented schlieren (BOS), and temperature and pressure instrumentation are used to characterize thermal-hydraulic performance of each channel. Cross correlation of time-resolved BOS images is then used to calculate time-averaged 2-D helium velocity fields. Flow in additively manufactured (AM) channels is examined in this manner, including both featureless channels and those containing baffling as a heat transfer enhancement. The flow distribution seen in the featureless case differs significantly from that seen in prior simulations, whereas the flow in the baffled case shows the predicted behavior of flow forced along the heated wall. This augmented flow distribution is seen to increase the heat transfer coefficient in the baffled test section. Here, strategies are discussed for ongoing and future validation of these simulations, with the aim of model deployment for blanket cooling design and optimization.

Additive manufacturing↗
DOE OSTI2020

Probing the Effects of NO x and SO x Impurities on Oxy-Fuel Combustion in Supercritical CO 2 : Shock Tube Experiments and Chemical Kinetic Modeling

The direct-fired supercritical carbon dioxide cycles are one of the most promising power generation methods in terms of their efficiency and environmental friendliness. Two important challenges in implementing these cycles are the high pressure (300 bar) and high CO 2 dilution (>80%) in the combustor. The design and development of supercritical oxy-combustors for natural gas require accurate reaction kinetic models to predict the combustion outcomes. The presence of a small amount of impurities in natural gas and other feed streams to oxy-combustors makes these predictions even more complex. During oxy-combustion, trace amounts of nitrogen present in the oxidizer is converted to NO x and gets into the combustion chamber along with the recirculated CO 2 . Similarly, natural gas can contain a trace amount of ammonia and sulfurous impurities that get converted to NO x and SO x and get back into the combustion chamber with recirculated CO 2 . In this work, a reaction model is developed for predicting the effect of impurities such as NO x and SO x on supercritical methane combustion. The base mechanism used in this work is GRI Mech 3.0. H 2 S combustion chemistry is obtained from Bongartz et al. while NOx chemistry is from Konnov. The reaction model is then optimized for a pressure range of 30–300 bar using high-pressure shock tube data from the literature. It is then validated with data obtained from the literature for methane combustion, H 2 S oxidation, and NO x effects on ignition delay. The effect of impurities on CH 4 combustion up to 16 atm is validated using NO x -doped methane studies obtained from the literature. In order to validate the model for high-pressure conditions, experiments are conducted at the UCF shock tube facility using natural gas identical mixtures with N 2 O as an impurity at ~100 bar. Current results show that there is a significant change in ignition delay with the presence of impurities. A comparison is made with experimental data using the developed model and predictions are found to be in good agreement. The model developed was used to study the effect of impurities on CO formation from sCO 2 combustors. It was found that NO x helps in reducing CO formation while the presence of H 2 S results in the formation of more CO. The reaction mechanism developed herein can also be used as a base mechanism to develop reduced mechanisms for use in CFD simulations.

Energy & Fuels↗
DOE OSTI2020

Evaluating the Effect of Vane Trailing Edge Flow on Turbine Rim Sealing

Abstract Modern gas turbine development continues to move toward increased overall efficiency, driven in part by higher firing temperatures that point to a need for more cooling air to prevent catastrophic component failure. However, using additional cooling flow bled from the upstream compressor causes a corresponding detriment to overall efficiency. A primary candidate for cooling flow optimization is purge flow, which contributes to sealing the stator–rotor cavity and prevents ingestion of hot main gas path (MGP) flow into the wheelspace. Previous research has identified that the external main gas path flow physics play a significant role in driving rim seal ingestion. However, the potential impact of other cooling flow features on ingestion behavior, such as vane trailing edge (VTE) flow, is absent in the open literature. This paper presents experimental measurements of rim cavity cooling effectiveness collected from a one-stage turbine operating at engine-representative Reynolds and Mach numbers. Carbon dioxide (CO2) was used as a tracer gas in both the purge flow and vane trailing edge flow to investigate flow migration into and out of the wheelspace. Results show that the vane trailing edge flow does in fact migrate into the rim seal and that there is a superposition relationship between individual cooling flow contributions. Computational fluid dynamics (CFD) simulations using unsteady Reynolds-averaged Navier–Stokes (URANS) were used to confirm VTE flow ingestion into the rim seal cavity. Radial and circumferential traverse surveys were performed to quantify cooling flow radial migration through the main gas path with and without vane trailing edge flow. The surveys confirmed that vane trailing edge flow is entrained into the wheelspace as purge flow is reduced. Local CO2 measurements also confirmed the presence of VTE flow deep in the wheelspace cavity.

