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DOE OSTI2021

Identifying geological structures through microseismic cluster and burst analyses complementing active seismic interpretation

At the Decatur carbon capture and storage site (IL, USA) CO 2 has been injected from 2011–2014 and from 2017 to present near the base of the Lower Mt. Simon Sandstone saline reservoir, resulting in microseismicity. Microseismicity is mainly located in the basement and distributed in distinct spatial clusters. The lack of significant impedance contrasts within the basement makes the interpretation of active-source seismic reflection data challenging, however, recent reprocessing allowed to resolve faults above and at the top of the basement. These faults generally do not coincide with the location of microseismic events and their continuation to the general depth of the seismic events cannot be assumed. This paper shows how the interpretation of the microseismicity can complement structural interpretations of active-source seismic reflection data. In particular, we analyze clusters and bursts (abrupt increases) of microseismicity, identify unresolved, smaller-scale weaknesses and extract statistical parameters. These parameters allow comparisons with the interpreted faults, and with fracture sets intercepted by boreholes. During injection at the Decatur site, the injection pressure was kept far below fracture pressure, nevertheless, seismic events were induced and spread far beyond the expected extent of the CO 2 plume. We argue that local stress transfers related to the CO 2 injection reactivated pre-existing fractures within the critically stressed basement. Finally, we conducted a slip tendency analysis for faults interpreted from active seismic, selected cluster, bursts and nodal planes from focal mechanisms to determine if the interpreted structures are optimally oriented with respect to the stress regime. Our results suggest that the orientation of fractures close to the injection well, generally shows slight deviations from the optimal orientation for slip. This might indicate either slight local deviations of the maximum horizontal stress azimuth from the average direction used in the analysis, or the lack of optimally oriented fractures at this location.

58 GEOSCIENCES↗

NASA NTRS2023

Sewage Treatment Plant #1 Area, SWMU 117 Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment Progress Report

This Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment (SA) Progress Report (SAPR) discusses the investigation activities and findings for the Sewage Treatment Plant #1 (STP1) Area located at Kennedy Space Center (KSC), Florida (Figure 1-1). This site has been designated Solid Waste Management Unit (SWMU) 117 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program, as the sewage treatment plant and associated areas were identified as a potential source of PFAS to the environment. This PFAS SAPR was prepared by Tetra Tech, Inc., for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0011-80KSC019F0070. This is the first progress report to document on-going SA activities; supplemental progress reports will be provided as additional data is collected. PFAS SA activities were conducted between April 2020 and March 2022 to collect additional data to supplement the existing datasets to better understand the extent of PFAS impacts to the environment in the STP1 Area. The SA for the STP1 Area covers an approximately 130-acre investigation area with multiple structures and buildings. The focus of the SA is the STP1 Complex and associated structures, including the former Polishing Pond, former Sludge Disposal8 Area, and former Spray Field. The STP1 Complex is located in the KSC Industrial Area, at the southwest corner of 4th Street SE and C Avenue SE. The STP1 Complex is located approximately ¼-mile south and downgradient of the Fire Station #1 site (SWMU 116), which is also currently undergoing a PFAS SA because of potential releases of PFAS-containing aqueous film-forming foam (AFFF). During the SA, a total of seven soil, 131 groundwater direct push technology (DPT), 24 groundwater monitoring well, and 11 surface water samples were collected between April 2020 and March 2022. Monitoring well samples were analyzed for 18 PFAS compounds, with all other samples analyzed for 28 PFAS compounds. The SA sample results were used along with historical results to evaluate the extent of PFAS impacts to the environment in the STP1 Area. Data generated to date and prior results were screened against the United States Environmental Protection Agency (USEPA) May 2022 Tap Water Regional Screening Levels (RSL) for groundwater and residential RSLs for soil (hazard quotient of 0.1). Surface water results were screened against the State of Florida Human Health Surface Water Screening Levels (SWSLs). Results from the SA showed exceedances of the applicable screening criteria for groundwater and surface water. Considering the current and historical dataset, PFOS is the prevalent PFAS compound. Based on these results, additional groundwater DPT and surface water sampling should be considered for PFAS analysis, focused on evaluating surface water bodies in the southeast portion of the Industrial Area, which discharge into the Banana River. Additionally, installation of monitoring wells should be considered to evaluate the interaction between the groundwater and surface water at the site. Collection of additional samples for TOC analysis should also be considered from representative groundwater (saturated soils) and surface water locations to further evaluate potential correlations between PFAS and TOC to provide a more comprehensive dataset to assist in fate and transport analyses.

