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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Transactive Control Design for Commercial Buildings to Provide Demand Response

Transactive coordination and control is a new type of distributed strategy that is designed to engage various self-interested distributed energy resources such as controllable loads, distributed generators, and energy storage for providing demand response. In this paper, a novel transactive control design is proposed for commercial buildings to coordinate the electricity demand of the package rooftop air conditioning units (RTUs) for demand response. Such a coordination is realized by introducing a local relationship between the temperature setpoint and the market clearing price. With the proposed market-based control, the demand of individual RTUs is adjusted by changing their temperature setpoints as a response to the market clearing prices. Two dierent scenarios of demand response are considered herein. One is to limit the peak demand of commercial buildings, in which a new market clearing strategy based on proportionalintegral- derivative control with respect to the demand limit is proposed. The other one is to perform price responsive control, in which the total demand of commercial buildings will respond to the real-time pricing. The proposed transactive strategy is also tested and demonstrated on a real occupied building located at Pacic Northwest National Laboratory. The eld testing results conrm the eectiveness of the proposed transactive coordination and control for commercial building with RTUs to provide demand response.

Huang, Sen↗
NASA NTRS2002

Real-Time Quantitative Analysis of H2, He, O2, and Ar by Quadrupole Ion Trap Mass Spectrometry

The use of a quadrupole ion trap mass spectrometer for quantitative analysis of hydrogen and helium as well as other permanent gases is demonstrated. The customized instrument utilizes the mass selective instability mode of mass analysis as with commercial instruments; however, this instrument operates at a greater RF trapping frequency and without a buffer gas. With these differences, a useable mass range from 2 to over 50 Da is achieved, as required by NASA for monitoring the Space Shuttle during a launch countdown. The performance of the ion trap is evaluated using part-per-million concentrations of hydrogen, helium, oxygen and argon mixed into a nitrogen gas stream. Relative accuracy and precision when quantitating the four analytes were better than the NASA-required minimum of 10% error and 5% deviation, respectively. Limits of detection were below the NASA requirement of 25-ppm hydrogen and 100-ppm helium; those for oxygen and argon were slightly higher than the requirement. The instrument provided adequate performance at fast data recording rates, demonstrating the utility of an ion trap mass spectrometer as a real-time quantitative monitoring device for permanent gas analysis.

Ottens, Andrew K.↗
NASA NTRS2014

Fatigue‐Related Countermeasures for Long‐Duration Exploration Missions

The NASA Human Research Program's (HRP) Behavioral Health and Performance Element (BHP) supports and conducts research to mitigate deleterious outcomes related to fatigue, sleep loss, circadian desynchronization, and work overload. Objective evidence indicates that within the context of the International Space Station (ISS), sleep is reduced and there is circadian misalignment. Despite chronic sleep loss and high workloads; however, astronauts successfully complete their missions. Contributing to their success is not only the tremendous skills and capabilities of each astronaut, but also the collaborative team efforts amongst the crew, between flight and ground crews, and through real‐time care provided by medical personnel. It is anticipated that risks to human health and performance will increase in the context of exploration missions, where crewmembers will venture to deep space for extended durations and in small vehicles with limited communication with home. Hence, fatigue‐related countermeasures are being developed and/or validated that include unobtrusive monitoring technologies to detect fatigue‐related performance decrements, environmental countermeasures, and sleep education and training for flight and ground crews. Given that fatigue is an issue in current ISS missions, the BHP works collaboratively with Space Medicine operations to collect data in the operational environment, to validate fatigue-related countermeasures, and provide evidence‐based mitigations. Our presentation will summarize fatigue‐related operational research that is underway through NASA's BHP in partnership with its operational counterparts. Efforts include studies evaluating the effects of hypnotics, lighting protocols as countermeasures for circadian entrainment, and investigations involving education and training. This presentation will further identify, based on flight and terrestrial evidence, additional sleep and circadian countermeasures that may still be needed to support exploration missions. Lessons learned from transitioning research deliverables into ISS operations will also be discussed.