Engineering↗
DOE OSTI2020

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. Here, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Development of an Efficient Conjugate Heat Transfer Modeling Framework to Optimize Mixing-Limited Combustion of Ethanol in a Diesel Engine

Mixing controlled combustion of alcohol fuels has been identified as a promising technology based on their low propensity for particulate and NOx production, but the higher heats of vaporization and auto-ignition temperatures of these fuels make their direct use in diesel engine architectures a challenge. To realize the potential of alcohol-fueled combustion, a computational fluid dynamics (CFD) modeling framework is developed, validated, and exercised to identify designs that maximize engine thermal efficiency. To evaluate the use of thermal barrier coatings (TBCs), a simplified one-dimensional (1D) conjugate heat transfer (CHT) modeling framework is employed. The addition of the 1D CHT model only increases the computational expense by 15% relative to traditional approaches, yet offers more accurate heat transfer predictions over constant temperature boundary conditions. The validated model is then used to explore a range of injector orientations and piston bowl geometries. Using a design of experiments (DoE) approach, several designs were identified that improved fuel-air mixing, shortened the combustion duration, and increased thermal efficiency. The most promising design was fabricated and tested in a Caterpillar 1Y3700 single-cylinder oil test engine (SCOTE). Engine testing confirmed the findings from the CFD simulations and found that the co-optimized injector and piston bowl design yielded over 2-percentage point increase in thermal efficiency at the same equivalence ratio (0.96) and over 6-percentage point increase at the same engine load (10.1 bar indicated mean effective pressure (IMEP)), while satisfying design constraints for peak pressure and maximum pressure rise rate.

Magnotti, Gina M.↗
DOE OSTI2022

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions. Part I: Correlation Development

Abstract High-temperature supercritical CO2 Brayton cycles are promising possibilities for future stationary power generation and hybrid electric propulsion applications. Heat exchangers are critical components in supercritical CO2 thermal cycles and require accurate correlations and comprehensive performance modeling under extreme temperatures and pressures. In this paper (Part I), new Colburn and friction factor correlations are developed to quantify shell-side heat transfer and friction characteristics of flow within heat exchangers in the shell-and-tube configuration. Using experimental and computational fluid dynamics (CFD) data sets from existing literature, multivariate regression analysis is conducted to achieve correlations that capture the effect of multiple critical geometric parameters. These correlations offer superior accuracy and versatility as compared to previous studies and predict the thermohydraulic performance of about 90% of the existing experimental and CFD data within ±15%. Supplementary thermohydraulic performance data are acquired from CFD simulations with supercritical CO2 as working fluid to validate the developed correlations and demonstrate its capability to be applied to supercrtical CO2 heat exchangers.

Engineering↗
DOE OSTI2022

Modeling of Supercritical CO2 Shell-and-Tube Heat Exchangers Under Extreme Conditions: Part II: Heat Exchanger Model

Abstract Heat exchangers play a critical role in supercritical CO2 Brayton cycles by providing necessary waste heat recovery. Supercritical CO2 thermal cycles potentially achieve higher energy density and thermal efficiency operating at elevated temperatures and pressures. Accurate and computationally efficient estimation of heat exchanger performance metrics at these conditions is important for the design and optimization of sCO2 systems and thermal cycles. In this paper (Part II), a computationally efficient and accurate numerical model is developed to predict the performance of shell-and-tube heat exchangers (STHXs). Highly accurate correlations reported in Part I of this study are utilized to improve the accuracy of performance predictions, and the concept of volume averaging is used to abstract the geometry and reduce computation time. The numerical model is validated by comparison with computational fluid dynamics (CFD) simulations and provides high accuracy and significantly lower computation time compared to existing numerical models. A preliminary optimization study is conducted and the advantage of using supercritical CO2 as a working fluid for energy systems is demonstrated.

Engineering↗
DOE OSTI2022

Investigation of Gas Turbine Internal Cooling Using Supercritical CO 2 —Effect of Surface Roughness and Channel Aspect Ratio

