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NASA NTRS2006

System Finds Horizontal Location of Center of Gravity

An instrumentation system rapidly and repeatedly determines the horizontal location of the center of gravity of a laboratory vehicle that slides horizontally on three air bearings (see Figure 1). Typically, knowledge of the horizontal center-of-mass location of such a vehicle is needed in order to balance the vehicle properly for an experiment and/or to assess the dynamic behavior of the vehicle. The system includes a load cell above each air bearing, electronic circuits that generate digital readings of the weight on each load cell, and a computer equipped with software that processes the readings. The total weight and, hence, the mass of the vehicle are computed from the sum of the load-cell weight readings. Then the horizontal position of the center of gravity is calculated straightforwardly as the weighted sum of the known position vectors of the air bearings, the contribution of each bearing being proportional to the weight on that bearing. In the initial application for which this system was devised, the center- of-mass calculation is particularly simple because the air bearings are located at corners of an equilateral triangle. However, the system is not restricted to this simple geometry. The system acquires and processes weight readings at a rate of 800 Hz for each load cell. The total weight and the horizontal location of the center of gravity are updated at a rate of 800/3 approx. equals 267 Hz. In a typical application, a technician would use the center-of-mass output of this instrumentation system as a guide to the manual placement of small weights on the vehicle to shift the center of gravity to a desired horizontal position. Usually, the desired horizontal position is that of the geometric center. Alternatively, this instrumentation system could be used to provide position feedback for a control system that would cause weights to be shifted automatically (see Figure 2) in an effort to keep the center of gravity at the geometric center.

Johnston, Albert S.↗

NASA NTRS2010

Effect of Target Location on Dynamic Visual Acuity During Passive Horizontal Rotation

The vestibulo-ocular reflex (VOR) generates eye rotation to compensate for potential retinal slip in the specific plane of head movement. Dynamic visual acuity (DVA) has been utilized as a functional measure of the VOR. The purpose of this study was to examine changes in accuracy and reaction time when performing a DVA task with targets offset from the plane of rotation, e.g. offset vertically during horizontal rotation. Visual acuity was measured in 12 healthy subjects as they moved a hand-held joystick to indicate the orientation of a computer-generated Landolt C "as quickly and accurately as possible." Acuity thresholds were established with optotypes presented centrally on a wall-mounted LCD screen at 1.3 m distance, first without motion (static condition) and then while oscillating at 0.8 Hz (DVA, peak velocity 60 deg/s). The effect of target location was then measured during horizontal rotation with the optotypes randomly presented in one of nine different locations on the screen (offset up to 10 deg). The optotype size (logMar 0, 0.2 or 0.4, corresponding to Snellen range 20/20 to 20/50) and presentation duration (150, 300 and 450 ms) were counter-balanced across five trials, each utilizing horizontal rotation at 0.8 Hz. Dynamic acuity was reduced relative to static acuity in 7 of 12 subjects by one step size. During the random target trials, both accuracy and reaction time improved proportional to optotype size. Accuracy and reaction time also improved between 150 ms and 300 ms presentation durations. The main finding was that both accuracy and reaction time varied as a function of target location, with greater performance decrements when acquiring vertical targets. We conclude that dynamic visual acuity varies with target location, with acuity optimized for targets in the plane of motion. Both reaction time and accuracy are functionally relevant DVA parameters of VOR function.

Appelbaum, Meghan↗

NASA NTRS2013

Efficient Creation of Overset Grid Hole Boundaries and Effects of Their Locations on Aerodynamic Loads

Recent developments on the automation of the X-rays approach to hole-cutting in over- set grids is further improved. A fast method to compute an auxiliary wall-distance function used in providing a rst estimate of the hole boundary location is introduced. Subsequent iterations lead to automatically-created hole boundaries with a spatially-variable o set from the minimum hole. For each hole boundary location, an averaged cell attribute measure over all fringe points is used to quantify the compatibility between the fringe points and their respective donor cells. The sensitivity of aerodynamic loads to di erent hole boundary locations and cell attribute compatibilities is investigated using four test cases: an isolated re-entry capsule, a two-rocket con guration, the AIAA 4th Drag Prediction Workshop Common Research Model (CRM), and the D8 \Double Bubble" subsonic aircraft. When best practices in hole boundary treatment are followed, only small variations in integrated loads and convergence rates are observed for different hole boundary locations.

