Results for “TIME” — Page 91

NASA NTRSDate not supplied

STAR-X: Survey and Time-domain Astrophysical Research eXplorer

STAR-X is a MIDEX mission proposed to NASA in December 2021. Comprising an X-ray telescope (XRT) provided by GSFC and MIT, a UV telescope (UVT) provided by the University of Colorado, and a spacecraft (SC) provided by Ball Aerospace, STAR-X is designed to conduct time-domain survey and to respond rapidly to transient events discovered by other observatories such as LIGO, Rubin LSST, Roman WFIRST, and SKA. The key features of the XRT are its excellent PSF (2.5 arc-seconds half-power diameter), large effective area (1,800 cm2 at 1 keV), and large field of view (1 deg2), making it more than an order of magnitude more capable and more sensitive than Chandra and Swift/XRT to conduct survey and to find and study transient sources. The UVT has a 30-cm aperture with 5 filters covering the 160nm to 340nm band, providing simultaneous spectral coverage with the XRT and enabling reverberation mapping of accretion disk geometries around black holes. The SC is highly autonomous and is capable of fast slewing, enabling efficient raster scans and time-domain surveys. In particular, in combination with a state-of-the-art mission operations center at the University of Colorado, the SC can respond to targets of opportunity within 2 hours 90% of the time. With its nearly equatorial low-earth orbit, STAR-X’s telescopes will have low particle background, enabling them to have unprecedented sensitivity for measuring faint diffuse emissions from clusters of galaxies. STAR-X is a timely response to Astro2020’s recommendation for a space-based, sustaining time-domain and multi-messenger program.

William W. Zhang↗

NASA NTRSDate not supplied

The Interplay of Humor and Team Cohesion Over Time

Team bonding and interpersonal relationships are increasingly important as we continue understanding the effects of the pandemic on team dynamics. Specifically, team cohesion, defined as having a sense of belonging and commitment towards the group, has been found to be important for key team outcomes such as performance, relationships, and productivity (Beal, Cohen, Burke, & McLendon, 2003; Bell & Marentette, 2011). Research has stressed the benefits of positive humor on cohesion, with humor creating positive emotions and decreasing social distance (e.g., Robert & Wilbanks, 2012; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). However, research around the impact of negative humor on cohesion is scarce. This study aims to examine the impact of both positive and negative humor on cohesion for teams over time. The longitudinal design allows us to examine the interplay of humor and cohesion throughout a team’s lifecycle to guide relationship development and management (Romero & Pescosolido, 2008). Humor is defined as remarks recognized as amusing or funny (Martin, 2007). Positive humor can be categorized as good-natured aimed to create mutual agreement, while negative humor is defined as aggressive, hostile, and unreciprocated (Coan & Gottman, 2007). Research has concluded the many benefits of positive humor, showing it can increase open communication, social support, and create a pleasant environment (Martineau, 1972; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). The Humor Wheel model suggests that positive humor is contagious due to the positive affect produced and mimicked (Lehmann-Willenbrock & Allen 2014). Research has suggested positive humor would increase over time based on the norm of reciprocity; those treated positively are likely to respond similarly (Gouldner, 1960). Multiple parties’ involvement in humor exchanges highlights the interpersonal nature of humor. Thus, long-term consequences on relationships should be considered, such as cohesion (Robert & Wilbanks, 2012). It would be instrumental to understand how humor interactions could weaken or strengthen group cohesion through relational and emotional bonds over time (Martineau, 1972; Romero & Pescosolido, 2008). Positive humor should fundamentally increase team cohesion (Mesmer-Magnus, Glew, & Viswesvaran, 2012; Walter & Baruch, 2008) by promoting group harmony, team member attractiveness, and ingroup identity (Holmes, 2006). Thus, it is expected there is a positive relationship between positive humor and team cohesion over time (hypothesis 1). Humor is not always beneficial, especially when it elicits negative emotions (Dikkers, Doosje, & de Lange, 2011). Negative humor can be used to humiliate, tease, or insult (Coan & Gottman, 2007; Collinson, 1988; Romero & Cruthirds, 2006). It tends to cause distress and damage relationships, and in turn, decrease organizational performance and production (Avolio, Howell, & Sosik, 1999; Janes & Olson, 2000; Yerkes, 2001). Research concludes negative humor may create subgroups and decrease cooperation (Romero & Arendt, 2011). Research has also indicated positive implications for negative humor under specific circumstances. Negative humor could help to solidify ingroup identity and strengthen bonds when directed outside the team (Terrion & Ashforth, 2002; Thomae & Pina, 2015). For example, sport teams may use negative humor towards their opponent before a game, which emphasizes their differences and lowers the status and power of their opponent, ultimately promoting solidarity and cohesion inside the group. For this study, it is anticipated that negative humor toward other teammates will decrease team cohesion over time due to the damage to relationships and negative affect produced (Duncan, Smeltzer, & Leap, 1990; Maples, Dupey, Torres‐Rivera, Phan, Vereen, & Garrett, 2001). We also predicted that negative humor focused on something or someone outside the team will increase cohesion by enhancing ingroup ties and creating a common identity (Thomae & Pina, 2015). Thus, the relationship between negative humor and cohesion will be moderated by the target of the humor. The relationship will be negative when the target is within the team and positive when the target is outside (hypothesis 2).

