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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Projective Integral Updates for High-Dimensional Variational Inference

Variational inference is an approximation framework for Bayesian inference that seeks to improve quantified uncertainty in predictions by optimizing a simplified distribution over parameters to stand in for the full posterior. Capturing model variations that remain consistent with training data enables more robust predictions by reducing parameter sensitivity. This work introduces a fixed-point optimization for variational inference that is applicable when every feasible log density can be expressed as a linear combination of functions from a given basis. In such cases, the optimizer becomes a fixed-point of projective integral updates. When the basis spans univariate quadratics in each parameter, the feasible distributions are Gaussian mean-fields and the projective integral updates yield quasi-Newton variational Bayes (QNVB). Other bases and updates are also possible. Since these updates require high-dimensional integration, this work begins by proposing an efficient quasirandom sequence of quadratures for mean-field distributions. Each iterate of the sequence contains two evaluation points that combine to correctly integrate all univariate quadratic functions and, if the mean-field factors are symmetric, all univariate cubics. More importantly, averaging results over short subsequences achieves periodic exactness on a much larger space of multivariate polynomials of quadratic total degree. The corresponding variational updates require four loss evaluations with standard (not second-order) backpropagation to eliminate error terms from over half of all multivariate quadratic basis functions. Furthermore, this integration technique is motivated by first proposing stochastic blocked mean-field quadratures, which may be useful in other contexts. A PyTorch implementation of QNVB allows for better control over model uncertainty during training than competing methods. Experiments demonstrate superior generalizability for multiple learning problems and architectures.

Gaussian mean-field↗
NASA NTRS1995

Earth horizon modeling and application to static Earth sensors on TRMM spacecraft

Data from Earth sensor assemblies (ESA's) often are used in the attitude determination (AD) for both spinning and Earth-pointing spacecraft. The ESA's on previous such spacecraft for which the ground-based AD operation was performed by the Flight Dynamics Division (FDD) used the Earth scanning method. AD on such spacecraft requires a model of the shape of the Earth disk as seen from the spacecraft. AD accuracy requirements often are too severe to permit Earth oblateness to be ignored when modeling disk shape. Section 2 of this paper reexamines and extends the methods for Earth disk shape modeling employed in AD work at FDD for the past decade. A new formulation, based on a more convenient Earth flatness parameter, is introduced, and the geometric concepts are examined in detail. It is shown that the Earth disk can be approximated as an ellipse in AD computations. Algorithms for introducing Earth oblateness into the AD process for spacecraft carrying scanning ESA's have been developed at FDD and implemented into the support systems. The Tropical Rainfall Measurement Mission (TRMM) will be the first spacecraft with AD operation performed at FDD that uses a different type of ESA - namely, a static one - containing four fixed detectors D(sub i) (i = 1 to 4). Section 3 of this paper considers the effect of Earth oblateness on AD accuracy for TRMM. This effect ideally will not induce AD errors on TRMM when data from all four D(sub i) are present. When data from only two or three D(sub i) are available, however, a spherical Earth approximation can introduce errors of 0.05 to 0.30 deg on TRMM. These oblateness-induced errors are eliminated by a new algorithm that uses the results of Section 2 to model the Earth disk as an ellipse.

Keat, J.↗
NASA NTRS2003

Analysis of Parallel Burn, No-Crossfeed TSTO RLV Architectures and Comparison to Parallel Burn with Crossfeed and Series Burn Architectures

Three dominant Two Stage To Orbit (TSTO) class architectures were studied: Series Burn (SB), Parallel Bum with crossfeed (PBw/cf), and Parallel Burn, no-crossfeed (PBncf). The study goal was to determine what factors uniquely affect PBncf architectures, how each of these factors interact, and to determine from a performance perspective whether a PBncf vehicle could be competitive with a PBw/cf or a SB vehicle using equivalent technology and assumptions. In all cases, performance was evaluated on a relative basis for a fixed payload and mission by comparing gross and dry vehicle masses of a closed vehicle. Propellant combinations studied were LOX: LH2 propelled booster and orbiter (HH) and LOX: Kerosene booster with LOX: LH2 orbiter (KH). The study observations were: 1) A PBncf orbiter should be throttled as deeply as possible after launch until the staging point. 2) A PBncf TSTO architecture is feasible for systems that stage at mach 7. 2a) HH architectures can achieve a mass growth relative to PBw/cf of <20%. 2b) KH architectures can achieve a mass growth relative to Series Burn of <20%. 3) Center of gravity (CG) control will be a major issue for a PBncf vehicle, due to the low orbiter specific thrust to weight ratio and to the position of the orbiter required to align the nozzle heights at liftoff. 4) Thrust to weight ratios of 1.3 at liftoff and between 1.0 and 0.9 when staging at mach 7 appear to be close to ideal for PBncf vehicles. 5) Performance for HH vehicles was better when staged at mach 7 instead of mach 5. The study suggests possible methods to maximize performance of PBncf vehicle architectures in order to meet mission design requirements.

