A compilation of spacecraft loads data from four Titan Centaur launch vehicle flights. Volume 3, part 4: Shock spectra of transients
Shock spectra are presented for the TC-3 (Viking B Spacecraft) flight.
Kachadourian, G.↗
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A compilation of spacecraft loads data from four Titan Centaur Launch Vehicle flights. Volume 4, part 1: Titan Stage 1 and Centaur First Burn oscillations
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.
Kachadourian, G.↗
A compilation of spacecraft loads data from four Titan Centaur Launch Vehicle flights. Volume 4, part 2: Titan Stage 1 and Centaur First Burn oscillations
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).
Kachadourian, G.↗
A compilation of spacecraft loads data from four Titan Centaur Launch Vehicle flights. Volume 4, part 3: Titan Stage 1 and Centaur First Burn oscillations
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).
Kachadourian, G.↗
A compilation of spacecraft loads data from four Titan Centaur launch vehicle flights. Volume 5, part 1: Time history plots
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.
Kachadourian, G.↗
A compilation of spacecraft loads data from four Titan Centaur launch vehicle flights. Volume 5, part 2: Time history plots
For abstract, see N77-28193.
Kachadourian, G.↗
A compilation of spacecraft loads data from four Titan Centaur launch vehicle flights. Volume 5, part 3: Time history plots
For abstract, see N77-28193.
Kachadourian, G.↗
Estimates of probability distribution of root-mean-square gust velocity of atmospheric turbulence from operational gust-load data by random-process theory
Explore the source record for details and available documents.
Press, Harry↗
Apollo Crew Loads Data Mining
Explore the source record for details and available documents.
Teresa Reiber↗
Study for the optimization of a transport aircraft wing for maximum fuel efficiency. Volume 1: Methodology, criteria, aeroelastic model definition and results
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.
Radovcich, N. A.↗
IM3 + EPRI Data Center Load Projections
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.
Burleyson, Casey [Pacific Northwest National Labor↗
Life support systems analysis and technical trades for a lunar outpost
The NASA/JPL life support systems analysis (LISSA) software tool was used to perform life support system analysis and technology trades for a Lunar Outpost. The life support system was modeled using a chemical process simulation program on a steady-state, one-person, daily basis. Inputs to the LiSSA model include metabolic balance load data, hygiene load data, technology selection, process operational assumptions and mission parameter assumptions. A baseline set of technologies has been used against which comparisons have been made by running twenty-two cases with technology substitutions. System, subsystem, and technology weights and powers are compared for a crew of 4 and missions of 90 and 600 days. By assigning a weight value to power, equivalent system weights are compared. Several less-developed technologies show potential advantages over the baseline. Solid waste treatment technologies show weight and power disadvantages but one could have benefits associated with the reduction of hazardous wastes and very long missions. Technology development towards reducing the weight of resupplies and lighter materials of construction was recommended. It was also recommended that as technologies are funded for development, contractors should be required to generate and report data useful for quantitative technology comparisons.
Ferrall, J. F.↗
Impact of remote sensing upon the planning, management and development of water resources. Summary of computers and computer growth trends for hydrologic modeling and the input of ERTS image data processing load
An analysis of current computer usage by major water resources users was made to determine the trends of usage and costs for the principal hydrologic users/models. The laws and empirical relationships governing the growth of the data processing loads were described and applied to project the future data loads. Data loads for ERTS CCT image processing were computed and projected through the 1985 era. The analysis showns significant impact due to the utilization and processing of ERTS CCT's data.
Castruccio, P. A.↗
A method for determining crack opening load from load-displacement data
The fatigue crack opening load is determined as the tangent point on the ascending load-displacement data between the curved portion and the upper linear region. A model for the 'unzipping' behavior of the crack indicates that the curved portion of the load-deflection curve is second order. The opening load is determined by a nonlinear, least squares fit of the data to the model, which optimally locates the tangent point of the two curves. The method provided consistent results for determining opening load P(op) for 7475-T731 aluminum using data from a crack tip opening gage.