Sewage Treatment Plant↗

DOE OSTI2018

An Analysis Workflow-Aware Storage System for Multi-Core Active Flash Arrays

Here, the need for novel data analysis is urgent in the face of a data deluge from modern applications. Traditional approaches to data analysis incur significant data movement costs, moving data back and forth between the storage system and the processor. Emerging Active Flash devices enable processing on the flash, where the data already resides. An array of such Active Flash devices allows us to revisit how analysis workflows interact with storage systems. By seamlessly blending together the flash storage and data analysis, we create an analysis workflow-aware storage system, AnalyzeThis. Our guiding principle is that analysis-awareness be deeply ingrained in each and every layer of the storage, elevating data analyses as first-class citizens, and transforming AnalyzeThis into a potent analytics-aware appliance. To evaluate the AnalyzeThis system, we have adopted both emulation and simulation approaches. In particular, we have evaluated AnalyzeThis by implementing the AnalyzeThis storage system on top of the Active Flash Array's emulation platform. We have also implemented an event-driven AnalyzeThis simulator, called AnalyzeThisSim, which allows us to address the limitations of the emulation platform, e.g., performance impact of using multi-core SSDs. The results from our emulation and simulation platforms indicate that AnalyzeThis is a viable approach for expediting workflow execution and minimizing data movement.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2007

Mobile Collection and Automated Interpretation of EEG Data

A system that would comprise mobile and stationary electronic hardware and software subsystems has been proposed for collection and automated interpretation of electroencephalographic (EEG) data from subjects in everyday activities in a variety of environments. By enabling collection of EEG data from mobile subjects engaged in ordinary activities (in contradistinction to collection from immobilized subjects in clinical settings), the system would expand the range of options and capabilities for performing diagnoses. Each subject would be equipped with one of the mobile subsystems, which would include a helmet that would hold floating electrodes (see figure) in those positions on the patient s head that are required in classical EEG data-collection techniques. A bundle of wires would couple the EEG signals from the electrodes to a multi-channel transmitter also located in the helmet. Electronic circuitry in the helmet transmitter would digitize the EEG signals and transmit the resulting data via a multidirectional RF patch antenna to a remote location. At the remote location, the subject s EEG data would be processed and stored in a database that would be auto-administered by a newly designed relational database management system (RDBMS). In this RDBMS, in nearly real time, the newly stored data would be subjected to automated interpretation that would involve comparison with other EEG data and concomitant peer-reviewed diagnoses stored in international brain data bases administered by other similar RDBMSs.

Mintz, Frederick↗

DOE OSTI2015

A COMPREHENSIVE PHOTOMETRIC STUDY OF THE CONTACT BINARY GN BOO WITH POSSIBLE MAGNETIC ACTIVITIES

Multi-color photometric data of GN Boo observed from 2010 to 2013 are presented. The intrinsic variations of the light curves are remarkable, and their phenomena are probable evidence of stronger magnetic activities on the surfaces of the components. Based on all CCD times of minimum light, a secular increase superimposed on a cyclic oscillation is found. The orbital period increases at a rate of dP/dt = +1.74 × 10{sup −7} days yr{sup −1}, which can be explained by mass transfer from the less massive component to the more massive one. The period and amplitude of the cyclic variation are P = 9.5632 yr and A = 0.0046 days, respectively, which correspond to the previous published results. Using the 2010 version of the W–D code, five sets of photometric solutions were derived from our new data. The results imply that the stellar spot, the degree of fill-out, and the temperature difference ΔT between the components of GN Boo are variable. It is inferred that the magnetic activities perhaps influence the outer radius of the component, causing the temperature of the component star and the level of contact to change.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Changing-look Active Galactic Nuclei from the Dark Energy Spectroscopic Instrument. II. Statistical Properties from the First Data Release