Whitmire, A.↗
NASA NTRS1992

A VLSI implementation of DCT using pass transistor technology

A VLSI design for performing the Discrete Cosine Transform (DCT) operation on image blocks of size 16 x 16 in a real time fashion operating at 34 MHz (worst case) is presented. The process used was Hewlett-Packard's CMOS26--A 3 metal CMOS process with a minimum feature size of 0.75 micron. The design is based on Multiply-Accumulate (MAC) cells which make use of a modified Booth recoding algorithm for performing multiplication. The design of these cells is straight forward, and the layouts are regular with no complex routing. Two versions of these MAC cells were designed and their layouts completed. Both versions were simulated using SPICE to estimate their performance. One version is slightly faster at the cost of larger silicon area and higher power consumption. An improvement in speed of almost 20 percent is achieved after several iterations of simulation and re-sizing.

Kamath, S.↗
DOE OSTI2025

Morphotype-resolved characterization of microalgal communities in a nutrient recovery process with ARTiMiS flow imaging microscopy

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. Furthermore, these results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1980

An initial assessment of the performance achieved by the Seasat-1 radar altimeter

This paper describes the results of an initial on-orbit engineering assessment of the performance achieved by the radar altimeter system flown on Seasat-1. Additionally, the general design characteristics of this system are discussed and illustrations of altimeter data products are provided. The instrument consists of a 13.5-GHz monostatic radar system that tracks in range only using a 1-m parabolic antenna pointed at the satellite nadir. Two of its unique features are a linear FM transmitter with 320-MHz bandwidth, which yields a 3.125-ns time-delay resolution, and microprocessor-implemented closed-loop range tracking, automatic gain control (AGC), and real-time estimation of significant wave height (SWH). Results presented herein show that the altimeter generally performed in accordance with its original performance requirements of measuring altitude to a precision of less than 10-cm rms, SWH to an accuracy of plus or minus 0.5 m or 10 percent whichever is greater, and ocean backscatter coefficient to an accuracy of plus or minus 1 dB, all over an SWH range of 1 to 20 m.

Townsend, W. F.↗
NASA NTRSDate not supplied

Monitoring Human Performance on Future Deep Space Missions Abstract

NASA and the commercial spacecraft community are working diligently to put the first woman on the moon in the 2024 timeframe. At the same time, NASA researchers are thinking about how to solve the even larger challenges that future deep space missions will bring. Space travel itself is difficult, but astronauts on deep space missions will face obstacles and unknowns never before experienced. In addition to the altered gravity and hostile/closed environment of a spacecraft, deep space crews will face increased radiation, isolation, and distance from Earth. During Extravehicular Activity (EVA, or “spacewalk”) operations, crew will experience increased physical and cognitive workload due to extended types, frequencies and durations of tasks performed on exploration missions in partial gravity environments. All of these stressors will impact crew physical and mental health and performance in difficult-to-anticipate ways. Crew autonomy may be one of the biggest challenges faced. Communication delays and blackouts will occur, and in those situations, the crew may not have access to the Mission Control Center (MCC). They may be forced to be solely dependent on each other and the available information onboard to stay alive, healthy, and achieve the mission. The only conceivable way to meet the challenges of Earth independence is to enable the crew to monitor their own health and performance -- preferably unobtrusively as they perform their duties. Technologies and techniques must be developed to aid the crew in these assessments. A deep space mission is expected to have relatively short periods of high cognitive demand, stress, and fatigue, alongside potentially long periods of cognitive underload during the transit, where boredom, loneliness, and depression can set in. Both ends of this spectrum are dangerous. Crew must be made aware when their task performance drops significantly, when their cognitive workload is too high, when they have lost situation awareness, or when they are too stressed or too fatigued to perform well. They must be able to identify these risks, and then mitigate them with countermeasures available onboard. A number of self-monitoring technologies are presently being explored by NASA to advance crew state determination capabilities. These range from real-time, physiological workload and situation awareness assessments, to crew health measurements determining physical and mental fitness for duty, to task performance metrics such as suit resource expenditures. For EVA tasks during surface exploration missions, biomedical information such as metabolic rate may be provided to crewmembers for situational awareness related to task performance efficiency. In addition, translation distances, hydration, nutrition, inspired CO2 exposure and other consumables usage rates may be useful input metrics for modeling individualized performance during tasks to inform crew or provide estimates of work efficiency. Oculomotor metrics such as gaze dwell time, pupilometry, and eye tracking collected in advanced helmet mounted displays could potentially be used to characterize crew situation awareness. This paper highlights some of these projects, and provides broader discussion about the need for advanced monitoring and smart technologies, as NASA takes the leap into the next generation of space exploration.

