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Power spectral density plots and associated time history plots are presented for the Titan-Centaur launch carrying the Viking dynamic simulator.
Power spectral density plots and associated time history plots are presented for the Titan-Centaur launch carrying the Viking A spacecraft.
Power spectral density plots and associated time history plots are presented for the Titan-Centaur launch carrying the Viking B spacecraft.
Time histories and shock-spectra of vibration and acceleration were obtained during the TC-1, TC-4 and TC-3 launch and staging events. Summary plots are presented and comparisons to Viking Program midfrequency sine test requirements are made.
Shock spectra are presented for the TC-1 (Viking dynamic simulator) flight.
Shock spectra are presented for the TC-4 (Viking A Spacecraft) flight.
Shock spectra are presented for the TC-3 (Viking B Spacecraft) flight.
Time history and PSD data are presented showing the character of the first longitudinal mode oscillation which occurred during the Centaur first burn of the TC-4 and TC-3 flights.
Power spectral density plots of vibration and strain data are presented for the Titan stage 1 burn and the Centaur main engine first burn of the TC-4 flight (Viking A spacecraft).
Power spectral density plots of vibration and strain data are presented for the Titan stage 1 burn and the Centaur main engine first burn of the TC-3 flight (Viking B spacecraft).
Extended time history plots are presented for the TC-1, TC-3, and TC-4 flights. Vibration, strain, acceleration, force, and moment data are included.
For abstract, see N77-28193.
For abstract, see N77-28193.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Economically optimal and safe operation of integrated energy systems (IES) requires optimization at many different time scales. A real-time optimization (RTO) workflow will attempt to maximize revenue and minimize operational costs on a time scale of minutes to hours. Such a workflow requires the use of a digital twin (DT), which is a virtual representation of a physical system. The DT is updated using real-time data from the physical system, and serves as a model in an optimization framework. The optimization results are then sent back to the physical system to complete the loop. This report details the progress made in developing building blocks for a DT/RTO framework. The Risk Analysis Virtual Environment (RAVEN) platform within the Framework for Optimization of Resources and Economics (FORCE) tool suite can perform many of the tasks required for building a DT and performing RTO. The first item of this report details RAVEN enhancements that enable RAVEN workflows to be run in various environments. Data communication between the physical system and its DT is essential for successful RTO. This includes preprocessing real-time data, loading data into a data warehouse, and querying the stored data. The second section of this report describes the progress made in implementing an adapter in Python in order for Deep Lynx to handle the data communication. Typical dispatch optimization frameworks are built on linear programming (LP). The prototype RTO workflow developed in this report uses an LP problem as a part of a receding-horizon- or economic model predictive control (EMPC) based optimization. The third section of this report details the framework of an RTO workflow in which the system consists of a simple electrical storage device. A DT can be built from a reduced-order model (ROM). Integrating a ROM into a typical LP optimization framework has been challenging because most optimization packages require the user to write algebraic expressions for the system model. The final section of this report shows how an externally built RAVEN ROM can be integrated in an RTO framework by using the Python package Pyomo. This demonstrates the RTO workflow capability from a software-only perspective and is an important step in demonstrating the capability to implement an RTO workflow for a physical system.
Work performed in the design of a transport aircraft wing for maximum fuel efficiency is documented with emphasis on design criteria, design methodology, and three design configurations. The design database includes complete finite element model description, sizing data, geometry data, loads data, and inertial data. A design process which satisfies the economics and practical aspects of a real design is illustrated. The cooperative study relationship between the contractor and NASA during the course of the contract is also discussed.
This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.