Search NASA⌕ Search

SEARCH · Search NASA

Results for “GAS TURBINE”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Computational Design to Advance AM Fabrication of High Gamma Prime Alloys for Hot Gas Path Components in Gas Turbine Engines: A Pathway to Enhanced Gas Turbine Efficiency and Energy Saving (Final CRADA Report)

Raising turbine inlet temperature is a key lever for improving industrial gas-turbine efficiency and power output, but it increases thermo-mechanical demands on hot-gas-path components. Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), enables complex internal cooling features in critical components such as turbine tip shoes that are difficult to produce by conventional casting. However, qualification of new high-temperature AM alloys and aggressive geometries is often limited by trial-and-error iteration of build parameters and post-build heat treatments, with cracking during stress relieving or hot isostatic pressing (HIP) being a recurring failure mode.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Uncertainty Quantification of Fatigue Behavior of Rough AM Surfaces and Microstructures to Enable Hydrogen Gas Turbine

Modifying fossil-fueled industrial gas turbines to utilize low or zero-carbon fuels, such as hydrogen or hydrogen-natural gas blends, is a complex endeavor. The successful implementation of this technology hinges on three key design criteria: (1) developing new fuel injectors capable of efficiently burning alternative fuels, (2) ensuring manufacturability to meet cost and time-to-market goals, and (3) achieving component durability in the demanding environment of an operating gas turbine. Additive manufacturing (AM) accelerates product development, yet concerns persist regarding the durability of parts with rough AM surfaces. A fully experimental approach to quantify the fatigue performance of rough AM microstructures is both costly and labor-intensive. To address this, ORNL and Solar Turbines Incorporated (Solar) employed a crystal plasticity finite element (CPFE) model to identify the factors influencing AM surface fatigue behavior. These CPFE findings, combined with targeted experimental data, were used to develop a computationally efficient surrogate model suitable for assessing the lifespan of gas turbine engine components.

08 HYDROGEN↗
DOE OSTI2024

Advanced Airfoils for Efficient Combined Heat Power Systems: Task 4.3 - Gas Turbine Machinery and Systems

Industrial gas turbines are commonly used in steam combined heat and power (CHP) applications. CHP applications have significant environmental and economic benefits that are consistent with the goals of the U.S. Department of Energy. This presentation provides a status update for a DOE effort investigating the impacts of advanced internal cooling technologies for small (5-10 MW) gas turbine CHP applications. The potential efficiency impacts are 2-3 percentage points based on the model and the cooling technologies investigated in this project.

Straub, Douglas↗
DOE OSTI2026

2026 Innovating the Gas Turbine Supply Chain Workshop

The 2026 Innovating the Gas Turbine Supply Chain Workshop was convened at the Oak Ridge National Laboratory’s Manufacturing Demonstration Facility (MDF) in Knoxville, Tennessee, on March 31–April 1, 2026. Organized by ORNL, NETL, and the Gas Turbine Association (GTA) including gas turbine original equipment manufacturers (OEMs) GE Vernova, MHI, Siemens Energy, and Solar Turbines. The workshop brought together approximately 70 participants representing OEMs, supply chain companies, national laboratories, universities, and federal government agencies. Edgar Lara-Curzio (ORNL) served as Workshop Chair.

