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At least 1,639 records · Page 91

A Comparison of Preconstruction and Operational Wake Loss Estimates for Land-Based Wind Plants

Recent studies suggest that biases between wind plant pre-construction energy yield estimates and actual energy production are decreasing over time. However, variability in energy yield prediction accuracy across different projects and wind energy consultants remains high. Wake effects are one of the largest categories comprising the pre-construction energy yield assessment process. To assess the accuracy of wake loss predictions, we compare pre-construction wake loss estimates provided by 8 consultants to the estimated operational wake losses for 10 North American wind plants, as part of the Wind Plant Performance Prediction (WP3) Benchmark project. We estimate operational wake losses using supervisory control and data acquisition (SCADA) data by comparing total wind plant energy production to the potential energy production based on the power produced by freestream wind turbines. In the presentation, we will discuss the overall wake loss prediction bias as well as the project-to-project variability in the prediction accuracy. Further, we will highlight challenges encountered when estimating operational wake losses, including the impact of complex terrain and the presence of neighboring wind plants.

benchmark↗

Skylab simulator visual displays and training for joint observing programs

The complexity of the Skylab Apollo Telescope Mount (ATM) experiment operations necessitated the use of high fidelity simulations of the onboard visual displays and pointing system for crew training. The displays which were simulated included the H-alpha displays, XUV monitor display, XUV/slit/white light display, X-ray image display, and the white light coronagraph display. The pointing simulation was achieved by projecting film sequences which were subsequently viewed by TV cameras. An optical system in front of the vidicons simulated the pointing, roll, and zoom capabilities of the ATM and sensing systems. The simulation enabled the Skylab crewmen to obtain valuable integrated training combining such tasks as target recognition and status assessment, complex and the time dependent pointing operations, malfunction analyses, and rapid responses to flare and other transient events.

Holt, A. C.↗

Parametric Analysis of a Hover Test Vehicle using Advanced Test Generation and Data Analysis

Large complex aerospace systems are generally validated in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. This is due to the large parameter space, and complex, highly coupled nonlinear nature of the different systems that contribute to the performance of the aerospace system. We have addressed the factors deterring such an analysis by applying a combination of technologies to the area of flight envelop assessment. We utilize n-factor (2,3) combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. The data generated is automatically analyzed through a combination of unsupervised learning using a Bayesian multivariate clustering technique (AutoBayes) and supervised learning of critical parameter ranges using the machine-learning tool TAR3, a treatment learner. Covariance analysis with scatter plots and likelihood contours are used to visualize correlations between simulation parameters and simulation results, a task that requires tool support, especially for large and complex models. We present results of simulation experiments for a cold-gas-powered hover test vehicle.

Gundy-Burlet, Karen↗

Optimal Energy Consumption Forecast for Grid Responsive Buildings: A Sensitivity Analysis

It is envisioned that building systems will become active participants in the smart grid operation by controlling their energy consumption to optimize complex criteria beyond ensuring local end-use comfort satisfaction. A forecast of the building energy consumption will be necessary to enable integration between building and grid operation. Such forecast will be affected by parametric and measurement uncertainty. In this paper we develop a methodology for quantifying the sensitivity of optimal hourly energy consumption forecasts to various sources model and measurement uncertainty. We demonstrate the approach for a building heating ventilation and air conditioning (HVAC) system use-case.

Kundu, Soumya↗

Advancements in Remote Ground Control Station Operator Pilot in Command Training Program for Beyond Visual Line of Sight Flight Operations

The training program for a Remote Ground Control Station Operator Pilot in Command (R-GCSO PIC) at NASA Langley Research Center marks a pivotal evolution in preparing operators for Beyond Visual Line of Sight (BVLOS) operations. This program, developed within the Advanced Air Mobility (AAM) project High Density Vertiplex (HDV) subproject, was crafted to bridge the gap between traditional Ground Control Station Operators (GCSO) and R-GCSO PICs, focusing on uncrewed aircraft systems (UAS). It encompassed extensive theoretical and practical training, including hands-on experience with advanced simulators and live flight operations, while ensuring a deep understanding of BVLOS complexities. The training leveraged NASA technologies like the MPATH (Measuring Performance for Autonomy Teaming with Humans) ground control station software and incorporated human factors principles to enhance operational readiness. This paper details the program's development, execution, and the critical insights gained, emphasizing the necessity of continuous adaptation in training methodologies to meet the evolving demands of UAS operations in the National Airspace System.

Ground control station operator↗

Computational Workbench for Multibody Dynamics

PyCraft is a computer program that provides an interactive, workbenchlike computing environment for developing and testing algorithms for multibody dynamics. Examples of multibody dynamic systems amenable to analysis with the help of PyCraft include land vehicles, spacecraft, robots, and molecular models. PyCraft is based on the Spatial-Operator- Algebra (SOA) formulation for multibody dynamics. The SOA operators enable construction of simple and compact representations of complex multibody dynamical equations. Within the Py-Craft computational workbench, users can, essentially, use the high-level SOA operator notation to represent the variety of dynamical quantities and algorithms and to perform computations interactively. PyCraft provides a Python-language interface to underlying C++ code. Working with SOA concepts, a user can create and manipulate Python-level operator classes in order to implement and evaluate new dynamical quantities and algorithms. During use of PyCraft, virtually all SOA-based algorithms are available for computational experiments.