We present the identification of changing-look active galactic nuclei (CL-AGNs) from the Dark Energy Spectroscopic Instrument First Data Release and Sloan Digital Sky Survey Data Release 16 at z ≤ 0.9. To confirm the CL-AGNs, we utilize spectral flux calibration assessment via an [O III ]-based calibration, pseudophotometry examination, and visual inspection. This rigorous selection process allows us to compile a statistical catalog of 561 CL-AGNs, encompassing 527 Hβ, 149 Hα, and 129 Mg II CL behaviors. In this sample, we find (1) a 283:278 ratio of turn-on to turn-off CL-AGNs. (2) The median Eddington ratio for CL-AGNs in the dim state is approximately λ Edd ∼ 0.01. (3) A strong correlation between the change in the luminosity of the broad emission lines (BELs) and variation in the continuum luminosity, with Mg II and Hβ displaying similar responses during CL phases. (4) The Baldwin–Phillips–Terlevich diagram for CL-AGNs shows no statistical difference from the general AGN catalog. (5) Five CL-AGNs are associated with asymmetrical mid-infrared flares, possibly linked to tidal disruption events. Given the large CL-AGN sample and the stochastic sampling of spectra, we propose that some CL phenomena are inherently due to typical AGN variability during low accretion rates, particularly for CL phenomenon only occurring on one BEL. Finally, we introduce a monotonically dimming CL phase for objects characterized by a gradual decline over decades in the light curve and the complete disappearance of entire BELs in faint spectra, indicative of a real transition in the accretion disk.

accretion↗

DOE OSTI2025

Changing-look Active Galactic Nuclei from the Dark Energy Spectroscopic Instrument. II. Statistical Properties from the First Data Release

We present the identification of changing-look active galactic nuclei (CL-AGNs) from the Dark Energy Spectroscopic Instrument First Data Release and Sloan Digital Sky Survey Data Release 16 at z≤ 0.9. To confirm the CL-AGNs, we utilize spectral flux calibration assessment via an [O iii]-based calibration, pseudophotometry examination, and visual inspection. This rigorous selection process allows us to compile a statistical catalog of 561 CL-AGNs, encompassing 527 Hβ, 149 Hα, and 129 Mg ii CL behaviors. In this sample, we find (1) a 283:278 ratio of turn-on to turn-off CL-AGNs. (2) The median Eddington ratio for CL-AGNs in the dim state is approximately λ$_{Edd}$ ∼ 0.01. (3) A strong correlation between the change in the luminosity of the broad emission lines (BELs) and variation in the continuum luminosity, with Mg ii and Hβ displaying similar responses during CL phases. (4) The Baldwin–Phillips–Terlevich diagram for CL-AGNs shows no statistical difference from the general AGN catalog. (5) Five CL-AGNs are associated with asymmetrical mid-infrared flares, possibly linked to tidal disruption events. Given the large CL-AGN sample and the stochastic sampling of spectra, we propose that some CL phenomena are inherently due to typical AGN variability during low accretion rates, particularly for CL phenomenon only occurring on one BEL. Finally, we introduce a monotonically dimming CL phase for objects characterized by a gradual decline over decades in the light curve and the complete disappearance of entire BELs in faint spectra, indicative of a real transition in the accretion disk.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

