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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Enhancement of HLW glass property-composition models

Since the WTP compositional space of HLW glasses is extremely large, development of HLW property-composition models is a multi-year task consisting of multiple phases. The primary focus of this work was to enhance the WTP HLW models of interest including PCT releases, spinel crystallization (T1%), viscosity, electrical conductivity, and TCLP-Cd response. Model development to predict nepheline formation upon CCC is the subject of a separate task. In particular, the earlier work has produced property-composition models for glass melt viscosity and glass melt electrical conductivity that showed good performance, while models for PCT releases and spinel crystallization (T1%) required improvement. Therefore, more efforts were directed in the present work to collect data and improve model performance for HLW glass PCT releases and spinel crystallization than for other properties. The present work is a continuation of earlier development phases and is responsive to the applicable Test Plan.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1988

Radiation effect on rocket engine performance

The effects of radiation on the performance of modern rocket propulsion systems operating at high pressure and temperature were recognized as a key issue in the design and operation of various liquid rocket engines of the current and future generations. Critical problem areas of radiation coupled with combustion of bipropellants are assessed and accounted for in the formulation of a universal scaling law incorporated with a radiation-enhanced vaporization combustion model. Numerical algorithms are developed and the pertaining data of the Variable Thrust Engine (VTE) and Space Shuttle Main Engine (SSME) are used to conduct parametric sensitivity studies to predict the principal intercoupling effects of radiation. The analysis reveals that low enthalpy engines, such as the VTE, are vulnerable to a substantial performance set back by the radiative loss, whereas the performance of high enthalpy engines such as the SSME, are hardly affected over a broad range of engine operation. Additionally, combustion enhancement by the radiative heating of the propellant has a significant impact in those propellants with high absorptivity. Finally, the areas of research related with radiation phenomena in bipropellant engines are identified.

Chiu, Huei-Huang↗
NASA NTRS1990

Radiation effect on rocket engine performance

Critical problem areas involving the effect of radiation on the combustion of bipropellants are addressed by formulating a universal scaling law in combination with a radiation-enhanced vaporization combustion model. Numerical algorithms are developed and data pertaining to the Variable Thrust Engine (VTE) and the Space Shuttle Main Engine (SSME) are used to conduct parametric sensitivity studies to predict the principal intercoupling effects of radiation. The analysis reveals that low-enthalpy engines, such as the VTE, are vulnerable to a substantial performance setback due to radiative loss, whereas the performance of high-enthalpy engines such as the SSME are hardly affected over a broad range of engine operation. Combustion enhancement by radiative heating of the propellant has a significant impact on propellants with high absorptivity.

Chiu, Huei-Huang↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
DOE OSTI2024

Thermal conductivity of 3D-printed block-copolymer-inspired structures

This study primarily focuses on examining the impact that geometric structure has on thermal conductivity of multi-phase constructs in different 3D-printed poly(lactic acid), PLA, samples. The investigated structures are inspired by morphologies formed by diblock copolymers: lamellae, hexagonally packed cylinders, and gyroid. This research also investigates how volume percentage and material combination influence the thermal conductivity of these structures. Further, the samples can be tailored to simulate various thermal management structures observed in practical applications, such as thermal interface materials in electronic devices. Thermal conductivity ratio is controlled using air, the least conductive material at 0.026 W/(m K), PLA at 0.136 W/(m K), and thermal paste at 5.11 W/(m K). Different models were tested against thermal conductivity measurements in order to capture the effect of material type (PLA-Air versus PLA-Thermal Paste), volume percentage, structure, and orientation. Simple, effective medium models were good predictions of thermal conductivity in lamellar structures, but it was necessary to develop models for conduction through cylindrical and gyroid structures. Finally, all results were normalized to find a universal model that is independent of structure and material. This approach provides a simple method to predict how to reduce or enhance transport properties and heat management capabilities of 3D printed objects.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of Numerical Model of Metal Foam with PCM for the Estimation of Effective Thermal Conductivity