Melissa M. Vazquez↗

NASA NTRSDate not supplied

Why Near Real-time/Low Latency is Important to Monitor the Changing World

An essential factor for remote sensing data products in impacting decision making is latency, or the time between earth observation and data are available to users. In many applications areas, latency plays an important or even decisive role where low latency earth observations help people to make timely, data-based decisions. Within the open and free NASA resources, NASA’s Land, Atmosphere Near real-time Capability for Earth Observing System (EOS) (LANCE) supports users interested in monitoring a wide variety of natural and man-made phenomena using near real-time (NRT) data products that are made available much quicker than routine processing allows. The combination of all available LANCE satellite products provides global coverage at multiple times per day, which makes it possible to meet user needs in various areas of applications including water resources, agriculture, air quality, wildland fire and many other disasters monitoring and management. As one of the prime users of LANCE, NASA’s Earth Science Applied Sciences Program promotes the use of LANCE data products to demonstrate applications in decision making and facilitates the end-user feedback to the science team to improve data products. One of the most critical applications that we expect data to be processed as close to the user as possible is wildland file response and management. User feedback indicates that LANCE NRT fire data products within 3 hours latency would meet the needs of the wildland fire community. Other examples of earth science application areas for which low latency is particularly important include detecting volcanic eruptions, early warning of disasters, tracking extreme weather events, and monitoring air quality.

Tian Yao↗

NASA NTRSDate not supplied

A Real-Time Optical Ground Receiver for Photon Starved Environments

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a photon-counting optical ground receiver for pulse-position modulated signals. The real-time receiver system includes a fiber interconnect, superconducting nanowire single-photon detectors (SNSPDs), and a real-time field programmable gate array (FPGA) based receiver. The fiber interconnect and SNSPDs are implemented with two different configurations. In the first, a 7-channel few-mode fiber photonic lantern couples the light from the telescope to 7 single-pixel few-mode fiber coupled SNSPDs. In the second configuration, a few-mode fiber couples light to a 16-pixel monolithic SNSPD array. The real-time FPGA-based receiver performs combining of up to 16 SNSPD channels, symbol timing recovery, demodulation, and decoding. The system is scalable with data rates ranging from 20 Mbps to 267 Mbps. It is compliant with the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Standard. This standard will be used in NASA deep space and other low photon flux missions, such as in the Orion Artemis-2 Optical Communications System (O2O) demonstration, planned for the first crewed flight of Orion. This paper describes the scalable real-time optical receiver system and presents characterization test results.