42 ENGINEERING↗
DOE OSTI2024

Using Additive Manufacturing to Repair Gas Turbine Hot Section Components

Ni-based superalloys are used in the hot sections of gas turbine engines due to their excellent high temperature performance. During service the material degrades due to exposure at high temperature and mechanical loads. Hence, utility provides often inspect, service, and repair components in gas turbine engines to ensure safe operation. A major challenge, however, is that the most heat-resistant alloys are generally considered ‘non-weldable’ rendering them difficult to repair via welding operations. In these cases components are often scrapped and then replaced by parts which must be re-manufactured. This burdens utilities with additional cost and supply chain issues can result in long term outages or reduced operating limtis. In this work EPRI and ORNL investigated a proposed repair strategy for gas turbine hot section components. Hot section superalloy GTD-111 was selected as a candidate repair material system and AM material ABD-900 the repair material. Sandwich structures were fabricated via electron beam melting additive manufacturing (EBM-AM) producing tensile bars with gage sections consisting of dissimilar ABD-900 / GTD-111 / ABD-900 material. Metallography revealed that the interface exhibited no deleterious phases or processing defeats. Creep rupture experiments on heat treated material demonstrates that the emulated repair coupons exhibit creep resistance between GTD-111 and ABD-900. This study demonstrates that the proposed EBM-AM repair strategy presents a viable opportunity towards enabling AM repair of gas turbine engine components.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Cooled Gas Turbine and Combined Cycle Analysis for NH 3 -CH 4 Fuel Mixes (Up to 100% NH 3 )

In this study, a cooled gas turbine (GT) analysis was conducted for varying levels of ammonia (NH 3 ) blends with methane. The ultimate goal is to have a gas turbine design that can be used for all the fuel blends (including 100% NH 3 ) without any changes to the system. The technological developments in the cooling system, gas turbine design, and materials that will be required for NH 3 combustion were identified and analyzed in this study to develop an advanced gas turbine design for NH 3 fuels. The study includes a combined cycle performance analysis with the NH 3 fuel blends using the advanced gas turbine design developed in this study. A techno-economic analysis was conducted for analyzing the impact of the NH 3 fuels on the levelized cost of electricity and cost sensitivities to fuel price and capacity factor.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Using Additive Manufacturing to Repair Gas Turbine Hot Section Components

Ni-based superalloys are used in the hot sections of gas turbine engines due to their excellent high temperature performance. During service the material degrades due to exposure at high temperature and mechanical loads. Hence, utility provides often inspect, service, and repair components in gas turbine engines to ensure safe operation. A major challenge, however, is that the most heat-resistant alloys are generally considered ‘non-weldable’ rendering them difficult to repair via welding operations. In these cases components are often scrapped and then replaced by parts which must be re-manufactured. This burdens utilities with additional cost and supply chain issues can result in long term outages or reduced operating limits. In this work EPRI and ORNL investigated a proposed repair strategy for gas turbine hot section components. Hot section superalloy GTD-111 was selected as a candidate repair material system and AM material ABD-900 the repair material. Sandwich structures were fabricated via electron beam melting additive manufacturing (EBM-AM) producing tensile bars with gage sections consisting of dissimilar ABD-900 / GTD-111 / ABD-900 material. Metallography revealed that the interface exhibited no deleterious phases or processing defeats. Creep rupture experiments on heat treated material demonstrates that the emulated repair coupons exhibit creep resistance between GTD-111 and ABD-900. This study demonstrates that the proposed EBM-AM repair strategy presents a viable opportunity towards enabling AM repair of gas turbine engine components.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Gaseous Hydrogen Embrittlement of L-PBF Ni-Based Superalloys for Service in Natural Gas Turbines

Efforts to improve efficiency of industrial gas turbine engines have focused on increasing operating temperatures by use of fuel-flexible gas blends. Ni-based superalloys are susceptible to hydrogen embrittlement (HE), leading to potential risk of premature component failure. Certain turbine components exposed to hydrogen-rich environments are manufactured from additive processes like laser powder bed fusion (L-PBF). The HE susceptibility was evaluated for three Ni-based superalloys: solid solution strengthened Alloy 625, γ’/γ’’-precipitation strengthened Alloy 718, and γ’-precipitation strengthened Haynes® 282®. L-PBF samples were pre-charged under medium and high pressure gaseous hydrogen before tensile testing at temperatures up to 260 °C using a fast strain rate. Selected samples were subjected to a service conditioning heat treatment prior to hydrogen charging to evaluate the change in susceptibility after prolonged service. The susceptibility to HE and HE mechanisms of these three alloys is compared.

additive manufacturing↗
DOE OSTI2024

Novel Hot Gas Components for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