Global warming due to climate change is a threat to humankind. Nuclear energy is one of the promising solutions to reduce fossil fuel usage. Nuclear energy can handle the base load, compensating for the volatility of renewable energy. If nuclear energy could achieve load following capability, the combination with renewable energy would be more suitable. Thermal energy storage (TES) is one of the options for enabling load following of nuclear reactors. The TES makes it possible to store surplus nuclear thermal energy and release it later as needed. In Idaho National Laboratory (INL), a new concept of latent heat TES integrated with high-temperature heat pipe has been proposed and is under development, which is called Heat pipe-Integrated Thermal Battery (HITB). HITB exchanges thermal energy between the reactor system and TES via heat pipe. The heat transferred to TES medium, made of phase change material (PCM), stores energy as sensible heat and/or latent heat. As PCM typically has poor thermal conductivity, however, various heat transfer enhancement techniques are required to achieve a rapid charging cycle. There are many techniques to enhance the heat transfer ability of TES medium such as disk, fin, and metal foam. Among them, metal foam is an appropriate option to enhance the heat transfer because it maximizes the heat transfer area through metal wicks. Metal foam is a lightweight metal structure that has a high porosity of over 0.9. The typical materials for metal foam are Aluminum, Copper, Nickel, and Silicon Carbide (SiC). Metal foam not only enhances heat transfer via conduction but also increases contact surface area. In the HITB design , the metal foam is being considered as one of the options to enhance the heat transfer of TES medium (PCM) [1]. To predict the enhanced thermal performance of TES, one should properly estimate the effective thermal conductivity of metal foam combined with PCM material or calculate heat transfer in distributed model. There are many experimental works that provides effective thermal conductivity of metal foam with various PCM [2,3]. Also, many theoretical models were developed based on the unit cell model of metal foam [4,5]. With a distributed model, on the other hand, detail heat transfer characteristics between metal foam and PCM material can be analyzed considering the geometry or buoyancy effect. However, due to the complex geometry of metal foam pores, the computational cost for three-dimensional modeling highly increases. Therefore, if metal foam structure can be modeled in simple and repetitive design, the computational cost would decrease Among the various metal foam models [2], lattice model is one of the simple and extendable design. The porosity and pores per inch (PPI) can be characterized by the size and spatial distance of lattice structure. If the three-dimensional metal foam model consists of lattice structure could properly estimate the heat transfer, which is characterized by effective thermal conductivity, it would be a good option to assess the thermal performance of metal foam with PCM. In this study, a three-dimensional numerical model was developed to simulate conductive heat transfer between metal foam and PCM. The three-dimensional lattice structure of square pillars was selected as a basic structure of the metal foam. The calculation result was characterized by the effective thermal conductivity of the whole domain. A sensitivity study was conducted for mesh size, domain size, and PPI to check whether the calculation result gives a converged result or not. Lastly, the effective thermal conductivity from the lattice model was compared with existing experimental data to validate the model result

25 ENERGY STORAGE↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Large Eddy Simulation of Low-Reynolds-Number Turbulent Flow of Low-Prandtl-Number Fluid in a Tight Lattice Bundle for Assessment of Reynolds-Averaged Navier-Stokes Turbulence Model

The MARVEL (Microreactor Applications Research Validation and Evaluation) microreactor utilizes natural circulation as core cooling mechanism and liquid metal as a primary coolant. Moreover, the reactor core has a pitch-to-diameter ratio of 1.056, which is considered a tight lattice configuration. Numerous studies have widely reported that Reynolds-Averaged Navier-Stokes (RANS) turbulence models inaccurately predict heat transfer in liquid metals and fail to capture flow pulsations that can occur within tight lattices, leading to further inaccuracies in simulation results. Therefore, evaluating the accuracy of RANS turbulence models in the thermal-hydraulic analysis of the MARVEL microreactor core is crucial for assessing reactor safety. In this study, a Large Eddy Simulation (LES) of the MARVEL microreactor core subchannel was conducted and compared with a RANS simulation to evaluate the accuracies and conservatism of the RANS model. The flow pulsation in a tight lattice predicted by LES enhanced the heat transfer, whereas the RANS model underpredicted it. Consequently, the RANS model predicted the peak cladding temperature higher than the LES model, but the discrepancy between the two approaches was not significant due to the good thermal characteristics of the liquid metal. It can be concluded that the steady-state RANS model is effective for the thermal analysis of liquid-metal-cooled MARVEL microreactor core and can provide conservative predictions from a safety analysis perspective.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS2011