Overset↗

NASA NTRS2013

Regression Analysis of Top of Descent Location for Idle-thrust Descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

trajectory prediction↗

NASA NTRS2018

International Space Station (ISS) Environmental Control and Life Support System (ECLSS) Vent Flow Reflection and Detection by Robotic External Leak Locator (RELL)

On-orbit Robotic External Leak Locator (RELL) (i.e., mass spectrometer and ion gauge) measurements on the International Space Station (ISS) are presented to show the detection of recurring Environmental Control and Life Support System (ECLSS) vents at multiple ISS locations and RELL pointing directions. The path of ECLSS effluents to the RELL detectors is not entirely obvious at some locations, but the data indicates that diffuse gas-surface reflection or scattering resulting from plume interaction with vehicle surfaces is responsible. RELL was also able to confirm the ISS ECLSS constituents and distinguish them from the ammonia leak based on the ion mass spectra and known venting times during its operation to locate a leak in the ISS port-side External Active Thermal Control System (EATCS) coolant loop.

Gas/Surface Reflections or Scattering↗

NASA NTRS2018

International Space Station (ISS) Environmental Control and Life Support System (ECLSS) Vent Flow Reflection and Detection by Robotic External Leak Locator (RELL)

Gas/Surface Reflections or Scattering↗

NASA NTRS2018

Spatial Correlation Structures in SMAP Near-Surface Soil Moisture (How Spatially Correlated are the Temporal Variations of Soil Moisture at Different Locations? and Why Is This of Interest?)

Spatial correlation structures can describe the degree to which soil moisture at a specified location co-varies in time with that at other, remote locations. Using four years of warm season SMAP Level 2 near-surface soil moisture data, we compute these spatial correlation structures for points across North America. The character of these structures is seen to differ geographically; the structures found for the west-central US, for example, are significantly more spatially extensive. We then demonstrate how these structures can potentially be used to reconstruct soil moisture fields during the pre-SMAP era. In this exercise, we consider as "truth" the soil moistures produced in a long-term offline land surface model simulation (1980-2014) that utilizes precipitation forcing based on a high density of precipitation gauges. Then, for a given location within the continent, we construct an"estimated" soil moisture time series based solely on historical soil moisture information simulated at least 300 km distant from the location, using the SMAP-based spatial correlation structures to determine how to make best use of the remote information. The reconstructed soil moistures are found to have significant skill relative to the assumed truth, suggesting that the same approach, when applied in areas of low rain gauge density (i.e., in areas for which historically simulated soil moistures are necessarily inaccurate), could provide useful historical soil moisture estimates through the SMAP-guided extraction of relevant information from neighboring gauged regions.

Koster, Randal↗

NASA NTRS2021

Locating the Isolator Shock-Train Leading Edge with Limited Pressure Information

Real-time detection and control of the isolator shock-train leading edge (STLE) is important to the performance of high-speed air-breathing engines, such as dual-mode scramjets. Typically, the STLE location is determined using wall static-pressure measurements, but there are often restrictions on the placement and overall number of the pressure transducers, reducing the viability and accuracy of such approaches. To address these issues, we introduce the adaptive pressure profile (APP) method for estimating the STLE location. This method does not require extensive prior characterization of the isolator or engine model. Instead, it uses real-time pressure measurements from a small number of transducers to adaptively learn the isolator pressure profile and subsequently uses this deduced profile to estimate the STLE location in a data-driven manner. The APP method works well in situations with sparse transducer placement. It produces accurate estimates when the STLE location is 1) not bounded by two or more transducers or 2) between two transducers that are several isolator duct heights apart. We demonstrate the efficacy of the APP method using simulations and experimental data from direct-connect isolator models. This validation shows that the APP method is accurate and robust for different flow regimes, transducer configurations, and model geometries.

Gregory J. Hunt↗

NASA NTRSDate not supplied

Simultaneous Ice Water Content Measurements at Multiple Locations on the NASA DC-8 Aircraft during the 2018 HIWC RADAR Flight Campaign