optical communications↗

NASA NTRSDate not supplied

A Real-Time Optical Ground Receiver for Photon Starved Environments

The National Aeronautics and Space Administration (NASA) Glenn Research Center (GRC) has developed a photon-counting optical ground receiver for pulse-position modulated signals. The real-time receiver system includes a fiber interconnect, superconducting nanowire single-photon detectors (SNSPDs), and a real-time field programmable gate array (FPGA) based receiver. The fiber interconnect and SNSPDs are implemented with two different configurations. In the first, a 7-channel few-mode fiber photonic lantern couples the light from the telescope to 7 single-pixel few-mode fiber coupled SNSPDs. In the second configuration, a few-mode fiber couples light to a 16-pixel monolithic SNSPD array. The real-time FPGA-based receiver performs combining of up to 16 SNSPD channels, symbol timing recovery, demodulation, and decoding. The system is scalable with data rates ranging from 20 Mbps to 267 Mbps. It is compliant with the Consultative Committee for Space Data Systems (CCSDS) Optical Communications Coding and Synchronization Standard. This standard will be used in NASA deep space and other low photon flux missions, such as in the Orion Artemis-2 Optical Communications System (O2O) demonstration, planned for the first crewed flight of Orion. This paper describes the scalable real-time optical receiver system and presents characterization test results.

optical communications↗

NASA NTRSDate not supplied

Ultra-Low Noise Laser and Optical Frequency Comb-Based Timing System for the Event Horizon Explorer (EHE) Mission

Very Long Baseline Interferometry (VLBI) requires a highly stable time reference and synchronization system to maintain coherence between observations recorded independently at multiple stations – for example, the array of ground-based radio telescopes that comprise the Event Horizon Telescope (EHT). Instability in the time reference or inaccuracy in the synchronization impacts phase conference and reduces the performance of the VLBI system. For space-based telescopes like the Event Horizon Explorer (EHE) mission, the precision timing system must meet the size, weight, and power (SWaP) constraints of the spacecraft platform and be capable of operating in the space environment. The EHE mission is a proposed extension to the EHT that can improve the angular and time resolution of observations by enabling longer interferometric baselines than are possible on the Earth and sampling a wide range of Fourier spatial frequencies throughout the orbital motion. In this effort, we demonstrate the performance of one option for the EHE mission: the use of a space-qualified, ultra-low noise laser developed as part of the Laser Interferometer Space Antenna (LISA) mission as the timing reference, and an optical frequency comb to transfer the stability of this laser to the microwave regime for instrumentation use. We describe the implementation of the microwave down-conversion, in which the LISA cavity-stabilized laser is locked to a self-referenced optical frequency comb to divide the optical frequency down to 100 MHz. The phase noise of the 100 MHz signal is measured and validated using a phase noise analyzer that is referenced to a separate laboratory ultra-stable laser system. We present the results of this experiment, which demonstrates that the performance of this system meets the EHE requirement: a relative frequency error of 1e-14 from 1 to 30 seconds.

Hannah Tomio↗

NASA NTRSDate not supplied

Identifying Large Transients within ARTEMIS Solar Wind Data for Nightside Time Domain Electromagnetic Sounding

Mysteries regarding the Moon’s internal composition persist. By studying the induced magnetic fields produced by the Moon in response to changes in the magnetic field of the surrounding solar wind, inferences can be made regarding the associated eddy currents and thus regarding the electrical conductivities of the lunar regions hosting these currents. In this manner, a greater understanding of the Moon’s inner geophysical properties can be garnered. This investigative strategy, known as electromagnetic sounding, can be employed using magnetic field data from the ARTEMIS satellites. In particular, data taken from time intervals in which one satellite is within the lunar wake and within 500 km of the surface while the other is relatively far from the surface immersed within the pristine solar wind. Per Faraday’s Law, the steeper the magnetic transient from the solar wind, the greater the current induced within the Moon, and per Ampère’s Law, the greater this induced current, the larger the magnetic field it produces. Larger signals generally feature higher signal-to-noise ratios (SNRs). Thus, larger transients tend to produce more valuable data in terms of sounding. The enhanced separation between source signal and reaction signal via the aforementioned positioning of the probes during time intervals of interest augments the SNRs as well. Here we discuss tools developed in Python (making use of the PySPEDAS package) that expedites the task of identifying large magnetic transients within these time frames of interest. These exceptional changes in magnetic field are then evaluated for use in electromagnetic sounding as described above. We have identified 51 major transient events (during times of interest) from 8/1/2011 to 7/31/2021. One key hurdle we overcame was identifying and navigating data gaps. These data gaps would often interfere with our time intervals of interest, necessitating an algorithm to avoid them.