Variability of Thermosphere and Ionosphere Responses to Solar Flares

We investigated how the rise rate and decay rate of solar flares affect the thermosphere and ionosphere responses to them. Model simulations and data analysis were conducted for two flares of similar magnitude (X6.2 and X5.4) that had the same location on the solar limb, but the X6.2 flare had longer rise and decay times. Simulated total electron content (TEC) enhancements from the X6.2 and X5.4 flares were 6 total electron content units (TECU) and approximately 2 TECU, and the simulated neutral density enhancements were approximately 15% -20% and approximately 5%, respectively, in reasonable agreement with observations. Additional model simulations showed that for idealized flares with the same magnitude and location, the thermosphere and ionosphere responses changed significantly as a function of rise and decay rates. The Neupert Effect, which predicts that a faster flare rise rate leads to a larger EUV enhancement during the impulsive phase, caused a larger maximum ion production enhancement. In addition, model simulations showed that increased E x B plasma transport due to conductivity increases during the flares caused a significant equatorial anomaly feature in the electron density enhancement in the F region but a relatively weaker equatorial anomaly feature in TEC enhancement, owing to dominant contributions by photochemical production and loss processes. The latitude dependence of the thermosphere response correlated well with the solar zenith angle effect, whereas the latitude dependence of the ionosphere response was more complex, owing to plasma transport and the winter anomaly.

Qian, Liying↗
NASA NTRS2016

Spacecraft Risk Posed by the 2016 Perseid Outburst

The Perseids are one of the more prolific annual showers, known for high rates and for producing bright meteors. Outbursts of this shower have been noted in the 1860s, the early 1990s, 2004, and 2009, with the 1993 outburst being especially active (peak ZHR above 300). The 1993 Perseids also affected the space-faring nations, as the launch of the STS-51 mission was delayed by NASA until after the shower maximum due to an inability to predict the shower intensity, and the ESA telecommunications satellite Olympus suffered a mission-ending anomaly attributed to a static discharge caused by a Perseid impact [1]. Rates were again high (peak ZHR around 200) in 2009, when the NASA/USGS imaging satellite Landsat-5 experienced a gyro anomaly just before the shower peak; however in this case, the satellite was recovered and normal operations resumed one week later [2]. It is interesting to note that both spacecraft anomalies were not what is typically expected from meteoroid strikes, i.e., physical damage or an attitude displacement due to transfer of momentum. It would appear that the very fast Perseids (59 km s(sup -1) have a marked ability to produce plasma upon impact, which can then serve as a conductive path for discharge currents. The shower is expected to outburst again in 2016, and we present the results from the MSFC Meteoroid Stream Model [4], which predicts enhanced activity on a level similar to that of 2009 as the Earth passes through several debris trails on the night of August 11-12 (UT). We then compare our results to those of other modelers.

Cooke, W. J.↗
DOE OSTI2025

5-minute Wind Power Data based on WFIP2 WRF Simulation

The second Wind Forecast Improvement Project (WFIP2) was a public-private partnership funded by the U.S. Department of Energy and NOAA, aimed at enhancing the forecast skill of numerical weather prediction models for turbine-height winds in regions with complex terrain. An 18-month Weather Research and Forecasting (WRF) model simulation was conducted over the Pacific Northwest, with model outputs validated against observational data collected during WFIP2. Simulated wind speeds were used to estimate wind power generation using reV (the Renewable Energy Potential Model developed by NREL) at ten wind project sites. Two sets of results were produced: one using wind speeds extracted from the model grid cell at the project centroid, and another using wind speeds from the actual turbine locations. For each dataset, power output was calculated using both actual turbine-specific power curves and nine generic power curves to convert wind speed into power.