Edmonds, Karina↗

Optimal operations of a nuclear-based integrated energy system: A mixed integer program approach

A nuclear-based integrated energy system (IES), consisting of multiple carbon-free energy generation and conversion technologies to meet heterogeneous end-use demands, offers a promising approach to decarbonizing the U.S. economy. Operating such an IES is challenging due to its complexity and the diverse end-use demands, such as heating and electricity. This paper aims to address the optimal operation of an IES composed of a small modular reactor (SMR), a steam manifold, a balance of plant (BOP), a high-temperature steam electrolysis (HTSE) system, a district heating (DH) network, and electrical grids. We formulate the system’s operation as a mixed integer linear programming (MILP) problem to maximize net revenues from sales of electricity and hydrogen. To evaluate the efficacy of the proposed model, we conduct a 24-hour simulation considering day-ahead (DA) electricity prices from CAISO and a varying DH demand profile with hourly resolution. The simulation results show that our model effectively optimizes the operation by selling electricity during on-peak periods and purchasing electricity for hydrogen production during off-peak periods, while satisfying operating constraints within the IES.

08 HYDROGEN↗

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A matrix-free approach for finite-strain hyperelastic problems using geometric multigrid

This paper investigates matrix-free algorithms for problems in quasi-static finite-strain hyperelasticity. Iterative solvers with matrix-free operator evaluation have emerged as an attractive alternative to sparse matrices in the fluid dynamics and wave propagation communities because they significantly reduce the memory traffic, the limiting factor in classical finite element solvers. Specifically, we study different matrix-free realizations of the finite element tangent operator and determine whether generalized methods of incorporating complex constitutive behavior might be feasible. Furthermore, in order to improve the convergence behavior of iterative solvers, we also propose a method by which to construct level tangent operators and employ them to define a geometric multigrid preconditioner. Additionally, the performance of the matrix-free operator and the geometric multigrid preconditioner is compared to the matrix-based implementation with an algebraic multigrid (AMG) preconditioner on a single node for a representative numerical example of a heterogeneous hyperelastic material in two and three dimensions. We find that matrix-free methods for finite-strain solid mechanics are very promising, outperforming linear matrix-based schemes by two to five times, and that it is possible to develop numerically efficient implementations that are independent of the hyperelastic constitutive law.

42 ENGINEERING↗

Lunar Daylight Exploration

With 1 rover, 2 astronauts and 3 days, the Apollo 17 Mission covered over 30 km, setup 10 scientific experiments and returned 110 kg of samples. This is a lot of science in a short time and the inspiration for a barebones, return-to-the-Moon strategy called Daylight Exploration. The Daylight Exploration approach poses an answer to the question, What could the Apollo crew have done with more time and today s robotics? In contrast to more ambitious and expensive strategies that create outposts then rely on pressurized rovers to drive to the science sites, Daylight Exploration is a low-overhead approach conceived to land near the scientific site, conduct Apollo-like exploration then leave before the sun goes down. A key motivation behind Daylight Exploration is cost reduction, but it does not come at the expense of scientific exploration. As a goal, Daylight Exploration provides access to the top 10 science sites by using the best capabilities of human and robotic exploration. Most science sites are within an equatorial band of 26 degrees latitude and on the Moon, at the equator, the day is 14 Earth days long; even more important, the lunar night is 14 days long. Human missions are constrained to 12 days because the energy storage systems required to operate during the lunar night adds mass, complexity and cost. In addition, short missions are beneficial because they require fewer consumables, do not require an airlock, reduce radiation exposure, minimize the dwell-time for the ascent and orbiting propulsion systems and allow a low-mass, campout accommodations. Key to Daylight Exploration is the use of piloted rovers used as tele-operated science platforms. Rovers are launched before or with the crew, and continue to operate between crew visits analyzing and collecting samples during the lunar daylight

Griffin, Brand Norman↗

Electronic Components for use in Extreme Temperature Aerospace Applications

Electrical power management and control systems designed for use in planetary exploration missions and deep space probes require electronics that are capable of efficient and reliable operation under extreme temperature conditions. Space-based infra-red satellites, all-electric ships, jet engines, electromagnetic launchers, magnetic levitation transport systems, and power facilities are also typical examples where the electronics are expected to be exposed to harsh temperatures and to operate under severe thermal swings. Most commercial-off-the-shelf (COTS) devices are not designed to function under such extreme conditions and, therefore, new parts must be developed or the conventional devices need to be modified. For example, spacecraft operating in the cold environment of deep space carry a large number of radioisotope heating units in order to maintain the surrounding temperature of the on-board electronics at approximately 20 C. At the other end, built-in radiators and coolers render the operation of electronics possible under hot conditions. These thermal measures lead to design complexity, affect development costs, and increase size and weight. Electronics capable of operation at extreme temperatures, thus, will not only tolerate the hostile operational environment, but also make the overall system efficient, more reliable, and less expensive. The Extreme Temperature Electronics Program at the NASA Glenn Research Center focuses on research and development of electronics suitable for applications in the aerospace environment and deep space exploration missions. Research is being conducted on devices, including COTS parts, for potential use under extreme temperatures. These components include semiconductor switching devices, passive devices, DC/DC converters, operational amplifiers, and oscillators. An overview of the program will be presented along with some experimental findings.

