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DOE OSTI2020

AATTENUATION—The Atmospheric Attenuation Model for CSP Tower Plants: A Look-Up Table for Operational Implementation

Attenuation of solar radiation between the receiver and the heliostat field in concentrated solar power (CSP) tower plants can reduce the overall system performance significantly. The attenuation varies strongly with time and the average attenuation at different sites might also vary strongly from each other. If no site specific attenuation data is available, the optimal plant design cannot be determined and rough estimations of the attenuation effect are required leading to high uncertainties of yield analysis calculations. The attenuation is caused mainly by water vapor content and aerosol particles in the lower atmospheric layer above ground. Although several on-site measurement systems have been developed during recent years, attenuation data sets are usually not available to be included during the plant project development. An Atmospheric Attenuation (AATTENUATION) model to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity, and barometric pressure measurements was developed and validated by the authors earlier. The model allows the accurate estimation of attenuation for sites with low attenuation and gives an estimation of the attenuation for less clear sites. However, the site-dependent coefficients of the AATTENUATION model had to be developed individually for each site of interest, which required time-consuming radiative transfer simulations, considering the exact location and altitude, as well as the pre-dominant aerosol type at the location. This strongly limited the application of the model despite its typically available input data. In this manuscript, a look-up table (LUT) is presented which enables the application of the AATTENUATION model at the site of interest without the necessity to perform the according complex radiative transfer calculations for each site individually. This enables the application of the AATTENUATION model for virtually all resource assessments for tower plants and in an operational mode in real time within plant monitoring systems around the world. The LUT also facilitates the generation of solar attenuation maps on the basis of long-term meteorological data sets which can be considered during resource assessment for CSP tower plant projects. The LUTs are provided together with this manuscript as supplementary files. The LUT for the AATTENUATION model was developed for a solar zenith angle (SZA) grid of 1°, an altitude grid of 100 m, 7 different standard aerosol types and the standard AFGL atmospheres for mid-latitudes and the tropics. The LUT was tested against the original version of the AATTENUATION model at 4 sites in Morocco and Spain, and it was found that the additional uncertainty introduced by the application of the LUT is negligible. With the information of latitude, longitude, altitude above mean sea level, DNI, relative humidity (RH), ambient temperature (Tair), and barometric pressure (bp), the attenuation can be now derived easily for each site of interest.

14 SOLAR ENERGY↗

DOE OSTI2023

Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images

Wind turbine blade leading edge erosion is a major source of power production loss and early detection benefits optimization of repair strategies. Two machine learning (ML) models are developed and evaluated for automated quantification of the areal extent, morphology and nature (deep, shallow) of damage from field images. The supervised ML model employs convolutional neural networks (CNN) and learns features (specific types of damage) present in an annotated set of training images. The unsupervised approach aggregates pixel intensity thresholding with calculation of pixel-by-pixel shadow ratio (PTS) to independently identify features within images. The models are developed and tested using a dataset of 140 field images. The images sample across a range of blade orientation, aspect ratio, lighting and resolution. Each model (CNN v PTS) is applied to quantify the percent area of the visible blade that is damaged and classifies the damage into deep or shallow using only the images as input. Both models successfully identify approximately 65% of total damage area in the independent images, and both perform better at quantifying deep damage. The CNN is more successful at identifying shallow damage and exhibits better performance when applied to the images after they are preprocessed to a common blade orientation.

Aird, Jeanie A.↗

DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1979

Upper-surface modifications for C sub l max improvement of selected NASA 6-series airfoils

The thickness of the upper surface of 64 airfoils was increased from the leading edge to the position of maximum thickness. The modifications were generated using a numerical optimization routine coupled with an aerodynamic analysis code. The type of modification presented can be used for aircraft design or for the retrofit of current aircraft to improve the stall characteristics and climb performance. The coordinates of the modified airfoils are presented with plots of the forward 45% of the profiles and pressure distributions for both the modified and unmodified sections at an angle of attack of 14 degrees.