Smith, Garrett↗
DOE OSTI2022

Fast response High Altitude Laser Optical Sensor (HALOS) for atmospheric water and temperature measurements

Advanced instrumentation and sensing methods are the key factor in the determination of many climate science research problems. In this Phase I STTR a laser-based diagnostic system for atmospheric temperature and water concentrations measurements was developed, this system, HALOS – High Altitude Laser Optical Sensor, employs a mid-infrared (MIR) quantum cascade laser (QCL) of the distributed feedback variety (DFB) powered by a 100 kHz rep rate ramp modulated current profile which enables spectral profiling. Measurements made during Phase I showed that HALOS can surpass the available hygrometers refresh rates while offering superior accuracy (<4%) from the low Frost Point (Tfrost) of -110°C to dew points (Tdew) at 35°C while being able to encompass sub ppm concentration at altitude and supersaturated conditions at ground level. Readings from HALOS include temperature sensitivity on the order of 0.2°C and absolute humidity sensitivity (AH) on the order of 0.010 μmol/m 3 (0.18 mg/m 3 ) at a data output rate exceeding 1 kHz. HALOS can be easily integrable in existing Department of Energy (DOE) or National Oceanic and Atmospheric Administration (NOAA) conventional or remote operated aircraft having either a fixed or rotary wing.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

Robust crossfeed design for hovering rotorcraft

Control law design for rotorcraft fly-by-wire systems normally attempts to decouple angular responses using fixed-gain crossfeeds. This approach can lead to poor decoupling over the frequency range of pilot inputs and increase the load on the feedback loops. In order to improve the decoupling performance, dynamic crossfeeds may be adopted. Moreover, because of the large changes that occur in rotorcraft dynamics due to small changes about the nominal design condition, especially for near-hovering flight, the crossfeed design must be 'robust.' A new low-order matching method is presented here to design robost crossfeed compensators for multi-input, multi-output (MIMO) systems. The technique identifies degrees-of-freedom that can be decoupled using crossfeeds, given an anticipated set of parameter variations for the range of flight conditions of concern. Cross-coupling is then reduced for degrees-of-freedom that can use crossfeed compensation by minimizing off-axis response magnitude average and variance. Results are presented for the analysis of pitch, roll, yaw, and heave coupling of the UH-60 Black Hawk helicopter in near-hovering flight. Robust crossfeeds are designed that show significant improvement in decoupling performance and robustness over nominal, single design point, compensators. The design method and results are presented in an easily-used graphical format that lends significant physical insight to the design procedure. This plant pre-compensation technique is an appropriate preliminary step to the design of robust feedback control laws for rotorcraft.

Catapang, David R.↗
NASA NTRS1993

Robust Crossfeed Design for Hovering Rotorcraft

Control law design for rotorcraft fly-by-wire systems normally attempts to decouple angular responses using fixed-gain crossfeeds. This approach can lead to poor decoupling over the frequency range of pilot inputs and increase the load on the feedback loops. In order to improve the decoupling performance, dynamic crossfeeds may be adopted. Moreover, because of the large changes that occur in rotorcraft dynamics due to small changes about the nominal design condition, especially for near-hovering flight, the crossfeed design must be 'robust'. A new low-order matching method is presented here to design robust crossfeed compensators for multi-input, multi-output (MIMO) systems. The technique identifies degrees-of-freedom that can be decoupled using crossfeeds, given an anticipated set of parameter variations for the range of flight conditions of concern. Cross-coupling is then reduced for degrees-of-freedom that can use crossfeed compensation by minimizing off-axis response magnitude average and variance. Results are presented for the analysis of pitch, roll, yaw and heave coupling of the UH-60 Black Hawk helicopter in near-hovering flight. Robust crossfeeds are designed that show significant improvement in decoupling performance and robustness over nominal, single design point, compensators. The design method and results are presented in an easily used graphical format that lends significant physical insight to the design procedure. This plant pre-compensation technique is an appropriate preliminary step to the design of robust feedback control laws for rotorcraft.