Patterson, Richard L.↗

Innovative Test Operations to Support Orion and Future Human Rated Missions

This paper describes how the Orion program is implementing new and innovative test approaches and strategies in an evolving development environment. The early flight test spacecraft are evolving in design maturity and complexity requiring significant changes in the ground test operations for each mission. The testing approach for EM-2 is planned to validate innovative Orion production acceptance testing methods to support human exploration missions in the future. Manufacturing and testing at Kennedy Space Center in the Neil Armstrong Operations and Checkout facility will provide a seamless transition directly to the launch site avoiding transportation and checkout of the spacecraft from other locations.

Koenig, William J.↗

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗

Operation of the H- Linac at FNAL

The Fermi National Accelerator Laboratory (FNAL) Linac has been in operation for 52 years. In approximately four years, it will be replaced by a new 800 MeV superconducting machine, the PIP-II SRF Linac. In the current configuration, the Linac delivers H- ions at 400 MeV and injects protons by charge exchange into the Booster synchrotron. Despite its age, the Linac is the most stable accelerator in the FNAL complex, reliably sending 22 mA in daily operations. We will discuss the status of the operation, beam studies, and plans.

43 PARTICLE ACCELERATORS↗

Ares I-X Overview

The Ares I-X flight test is the first development flight of the Ares I crew launch vehicle. This mission, first conceived in 2006, will be launching later in 2009. Its primary mission objectives will be to demonstrate flight and roll control of a dynamically similar vehicle, perform a separation event and measure its shock effects, stack and recover a first stage booster, and demonstrate ground operations. All of the primary flight test vehicle s hardware is at Kennedy Space Center, and is being stacked in the Vehicle Assembly Building for a liftoff at Launch Complex 39B. Mission hardware specific to Ares I-X also is being installed at Launch Complex 39, which has been supporting Space Shuttle operations. This presentation will provide a status and preview of the upcoming mission.

Davis, Stephan R.↗

A Controller-in-the Loop Simulation of Ground-Based Automated Separation Assurance in a NextGen Environment

A controller-in-the-loop simulation was conducted in the Airspace Operations Laboratory (AOL) at the NASA Ames Research Center to investigate the functional allocation aspects associated with ground-based automated separation assurance in a far-term NextGen environment. In this concept, ground-based automation handled the detection and resolution of strategic and tactical conflicts and alerted the controller to deferred situations. The controller was responsible for monitoring the automation and managing situations by exception. This was done in conditions both with and without arrival time constraints across two levels of traffic density. Results showed that although workload increased with an increase in traffic density, it was still manageable in most situations. The number of conflicts increased similarly with a related increase in the issuance of resolution clearances. Although over 99% of conflicts were resolved, operational errors did occur but were tied to local sector complexities. Feedback from the participants revealed that they thought they maintained reasonable situation awareness in this environment, felt that operations were highly acceptable at the lower traffic density level but were less so as it increased, and felt overall that the concept as it was introduced here was a positive step forward to accommodating the more complex environment envisioned as part of NextGen.

Homola, J.↗

General aviation single pilot IFR autopilot study

Five levels of autopilot complexity were flown in a single engine instrument flight rules (IFR) simulation for several different IFR terminal operations. A comparison was made of the five levels of complexity ranging from no-autopilot to a fully coupled lateral and vertical guidance mode to determine the relative benefits vs. complexity/cost of state of the art autopilot capability in the IFR terminal area. Of the five levels tested, the heading select mode made the largest relative difference in decreasing workload and simplifying the approach task. It was also found that the largest number of blunders was detected with the most highly automated mode. The data also showed that, regardless of the autopilot mode, performance during an IFR approach was highly dependent on the type of approach being flown. These results indicate that automation can be useful when making IFR approaches in a high workload environment, but also that some disturbing trends are associated with some of the higher levels of automation found in state of the art autopilots.

Bergeron, H. P.↗

Saturn S-2 production operations techniques: Production welding. Volume 1: Bulkhead welding

The complex Saturn S-2 welding processes and procedures required considerable development and refinement to establish a production capability that could consistently produce aluminum alloy welds within specified requirements. The special processes and techniques are defined that were established for the welding of gore-to-gore and manhole- or closeout-to-gore.

Abel, O. G.↗