Szelazek, C. A.↗

NASA NTRS1980

A study of wing body blending for an advanced supersonic transport

Increases in supersonic cruise lift drag ratio were sought at Mach numbers 2.2 and 2.7 using wing body planform and thickness blending. Constrained twist and camber optimization was performed in the presence of nacelles. Wing and fuselage thickness distributions were optimized for either minimum volume wave drag or minimum total pressure wave drag. The zero leading edge suction lift drag ratios were determined for three wing planforms. The magnitude of the effect of leading edge suction on attainable lift drag ratio was defined on one planform and estimation of available leading edge suction was made.

Goebel, T. P.↗

NASA NTRS1991

Prospects and progress of high Tc superconductivity for space applications

Current research in the area of high temperature superconductivity is organized around four key areas: communications and data, sensors and cryogenics, propulsion and power, and space materials technology. Recently, laser ablated YBa2Cu3O(7-x) films on LaAlO3 produced far superior RF characteristics when compared to metallic films on the same substrate. The achievement has enabled a number of unique microwave device applications, such as low insertion loss phase shifters and high-Q filters. Melt texturing and melt-quenched techniques are being used to produce bulk material with optimized magnetic properties. These yttrium-enriched materials possess enhanced flux pinning characteristics and could lead to prototype cryocooler bearings. Significant progress has also occurred in bolometer and current lead technology. Studies were conducted to evaluate the effect of high temperature superconducting materials on the performance and life of high power magnetoplasma-dynamic thrusters. Extended studies were also performed to evaluate the benefit of superconducting magnetic energy storage for LEO space station, lunar, and Mars mission applications.

Romanofsky, Robert R.↗

NASA NTRS1993

New proof of the Jacobi necessary condition

A new proof is given for Jacobi's necessary condition. It is shown that the existence of a conjugate point in the interior of an extremal implies the existence of control perturbations which lead to a reduction in cost. The present analysis is restricted to linear-quadratic optimal control problems.

Seywald, Hans↗

NASA NTRS1999

Toward a More Flexible Web-Based Framework for Multidisciplinary Design

In today's competitive environment, both industry and government agencies are under pressure to reduce the time and cost of multidisciplinary design projects. New tools have been introduced to assist in this process by facilitating the integration of and communication among diverse disciplinary codes. One such tool, a framework for multidisciplinary design, is defined as a hardware-software architecture that enables integration, execution, and communication among diverse disciplinary processes. An examination of current frameworks reveals weaknesses in various areas, such as sequencing, monitoring, controlling, and displaying the design process. The objective of this research is to explore how Web technology can improve these areas of weakness and lead toward a more flexible framework. This article describes a Web-based system that optimizes and controls the execution sequence of design processes in addition to monitoring the project status and displaying the design results.

Rogers, J. L.↗

NASA NTRS2002

Probabilistic Analysis of Gas Turbine Field Performance

A gas turbine thermodynamic cycle was computationally simulated and probabilistically evaluated in view of the several uncertainties in the performance parameters, which are indices of gas turbine health. Cumulative distribution functions and sensitivity factors were computed for the overall thermal efficiency and net specific power output due to the thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design, enhance performance, increase system availability and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in the gas turbine health determination and to the identification of both the most critical measurements and parameters. Probabilistic analysis aims at unifying and improving the control and health monitoring of gas turbine aero-engines by increasing the quality and quantity of information available about the engine's health and performance.