Catapang, David R.↗
NASA NTRS2009

Long Term Measurement of the Vapor Pressure of Gold in the Au-C System

Incorporating the {Au(s,l) + graphite} reference in component activity measurements made with the multiple effusion-cell vapor source mass spectrometry (multicell KEMS) technique provides a fixed temperature defining ITS-90 (T(sub mp)(Au) = 1337.33K) and a systematic method to check accuracy. Over a 2 year period delta H sub(298)Au was determined by the 2nd and 3rd law methods in 25 separate experiments and were in the ranges 362.2 plus or minus 3.3 kJmol(sup -1) and 367.8 plus or minus 1.1 kJmol(sup -1), respectively. This 5 kJmol-1 discrepancy is transferred directly to the measured activities. This is unacceptable and the source of this discrepancy needs to be understood and corrected. Accepting the 2nd law value increases p(Au) by about 50 percent, brings the 2nd and 3rd law values into agreement and removes the T dependence in the 3rd law values. While compelling, there is no way to independently determine instrument sensitivities, S(sub Au), with T in a single experiment with KEMS. This lack of capability is stopping a deeper understanding of this problem. In addition, the Au-C phase diagram suggests a eutectic invariant reaction: L-Au(4.7at%C) = FCC-Au(0.08at%C) + C(graphite) at T(sub e) approximately 1323K. This high C concentration in Au(l) must reduce p(Au) in equilibrium with {Au(s,l) + graphite} and raises some critical questions about the Gibbs free energy functions of Au(s,l) and the Au fixed point (T(sub mp)(Au) = 1337.33K) which is always measured in graphite.

Copland, Evan H.↗
NASA NTRS2010

Handheld White Light Interferometer for Measuring Defect Depth in Windows

Accurate quantification of defects (scratches and impacts) is vital to the certification of flight hardware and other critical components. The amount of damage to a particular component contributes to the performance, reliability, and safety of a system, which ultimately affects the success or failure of a mission or test. The launch-commit criteria on a Space Shuttle Orbiter window are governed by the depth of the defects that are identified by a visual inspection. This measurement of a defect is not easy to obtain given the environment, size of the defect, and location of the window(s). The determination of depth has typically been performed by taking a mold impression and measuring the impression with an optical profiling instrument. Another method of obtaining an estimate of the depth is by using a refocus microscope. To use a refocus microscope, the surface of the glass and bottom of the defect are, in turn, brought into focus by the operator. The amount of movement between the two points corresponds to the depth of the defect. The refocus microscope requires a skilled operator and has been proven to be unreliable when used on Orbiter windows. White light interferometry was chosen as a candidate to replace the refocus microscope. The White Light Interferometer (WLI) was developed to replace the refocus microscope as the instrument used for measuring the depth of defects in Orbiter windows. The WLI consists of a broadband illumination source, interferometer, detector, motion control, displacement sensor, mechanical housing, and support electronics. The illumination source for the WLI is typically a visible light emitting diode (LED) or a near-infrared superluminescent diode (SLD) with power levels of less than a milliwatt. The interferometer is a Michelson configuration consisting of a 1-in. (2.5-cm) cube beam splitter, a 0.5-in. (1.3-cm) optical window as a movable leg (used to closely match the return intensity of the fixed leg from the window), and a mirrored prism to fold the optics into the mechanical housing. The detector may be one of many C-mount CCD (charge-coupled device) cameras. Motion is provided by a commercial nanostepping motor with a serial interface. The displacement sensor is a custom device specifically designed for this application. The mechanical housing and support electronics were designed to integrate the various components into an instrument that could be physically handled by a technician and easily transported.

