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Existing Hydropower Asset (EHA) Annual Gross Generation is a geospatial point-level dataset containing annual gross generation over time (2003-2024) and key characteristics of operational U.S. pumped storage and hybrid plants with 1 megawatt or greater of nameplate capacity. EIA 923 and EHA are the primary sources of the derived data. Hydropower units are excluded.
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Existing Hydropower Asset (EHA) Annual Net Generation is a geospatial point-level dataset containing annual net generation over time (2003-2024) and key characteristics of operational U.S. hydropower plants with 1 megawatt or greater of nameplate capacity. EIA 923 and EHA are the primary sources of the derived data. Pumped storage and hybrid plants are excluded.
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Flight computers and flight software are used for GN&C (guidance, navigation, and control), engine controllers, and avionics during missions. The software development requires the generation of a considerable amount of code. The engineers who generate the code make mistakes and the generation of a large body of code with high reliability requires considerable time. Computer-aided software engineering (CASE) tools are available which generates code automatically with inputs through graphical interfaces. These tools are referred to as code generators. In theory, code generators could write highly reliable code quickly and inexpensively. The various code generators offer different levels of reliability checking. Some check only the finished product while some allow checking of individual modules and combined sets of modules as well. Considering NASA's requirement for reliability, an in house manually generated code is needed. Furthermore, automatically generated code is reputed to be as efficient as the best manually generated code when executed. In house verification is warranted.
A computational study of a gust field generated by a gust generator in a low-speed wind tunnel. The gust generator is designed for the University of Washington Aeronautical Laboratory (UWAL) Kirsten wind tunnel for a gust load alleviation (GLA) control experiment of a Common Research Model (CRM) flexible wing utilizing the Variable Camber Continuous Trailing Edge Flap (VCCTEF). The gust generator comprises four horizontal NACA 0015 gust generator vanes placed upstream of the test section. Computational fluid dynamics simulations using a two-dimensional (2D) Unsteady-Reynolds-Averaged-Navier-Stokes (URANS) with k-ω Shear Stress Transport (SST) turbulence model provide detailed time-resolved information about the generated flow by the gust generator under prescribed sinusoidal motion. The characteristics of the induced flow by the gust generator are analyzed. A gust propagation model of the gust field is investigated. An unsteady lift model is developed using a varying-fidelity approach which includes a 2D interference aerodynamic model of the combined gust generator-wing system. The computed integrated unsteady lift is compared to experimental data for validation of the unsteady lift model. Both the amplitude and transport delay are found to be accurately captured by the unsteady lift model.
Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.
This study investigates the application of generative artificial intelligence techniques, particularly conditional generative adversarial networks (cGAN), in real-world engineering contexts, with a specific focus on synthetic data generation for critical heat flux (CHF). Utilizing a dataset comprising more than 20,000 real experimental CHF measurements, we conduct a series of experiments to examine cGAN’s behavior. These experiments encompass varying sizes of the training dataset, training cGAN on data from diverse experimental sources to generate new data on unseen experimental setups, and assessing the impact of excluding various input features on cGAN’s data generation accuracy. Our findings underscore the pronounced data dependency of cGAN for reliable performance, with decreased efficacy observed with smaller training dataset sizes. Notably, cGAN exhibits varying performance when trained on data from different experiments, with superior predictive capabilities observed for certain experiment sources compared to others. For instance, when cGAN was trained on data from Smolin et al.’s experiments or Zenkevich et al., it exhibited relatively good performance in generating the data from Becker et al., Kirillov et al., and Alekseev et al. experiments. In contrast, when trained with Alekseev et al.’s data and tasked with generating other experimental setups, cGAN showed notably poor performance. In both scenarios, cGAN’s performance was inferior compared to training on samples from all experiments concurrently. A feature importance analysis highlights the significant influence of parameters such as mass flux and heated length on accurate CHF generation, while other parameters like diameter and pressure have less impact. Inlet temperature is identified as a moderating factor by cGAN.
General curvilinear coordinate systems are considered along with the error induced by coordinate systems, basic differential models for coordinate generation, elliptic grid generation, conformal grid generation, algebraic grid generation, orthogonal grid generation, patched coordinate systems, and solid mechanics applications of boundary fitted coordinate systems. Attention is given to coordinate system control and adaptive meshes, the application of body conforming curvilinear grids for finite difference solution of external flow, the use of solution adaptive grids in solving partial differential equations, adaptive gridding for finite difference solutions to heat and mass transfer problems, and the application of curvilinear coordinate generation techniques to the computation of internal flows. Other topics explored are related to the solution of nonlinear water wave problems using boundary-fitted coordinate systems, the numerical modeling of estuarine hydrodynamics on a boundary-fitted coordinate system, and conformal grid generation for multielement airfoils.