In this paper, an experimental and numerical investigation of internal cooling channels with rib turbulators is presented with sCO 2 as the working fluid at process conditions (pressure-20.7 MPa and temperature up to 150 °C). The effect of channel aspect ratio up to 2:1 on thermal-hydraulic performance is explored in additively manufactured rectangular channels and square channels, both with and without 60 deg ribs on the top and bottom sides. The Wilson-plot method is employed to experimentally measure channel-averaged Nusselt number over a Reynolds number range up to 370,000. The friction factor is calculated from pressure drop and mass flow rate and additionally, the overall thermal performance factor (TPF) is reported. A companion computational fluid dynamics (CFD) simulation is performed for the rib turbulated cooling configurations reported in the experiments using the Reynolds average Navier–Stokes-based turbulence model. The objective of the numerical study is to gain insight into the local heat transfer augmentation in the ribbed channels as a result of varying the aspect ratio, channel configuration (square versus rectangular), operating conditions (Reynolds number) and the surface roughness, an inherent outcome of the additive manufacturing process. Surface roughness is simulated using sand grain roughness height (K S ) calculated from the experimental data, and a comparison is presented with the corresponding channel configuration with varying surface roughness heights starting from smooth surfaces (K S = 0). Experimental results indicate that the heat transfer augmentation is negligible in the rectangular channels with ribs on the long side compared to the square channel. However, it is enhanced by 60% in comparison to placing ribs on the shorter side. The TPF remains constant at around 1 for the entire range of Reynolds numbers consistent with prior work at the National Energy Technology Laboratory (NETL). Here, the simulation results highlight that increased surface roughness can have a favorable considerable influence on Nusselt number and overall thermal performance enhancement.

42 ENGINEERING↗
DOE OSTI2022

Design of an Additively Manufactured Recuperator With 800 °C Inlet Temperature for sCO2 Power Cycle Application

Abstract Recuperators with design temperatures at and above 800 °C can further increase the thermal efficiency of supercritical CO2 power cycles by enabling higher turbine exhaust temperatures. Mar-M247 is a well-suited nickel-based superalloy for high temperature service due to its high creep strength that prevents excessive material thickness being required for pressure containment. Additive manufacturing using a high-speed laser-directed energy deposition (L-DED) process presents a promising solution, with build trials demonstrating the ability to produce nonconventional flow channels for enhanced heat transfer. A design process is presented that includes aerothermal and mechanical evaluation to maximize performance within the constraints of the manufacturing process. A 2-D heat transfer network and pressure drop code allows prediction of flow distribution and its effect on overall thermal performance. Established literature correlations, along with CFD simulation, inform the prediction of heat transfer coefficients and friction factors for the flowpaths and enhancement features in the heat exchanger core. Mechanical evaluation using finite element analysis (FEA) modeling with the intent of the ASME Boiler & Pressure Vessel Code (BPVC) Section VIII, Div. 2 assesses the operational safety of the design. The detailed design features annular finned passages that take advantage of helical flow paths to distribute the flow from separated headers to shared heat transfer surfaces. Performance predictions for the recuperator at a 50 kW scale provide insights into the feasibility of the additively manufactured (AM) process to produce recuperators on a commercial scale that extend existing operating envelopes.

Engineering↗
DOE OSTI2023

Analytical Study and Experimental Verification of Shear-Thinning Ink Flow in Direct Ink Writing Process

Abstract Direct ink writing (DIW) process is a facile additive manufacturing technology to fabricate three-dimensional (3D) objects with various materials. Its versatility has attracted considerable interest in academia and industry in recent years. As such, upsurging endeavors are invested in advancing the ink flow behaviors in order to optimize the process resolution and the printing quality. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and its underlying theories. Here, we present a comprehensive analytical study of non-Newtonian ink flow behavior during the DIW process. Different syringe-nozzle geometries are modeled for the comparative case studies. By using the computational fluid dynamics (CFD) simulation method, we reveal the shear-thinning property during the ink extrusion process. Besides, we study the viscosity, shear stress, and velocity fields, and analyze the advantages and drawbacks of each syringe-nozzle model. On the basis of these investigations and analyses, we propose an improved syringe-nozzle geometry for stable extrusion and high printing quality. A set of DIW printing experiments and rheological characterizations are carried out to verify the simulation studies. The results developed in this work offer an in-depth understanding of the ink flow behavior in the DIW process, providing valuable guidelines for optimizing the physical DIW configuration toward high-resolution printing and, consequently, improving the performance of DIW-printed objects.

Engineering↗
DOE OSTI2024

Characterization of Flex-Fuel Prechamber Enabled Mixing-Controlled Combustion With Gasoline/Ethanol Blends at High Load

This numerical study focuses on the characterization of prechamber-enabled mixing-controlled combustion (PC-MCC) at ~18 bar brake mean effective pressure (BMEP) and 2200 rpm with 10% by volume ethanol-gasoline blend (E10) and pure ethanol (E100). Computational fluid dynamic (CFD) simulations of a stock and prechamber retrofitted single-cylinder Caterpillar C9.3B are carried out using CONVERGE. Prechamber equivalence ratio at spark timing, prechamber spark timing advance, and main chamber injection strategy are assessed with respect to their impact on ignition assistance performance and emissions characteristics relative to a diesel baseline at the same boundary conditions. Simulation results indicate that PC-MCC is flex-fuel capable and operates well for both E10 and E100 at the operating conditions considered. The results demonstrate that the use of a pilot-main injection strategy enables spark timing in the prechamber to be advanced and thus reduces spark plug firing pressure while maintaining robust ignition assistance. Results also indicate that the rich prechamber operation is favored for improved ignition assistance capabilities. Furthermore, the findings of this work suggest that a heavy-duty vehicle using a PC-MCC engine can utilize any blend of gasoline and ethanol, up to including pure ethanol, with no major sacrifices in performance relative to the diesel engine.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