Gorla, Rama S. R.↗

NASA NTRS1997

The effects of programmed administration of human parathyroid hormone fragment (1-34) on bone histomorphometry and serum chemistry in rats

PTH treatment can result in dramatic increases in cancellous bone volume in normal and osteopenic rats. However, this potentially beneficial response is only observed after pulsatile treatment; continuous infusion of PTH leads to hypercalcemia and bone abnormalities. The purpose of these studies was to determine the optimal duration of the PTH pulses. A preliminary study revealed that human PTH-(1-34) (hPTH) is cleared from circulation within 6 h after sc administration of an anabolic dose of the hormone (80 microg/kg). To establish the effects of gradually extending the duration of exposure to hPTH without increasing the daily dose, we programmed implanted Alzet osmotic pumps to deliver the 80 microg/kg x day dose of the hormone during pulses of 1, 2, and 6 h/day, or 40 microg/kg x day continuously. Discontinuous infusion was accomplished by alternate spacing of external tubing with hPTH solution and sesame oil. After 6 days of treatment, we evaluated serum chemistry and bone histomorphometry. As negative and positive controls, groups of rats received pumps that delivered vehicle only and 80 microg/kg x day hPTH by daily sc injection, respectively. Dynamic and static bone histomorphometry revealed that the daily sc injection and 1 h/day infusion dramatically increased osteoblast number and bone formation in the proximal tibial metaphysis, whereas longer infusion resulted in systemic side-effects, including up to a 10% loss in body weight, hypercalcemia, and histological changes in the proximal tibia resembling abnormalities observed in patients with chronic primary hyperparathyroidism, including peritrabecular marrow fibrosis and focal bone resorption. Infusion for as little as 2 h/day resulted in minor weight loss and changes in bone histology that were intermediate between sc and continuous administration. The results demonstrate that the therapeutic interval for hPTH exposure is brief, but that programmed administration of implanted hormone is a feasible alternative to daily injection as a route for administration of the hormone.

Non-NASA Center↗

NASA NTRS2013

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Flight-Test Results

A peak-seeking control algorithm for real-time trim optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control algorithm is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are used for optimization of fuel flow. Results from six research flights are presented herein. The optimization algorithm found a trim configuration that required approximately 3 percent less fuel flow than the baseline trim at the same flight condition. The algorithm consistently rediscovered the solution from several initial conditions. These results show that the algorithm has good performance in a relevant environment.

flight optimization↗

NASA NTRS2013

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Architecture and Performance Predictions

A peak-seeking control approach for real-time trim configuration optimization for reduced fuel consumption has been developed by researchers at the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center to address the goals of the NASA Environmentally Responsible Aviation project to reduce fuel burn and emissions. The peak-seeking control approach is based on a steepest-descent algorithm using a time-varying Kalman filter to estimate the gradient of a performance function of fuel flow versus control surface positions. In real-time operation, deflections of symmetric ailerons, trailing-edge flaps, and leading-edge flaps of an F/A-18 airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) are controlled for optimization of fuel flow. This paper presents the design and integration of this peak-seeking controller on a modified NASA F/A-18 airplane with research flight control computers. A research flight was performed to collect data to build a realistic model of the performance function and characterize measurement noise. This model was then implemented into a nonlinear six-degree-of-freedom F/A-18 simulation along with the peak-seeking control algorithm. With the goal of eventual flight tests, the algorithm was first evaluated in the improved simulation environment. Results from the simulation predict good convergence on minimum fuel flow with a 2.5-percent reduction in fuel flow relative to the baseline trim of the aircraft.

performance optimization↗

NASA NTRS2013

Peak-Seeking Optimization of Trim for Reduced Fuel Consumption: Flight-test Results

flight optimization↗

DOE OSTI2025

Chemical Reactor Network Modeling of Ammonia Rich-Quench-Lean Combustion Using a Partially Stirred Reactor Approach

Ammonia is a promising alternative fuel, but its use is challenging due to low flammability and high nitrogen oxide (NOx) emissions. Two-stage rich-quench-lean (RQL) combustion strategies have shown promise in reducing NOx emissions. This approach involves two stages: a rich stage that oxidizes part of the fuel and decomposes ammonia into hydrogen, and a lean stage that burns out the hydrogen and residual ammonia. Researchers used a chemical reactor network model to study the effects of heat loss and mixing on emissions performance. They found that heat loss and reduced mixing rates can lead to increased NOx emissions and N2O formation. The results will inform the development of optimized two-stage RQL combustors for ammonia, with a focus on minimizing NOx emissions and improving overall efficiency.