Program verification using Hoare-style techniques requires many logical annotations. We have previously developed a generic annotation inference algorithm that weaves in all annotations required to certify safety properties for automatically generated code. It uses patterns to capture generator- and property-specific code idioms and property-specific meta-program fragments to construct the annotations. The algorithm is customized by specifying the code patterns and integrating them with the meta-program fragments for annotation construction. However, this is difficult since it involves tedious and error-prone low-level term manipulations. Here, we describe an annotation schema compiler that largely automates this customization task using generative techniques. It takes a collection of high-level declarative annotation schemas tailored towards a specific code generator and safety property, and generates all customized analysis functions and glue code required for interfacing with the generic algorithm core, thus effectively creating a customized annotation inference algorithm. The compiler raises the level of abstraction and simplifies schema development and maintenance. It also takes care of some more routine aspects of formulating patterns and schemas, in particular handling of irrelevant program fragments and irrelevant variance in the program structure, which reduces the size, complexity, and number of different patterns and annotation schemas that are required. The improvements described here make it easier and faster to customize the system to a new safety property or a new generator, and we demonstrate this by customizing it to certify frame safety of space flight navigation code that was automatically generated from Simulink models by MathWorks' Real-Time Workshop.
The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.
Model-based design and automated code generation are increasingly used at NASA to produce actual flight code, particularly in the Guidance, Navigation, and Control domain. However, since code generators are typically not qualified, there is no guarantee that their output is correct, and consequently auto-generated code still needs to be fully tested and certified. We have thus developed AUTOCERT, a generator-independent plug-in that supports the certification of auto-generated code. AUTOCERT takes a set of mission safety requirements, and formally verifies that the autogenerated code satisfies these requirements. It generates a natural language report that explains why and how the code complies with the specified requirements. The report is hyper-linked to both the program and the verification conditions and thus provides a high-level structured argument containing tracing information for use in code reviews.
The results of a dynamic interaction investigation to characterize any disturbances caused by interfacing the Mod 0A wind turbine (150 kW configuration) with the Block Island utility diesel generator grid are reported. The tests were run when only two diesel generators were on line, and attention was given to power, frequency, and voltage time profiles. The interconnected system was examined in the start-up and synchronization phase, normal shutdown and cut-out of the wind turbine, during fixed pitch generation, and during variable pitch operation. Governors were installed on the diesel generators to accommodate the presence of wind-derived electricity. The blade pitch control was set to maintain power at 150 kW or below. Power and voltage transients were insignificant during start-up and shutdown, and frequency aberrations were within the range caused by load fluctuations. It is concluded that wind turbine generation can be successfully implemented by an isolated utility, even with a significant penetration to the total grid output.
This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment.
This dataset accompanies the publication "ProtNHF: Neural Hamiltonian Flows for Controllable Protein Sequence Generation". This paper introduces a new AI model for protein sequence generation. This dataset contains data related to experiments discussed in the publication. This includes generated sequences and evaluation metrics supporting all unconditional and bias-controlled experiments in the ProtNHF paper.
A windpowered generator system is described which uses a windmill to convert mechanical energy to electrical energy for a three phase (network) voltage of constant amplitude and frequency. The generator system controls the windmill by the number of revolutions so that the power drawn from the wind for a given wind velocity is maximum. A generator revolution which is proportional to wind velocity is achieved. The stator of the generator is linked directly to the network and a feed converter at the rotor takes care of constant voltage and frequency at the stator.
This paper discusses the application of field modulated generator systems (FMGS) to dispersed solar-thermal-electric generation from a parabolic dish field with electric transport. Each solar generation unit is rated at 15 kWe and the power generated by an array of such units is electrically collected for insertion into an existing utility grid. Such an approach appears to be most suitable when the heat engine rotational speeds are high (greater than 6000 r/min) and, in particular, if they are operated in the variable speed mode and if utility-grade a.c. is required for direct insertion into the grid without an intermediate electric energy storage and reconversion system. Predictions of overall efficiencies based on conservative efficiency figures for the FMGS are in the range of 25 per cent and should be encouraging to those involved in the development of cost-effective dispersed solar thermal power systems.
The effect of radiative heating and cooling by clouds on the available potential energy (APE) is theoretically discussed. It is shown that the cloud radiative contribution to the generation of APE is determined by the net cloud radiative heating and the efficiency factor, which is a function of the temperature distribution of the atmosphere. Results are presented for low and middle cloud effects for three atmospheric layers. Cloud radiative heating is found to be a single function of cloud optical thickness for all classes designed in terms of cloud top heights and optical thickness. Low clouds at low latitudes destroy APE an midclouds generate APE. A concept is developed to relate the cloud radiative heating to cloud heights and optical depths. Cloud-generated radiative heating is computed for January zonal mean conditions for low and midclouds. For both cases, the strongest influence is found in the low troposphere, with marked differences in signs and magnitudes. At extratropical latitudes, both cloud classes generate net radiative cooling. In the tropics, the effect of low cloud changes from net cooling to the net heating as the optical thickness increases, and midclouds cause net heating. A mechanism is described whereby this dependence produces a strong positive feedback effect on the development of SST anomalies in the tropical oceans.