Youngquist, Robert↗
DOE OSTI2021

Griffin Software Development Plan

Griffin is a MOOSE-based reactor physics application for advanced reactor multiphysics modeling and simulation. The application is developed in a consistent multiphysics environment with strong software quality assurance. Griffin inherited most of the capabilities of MAMMOTH/Rattlesnake and is adopting the capabilities from PROTEUS that are needed in the code. The toolset includes a variety of deterministic radiation transport solvers for fixed source, k-eigenvalue, ad-joint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code contains the cross-section preparation capabilities applicable to fast and thermal reactors, including TRISO-fueled reactors. Core management capabilities include core performance, fuel depletion and shuffling, equilibrium core calculation, pebble-bed reactor run-in and equilibrium core, molten-salt reactor delayed neutron precursor drift, and control rod and drum movement with cusping correction. This software development plan presents the current and future capabilities and features in Griffin for the design and analysis of non-light-water reactor systems in steady-state and transient conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2007

Analysis of Crossflow Transition Flight Experiment aboard the Pegasus Launch Vehicle

The Pegasus wing-glove flight experiment was designed to provide crossflow transition data at high Mach numbers, specifically to help validate stability based predictions for transition onset in a flight environment. This paper provides an analysis of the flight experiment, with emphasis on computational results for crossflow disturbances and the correlation of disturbance growth factors with in-flight transition locations via the e(sup N) method. Implications of the flight data for attachment line stability are also examined. Analysis of the thermocouple data reveals that transition (from turbulent to laminar flow) was first detected during the ascending flight of the rocket when the free stream Mach number exceeded about 4. Therefore, computations have been performed for flight Mach numbers of 4.13, 4.35, 4.56 and 4.99. Due to continually decreasing unit Reynolds number at higher altitudes, the entire wing-glove boundary layer became laminar at the highest flight Mach number computed above. In contrast, the boundary layer flow over the inboard tile region remained transitional up to and somewhat beyond the time of laminarization over the instrumented glove region. Linear stability predictions confirmed that the tile boundary layer is indeed more unstable to crossflow disturbances than the much colder stainless steel glove boundary layer. The transition locations based on thermocouple data from both the glove and the tile regions are found to correlate with stationary-crossflow N-factors within the range of 7 to 12.4 and with traveling mode N-factors between 7.6 and 14.1. Data from the thermocouples and hot film sensors indicates that transition from turbulent to laminar flow (i.e., laminarization) at a fixed point over the glove is generally completed within a flight time interval of 3 seconds. However, the times at which transition begins and ends as inferred from the hot film sensors are found to differ by about 2 seconds from the corresponding estimates based on the thermocouple data.

Malik, Mujeeb R.↗
NASA NTRS2017

Transformational Solar Array Option I Final Report

This report summarizes the work performed under NASA contract NNC16CA19C from May 2, 2017 through April 2, 2018. This work is directed toward meeting the goals of the associated NASA NRA and, of course, the requirements of the contract. In brief, the goals are: (1) Over 47% beginning of life cell efficiency at 5 AU and -125 °C (2) Over 32% end of life efficiency at the blanket level at 50 W m-2, -125 °C and 4E15 1 MeV e cm-2 (3) Over 8 W kg-1 at EOL for the entire array including structure, deployment, and pointing mechanisms using beginning of life performance. (4) A stowed packaging density of greater than 66 kW m-3 (5) An ability to survive launch and numerous deploy retract cycles without degradation (6) An output higher than 300 V (7) An ability to operate in a plasma generated by xenon thrusters, typically 1E8 cm-3 ions with an average energy of 2 eV (8) A design compatible with electrostatic and magnetic cleanliness (9) Record breaking inverted metamorphic (IMM) 6 junction solar cells (10) IMM solar cells that have no anomalous flat spot behavior at low irradiance and low temperature (11) A mock-up production line for the low-cost manufacture of spacecraft blanket arrays. The Option I phase of the project continued efforts, started in the base-phase, to eliminate or reduce to very low levels the flat spots that reduce power to an unacceptable value in a significant percentage of cells and to reduce outgassing contamination of the concentrators to acceptable levels. Option I adds tasks to increase the efficiency of IMM cells from those produced in the Base Phase, to eliminate delamination of the coatings that were present in previous versions of the concentrator mirrors, to evaluate pressure sensitive adhesive as a method of fixing solar cell assemblies to blankets, to design a magnetically clean brake for ROSA, to test the robustness of a sample blanket in deploy and retract, to test for the adequate performance of a blanket in vibration and thermal environments, and to define the capital equipment needed to optimize production of the Transformational Array. 5 Work for this Final Report showed that the greatest likely improvement in the solar cells would be by emphasizing the effort for the IMM4 solar cells and stopping work on other IMM cells. For this phase, the solar cell work was primarily on the IMM4 cells with little work on IMM5 and none on IMM6 cells.

