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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Submodule-Level Decentralized Control for Cascaded Quadruple Active Bridge-Based Medium-Voltage Converters

High-power, low-voltage DC (LVDC) loads, such as AI data centers, electric vehicle fast-charging hubs, and hydrogen electrolyzers, are accelerating the deployment of solid-state transformers (SSTs) that directly interface medium-voltage AC (MVAC) to LVDC, streamlining power delivery. This paper proposes a submodule-level decentralized control strategy for an SST comprising a cascaded H-bridge (CHB) active rectifier on the MVAC side and per-module quadruple-active-bridge (QAB) converters on the LVDC side with local controllers. The proposed strategy eliminates the need for high-fidelity, real-time cross-isolation communication by synchronizing all active bridges to a common heartbeat signal and enabling the primary- and secondary-side controllers to generate independent local control signals (phase shifts). The resultant phase shifts naturally govern average and double-line frequency power transfer between active bridges, enabling each side to meet its control objectives using only local measurements. Detailed controller design considerations are presented based on a stability analysis. Comprehensive electromagnetic-transient simulations demonstrate tight DC-link voltage regulation, high-quality grid currents, and robust operation under load transients and grid disturbances, without real-time communication among the submodule controllers. These results indicate that the proposed scheme can significantly enhance the reliability, scalability, and flexibility of cascaded bridge-based medium-voltage converters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Big Data Analytic for Cascading Failure Analysis

With the challenges of increased grid dynamics and more variability of power generation from renewable energy sources, rapidly increasing complexity in the grid model, and abundant data from measurements and simulations, the requirements for computational analysis have also increased dramatically. Power system operators and engineers need to analyze more scenarios, extract meaningful information from a larger set of data, and respond more quickly when faced with these new challenges for large-scale applications, such as contingency analysis. This paper proposes a novel big data analysis approach for power system cascading analysis, prevention, and remediation. The developed techniques will be capable of cascading analysis, better assessment of the system’s vulnerability level, as well as proposing potential remediation. Case studies using IEEE 118-bus system and a 500-bus system, with a comparison against a commercial tool, validate the advantages of the developed big data approach: accurate prediction, and more importantly, faster and effective correction actions.The developed techniques could be further used for other power system applications.

big data, cascading analysis, corrective action, m↗
DOE OSTI2017

An Efficient Modulation Strategy for Cascaded Photovoltaic Systems Suffering From Module Mismatch

Modular multilevel cascaded converter (MMCC) is a promising technique for medium/high-voltage high-power photovoltaic systems due to its modularity, scalability, and capability of distributed maximum power point tracking (MPPT) etc. However, distributed MPPT under module-mismatch might polarize the distribution of ac output voltages as well as the dc-link voltages among the modules, distort grid currents, and even cause system instability. For the better acceptance in practical applications, such issues need to be well addressed. Based on mismatch degree that is defined to consider both active power distribution and maximum modulation index, this paper presents an efficient modulation strategy for a cascaded-H-bridge-based MMCC under module mismatch. It can operate in loss-reducing mode or range-extending mode. By properly switching between the two modes, performance indices such as system efficiency, grid current quality, and balance of dc voltages, can be well coordinated. In this way, the MMCC system can maintain high-performance over a wide range of operating conditions. As a result, effectiveness of the proposed modulation strategy is proved with experiments.

14 SOLAR ENERGY↗
DOE OSTI2016

Estimating the Propagation of Interdependent Cascading Outages with Multi-Type Branching Processes

In this paper, the multi-type branching process is applied to describe the statistics and interdependencies of line outages, the load shed, and isolated buses. The offspring mean matrix of the multi-type branching process is estimated by the Expectation Maximization (EM) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of outages is estimated by the multi-type branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of outages. Compared with empirically estimating the joint distribution of the total outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total outages even when there are no data of them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Trace-gas sensing using the compliance voltage of an external cavity quantum cascade laser

Quantum cascade lasers (QCLs) are increasingly being used to detect, identify, and measure levels of trace gases in the air. External cavity QCLs (ECQCLs) provide a broadly-tunable infrared source to measure absorption spectra of chemicals and provide high detection sensitivity and identification confidence. Applications include detecting chemical warfare agents and toxic industrial chemicals, monitoring building air quality, measuring greenhouse gases for atmospheric research, monitoring and controlling industrial processes, analyzing chemicals in exhaled breath for medical diagnostics, and many more. Compact, portable trace gas sensors enable in-field operation in a wide range of platforms, including handheld units for use by first responders, fixed installations for monitoring air quality, and lightweight sensors for deployment in unmanned aerial vehicles (UAVs). We present experimental demonstration of a new chemical sensing technique based on intracavity absorption in an external cavity quantum cascade laser (ECQCL). This new technique eliminates the need for an infrared photodetector and gas cell by detecting the intracavity absorption spectrum in the compliance voltage of the laser device itself. To demonstrate and characterize the technique, we measure infrared absorption spectra of chemicals including water vapor and Freon-134a. Sub-ppm detection limits in one second are achieved, with the potential for increased sensitivity after further optimization. The technique enables development of handheld, high-sensitivity, and high-accuracy trace gas sensors for in-field use.