Carman, C. Davis↗
Tabulated Pressure Coefficient Data from a Tail Loads Investigation on a 1/15-Scale Model of the Goodyear XZP5K Airship
This paper contains tail and hull loads data obtained in an investigation of a l/15-scale model of the Goodyear XZP5K airship. Data are presented in the form of tabulated pressure coefficients over a pitch and yaw range of +/-20 deg and 0 deg to 30 deg respectively, with various rudder and elevator deflections. Two tail configurations of different plan forms were tested on the model. The investigation was conducted in the Langley full-scale tunnel at a Reynolds number of approximately 16.5 x 10(exp 6) based on hull length, which corresponds to a Mach number of about 0.12.
Cannon, Michael D.↗
An application of the Multi-Purpose System Simulation /MPSS/ model to the Monitor and Control Display System /MACDS/ at the National Aeronautics and Space Administration /NASA/ Goddard Space Flight Center /GSFC/
The Multi-Purpose System Simulator (MPSS) model was used to investigate the current and projected performance of the Monitor and Control Display System (MACDS) at the Goddard Space Flight Center in processing and displaying launch data adequately. MACDS consists of two interconnected mini-computers with associated terminal input and display output equipment and a disk-stored data base. Three configurations of MACDS were evaluated via MPSS and their performances ascertained. First, the current version of MACDS was found inadequate to handle projected launch data loads because of unacceptable data backlogging. Second, the current MACDS hardware with enhanced software was capable of handling two times the anticipated data loads. Third, an up-graded hardware ensemble combined with the enhanced software was capable of handling four times the anticipated data loads.
Mill, F. W.↗
Techno-Economic Assessment of Data Center Load Demand Powered by Small Modular Reactors and Distributed Energy Resources
The rapid increase in data center energy demand, driven by AI and large-scale data processing, poses significant challenges to global energy infrastructure. Data centers require substantial and reliable energy for continuous operations and high-performance computing. Current electrical grids face issues such as transmission bottlenecks and aging infrastructure, making it difficult to meet these demands. Integrating inverter-based-resources (IBRs) like solar and wind presents both opportunities and challenges due to their intermittent nature. Small Modular Reactors (SMRs) offer a promising solution with their enhanced safety, modularity, reliability, and scalability, providing consistent base load power ideal for data center operations. This study presents a comprehensive techno-economic assessment of powering data center load demand using a combination of SMRs and IBRs with grid-connected and islanded mode. This study utilized Idaho National Laboratory’s (INL) HPC data center hourly load profiles and Xendee microgrid optimization platform to conduct the analysis. In this configuration, SMRs serves as the primary base load power source, consistently providing a steady supply of electricity necessary to meet the minimum load demand of the data center with support from the IBRs. Key performance indicators such as Levelized Cost of Electricity (LCOE), Net Present Value (NPV) has been calculated to assess the economic feasibility. The findings from this research will underscore the strategic benefits of integrating SMR plant with DERs – particularly for critical infrastructure load such as data centers.
14 - SOLAR ENERGY↗
Portable Load Measurement Device for Use During ARED Exercise on ISS
The Advanced Resistive Exercise Device (ARED) (Fig.1) is unique countermeasure hardware available to crewmembers aboard the International Space Station (ISS) used for resistance exercise training to protect against bone and muscle loss during long duration space missions. ARED instrumentation system was designed to measure and record exercise load data, but: - Reliably accurate data has not been available due to a defective force platform. - No ARED data has been recorded since mid-2011 due to failures in the instrumentation power system. ARED load data supports on-going HRP funded research, and is available to extramural researchers through LSDA-Repository. Astronaut Strength, Conditioning, and Rehabilitation specialists (ASCRs) use ARED data to track training progress and advance exercise prescriptions. ARED load data is necessary to fulfill medical requirements. HRP directed task intends to reduce to program risk (HRP IRMA Risk 1735), and evaluate the XSENS ForceShoeTM as a means of obtaining ARED load data during exercise sessions. The XSENS ForceShoes"TM" will fly as a hardware demonstration to ISS in May 2014 (39S). Additional portable load monitoring devices (PLMDs) are under evaluation in the ExPC Lab. PLMDs are favored over platform redesign as they support future exploration needs.
Hanson, A.↗