Solar activity variations in midlatitude thermospheric meridional winds

Upper thermospheric meridional wind data at midlatitudes and for low magnetic activity are examined for solar activity variations following an analysis scheme suggested by a Coordinated Analysis of the Thermosphere workshop. Wind data from incoherent scatter, Fabry-Perot, and F2 peak heights show decreasing diurnal amplitudes with increasing solar activity during all seasons, except for Saint Santin data, which show a slight increase in summer. Equivalent winds from F2 peak height data have strong decreases in diurnal amplitude in all seasons. The coupled thermosphere ionosphere model and thermosphere ionosphere global circulation model predictions of diurnal amplitude, while differing considerably in magnitude, also show decreasing amplitudes during all seasons except summer, while the HWM90 empirical model amplitudes increase slightly with solar activity during all seasons. The diurnal mean wind trends with solar activity are fairly weak, except for Millstone Hill incoherent scatter radar, which shows a shift from strong southward to near zero or northward wind with increasing activity. Model results for the mean generally fall within the band of measurements. Near midnight, most of the data also show that the typically southward winds weaken with increasing solart activity in all seasons except summer, when results are mixed. There are significant differences between the trends and between absolute values for the various data sets and models which need further investigation.

Hedin, A. E.↗

NASA NTRS2019

Application of Synoptic Magnetograms for Prediction of Solar Activity Using Ensemble Kalman Filter

Solar activity predictions using the data assimilation approach have demonstrated great potential to build reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to a non-linear dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. These developments assume an empirical relationship between the mean toroidal magnetic field flux and the sunspot number. Estimated from the sunspot number series, variations of the toroidal field have been used to assimilate the data into the Parker-Kleeorin-Ruzmakin (PKR) dynamo model by applying the EnKF method. The dynamo model describes the evolution of the toroidal and poloidal components of the magnetic field and the magnetic helicity. Full-disk magnetograms provide more accurate and complete input data by constraining both the toroidal and poloidal global field components, but these data are available only for the last four solar cycles. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of limited time series observations on the accuracy of solar activity predictions. We present EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach.

Kitiashvili, Irina N.↗

DOE OSTI2018

PlantSEED enables automated annotation and reconstruction of plant primary metabolism with improved compartmentalization and comparative consistency

Genome-scale metabolic reconstructions help us to understand and engineer metabolism. Next-generation sequencing technologies are delivering genomes and transcriptomes for an ever-widening range of plants. While such omic data can, in principle, be used to compare metabolic reconstructions in different species, organs and environmental conditions, these comparisons require a standardized framework for the reconstruction of metabolic networks from transcript data. We previously introduced PlantSEED as a framework covering primary metabolism for 10 species. We have now expanded PlantSEED to include 39 species and provide tools that enable automated annotation and metabolic reconstruction from transcriptome data. The algorithm for automated annotation in PlantSEED propagates annotations using a set of signature k-mers (short amino acid sequences characteristic of particular proteins) that identify metabolic enzymes with an accuracy of about 97%. PlantSEED reconstructions are built from a curated template that includes consistent compartmentalization for more than 100 primary metabolic subsystems. Together, the annotation and reconstruction algorithms produce reconstructions without gaps and with more accurate compartmentalization than existing resources. These tools are available via the PlantSEED web interface at , which enables users to upload, annotate and reconstruct from private transcript data and simulate metabolic activity under various conditions using flux balance analysis. We demonstrate the ability to compare these metabolic reconstructions with a case study involving growth on several nitrogen sources in roots of four species.

Flux Balance Analysis↗

NASA NTRS1992

Visualization techniques to aid in the analysis of multi-spectral astrophysical data sets

This report describes our project activities for the period Sep. 1991 - Oct. 1992. Our activities included stabilizing the software system STAR, porting STAR to IDL/widgets (improved user interface), targeting new visualization techniques for multi-dimensional data visualization (emphasizing 3D visualization), and exploring leading-edge 3D interface devices. During the past project year we emphasized high-end visualization techniques, by exploring new tools offered by state-of-the-art visualization software (such as AVS3 and IDL4/widgets), by experimenting with tools still under research at the Department of Computer Science (e.g., use of glyphs for multidimensional data visualization), and by researching current 3D input/output devices as they could be used to explore 3D astrophysical data. As always, any project activity is driven by the need to interpret astrophysical data more effectively.