Moons↗

NASA NTRSDate not supplied

Improving the Time Resolution of Hyper-Spectral Infrared Sounding Observations With Trajectory Enhancement

Low Earth orbit (LEO) hyper-spectral infrared (IR) sounders have significant potential for characterizing the complex evolution of thermodynamic environments favorable for convective initiation and ongoing convection. The snapshots at fixed local times are unable to provide the temporal resolution needed to resolve the rapidly evolving convective environment. A novel methodology using trajectory modeling coupled with satellite soundings was developed to create proximity soundings near NCEI Storm Events to investigate differences in severe weather environments (Kalmus et al., 2019, https://doi.org/10.1175/MWR-D-18-0055.1). This methodology was recently extended to entire satellite swaths forward in time up to six hours into the future and was evaluated during NOAA’s Hazardous Weather Testbed (HWT) in a quasi-operational setting (Kahn et al., 2023, https://doi.org/10.1175/WAF-D-22-0204.1). This methodology is based on parcel forward-trajectory calculations from the satellite observing time to recreate future soundings of temperature and moisture at regularly gridded intervals in space and time. Using coincident Multi-Radar Multi-Sensor (MRMS) rainfall estimates, we show that convective available potential energy (CAPE) is increased and convective inhibition (CIN) is decreased at times and locations where convection initiated. This methodology was evaluated with ERA5 data sampled to mimic the satellite observing swaths (Richardson et al., 2023, https://doi.org/10.5194/egusphere-2023-97). Approximately 60–90% of the temporal and spatial variability in temperature and humidity is explained by parcel advection using trajectory modeling. This method will be applied to the multi-decadal LEO satellite record and could be used to examine capabilities of future satellite missions such as the GeoXO mission.

Emily Berndt↗

NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗

NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

NASA NTRSDate not supplied

Analysis of Ice Mass Growth Over Time on the CRM65 Midspan Hybrid Model

The Aeronautics Research Mission Directorate at NASA is developing and applying tools to enable future technologies towards sustainable flight. Aircraft icing has been identified as a potential barrier to entry into service for innovative designs necessitating improvements to computational ice accretion tools. NASA is developing the Glenn Icing Computational Environment (GlennICE) to address deficiencies in the computational modeling capabilities of previously developed ice accretion solvers. To benchmark and improve the ability to model highly three-dimensional ice accretion, high quality validation data against experimental data is required. The CRM65 Midspan Hybrid geometry was previously tested at the NASA Icing Research Tunnel to generate experimental data for swept wing geometries typical for commercial transport aircraft. As a part of a 2018 icing test campaign, experimental data characterizing the relationship between ice accretion time and ice mass growth was obtained and can be leveraged for use in validation of computational tools. The desire for computational ice accretion solvers to predict ice shapes profiles accreted experimentally has often overshadowed the comparison to the mass and bulk volume of ice accreted. To address this deficiency, an analysis is presented in which GlennICE is applied to simulations of the CRM65 Midspan Hybrid model tested in the NASA Icing Research Tunnel. Results from the computational fluid dynamics simulations compared favorably to the experimental pressure coefficient data, thus validating the modeling setup. The experimental data showed excellent repeatability for the 15.0 minute accretion time. The comparisons between the experimental and computational ice mass over time showed good agreement up to 10.0 minutes after which the ice mass was underpredicted. The experimental ice mass was largely linear with some nonlinear data. The bulk volume of ice accreted experimentally compared well to GlennICE for the scanned ice shapes and mean combined cross section ice shapes, but was underpredicted for the maximum combined cross section ice shapes at longer accretion times. The experimental minimum combined cross section, mean combined cross section, and maximum combined cross section profiles when compared to GlennICE show good agreement for the mean combined cross section up to 15.0 minutes. The analyses show that with a single-shot method, GlennICE currently underpredicts the ice mass for longer accretion times, is not able to match the bulk volume of the maximum combined cross section due to dominating scallop features, and future work is required to generate a more generalized ice bulk density model.