infrared spectroscopy, quantum cascade laser, trac↗
DOE OSTI2012

Influence of the vacuum-lunar regolith interface on the generation of radio emission by a cascade shower from an ultrahigh-energy particle

We present the results of our calculation of the radio emission field produced by an electron-photon shower from an ultrahigh-energy particle under the surface of the Moon. We consider two cases of cascade propagation: the first in the lunar regolith-vacuum direction at small angles to the interface; and the second in the opposite direction when the particle generates a cascade almost immediately after it has crossed the vacuum-regolith interface. To calculate the relative energy density of the emission emerged at the surface, we have used the method of decomposing spherical waves into plane ones. The intensity of the refracted waves for high frequencies has been found to depend strongly on the shower inclination angle.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Spatial distribution of Cherenkov light from cascade showers in water

The spatial distribution of the Cherenkov light generated by cascade showers is analyzed using the NEVOD Cherenkov water detector. The dependence of the Cherenkov light intensity on the depth of shower development at various distances from the shower axis is investigated for the first time. The experimental data are compared with the Cherenkov light distributions predicted by various models for the scattering of cascade particles.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Ratio method for estimating uncertainty in calculated gamma cascades

The assessment of uncertainty on deduced quantities obtained through both measurement and modeling must include contributions from both components. There are several methods to estimate the uncertainty due to modeling, such as the parametric uncertainty and that stemming from model bias. However, in the case where experimental data exists for partial cross sections, such as discrete gammas emitted in the de-excitation of the product nucleus following the reaction, the discrepancy between the measured and modeled gamma cascades provides more information and allows for uncertainty estimation that can account for all types of model and data uncertainty. This work presents a method for estimating that uncertainty, using ratios of gammas to get a measure of the accuracy of different parts of the modeled gamma cascade. The gamma with the lowest intensity uncertainty is shown to be the best for determining the channel cross section with realistic uncertainties, indicating that it should be used rather than the most intense gamma or a sum of gammas. In conclusion, this method provides both a simple procedure for calculating realistic uncertainties and identifies the best gamma for use in converting a set of measured partial gamma cross sections to the deduced total channel cross section.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Focusing metasurface quantum-cascade laser with a near diffraction-limited beam

A terahertz vertical-external-cavity surface-emitting-laser (VECSEL) is demonstrated using an active focusing reflectarray metasurface based on quantum-cascade gain material. The focusing effect enables a hemispherical cavity with flat optics, which exhibits higher geometric stability than a plano-plano cavity and a directive and circular near-diffraction limited Gaussian beam with M 2 beam parameter as low as 1.3 and brightness of 1.86 × 10 6 Wsr –1 m –2 . As a result, this work initiates the potential of leveraging inhomogeneous metasurface and reflectarray designs to achieve high-power and high-brightness terahertz quantum-cascade VECSELs.

36 MATERIALS SCIENCE↗
DOE OSTI2013

ARRA: Reconfiguring Power Systems to Minimize Cascading Failures - Models and Algorithms

Building on models of electrical power systems, and on powerful mathematical techniques including optimization, model predictive control, and simluation, this project investigated important issues related to the stable operation of power grids. A topic of particular focus was cascading failures of the power grid: simulation, quantification, mitigation, and control. We also analyzed the vulnerability of networks to component failures, and the design of networks that are responsive to and robust to such failures. Numerous other related topics were investigated, including energy hubs and cascading stall of induction machines

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1957

Comparison of NACA 65-series compressor-blade pressure distributions and performance in a rotor and in cascade

An investigation has been conducted to compare the performance of NACA 65-series compressor blades in two-dimensional cascade with that in an axial flow compressor. Blade pressure distributions were obtained by the use of a mercury-seal pressure-transfer device. The comparison indicated that cascade data accurately predicted the turning angle and blade pressure distribution obtained in the compressor at design conditions.