Ice water content measurements were made simultaneously at three locations on the NASA DC-8 in natural, glaciated conditions during the 2018 High Ice Water Content RADAR flight campaign. The purpose of these measurements was to further evaluate efficiency factors of hot-wire total water content probes in glaciated conditions and investigate the enhancement of ice crystal concentrations near fuselage surfaces due to flow field inertial effects, and ice crystals impacting the nose, breaking up, and flowing downstream. The total water content measurements were made using either Science Engineering Associates Ice Crystal Detectors or Robust Probes. Three common sensors were mounted on an underwing canister considered to be in near free-flow conditions, a standoff from a fuselage window, and the nose of the fuselage near the pitot probes. The Ice Crystal Detector total water content sensor and Robust Probe sensor collection and retention efficiencies were evaluated through comparisons with the underwing Ice Crystal Detector and Robust Probe measurements to the IKP2 isokinetic evaporator probe, which provided the reference ice water content measurement. Local ice water content at the nose position was evaluated by comparing ratios of the nose and underwing ice crystal detectors to the IKP2. Local ice water content at the window-standoff location was also evaluated by comparing total water content sensor measurements from the window ice crystal detector to the measurements made with the underwing and nose Ice Crystal Detectors The key findings were: 1) the Ice Crystal Detector total water content sensor ice water content efficiency factor was similar to previous estimates, but reduced with increased ice crystal median mass diameter; 2) the ice water content at the fuselage nose location near the DC-8 pitot probes was approximately 2.5 times the freestream values – although this estimate is affected by a higher probe efficiency factor due to smaller particles in the debris cloud from impacts upstream of the probe; and 3) the ice water content at the 17” standoff from the port window varied from about 50% to 3 times freestream values in a complicated manner. Similar measurement locations are not uncommon on cloud research aircraft, where ice particle measurements may be subject to similar uncertainties.

Aircraft Icing↗

NASA NTRSDate not supplied

Simultaneous Ice Water Content Measurements at Multiple Locations on the NASA DC-8 Aircraft during the 2018 HIWC RADAR Flight Campaign

o Aircraft Icing↗

NASA NTRS2021

Simultaneous Ice Water Content Measurements at Multiple Locations on the NASA DC-8 Aircraft during the 2018 HIWC RADAR Flight Campaign

Ice water content measurements were made simultaneously at three locations on the NASA DC-8 in natural, glaciated conditions during the 2018 High Ice Water Content RADAR flight campaign. The purpose of these measurements was to further evaluate efficiency factors of hot-wire total water content probes in glaciated conditions and investigate the enhancement of ice crystal concentrations near fuselage surfaces due to flow field inertial effects, and ice crystals impacting the nose, breaking up, and flowing downstream. The total water content measurements were made using either Science Engineering Associates Ice Crystal Detectors or Robust Probes. Three common sensors were mounted on an underwing canister considered to be in near free-flow conditions, a standoff from a fuselage window, and the nose of the fuselage near the pitot probes. The Ice Crystal Detector concave total water content sensor and Robust Probe sensor collection and retention efficiencies were evaluated through comparisons with the underwing Ice Crystal Detector and Robust Probe measurements to the Isokinetic Probe version 2 (IKP2), which provided the reference ice water content measurement. Local ice water content at the nose position was evaluated by comparing ratios of the nose and underwing ice crystal detectors to the IKP2. Local ice water content at the window-standoff location was also evaluated by comparing total water content sensor measurements from the window probes to the measurements made with the underwing and nose probes. The key findings were: (1) the Ice Crystal Detector concave water content sensor efficiency factor to glaciated conditions was similar to previous estimates, but reduced with increased ice crystal median mass diameter; (2) the ice water content at the fuselage nose location near the DC-8 pitot probes was approximately 2.5 times the freestream values—although this estimate is affected by a higher probe efficiency factor due to smaller particles in the debris cloud from impacts upstream of the probe; and (3) the ice water content at the 17 in. standoff from the port window varied from about 50 percent to nearly three times freestream values in a complicated manner. Similar measurement locations are not uncommon on cloud research aircraft, where ice particle measurements may be subject to similar uncertainties.

Aircraft Icing↗

DOE OSTI2024

Waveform emission location determination systems and associated methods

Waveform emission location determination systems and associated methods are described. According to one aspect, a waveform emission location determination system includes a plurality of detectors configured to receive a waveform emitted by a source and to generate electrical signals corresponding to the waveform, processing circuitry configured to access data corresponding to the electrical signals generated by the detectors, use the data to determine a plurality of spheres, and wherein a surface of each of the spheres contains a location of the source when the waveform was emitted by the source, determine an intersection of the spheres, and use the intersection of the spheres to determine the location of the source when the waveform was emitted by the source.