Icing↗

NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗

DOE OSTI2020

Quantum self-authenticating timing system

An improved method of distributing timing information is provided. The method includes transmitting encrypted timing signals from two or more beacons at different locations. The encrypted timing signals are transmitted at regular intervals and are received by a receiver. The receiver then performs a logic operation on the encrypted timing signals and validates, based on the logic operation, the authenticity of the timing signals. The logic operation also results in a decrypted message from the beacons, which can contain additional information, for example, data to be sent back to the beacons to verify receipt.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Providing Levelized Cost and Waiting Time Inputs for HDV Hydrogen Refueling Station Planning: A Case Study of U.S. I-75 Corridor

Widespread use of diesel fuels in freight transportation leads to greenhouse gas (GHG) emissions that play an important role in air pollution. The air pollution, as well as the energy crisis, drives the transition from diesel fuel to cleaner and more energy-efficient fuels such as hydrogen fuels. With such a transition, the strategic planning and optimization of supporting refueling infrastructure along the national highway for heavy-duty vehicles (HDVs) become a necessity. Cost and service level are two essential factors to be considered in hydrogen refueling station location and capacity optimization. The service level in this study is assessed by the delay (waiting time) at fueling stations as well as the fueling demand fulfilled. To this end, this study presents a methodology to provide waiting time (delay) and levelized hydrogen fuel cost inputs under different station configurations (number of dispensers and fill rates) for hydrogen refueling station planning. Determining the station configurations based on the trade-off between the two inputs is further discussed. Particularly, this study points out that the levelized cost and waiting time are affected by hourly demand patterns and estimates the two inputs under different peak hour fueling demand scenarios. The results suggest that, with the same daily demand, the cost, as well as the waiting time, increases obviously with the peak hour demand. In the case study, the cost grows at least 30% when the peak hour visit frequency of the daily total visits increases from 5% (evenly distributed pattern) to 10% (the most common pattern for existing diesel fueling stations along I-75). Accounting for this peak hour effect in hydrogen refueling station planning is recommended. Overall, the waiting time model and cost analysis provide key inputs for optimizing hydrogen refueling station location and configurations based on anticipated refueling demand patterns. The paper is the second in a series that aims to build a comprehensive modeling plan and optimize hydrogen refueling infrastructure along the Interstate 75 (I-75) corridor for HDVs.

Liu, Yuandong↗

DOE OSTI2022

Distribution Function Instead of Steady-State Assumption in Time-Series Simulation

The quasi-steady-state assumption in time-series simulation is inadequate to model phenomena of interest to energy system integration such as inverter clipping, sell-back of excess electricity to a utility and utility hosting capacity, and battery throughput. Researchers are working on stochastic modeling, higher-resolution time series data, and machine learning approaches to address this need. This poster describes a distribution function that allows a maximum value, minimum value, and shape to the curve that can vary within each time-step based only on data inputs at the resolution of that time step (eg. Hourly). The distribution function is not used to synthesize high-resolution data, rather scalar integrals are used to calculate the quantities of interest within each time step. The form of the distribution function shows significant reduction in error when compared to 1 minute data and commercial software employing the distribution function (HOMER Pro version 14.3 and higher) shows a much improved estimate of inverter clipping in an example.