17 WIND ENERGY↗
NASA NTRS1994

Heating of the nighttime D region by very low frequency transmitters

VLF signals propagating in the Earth-ionosphere waveguide are used to probe the heated nighttime D region over three U.S. Navy very low frequency (VLF,3-30 kHz) transmitters. Ionospheric cooling and heating are observed when a transmitter turns off and on in the course of normal operations. Heating by the 24.0-kHz NAA transmitter in Cutler, Maine, (1000 kW radiated power) was observed by this method in 41 of 52 off/on episodes during December 1992, increasing the amplitude and retarding the phase of the 21.4-kHz NSS probe wave propagating from Annapolis, Maryland, to Gander, Newfoundland, by as much as 0.84 dB and 5.3 deg, respectively. In 6 of these 41 episodes, the amplitude of the 28.5-kHz NAU probe wave propagating from Puerto Rico to Gander was also perturbed by as much as 0.29 dB. The latter observations were unexpected due to the greater than 770 km distance between NAA and the NAU-Gander great circle path. Heating by the NSS (21.4 kHz, 265 kW) and NLK (24.8 kHz, 850 kW) transmitters was observed serendipitously in data from earlier measurements of the amplitudes of VLF signals propagating in the Earth-ionosphere waveguide. A three-dimensional model of wave absorption and electron heating in a magnetized, weakly ionized plasma is used to calculate the extent nad shape of the collision frequency (i.e., electron temperature) enhancement above a VLF transmitter. The enhancements are annular, with a geomagnetic north-south asymmetry and a radius at the outer half-maximum of the collision frequency enhancement of about 150 km. Heating by the NAA transmitter is predicted to increase the nighttime D region electron temperature by as much as a factor of 3. The calculated changes in the D region conductivity are used in a three-dimensional model of propagation in the Earth-ionosphere wavelength to predict the effect of the heated patch on a subionospheric VLF probe wave. The range of predicted scattered field amplitudes is in general consistent with the observed signal perturbations. Discrepanices in the predictions are attributed to lack of knowledge of the D region electron density profile along the probe wave great circle paths.

Rodriguez, Juan V.↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2022

Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. Here, the results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Synchronous Machine Governor Upgrade

Conventional generation sources play a critical role in the stability and reliability of the electrical grid, particularly as we transition towards more renewable energy sources. To understand and accurately emulate their behavior for optimizing grid operations and ensuring seamless integration with renewable technologies, it is essential to better emulate the grid- and plant-level impacts of conventional generation sources, such as natural gas (NG) driven heat recovery steam generators (HRSGs) and combustion turbines (CTs). Therefore, a governor model is developed in a programmable logic controller (PLC) to investigate the performance of the conventional generator under various dynamic operating conditions and to identify the impact on grid stability in a controlled environment. The governor model aims to enable the hardware-in-the-loop (HIL) based emulation of these conventional generation sources using the existing 2 MVA synchronous machine/generator that is driven by a flexible 2.5 MW variable speed drive. This setup will allow us to replicate the dynamic characteristics and response behaviors of NG-driven HRSGs and CTs. The controls for the emulated conventional plants follow the industry standard and are adjustable, ensuring they accurately reflect the operational capabilities and limitations of real-world systems. These controls include load-following capabilities, ramp rates, startup and shutdown sequences, and emissions characteristics. By incorporating these adjustable controls, we aim to capture the nuanced impacts of conventional generation, such as their ability to provide ancillary services like frequency regulation, voltage support, and spinning reserve. In this report, we simulate two types of dynamic operations: grid-connected and islanding. For each dynamic operation, representative starting sequences are tested, including turbine purge, ignition, speed ramping up, generator excitation and synchronizing, and breaker close. The HIL based tests provides insights for field deployment, specifically the high-fidelity governor model provides results to predict the potential stability and reliability risk and suggest possible integration measures (e.g., generation and load balancing, tuning of governor control parameters). Ultimately, this enhanced emulation capability will be integrated into our Advanced Research on Integrated Energy Systems (ARIES), enabling us to conduct comprehensive studies on the interactions between conventional and renewable energy sources. By better understanding these interactions, we can develop strategies to optimize the overall performance and reliability of the grid. This will support the deployment of advanced grid management techniques, such as demand response, grid-forming inverters, and energy storage systems. The main contributions are summarized as follows: (1) This report introduces a PLC-based governor model for gas turbines. This model accurately simulates the dynamic behavior of conventional generation sources under various operational scenarios; (2) The model is integrated with an HIL testbed that includes a 2.5 MW variable speed drive and a 2 MVA synchronous machine. This setup enables realistic, real-time emulation of conventional power plants, particularly NG driven HRSGs and CTs; (3) The developed model is adaptable to various gas turbine configurations and allows for precise control over parameters such as MW ramp rates. This flexibility makes it a valuable tool for future research and industry collaboration; and (4) By incorporating the model into the National Renewable Energy Laboratory's Advanced Research on Integrated Energy Systems, the report lays the groundwork for future studies on interactions between conventional and renewable energy sources, enhancing the ability to develop advanced grid management strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