Kritina Holden↗
NASA NTRSDate not supplied

Building a Real-Time Predictive Flood Model for Improving Early Warning Systems in Ellicott City, Maryland

As flood events in the United States grow in frequency and intensity, the use of applied remote sensing analyses is increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP National Program partnered with the Howard County government in Maryland to investigate the use of machine learning for advanced flood risk detection, and to test the feasibility of integrating this approach into the county’s flood early warning system. To strengthen the efforts of the Howard County Office of Emergency Management (OEM), the project developed a prediction model capable of hindcasting the two severe flash flood events that devastated Ellicott City, and transitioned to an Long Short-Term Memory (LSTM) based sequence-to-sequence deep learning model with 8-hour forecast capability. The team combined data inputs from public sources including river and precipitation gauges, NASA and NOAA Earth observations, and numerical weather model products using scripts written in the Google Colaboratory Python scripting environment. In addition to designing the deep learning architecture, the team trained and tested the model, and evaluated its performance using the Nash-Sutcliffe Efficiency (NSE). The final product, called the Sequentially Trained Real-time EstimAted Model (STREAM), predicts stage height for the Hudson Branch gauge in Ellicott City using data products available in near real-time, including the High-Resolution Rapid Refresh (HRRR) model’s accumulated precipitation forecasts supplemented by stream gauge data from the OEM and the U.S. Geological Survey. STREAM was incorporated into an online dashboard in a user-friendly interface capable of triggering the alarms that initiate the OEM’s emergency response protocols up to 8 hours in advance of a predicted severe flood event. The project demonstrated the potential for the integration of open data and Earth observations into a flood risk forecasting tool capable of informing near real-time decision making.

Ryan Hammock↗
NASA NTRS1995

Concept Designed and Developed for Distortion- Tolerant, High-Stability Engine Control

Engine Control Future aircraft turbine engines, both commercial and military, must be able to successfully accommodate expected increased levels of steady-state and dynamic engine-face distortion. Advanced tactical aircraft are likely to use thrust vectoring to enhance their maneuverability. As a result, the engines will see more extreme aircraft angles-of-attack and sideslip levels than are currently encountered with present-day aircraft. Also, the mixed-compression inlets needed for the High Speed Civil Transport will likely encounter disturbances similar to those seen by tactical aircraft, in addition to planar pulse, inlet buzz, and high distortion levels at low flight speed and off-design operation. The current approach of incorporating a sufficient component design stall margin to tolerate these increased levels of distortion would significantly reduce performance. The objective of the High Stability Engine Control (HISTEC) program is to design, develop, and flight demonstrate an advanced, high-stability, integrated engine-control system that uses measurement-based, real-time estimates of distortion to enhance engine stability. The resulting distortion-tolerant control reduces the required design stall margin, with a corresponding increase in performance and decrease in fuel burn. The HISTEC concept has been designed and developed, and the software implementing the concept has successfully accommodated time-varying distortion. The NASA Lewis Research Center is currently overseeing the development and validation of the hardware and software necessary to flight test the HISTEC concept. HISTEC is a contracted effort with Pratt & Whitney of West Palm Beach, Florida. The HISTEC approach includes two major systems: A Distortion Estimation System (DES) and Stability Management Control (SMC). DES is an aircraft-mounted, high-speed processor that estimates the amount and type of distortion present and its effect on the engine. It uses high-response pressure measurements at the engine face to calculate indicators of the type and extent of distortion in real time. From these indicators, DES determines the effects of distortion on the propulsion systems and the corresponding engine match point necessary to accommodate it. DES output consists of fan and compressor pressure ratio trim commands that are passed to the SMC. In addition, DES uses maneuver information, consisting of angle-of-attack and sideslip from the flight control, to anticipate high inlet distortion conditions. The SMC, which is contained in the engine-mounted, Improved Digital Electronic Engine Control (IDEEC), includes advanced control laws to directly control the fan and compressor transient operating line (pressure ratio). These advanced control laws, with a multivariable design, have the potential for higher bandwidth and the resulting more precise control of engine match. The ability to measure and assess the distortion effects in real time coupled with a high-response controller improves engine stability at high levels of distortion. The software algorithms implementing DES have been designed, developed, and demonstrated, and integration testing of the DES and SMC software has been completed. The results show that the HISTEC system will be able to sense inlet distortion, determine the effect on engine stability, and accommodate distortion by maintaining an adequate margin for engine surge. The Pratt &Whitney Comprehensive Engine Diagnostic Unit was chosen as the DES processor. An instrumented inlet case for sensing distortion was designed and fabricated. HISTEC is scheduled for flight test on the ACTIVE F-15 aircraft at the NASA Dryden Flight Research Center in Edwards, California, in late 1996.