CASCADES↗
NASA NTRS1995

Cascade Error Projection Learning Algorithm

A detailed mathematical analysis is presented for a new learning algorithm termed cascade error projection (CEP) and a general learning frame work. This frame work can be used to obtain the cascade correlation learning algorithm by choosing a particular set of parameters.

learning algorithm neural network cascade error pr↗
NASA NTRS2015

Complementary Aerodynamic Performance Datasets for Variable Speed Power Turbine Blade Section from Two Independent Transonic Turbine Cascades

Two independent experimental studies were conducted in linear cascades on a scaled, two-dimensional mid-span section of a representative Variable Speed Power Turbine (VSPT) blade. The purpose of these studies was to assess the aerodynamic performance of the VSPT blade over large Reynolds number and incidence angle ranges. The influence of inlet turbulence intensity was also investigated. The tests were carried out in the NASA Glenn Research Center Transonic Turbine Blade Cascade Facility and at the University of North Dakota (UND) High Speed Compressible Flow Wind Tunnel Facility. A large database was developed by acquiring total pressure and exit angle surveys and blade loading data for ten incidence angles ranging from +15.8deg to −51.0deg. Data were acquired over six flow conditions with exit isentropic Reynolds number ranging from 0.05×106 to 2.12×106 and at exit Mach numbers of 0.72 (design) and 0.35. Flow conditions were examined within the respective facility constraints. The survey data were integrated to determine average exit total-pressure and flow angle. UND also acquired blade surface heat transfer data at two flow conditions across the entire incidence angle range aimed at quantifying transitional flow behavior on the blade. Comparisons of the aerodynamic datasets were made for three "match point" conditions. The blade loading data at the match point conditions show good agreement between the facilities. This report shows comparisons of other data and highlights the unique contributions of the two facilities. The datasets are being used to advance understanding of the aerodynamic challenges associated with maintaining efficient power turbine operation over a wide shaft-speed range.

Cascade↗
NASA NTRS2015

Complementary Aerodynamic Performance Datasets for Variable Speed Power Turbine Blade Section from Two Independent Transonic Turbine Cascades

Two independent experimental studies were conducted in linear cascades on a scaled, two-dimensional mid-span section of a representative Variable Speed Power Turbine (VSPT) blade. The purpose of these studies was to assess the aerodynamic performance of the VSPT blade over large Reynolds number and incidence angle ranges. The influence of inlet turbulence intensity was also investigated. The tests were carried out in the NASA Glenn Research Center Transonic Turbine Blade Cascade Facility and at the University of North Dakota (UND) High Speed Compressible Flow Wind Tunnel Facility. A large database was developed by acquiring total pressure and exit angle surveys and blade loading data for ten incidence angles ranging from +15.8deg to −51.0deg. Data were acquired over six flow conditions with exit isentropic Reynolds number ranging from 0.05×106 to 2.12×106 and at exit Mach numbers of 0.72 (design) and 0.35. Flow conditions were examined within the respective facility constraints. The survey data were integrated to determine average exit total-pressure and flow angle. UND also acquired blade surface heat transfer data at two flow conditions across the entire incidence angle range aimed at quantifying transitional flow behavior on the blade. Comparisons of the aerodynamic datasets were made for three "match point" conditions. The blade loading data at the match point conditions show good agreement between the facilities. This report shows comparisons of other data and highlights the unique contributions of the two facilities. The datasets are being used to advance understanding of the aerodynamic challenges associated with maintaining efficient power turbine operation over a wide shaft-speed range.

Turbine↗
NASA NTRSDate not supplied

Flow Reconstruction in A Transonic Turbine Cascade Using Physics-Informed Neural Networks (PINNS)

This paper investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. The 2-D flow field in a transonic turbine cascade is reconstructed in three ways: the traditional forward approach (PINN not trained on experimental data), by training the PINN using discrete sets of experimentally measured pressure at midspan, and in the inverse sense where no inlet or outlet pressure boundary conditions are applied. Comparisons between the PINN solutions to measured data are made. This is repeated for three different turbine blades with distinct loading characteristics. Good agreement is shown between a CFD calculation of the CMC7 blade, and the PINN model trained with all data. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. The forward problem results deviate the most from experimental data but show promise. Solutions from the assisted training cases show that the PINN can reconstruct the flow field with acceptable accuracy when trained on measurements along the entire blade. In the inverse case, it is shown that to simultaneously achieve acceptable errors for inlet Mach number and outlet isentropic Mach number, the PINN must be trained on the static pressure data along the entire blade.

Machine Learning↗
NASA NTRSDate not supplied

GT2024-128885: Flow Reconstruction in a Transonic Turbine Cascade using Physics-Informed Neural Networks (PINNs)

This presentation investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. PINNs are a machine learning method trained on losses calculated from reconstructed governing equations, assigned boundary/initial conditions, and measured data. We reconstruct the 2-D flow field in a transonic turbine cascade in two ways: the traditional forward approach (without training/experimental data) and by training the PINN using experimental data. We then compare the PINN solutions to measured data. This is repeated for three different turbine blades with distinct loading characteristics. The experimental data used for training is the static pressure measurements along the suction and pressure sides of each blade. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. It's shown that the PINN can reconstruct the flow field in all cases with acceptable errors. Cases where the PINN is trained on all the data, and even half the data, resulted in the lowest errors. The exit Mach number is inferred for each case and compared to the experimentally calculated value.

Machine Learning↗