Domik, Gitta↗

DOE OSTI2013

Data sharing report characterization of population 7: Personal protective equipment, dry active waste, and miscellaneous debris, surveillance and maintenance project Oak Ridge National Laboratory Oak Ridge, Tennessee

The U.S. Department of Energy (DOE) Oak Ridge Office of Environmental Management (EM-OR) requested that Oak Ridge Associated Universities (ORAU), working under the Oak Ridge Institute for Science and Education (ORISE) contract, provide technical and independent waste management planning support under the American Recovery and Reinvestment Act (ARRA). Specifically, DOE EM-OR requested that ORAU plan and implement a sampling and analysis campaign targeting certain URS|CH2M Oak Ridge, LLC (UCOR) surveillance and maintenance (S&M) process inventory waste. Eight populations of historical and reoccurring S&M waste at the Oak Ridge National Laboratory (ORNL) have been identified in the Waste Handling Plan for Surveillance and Maintenance Activities at the Oak Ridge National Laboratory, DOE/OR/01-2565&D2 (WHP) (DOE 2012) for evaluation and processing to determine a final pathway for disposal. Population 7 (POP 7) consists of 56 containers of aged, low-level and potentially mixed S&M waste that has been staged in various locations around ORNL. Several of these POP 7 containers primarily contain personal protective equipment (PPE) and dry active waste (DAW), but may contain other miscellaneous debris. This data sharing report addresses the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 (CERCLA) specified waste in a 13-container subpopulation (including eight steel boxes, three 55-gal drums, one sealand, and one intermodal) that lacked sufficient characterization data for possible disposal at the Environmental Management Waste Management Facility (EMWMF) using the approved Waste Lot (WL) 108.1 profile.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Data Qualification Report: SRNL Glass Composition-Properties (ComPro) Database

The Savannah River National Laboratory Glass Composition-Properties (ComPro) database is an extensive database containing pertinent composition and durability data to support the accelerated clean-up mission at the Defense Waste Processing Facility. The activities described in this data qualification report were performed to support the information contained in the database. There were two objectives of the original data qualification process. The first objective was to review supporting documentation to determine if DOE/RW-0333P Quality Assurance Requirements and Description had been implemented during the original work. If the DOE/RW-0333P Quality Assurance Requirements and Description had not been directly implemented during the original work, the second objective was to determine if the controls that were used were adequate to meet the intent of the DOE/RW-0333P Quality Assurance Requirements and Description. The results of these two objectives and the activities performed to support these decisions are described in this document. An assessment of each dataset was made to determine if the data were RW-0333P Compliant, RW-0333P Equivalent or Non-RW-0333P Compliant. The original data qualification was performed in accordance with E7, Conduct of Engineering Manual, Procedure 3.70, Revision 4, Qualification of Data. The specific method that was used was Equivalent Controls as described in E7, 3.70. Revision 2 of this document adds supporting information for the RW-0333P Compliant datasets added to Revision 3 of the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Knowledge extraction and transfer in data-driven fracture mechanics

Significance Data-driven approaches have launched a new paradigm in scientific research that is bound to have an impact on all disciplines of science and engineering. However, at this juncture, the exploration of data-driven techniques in the century-old field of fracture mechanics is highly limited, and there are key challenges including accurate and intelligent knowledge extraction and transfer in a data-limited regime. Here, we propose a framework for data-driven knowledge extraction in fracture mechanics with rigorous accuracy assessment which employs active learning for optimizing data usage and for data-driven knowledge transfer that allows efficient treatment of three-dimensional fracture problems based on two-dimensional solutions.