Gaddy, Edward↗
NASA NTRS1950

Theoretical Investigation of a Proportional-Plus-Flicker Automatic Pilot

The proportional-plus-flicker automatic pilot operates by a nonlinear principle whereby a fast-acting flicker servomotor response is combined with a low-speed proportional servomotor response for the purpose of obtaining supersonic stability and control. Essentially, the autopilot maintains a zero reference about which the output is proportional to the input. However, a flicker response overrides this proportional response at a fixed angle of gimbal displacement on either side of the zero gyroscope reference. Therefore, in contrast to other high speed control systems, the design requirements are simplified because the two components of the proportional-flicker control system are easy to build separately and they can be combined in a relatively simple manner. By application of the proportional-flicker principle, satisfactory stability can be obtained by the proper adjustment of the variable factors in the autopilot mechanism; namely, the proportional gain, the amplitude of flicker control deflection, the autopilot time-lag factor (the time-lag between flicker and proportional operation), and the point in the range that the autopilot switches from a flicker to a proportional system. There is a possibility that these factors can be adjusted so that a more rapid response time (the time to reach steady state) is obtained with the non-linear proportional-flicker autopilot than with a purely linear proportional autopilot. For the main part of this analysis, the proportional part of the system is approximated by a zero-phase-lag proportional autopilot with the assumption that the control surface moves instantaneously at the point where the system switches from flicker to proportional. Good correlation is shown between the results obtained by this method and results obtained by using a close approximation of an actual autopilot transfer function for proportional autopilot operation.

Seaberg, Ernest C.↗
NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗
NASA NTRS2019

Using Science Operations Tools and Methods from NASA for Remote Presence Ocean Exploration on Earth: E/V Nautilus Cruise NA108

NASA Ames' Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade.In the last two years, we have participated in SUBSEA, a multi-institution collaborative project. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC). In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule.The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team's ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA's Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island's Graduate School of Oceanography (GSO). ISC is GSO's telepresence operations facility.

Lees, David S.↗
NASA NTRS2020

Conducting Efficient Remote Science and Planning Operations for Ocean Exploration Using Exploration Ground Data Systems (xGDS)

NASA Ames’ Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing information in time and space, including video and still images, scientific instrument data, and science and operations notes in geographic and temporal context. We have deployed xGDS at multiple NASA field analog missions over the past decade. In the last two years, we have participated in SUBSEA, a multi-institution collaborative project*. SUBSEA used the research ship E/V Nautilus along with its two remotely operated vehicles (ROVs), Hercules and Argus, to explore deep ocean volcanic vents as an analog for ocean worlds (e.g. Enceladus). This work allowed us to compare the existing oceanographic operations methods and technologies used for ocean exploration with corresponding tools and approaches developed and used at NASA. In the first year of SUBSEA we observed existing remote science operations from the Inner Space Center (ISC)**. In the second year, we deployed xGDS at ISC to complement existing capabilities with xGDS tools designed to support remote Nautilus science operations from the ISC. During operations, video, ROV position and instrument telemetry were streamed from the ship to the ISC. As the science team watched dive operations, they could annotate the data with observations that were relevant to their work domain. Later, the team members could review the data at their own pace to collaboratively develop a dive plan for the next day, which had to be delivered on a fixed daily schedule. The opportunity to compare operations under different conditions enabled us to make several key observations about conducting remote science and planning operations efficiently: (i) Reviewing data collaboratively and interactively with temporal and spatial context was critical for the remote science team’s ability to plan dive operations on the Nautilus. (ii) Science team members were actively engaged with the remote dive operations because they could interact with the collected data and visualize it as they desired. (iii) Being able to replay past events at accelerated speeds, and jump to points in time and spaced based on search results, provided efficient access to critical points of interest in a massive volume of data, so the remote science team could deliver plans on time. * SUBSEA (Systematic Underwater Biogeochemical Science and Exploration Analog) is a multi-institution collaboration supported by NASA, NOAA’s Office of Exploration Research (OER), the Ocean Exploration Trust (OET) and the University of Rhode Island’s Graduate School of Oceanography (GSO).