Hughes, Michael S.↗

NASA NTRS2022

Observations of Particle Number Size Distributions and New Particle Formation in Six Indian Locations

Atmospheric new particle formation (NPF) is a crucial process driving aerosol number concentrations in the atmosphere; it can significantly impact the evolution of atmospheric aerosol and cloud processes. This study analyses at least 1 year of asynchronous particle number size distributions from six different locations in India. We also analyze the frequency of NPF and its contribution to cloud condensation nuclei (CCN) concentrations. We found that the NPF frequency has a considerable seasonal variability. At the measurement sites analyzed in this study, NPF frequently occurs in March–May (pre-monsoon, about 21 % of the days) and is the least common in October–November (post-monsoon, about 7 % of the days). Considering the NPF events in all locations, the particle formation rate (J_(SDS)) varied by more than 2 orders of magnitude (0.001–0.6 /cu.cm s) and the growth rate between the smallest detectable size and 25 nm (GR_(SDS-25 nm)) by about 3 orders of magnitude (0.2–17.2 nm/h). We found that JSDS was higher by nearly 1 order of magnitude during NPF events in urban areas than mountain sites. GRSDS did not show a systematic difference. Our results showed that NPF events could significantly modulate the shape of particle number size distributions and CCN concentrations in India. The contribution of a given NPF event to CCN concentrations was the highest in urban locations (4.3 × 10^(3) /cu.cm per event and 1.2 × 10^(3)/cu.cm per event for 50 and 100 nm, respectively) as compared to mountain background sites (2.7 × 10^(3)/cu.cm per event and 1.0 × 10^(3)/cu.cm per event, respectively). We emphasize that the physical and chemical pathways responsible for NPF and factors that control its contribution to CCN production require in situ field observations using recent advances in aerosol and its precursor gaseous measurement techniques.

particle number size distributions↗

DOE OSTI2020

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Evaluation of thermostat location for multizone commercial building performance

In multi-zone buildings, it is often found that a single shared thermostat controls more than one conditioned zones. Although these shared zones are supposed to have similar thermal needs (e.g., cooling and heating load), in reality, they are not mainly due to different orientations, sizes of windows, occupancy, space types, etc. This can cause unnecessary energy waste or thermal discomfort for the occupants. How to quantify this impact in multizone buildings remains a research gap. Therefore, this study aims to evaluate the impact of different sensor (i.e., thermostat) locations for multizone commercial buildings through a comprehensive modeling study. Here, two different scenarios for the sensor locations were selected to evaluate the impact in terms of energy and thermal comfort. The scenario (1) is that one to five sensors distributed among the five zones, but the sensor readings from selected zones will be used for no-sensor zones, which is no-mean sensor scenario. The scenario (2) is that one to five sensors distributed among the five zones, but the average temperature from the shared zones will be used for each of the shared zones, which is a mean sensor scenario. The uncertainty analysis was performed for different sensor location scenarios.(a)The major findings from an energy perspective, for scenario (1), the differences of cooling energy go as high as 17% more or 12% less, compared with the baseline. For heating energy consumption, the discrepancies go as high as 51% more or 52% less, compared with baseline. For site energy consumption, the discrepancies go as high as 3.2% more or 3.2% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.2% more or as low as 1.0% less, compared with baseline. For scenario (2), the discrepancies of cooling energy go as high as 3% more, or 0.5% less, compared with the baseline. For heating energy consumption, the discrepancies, are go as high as 10.1% more or as low as 3.0% less, compared with baseline. For site energy consumption, the discrepancies go as high as 1.3% more or 0.3% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.1% more or as low as 1.0% less, compared with baseline.(b) In terms of the indoor thermal comfort, for the no-mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 1,200 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 740 h, compared with the baseline. For the mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 750 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 50 h, compared with the baseline.

42 ENGINEERING↗

DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator (Q1 2020)

The US. Department of Energy’s (DOE’s) Alternative Fueling Station Locator contains information on public and private non-residential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the first calendar quarter of 2020 (Q1). Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with the amount projected to meet charging demand by 2030. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2020

The U.S. Department of Energy’s Alternative Fueling Station Locator contains information on public and private non-residential alternative fueling stations in the United States and Canada and currently tracks ethanol (E85), biodiesel, compressed natural gas, electric vehicle (EV) charging, hydrogen, liquefied natural gas, and propane stations. Of these fuels, EV charging continues to experience rapidly changing technology and growing infrastructure. This report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2020. Using data from the Station Locator, this report breaks down the growth of public and private charging infrastructure by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with the amount projected to meet charging demand by 2030. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape for EV charging. This is the second report in a new series. The first report for the first calendar quarter of 2020 can be found in the publication databases of the Alternative Fuels Data Center and the National Renewable Energy Laboratory.

24 POWER TRANSMISSION AND DISTRIBUTION↗