battery↗

DOE OSTI2022

Time Evolution in Quantum Cosmology

The quantum description of time evolution in non-linear gravitational systems such as cosmological space-times is not well understood. We show, in the simplified setting of mini-superspace, that time evolution of this system can be obtained using a gauge fixed path integral, as long as one does not integrate over proper time. Using this gauge fixed action we can construct a Hamiltonian in the coherent - or classical - state basis. We show that by construction the coherent states satisfy the classical dynamical equations of General Relativity. They do not satisfy the Hamiltonian constraint. A consequence of this is that the Wheeler-DeWitt equation should not be satisfied in quantum gravity. Classical states have a natural non-trivial time evolution since they are not eigenstates of the Hamiltonian. A general feature of the unconstrained quantum theory of gravity is the prediction of a pressureless dark matter component of either sign energy density in the classical universe which may lead to novel phenomenology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Automated tracking of prefabricated components for areal-time evaluator to optimize and automateinstallation

Trade associations for prefabricated construction estimate that about 50% of prefabricated wall projects have alignment problems that lead to defects and rework. Additionally, component installation times average between 30 and 60 minutes per component. To address these issues, a real-time evaluator (RTE) system was introduced to decrease cost and automate prefabricated component installation by reducing the installation time, decreasing rework, and enhancing energy performance through higher installation quality. The RTE uses commonly available hardware and software to perform autonomous tracking to measure the real-time location and orientation of components as they are crane-lifted and installed. The hardware, software, and algorithms that allow the autonomous tracking of components are detailed. An algorithm to automate the initial search for a component with three attached retroreflectors is proposed. Algorithms to automate the measurement of component position and orientation are also proposed. Simple lab-scale proof-of-concept experiments were conducted to assess the algorithms for automation of component searching, measurement of real-time movement, and measurement of component orientation. With additional development, the system can be used as a tool to generate the commands for autonomous crane operation or single-task construction robots.

Hayes, Nolan↗

DOE OSTI2021

Stepwise Dynamic Calibration of a Hydromechanical Simulation Using Time-Lapse Vertical Seismic Profile

This study aims to develop a methodology for calibrating subsurface stress changes through time-lapse Vertical Seismic Profiling (VSP) integration. The selected study site is the 13-10A injector well within the ongoing CO2-EOR operation of the Farnsworth Field Unit. The Time-lapse VSP dataset carries the combined effects of fluid substitution and mean effective stress changes, thereby providing a dataset amenable for the calibration of production and injection-induced stress changes. The concept is similar to calibrating a reservoir simulation model in that the process honor real field data to set up an inverse problem. The solution optimizes the independent and impactful geomechanical parameters that replicate the observed time-lapse seismic velocity changes. This stress calibration is enabled by 4D geomechanical modeling and the VSP Integration workflow. This calibration benefits from extensive geological, geophysical and geomechanical characterization through 3D seismic data, geophysical well logs, and core assessed as part of the 1D MEM conducted on the 13-10A subject well. These data are used to develop a site-specific rock physics model. The Biot Gassmann workflow combines rock physics and reservoir simulation outputs to determine the fluid substitution contribution to seismic velocity change. Additionally, modeled seismic velocity attributed to mean effective stress are determined from the geomechanical simulation outputs, and the stress-velocity relationship developed from the ultrasonic seismic velocity measurements on the extracted Morrow B core. A penalty function is then formed between the modeled seismic velocities and the observed time-lapse VSP dataset. Four independent and impactful geomechanical parameters have been determined. These are the bulk modulus and shear modulus for zero porosity and the shear and compressional seismic velocity to mean effective stress derivatives. The dataset of numerous coupled hydromechanical- geomechanical simulation realizations is built by combining variations of the four stated geomechanical parameters. A machine learning-assisted workflow comprised of an artificial neural network and a particle swarm optimizer are used to converge on the optimal geomechanical parameters. The successful execution of this workflow has affirmed the suitability of acoustic time-lapse measurements for 4D-VSP geomechanical stress calibration pending measurable stress sensitivities within the anticipated effective stress changes and the availability of suitable and reliable datasets for petroelastic modeling.

02 PETROLEUM↗