Cohen, Tamar E.↗
NASA NTRS2004

Method Developed for Noninterference Measurement of Blade Damping

Although noninterference optical instrumentation has been previously used to monitor and measure rotor blade vibrations, it has not been used at the NASA Glenn Research Center for the determination of damping. This article describes such a measurement in Glenn's Spin Rig facility. The optical system was chosen because installation of the slip rig for this particular configuration was not feasible, ruling out strain gauge instrumentation. The shaft in this facility was suspended with two radial magnetic bearings, and the excitation was supplied to the bearings by a signal generator. For the first mode, the direction of excitation force was always perpendicular to the blade as described by Morrison. The preceding graph shows blade displacements, as measured by an optical probe during a frequency sweep designed to locate the first mode resonance. The resonance determines the frequency at which the shaft is excited. Because the sampling rate was fixed to only once per revolution with these transducers, and the first mode frequency was much larger than the rotational frequency, the data in this figure were undersampled with an apparent frequency that was only 1 Hz at the beginning of the sweep, about 14 Hz at the maximum amplitude, and about 20 Hz at the end of the sweep. The true excitation frequency varied during the sweep from 320 to 340 Hz, and the resonance occurred at 334 Hz. Damping is determined by applying a frequency burst to the shaft. Upon cessation of the burst, the blade undergoes free decay. The apparent damping is then obtained by a least squares fit of the theoretical cosine decay function through the experimental points. The following graph illustrates this procedure. Because of frequency aliasing, the apparent measured damping must be divided by the ratio of the true resonant frequency to the apparent measured frequency to obtain the true damping. Note that the contribution of the shaft vibration to the blade vibration could be neglected because it was shown that it is an order of magnitude smaller and that its decay is an order of magnitude faster.

Kurkov, Anatole P.↗
DOE OSTI2023

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. As a result, the adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Soil Water Retention and Hydraulic Conductivity Data and Model at Snodgrass Mountain in East River Watershed, Colorado 2020-2025