Additive Manufacturing (AM), also known as 3D printing, has emerged as a manufacturing method that enables new design freedom for gas turbine engine manufacturers. However, the material selection for AM processable high-temperature super alloys is currently limited. Additionally, the heat transfer performance of AM enabled micro-cooling architectures is not yet well understood. Accordingly, in support of advanced manufacturing and engine performance development, Oak Ridge National Laboratory (ORNL)and Solar Turbines (Solar) conducted a multidisciplinary project to generate both AM super alloy material properties data and micro-channel performance data for two AM super alloys. The data supported the design and analysis of an internally cooled turbine hot section AM tip shoe component. This data was used to analytically predict the reduction in operating temperature of a gas turbine tip shoe. The work concluded that the cooling flow required to cool the tip shoe can be tuned to suit the efficiency improvements desired in an industrial gas turbine.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncertainty Quantification of Fatigue Behavior of Rough AM Surfaces and Microstructures to Enable Hydrogen Gas Turbine Combustion

Modification of fossil-fueled industrial gas turbines to accept no/low carbon fuels (Hydrogen, H2/natural gas blends) is a significant undertaking. Successful deployment of this technology sits at the intersection of three design criteria (1) new functional fuel injectors that can burn these fuels, (2) manufacturability to meet cost and time-to-market targets, and (3) durability in the harsh environment of an operating turbine. Additive manufacturing (AM) provides accelerated product development. However, uncertainty remains around the durability of parts with rough AM surfaces. A fully experimental approach towards quantifying fatigue performance of rough AM microstructures is costly and laborious. Instead, Solar Turbines Incorporated (Solar) proposes the use of a crystal plasticity finite element (CPFE) model to quantify the factors that drive AM surface fatigue behavior. Solar will use the CPFE results, along with targeted experimental data, to train a computationally efficient surrogate model that can be incorporated into existing turbine part lifing methods.

08 HYDROGEN↗
DOE OSTI2024

Computational Study of Additively Manufactured Internally Cooled Airfoils for Industrial Gas Turbine Applications

Internal cooling features such as pin-fins, impingement jets, and rib-turbulators are necessary to keep turbine components cool, but if sufficiently advanced can potentially also eliminate the need for film cooling on turbine blades particularly in industrial gas turbines where temperatures are not extreme. Furthermore, by leveraging additive manufacturing, other advanced designs such as lattice and incremental impingement configurations are possible and have recently been experimentally tested. While the performance of such configurations has been quantified through means of overall cooling effectiveness, it is not as clear why certain designs were better than others, or what the mechanisms were behind the observed external cooling patterns. The purpose of this study was to computationally analyze different advanced turbine blade internal cooling designs previously tested by the National Energy Technology Laboratory.

CFD↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Understanding Environmental Barrier Coating Lifetimes and Performance for Industrial Gas Turbines

Hydrogen or hydrogen blend fuels are expected to replace natural gas in land-based industrial gas turbines (IGTs) to support a greener power economy. Silicon carbide (SiC) base ceramic matrix composites (CMCs) are considered for replacement of Ni-based superalloys to facilitate future efficiency improvements. SiC CMCs require environmental barrier coatings (EBCs) to mitigate volatilization from high-temperature steam, thus making the EBC lifetime critical information for identifying CMC component lifetimes. Here, the goal of this project is to determine the maximum bond coating temperature underneath the EBC for achieving an IGT component lifetime goal of 25,000 h, which is far greater than current CMC component lifetime requirements for aeroturbine applications. To provide data for the lifetime model, laboratory testing used atmospheric plasma-sprayed rare-earth silicate EBCs on monolithic SiC substrates with an intermediate Si bond coating. Specimens exposed to 1-h thermal cycles in flowing air–steam environments and reaction kinetics were assessed from 700 °C to 1350 °C by measuring the thickness of the thermally grown silica scales. The silica growth and phase transformation appear critical in predicting EBC lifetime and several strategies have been explored to reduce the oxide growth rate and improve EBC durability at elevated temperatures. Advanced characterization using Raman spectroscopy has helped clarify this system.