ammonia combustion↗

DOE OSTI2024

ToF-SIMS in material research: A view from nanoscale hydrogen detection

Hydrogen in materials has attracted tremendous interest as its incorporation leads to significant alterations in nanoscale structure, composition, and chemistry, impacting functional properties. It has also been integral to nuclear fusion reactors and is considered a future clean energy source. However, nanoscale characterization and manipulation of hydrogen in materials are challenging as only a selected few analytical techniques can readily detect hydrogen, among which time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a unique and powerful one due to its excellent detection limit along with decent depth and lateral resolutions. Here, in this review, we discuss, using selected examples, how to detect and quantify hydrogen in materials by ToF-SIMS and its impact on revealing the hydrogenation/protonation-induced novel functional states in different classes of materials. In addition, we present our protocols on sample preparation and experimental conditions optimization, allowing us to achieve the best possible results. Finally, we highlight future research directions that can lead to the discovery of novel functional states and ultimately provide a deeper understanding of scientific questions in materials science.

36 MATERIALS SCIENCE↗

NASA NTRS1991

Approaches for optimizing the first electronic hyperpolarizability of conjugated organic molecules

Conjugated organic molecules with electron-donating and -accepting moieties can exhibit large electronic second-order nonlinearities, or first hyperpolarizabilities, beta. The present two-state, four-orbital independent-electron analysis of beta leads to the prediction that its absolute value will be maximized at a combination of donor and acceptor strengths for a given conjugated bridge. Molecular design strategies for beta optimization are proposed which give attention to the energetic manipulations of the bridge states. Experimental results have been obtained which support the validity of this approach.

Marder, S. R.↗

NASA NTRS1993

Recent developments in equivalent plate modeling for wing shape optimization

A new technique for structural modeling of airplane wings is presented taking transverse shear effects into account. The kinematic assumptions of first order shear deformation plate theory in combination with numerical analysis based on simple polynomials which define geometry, construction and displacement approximations lead to analytical expressions for elements of the stiffness and mass matrices and load vector. Contributions from the cover skins, spar and rib caps and spar and rib webs are included as well as concentrated springs and concentrated masses. Limitations of current equivalent plate wing modeling techniques based on classical plate theory are discussed, and the improved accuracy of the new equivalent plate technique is demonstrated through comparison to finite element analysis and test results. Analytical derivatives of stiffness, mass and load terms with respect to wing shape lead to analytic sensitivities of displacements, stresses and natural modes with respect to planform shape and depth distribution. This makes the new capability an effective structural tool for wing shape optimization.

Livne, Eli↗

DOE OSTI2022

Optimal Electric Grid Black Start Restoration Subject to Intentional Threats

Efficient restoration of the electric grid from significant disruptions – both natural and manmade – that lead to the grid entering a failed state is essential to maintaining resilience under a wide range of threats. Restoration follows a set of black start plans, allowing operators to select among these plans to meet the constraints imposed on the system by the disruption. Restoration objectives aim to restore power to a maximum number of customers in the shortest time. Current state-of-the-art for restoration modeling breaks the problem into multiple parts, assuming a known network state and full observability and control by grid operators. These assumptions are not guaranteed under some threats. This paper focuses on a novel integration of modeling and analysis capabilities to aid operators during restoration activities. A power flow-informed restoration framework, comprised of a restoration mixed-integer program informed by power flow models to identify restoration alternatives, interacts with a dynamic representation of the grid through a cognitive model of operator decision-making, to identify and prove an optimal restoration path. Application of this integrated approach is illustrated on exemplar systems. Validation of the restoration is performed for one of these exemplars using commercial solvers, and comparison is made between the steps and time involved in the commercial solver, and that required by the restoration optimization in and of itself, and by the operator model in acting on the restoration optimization output. Publications and proposals developed under this work, along with a path forward for additional expansion of the work, and summary of what was achieved, are also documented.

24 POWER TRANSMISSION AND DISTRIBUTION↗