ENGINE COMBUSTION SYSTEM OPTIMIZATION USING CFD AND MACHINE LEARNING: A METHODOLOGICAL APPROACH

Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (ML) offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine learning Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variables limits extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a Research Octane Number (RON) of 80. The ML-GGA approach yielded > 2% improvements in the merit function, compared to the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared to traditional approaches.

engine optimization↗
DOE OSTI2024

Evaluation of the Performance and Exhaust Emissions of a 4 Cylinder CI Engine Operating With Dimethyl Ether (DME) and Propane Blends

In response to stringent emissions regulations and the need for higher efficiency engines, the utilization of DME and propane fuel blends in compression ignition (CI) engines has gained interest in the automotive industry. In this study, a range of DME-propane blends are explored in a CI combustion strategy at high injection pressures. A GT-Power model of a 2.2 L Hyundai CI engine was developed to facilitate evaluation of the impacts of variations of DME and propane blends at a light and medium engine speed-torque-load operating condition; specifically at 1500 rpm, 50 Nm and 2.84 bar brake mean effective pressure (BMEP); and 2000 rpm, 150 Nm, 8.53 bar BMEP speed-torque-load combination. Here, the GT-Power model was validated using Computational Fluid Dynamics (CFD) simulations. The results indicate that high diesel-like efficiencies can be achieved with a 100% DME mass substitution and up to 50% propane-DME blends could be implemented without a significant penalty on engine performance indicators. Significant brake specific nitrogen oxides (BSNOx) reductions were also observed along with reductions in carbon dioxide (CO2) and soot when leveraging these fuel blends.

computational fluid dynamics↗
DOE OSTI2025

Using 3D Measurements from MRI to Improve VVUQ

Magnetic resonance velocimetry (MRV) is a diagnostic that can measure 3D, three-component turbulent velocity fields in arbitrarily complex flow configurations. The approach uses magnetic resonance imaging (MRI), which is a system commonly available in radiology departments or medical research centers. MRV acquires signal from hydrogen protons in water channel flows. Despite the obvious utility of the measurements for investigating a variety of flows, comparisons with computational fluid dynamic (CFD) simulations largely retain traditional metrics, comparing velocity profiles and planes at discrete regions within the flow rather than using the 3D nature of the measured field. In this effort, MRV was conducted in a simple water channel with six spanwise-centered periodic obstacles roughly shaped as cubes. At a fully turbulent Reynolds number of 15,000, the channel includes two flows: a main streamwise flow and, in the wake of the second obstacle, an injected flow oriented perpendicular to the streamwise flow. The flow geometry includes partial obstacles on the side walls and is inherently 3D because of the interaction of the two streams and the wake features, making the flow challenging to completely measure with traditional optical techniques. Aside from line profiles and planar comparisons, two 3D metrics are used to compare the data with a steady Reynolds-averaged Navier–Stokes (RANS) simulation result. A brief discussion about the comparison is provided, including comments about uncertainty.

Benson, Mike [ORNL] (ORCID:000000023210116X)↗
DOE OSTI2020

Gas Generation Models for Thermal Runaway in Lithium Ion Batteries

A numerical model is implemented to study cell venting, internal pressure, and gas-phase dynamics behavior of Li-ion cells undergoing thermal runaway. A k-ɛ Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer’s equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. A series of computational fluid dynamics (CFD) simulations are conducted on a single cell at various states-of-charge (100%, 50%, 25%) to demonstrate detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is shown that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.

Kim, Jinyong↗
DOE OSTI2021

Deeper Fluids

A framework for building and training neural network surrogates for CFD simulation, with a focus on quickly simulating volume fraction dynamics for carbon capture efficiency prediction. Augments the Deep Fluids (Kim et al., 2019) approach as described in our paper "Latent Space Simulation for Carbon Capture Design Optimization" (Bartoldson et al., 2021).

Nguyen, PhuocChanh N↗
DOE OSTI2022

Sulfur TES ML [SWR-23-02]

Sulfur TES ML contains code originally created to develop surrogate ML models to mimic the utility of CFD simulations of Sulfur TES systems. It has been adapted to provide a comprehensive package for building, training, validating, testing, and optimizing regression models with customizable features and targets.

Menear, Kevin↗