Review of Slow-Wave Structures

The majority of recent theoretical and experimental reports published in the literature dealing with helical slow-wave structures focus on the dispersion characteristics and their effects due to the finite helix wire thickness and attenuation, dielectric loading, metal loading, and the introduction of plasma. In many papers, an effective dielectric constant is used to take into account helix wire dimensions and conductivity losses, while the propagation constant of the signal and the interaction impedance of the structure are found to depend on the surface resistivity of the helix. Also, various dielectric supporting rods are simulated by one or several uniform cylinders having an effective dielectric constant, while metal vane loading and plasma effects are incorporated in the effective dielectric constant. The papers dealing with coupled cavities and folded or loaded wave guides describe equivalent circuit models, efficiency enhancement, and the prediction of instabilities for these structures. Equivalent circuit models of various structures are found using computer software programs SUPERFISH and TOUCHSTONE. Efficiency enhancement in tubes is achieved through dynamic velocity and phase adjusted tapers using computer techniques. The stability threshold of unwanted antisymmetric and higher order modes is predicted using SOS and MAGIC codes and the dependence of higher order modes on beam conductance, section length, and effective Q of a cavity is shown.

Wallett, Thomas M.↗
NASA NTRS1993

Fabrication and properties of functionally graded NiAl/Al2O3 composites

A modified sedimentation process was used in the production of a functionally gradient material (FGM), NiAl/Al2O3. A simple finite element model was used to guide our design and fabrication efforts by estimating residual stress states as a function of composite structure. This approach could lead to tailored designs that enhance or avoid specific residual stress states. Thermal cycling tests were factored into the model to predict time dependent or steady-state internal temperature and stress profiles. Four-point bend tests were conducted to establish the mechanical load-displacement behavior of a single interlayer FGM at room temperature, 800 and 1000 K. Room temperature bend strength of the FGM was 3-4 times that of the base NiAl. At elevated temperatures, composite fracture occurred in a gradual, noncatastrophic mode involving NiAl retardation of a succession of cracks originating in the alumina face.

Miller, D. P.↗
NASA NTRS1987

Prediction and experimental observation of damage dependent damping in laminated composite beams

The equations of motion are developed for laminated composite beams with load-induced matrix cracking. The damage is accounted for by utilizing internal state variables. The net result of these variables on the field equations is the introduction of both enhanced damping, and degraded stiffness. Both quantities are history dependent and spatially variable, thus resulting in nonlinear equations of motion. It is explained briefly how these equations may be quasi-linearized for laminated polymeric composites under certain types of structural loading. The coupled heat conduction equation is developed, and it is shown that an enhanced Zener damping effect is produced by the introduction of microstructural damage. The resulting equations are utilized to demonstrate how damage dependent material properties may be obtained from dynamic experiments. Finaly, experimental results are compared to model predictions for several composite layups.

Allen, D. H.↗