08 HYDROGEN↗
DOE OSTI2024

Understanding EBC Lifetimes and Performance for Industrial Gas Turbines

Hydrogen or hydrogen blend fuels are expected to replace natural gas in land-based industrial gas turbines (IGTs) to support a greener power economy. Silicon carbide (SiC) base ceramic matrix composites (CMCs) are considered for replacement of Ni-based superalloys to facilitate future efficiency improvements. SiC CMCs require environmental barrier coatings (EBCs) to mitigate volatilization from high-temperature steam, thus making the EBC lifetime critical information for identifying CMC component lifetimes. The goal of this project is to determine the maximum bond coating temperature underneath the EBC for achieving an IGT component lifetime goal of 25,000 h, which is far greater than current CMC component lifetime requirements for aero-turbine applications. To provide data for the lifetime model, laboratory testing used plasma-sprayed rare-earth silicate EBCs on monolithic SiC substrates with an intermediate Si bond coating. Specimens exposed to 1-h thermal cycles in flowing air-steam environments and reaction kinetics were assessed from 700°-1350°C by measuring the thickness of the thermally grown silica scales. The silica growth and phase transformation appear critical in predicting EBC lifetime and several strategies have been explored to reduce the oxide growth rate and improve EBC durability at elevated temperatures. Advanced characterization using Raman spectroscopy has helped clarify this system.

Ridley, Mackenzie↗
DOE OSTI2024

DNS of ignition and flame stabilization in a simplified gas turbine premixer

With the increasing need for fuel flexibility, mitigation of auto-ignition (AI) inside gas turbine (GT) premixers becomes crucial. They must be designed to yield a sufficiently homogeneous fuel-air mixture to achieve low emissions while at the same time avoiding the occurrence of AI and subsequent flame stabilization. This challenge requires a detailed understanding of turbulent mixing and chemistry interactions. In the present work, a direct numerical simulation (DNS) of an array of jets in crossflow (JICF), representative of an industrial GT premixer, is reported to shed light on these complex phenomena. It is found that AI kernels form in the aft part of the premixer and coalesce into a flame front that then propagates upstream, mainly through the boundary layer, and successively engulfs the jets. This, therefore, suggests a significant role of the jet array pattern on the flame stabilization. It is noted that AI kernels continue to form independently during the whole time of the simulation. To clarify the contribution of AI and diffusion in the ignition kernels and the main flame, chemical explosive mode analysis (CEMA) is employed jointly with a kernel tracking algorithm. It is found that during the initial formation of the flame, many ignition kernels form in mixtures with low scalar dissipation rate and large contribution from AI mode. As they quickly grow, they merge into a single flame front that becomes increasingly more diffusion-assisted over time, balancing the AI mode. Turbulence is shown to have a significant enhancing effect in lean premixed flames, but further analysis is required to fully characterize it. These findings are relevant for the industrial premixer studied, and also for novel micromix concepts that may be used in the next generation of GT combustion systems.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Cyber-Physical Simulation of the Cold Startup of Solid Oxide Fuel Cell – Gas Turbine (SOFC-GT) Hybrid Systems

This work introduces experimental studies for the cold startup process (CPS) of the SOFC-GT hybrid system using the cyber-physical simulation approach. The physical gas turbine is coupled with a cyber-physical SOFC stack, which is represented using the integration of a real time dynamic SOFC model with physical components (e.g., pressure chamber, natural gas burner, etc.). Different ramp rates of the turbine speed were tested out during the startup processes. Bypass valves were also used to manipulate the airflow during SOFC-GT hybrid system start-up process. Different ramp rates enable the rapid start-up of the turbine to avoid surge and stall, meanwhile enable acceptable warm rate of the fuel cell stack without damaging the cell material. CPS can enable dynamic characterizations of highly integrated systems at lower cost.

Zhou, Nana↗
DOE OSTI2024

Ammonia Combustion for Gas Turbine Engines

Presentation given at the 2024 University Turbines System Research conference describing NETL Research and Innovation Center work associated with ammonia combustion for gas turbine engines.

Bedick, Clinton↗