Source record↗
NASA NTRSDate not supplied

Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation

National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.

Machine Learning (ML)↗
NASA NTRS1990

Stereopsis cueing effects on hover-in-turbulence performance in a simulated rotorcraft

The efficacy of stereopsis cueing in pictorial displays was assessed in a real-time piloted simulation experiment of a rotorcraft precision hover-in-turbulence task. Seven pilots endeavored to maintain a hover by visually aligning a set of inner and outer wickets (major elements of a real-world pictorial display, thus attaining the desired hover position, in a full factorial experimental design. The display conditions examined included the presence or absence of a velocity display element (a velocity head-up display) as well as the stereopsis cueing conditions, which included non-stereo (binoptic or monoscopic - no depth cues other than those provided by a perspective, real-world display), stereo 3-D, and hyper stereo (telestereoscopic). Subjective and objective results indicated that the depth cues provided by the stereo displays enhanced the situational awareness of the pilot and enabled improved hover performance to be achieved. The velocity display element also improved the hover performance, with the best hover performance being achieved with the combined use of stereo and the velocity display element. Pilot control input data revealed that less control action was required to attain the improved hover performance with the stereo displays.

Parrish, Russell V.↗
NASA NTRS2001

Psychophysiological Control of Acognitive Task Using Adaptive Automation

The major focus of the present proposal was to examine psychophysiological variables related to hazardous states of awareness induced by monitoring automated systems. With the increased use of automation in today's work environment, people's roles in the work place are being redefined from that of active participant to one of passive monitor. Although the introduction of automated systems has a number of benefits, there are also a number of disadvantages regarding worker performance. Byrne and Parasuraman have argued for the use of psychophysiological measures in the development and the implementation of adaptive automation. While both performance based and model based adaptive automation have been studied, the use of psychophysiological measures, especially EEG, offers the advantage of real time evaluation of the state of the subject. The current study used the closed-loop system, developed at NASA-Langley Research Center, to control the state of awareness of subjects while they performed a cognitive vigilance task. Previous research in our laboratory, supported by NASA, has demonstrated that, in an adaptive automation, closed-loop environment, subjects perform a tracking task better under a negative than a positive, feedback condition. In addition, this condition produces less subjective workload and larger P300 event related potentials to auditory stimuli presented in a concurrent oddball task. We have also recently shown that the closed-loop system used to control the level of automation in a tracking task can also be used to control the event rate of stimuli in a vigilance monitoring task. By changing the event rate based on the subject's index of arousal, we have been able to produce improved monitoring, relative to various control groups. We have demonstrated in our initial closed-loop experiments with the the vigilance paradigm that using a negative feedback contingency (i.e. increasing event rates when the EEG index is low and decreasing event rates when the EEG index is high) results in a marked decrease of the vigilance decrement over a 40 minute session. This effect is in direct contrast to performance of a positive feedback group, as well as a number of other control groups which demonstrated the typical vigilance decrement. Interestingly, however, the negative feedback group performed at virtually the same level as a yoked control group. The yoked control group received the same order of changes in event rate that were generated by the negative feedback subjects using the closed-loop system. Thus it would appear to be possible to optimize vigilance performance by controlling the stimuli which subjects are asked to process.