This data package includes soil water retention and hydraulic conductivity data and model fitting results from measurements of ex-situ soil samples and in-situ soil sensors at Snodgrass Mountain. Soil water retention curves (SWRC) characterize soil water content as a function of soil water potential. SWRC depends on soil texture and pore structure and can be used to describe the constraints on biogeochemical processes in terms of soil water availability. In this data package, the sample identification follows the format SG-X-Y, where SG refers to Snodgrass Mountain, X is the location identifier, and Y is the depth identifier at the same X (shallow Y=1). Specifically, SG-EHS is associated with ecohydrology sites under the East-Taylor Watershed Community Observatory Sites directory, and SG-ERTn (upslope n=1) are points along the Snodgrass electrical resistivity tomography transect not associated with the existing site names in the directory. The sample and location information can be found in metadata.csv. Sampling and Measurements Each sample falls into one of the three sampling methods – (1) intact cores, (2) repacked samples, or (3) soil sensors – and one of the two measurement methods – (a) laboratory or (b) in-situ. Both intact cores and repacked samples were measured using the laboratory methods, which include measurements of soil water potential (HYPROP & WP4C, METER), saturated (KSAT, METER) and unsaturated hydraulic conductivity (HYPROP). The in-situ method uses a pair of co-located soil sensors to measure volumetric water content (TEROS12, METER) and soil water potential (TEROS21, METER), and the hydraulic conductivity was not measured. In comparison, the laboratory methods progress from full saturation to dry conditions, and the in-situ method includes both dry-to-wet and wet-to-dry cycles. The sampling and measurement methods for each sample can be found in metadata.csv, and more information about the measurements is detailed in the Methods section below. Models Retention and hydraulic conductivity data were fitted with four van-Genuchten-type models (specified by “model_name” column in the files): (1) traditional constrained van Genuchten model (“vG_constrained”), (2) traditional unconstrained van Genuchten model (“vG_unconstrained”), (3) PDI-variant of the constrained van Genuchten model (“vG_constrained_PDI”), and (4) PDI-variant of the unconstrained van Genuchten model (“vG_unconstrained_PDI”). The difference between the constrained (1: n) and the unconstrained (2: n, m) van Genuchten models is the number of pore-size distribution parameters in the model equations, giving the unconstrained model more degrees of freedom when fitting the data. Between the traditional and the PDI-variant models, model fitting differs the most at the dry end of the measurements. The traditional models allow infinite suction at the residual water content (water content does not drop below residual water content), and the PDI-variant models enforce a soil water potential value of pF=6.8 (~ -630 MPa) at oven-dryness (water content reaches 0). The inclusion of the van-Genuchten-type models is due to their common application. If other retention models are required, users can access the data in data.csv for further data fitting. More information about the models can be found in the Methods section below. Fitting Tasks The model fitting can be categorized into three levels of tasks (specified by “fitting_task” column in the files). Level 1 (“fit_retention”) only includes retention data fitting (the only level available for the in-situ method). Level 2 (“fit_retention_conductivity”) includes both retention and hydraulic conductivity data fitting, and the saturated hydraulic conductivity (Ks, a parameter of the hydraulic conductivity functions) is fixed by the measurements from KSAT. Level 3 (“fit_retention_conductivity_Ks”) also includes both retention and hydraulic conductivity data fitting, but Ks is a fitted parameter without the constraints from KSAT measurements. Among the same retention models (e.g. vG_constrained models of the same sample), level 1 should produce the best retention data fitting. Level 2 should have the highest misfit of the retention and hydraulic conductivity data, because the retention and hydraulic conductivity functions share common model parameters, and the unsaturated hydraulic conductivity (HYPROP) data fitting is subject to Ks measured independently by KSAT. Level 3 should have mid-level misfits of the retention and hydraulic conductivity data. While level 3 fits the hydraulic conductivity data better than level 2, the fitted Ks value might be unreasonable due to the lack of constraints at the wet end of the measurements. General recommendation when using this data package: (1) Choice of sampling methods: Intact cores and in-situ soil sensors could be prioritized because these sampling methods are less destructive. While the repacked samples were packed to the target bulk density (estimated post-sampling, when sample volume was known), these samples had altered pore structures. Nevertheless, intact cores might suffer from sample gaps that would lead to overestimation of Ks (sample gaps can be inferred from the “soil_sample_volume” column in metadata.csv when the value is < 249). In-situ method also has higher uncertainty in characterizing the wet end of the SWRC because of sensor limitations and the difficulty in reaching full saturation under natural conditions. (2) Choice of fitting tasks: When only retention data is needed, level 1 (“fit_retention”) should be prioritized. When both retention and hydraulic conductivity data are needed, level 2 (“fit_retention_conductivity”) could be prioritized. (3) Choice of models: This could depend on what the downstream models call for. If no specific model is required, model misfit could be used as a ranking criterion. Model misfit values in terms of RMSE can be found in model_parameters.csv. The following files are included in this data package: (1) metadata.csv – This file includes the general information of each sample, including location (description, geocoordinates, elevation), sampling and measurements details (method, depth, time or period, volume, instruments), and soil physical properties (bulk density, saturated hydraulic conductivity, only applicable to physical soil samples). (2) data.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity data of each sample. Column “instrument” specifies the instrument (HYPROP, WP4C, or TEROS) used to perform the measurements. (3) model_fit.csv – This file includes soil water potential, volumetric water content, and unsaturated hydraulic conductivity fitted from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the variable does not apply to that fitting task. (4) model_parameters.csv – This file includes the fitted model parameters, model misfits, and conventional water content thresholds (field capacity and wilting point) from the four models and three fitting tasks. Column “model_name” specifies the retention model used, and “fitting_task” specifies the level of data fitting. Missing values indicate that the parameter does not apply to that model and/or that fitting task. (5) data_Ks.csv – This file includes the saturated hydraulic conductivity measurements from KSAT. (6) /figure/*.png – This folder includes three quick visualizations of the data, retention model fitting results and misfits, and hydraulic conductivity model fitting results, misfits, and parameters. The model fitting results are separated by samples and fitting tasks and colored by models. Zoom-in required. (7) /hyprop/*.bdhx – This folder includes proprietary hyprop files that require the free Labros SoilView-Analysis (METER) to open. Users can explore data fitting using other retention models (i.e. Brooks-Corey, Fredlund-Xing, Kosugi, bimodal models). Be aware that Ks value is pre-entered under “Fitting tab, Conductivity functions parameters” for level 2 fitting. If the value is lost, please refer to metadata.csv under “Ks” column. (8) Six file-level metadata that summarize file, header, column, and variable information of all files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

EARTH SCIENCE > LAND SURFACE > SOILS↗