Liu, Xing↗

DOE OSTI2022

Microbial extracellular enzyme activity with simulated climate change

It is critical to understand the consequences of environmental change for the microbial regulation of carbon and nutrient cycling. Specifically, understanding microbial community traits, such as extracellular enzyme activity, can help inform nutrient cycling models and address knowledge gaps. We analyzed data on extracellular enzyme activities and litter decomposition from an 18-month experiment in which microbial communities were reciprocally transplanted along a climate gradient in Southern California. Communities were from desert, scrubland, grassland, pine–oak, and subalpine ecosystems. We aimed to test how enzyme activities responded to climate change following transplantation and how those responses related to decomposition rates. We hypothesized that microbial communities would specialize on their native climate conditions, resulting in higher enzyme activities when transplanted back into their native climate. We investigated the relationship between extracellular enzyme Vmax values, substrate mass loss, and microbial biomass as well as variation in these variables across the climate gradient. We found little evidence for climate specialization, and there was rarely a reduction in enzyme functioning after microbial communities were transplanted into new climate conditions. Moreover, observed differences in decomposition were not related to changes in extracellular enzyme potential, although there were significant differences in enzyme activities and decomposition rates across sites. These results suggest that direct, physiological impacts of climate are likely to be important for enzyme-mediated decomposition, but climate specialization will not constrain the microbial response to climate change in our system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

The Development of Vocational Vehicle Drive Cycles and Segmentation

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize the on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical real-world drive cycle data captured from medium- and heavy vehicles operating across the United States. The data encompass data from existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topology ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. The range of fleets, geographic locations, and total number of vehicles analyzed ensures results that include the influence of these factors. While no analysis will be perfect without unlimited resources and data, it is the researchers understanding that the Fleet DNA database is the largest and most thorough publicly accessible vocational vehicle usage database currently in operation. This report includes an introduction to the Fleet DNA database and the data contained within, a presentation of the results of the statistical analysis performed by NREL, review of the logistic model developed to predict cluster membership, and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work are also included in the report content.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Leveraging Big Data Analysis Techniques for U.S. Vocational Vehicle Drive Cycle Characterization, Segmentation, and Development

Under a collaborative interagency agreement between the U.S. Environmental Protection Agency and the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) performed a series of in-depth analyses to characterize on-road driving behavior including distributions of vehicle speed, idle time, accelerations and decelerations, and other driving metrics of medium- and heavy-duty vocational vehicles operating within the United States. As part of this effort, NREL researchers segmented U.S. medium- and heavy-duty vocational vehicle driving characteristics into three distinct operating groups or clusters using real-world drive cycle data collected at 1 Hz and stored in NREL's Fleet DNA database. The Fleet DNA database contains millions of miles of historical drive cycle data captured from medium- and heavy-duty vehicles operating across the United States. The data encompass existing DOE activities as well as contributions from valued industry stakeholder participants. For this project, data captured from 913 unique vehicles comprising 16,250 days of operation were drawn from the Fleet DNA database and examined. The Fleet DNA data used as a source for this analysis has been collected from a total of 30 unique fleets/data providers operating across 22 unique geographic locations spread across the United States. This includes locations with topographies ranging from the foothills of Denver, Colorado, to the flats of Miami, Florida. This paper includes the results of the statistical analysis performed by NREL and a discussion and detailed summary of the development of the vocational drive cycle weights and representative transient drive cycles for testing and simulation. Additional discussion of known limitations and potential future work is also included.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1976

A mathematical model of an active control landing gear for load control during impact and roll-out

A mathematical model of an active control landing gear (ACOLAG) was developed and programmed for operation on a digital computer. The mathematical model includes theoretical subsonic aerodynamics; first-mode wing bending and torsional characteristics; oleo-pneumatic shock strut with fit and binding friction; closed-loop, series-hydraulic control; empirical tire force-deflection characteristics; antiskid braking; and sinusoidal or random runway roughness. The mathematical model was used to compute the loads and motions for a simulated vertical drop test and a simulated landing impact of a conventional (passive) main landing gear designed for a 2268-kg (5000-lbm) class airplane. Computations were also made for a simply modified version of the passive gear including a series-hydraulic active control system. Comparison of computed results for the passive gear with experimental data shows that the active control landing gear analysis is valid for predicting the loads and motions of an airplane during a symmetrical landing. Computed results for the series-hydraulic active control in conjunction with the simply modified passive gear show that 20- to 30-percent reductions in wing force, relative to those occurring with the modified passive gear, can be obtained during the impact phase of the landing. These reductions in wing force could result in substantial increases in fatigue life of the structure.

Mcgehee, J. R.↗