Freeman, Frederick↗
DOE OSTI2023

Comparison of machine learning systems trained to detect Alfvén eigenmodes using the CO 2 interferometer on DIII-D

Abstract A Machine-Learning (ML) based detection scheme that automatically detects Alfvén Eigenmodes (AE) in a labelled DIII-D database is presented here. Controlling AEs is important for the success of planned burning plasma devices such as ITER, since resonant fast ions can drive AEs unstable and degrade the performance of the plasma or damage the first walls of the machine vessel. Artificial Intelligence could be useful for real-time detection and control of AEs in steady-state plasma scenarios by implementing ML-based models into control algorithms that drive actuators for mitigation of AE impacts. Thus, the objective is to compare differences in performance between using two different recurrent neural network systems (Reservoir Computing Network and Long Short Term Memory Network) and two different representations of the C O 2 phase data (simple and crosspower spectrograms). All C O 2 interferometer chords are used to train both models, but only one is processed during each training step. The results from the model and data comparison show higher performance for the RCN model (True Positive Rate = 90% and False Positive Rate = 14%), and that using simple magnitude spectrograms is sufficient to detect AEs. Also, the vertical C O 2 interferometer chord passing near the center is better for ML-based detection of AEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

High-precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this work presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high-frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. Finally, the mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

42 ENGINEERING↗
DOE OSTI2016

Impacts of high resolution data on traveler compliance levels in emergency evacuation simulations

In this article, we conducted a comparison study of evacuation assignment based on Traffic Analysis Zones (TAZ) and high resolution LandScan USA Population Cells (LPC) with detailed real world roads network. A platform for evacuation modeling built on high resolution population distribution data and activity-based microscopic traffic simulation was proposed. This platform can be extended to any cities in the world. The results indicated that evacuee compliance behavior affects evacuation efficiency with traditional TAZ assignment, but it did not significantly compromise the performance with high resolution LPC assignment. The TAZ assignment also underestimated the real travel time during evacuation. This suggests that high data resolution can improve the accuracy of traffic modeling and simulation. The evacuation manager should consider more diverse assignment during emergency evacuation to avoid congestions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Operating organic light-emitting diodes imaged by super-resolution spectroscopy

Super-resolution stimulated emission depletion microscopy is adapted here for materials characterization that would not otherwise be possible. With the example of organic lightemitting diodes (OLEDs), spectral imaging with pixel-by-pixel wavelength discrimination allows us to resolve local-chain environment encoded in the spectral response of the semiconducting polymer, and correlate chain packing with local electroluminescence by using externally applied current as the excitation source. We observe nanoscopic defects that would be unresolvable by traditional microscopy. They are revealed in electroluminescence maps in operating OLEDs with 50 nm spatial resolution. We find that brightest emission comes from regions with more densely packed chains. Conventional microscopy of an operating OLED would lack the resolution needed to discriminate these features, while traditional methods to resolve nanoscale features generally cannot be performed when the device is operating. Furthermore, this points the way towards real-time analysis of materials design principles in devices as they actually operate.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Simulated field-modulated x-ray absorption in titania

Here, we present a method to compute the x-ray absorption near-edge structure (XANES) spectra of solid-state transition metal oxides using real-time time-dependent density functional theory, including spin–orbit coupling effects. This was performed on bulk-mimicking anatase titania (TiO 2 ) clusters, which allows for the use of hybrid functionals and atom-centered all electron basis sets. Furthermore, this method was employed to calculate the shifts in the XANES spectra of the Ti L-edge in the presence of applied electric fields to understand how external fields can modify the electronic structure, and how this can be probed using x-ray absorption spectroscopy. Specifically, the onset of t 2g peaks in the Ti L-edge was observed to red shift and the e g peaks were observed to blue shift with increasing fields, attributed to changes in the hybridization of the conduction band (3d) orbitals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Embedded Fiber Bragg Grating (FBG) Sensors Fabricated by Ultrasonic Additive Manufacturing for High-Frequency Dynamic Strain Measurements

This article demonstrates high-frequency dynamic strain measurements using fiber Bragg grating (FBG) sensors embedded in metal parts. Using an ultrasonic additive manufacturing (UAM) process, FBGs inscribed in polyimide coated optical fibers were embedded in aluminum parts. An electromagnetic shaker was used to exert dynamic events on the embedded FBG sensors with frequencies from 1 to 10 kHz. The high-speed interrogation of FBG sensors was accomplished using a tunable vertical-cavity surface-emitting laser (VCSEL) and a high-speed interrogation system sampling at 120 kHz. The strain response measured by the FBG sensors was compared with real-time measurements using a laser velocimeter. A finite-element analysis (FEA) was performed to simulate responses to both static strain and high-frequency dynamic strain. In conclusion, results show that strains as small as 2.5$με$ can be resolved at frequencies up to 10 kHz.

36 MATERIALS SCIENCE↗

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