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At least 1,621 records · Page 90

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NASA NTRS2002

The Aura Mission and the "A-Train"

By the end of 2004 the A-Train will have been assembled in orbit. The A-Train consists of five low earth orbit satellites flying within 15 minutes of each other. The first in the train is Aqua, launched early this year, in an ascending node orbit with 1 :30 PM crossing time. In January 2004, Aura will be launched, following 15 minutes behind Aqua. Aura's focus is atmospheric chemistry, with both forward looking, and backward looking instruments along with nadir sounders. Aura's limb microwave instrument will also be able to make temperature and humidity measurements into the troposphere. By mid-2004, CALIPSO, a cloud lidar mission and CloudSat, a cloud radar mission will be co-manifested. These two spacecraft will follow, and maneuver about 1 minute behind Aqua. The lidar on CALIPSO will provide aerosol and cloud height information which can enhance the aerosol information from Aqua s MODIS instrument. CloudSat s radar will enhance the information from AMSR-E, AIRS and HSB on Aqua. The fifth satellite to be launched in 2004 is PARASOL which will use the POLDER instrument to provide polarization information which can be used to determine aerosol composition and size distribution.

Schoeberl, Mark R.↗

NASA NTRS2010

Performance Support Tools for Space Medical Operations

Early Constellation space missions are expected to have medical capabilities similar to those currently on board the Space Shuttle and International Space Station (ISS). Flight surgeons on the ground in Mission Control will direct the Crew Medical Officer (CMO) during medical situations. If the crew is unable to communicate with the ground, the CMO will carry out medical procedures without the aid of a flight surgeon. In these situations, use of performance support tools can reduce errors and time to perform emergency medical tasks. The research presented here is part of the Human Factors in Training Directed Research Project of the Space Human Factors Engineering Project under the Space Human Factors and Habitability Element of the Human Research Program. This is a joint project consisting of human factors teams from the Johnson Space Center (JSC) and the Ames Research Center (ARC). Work on medical training has been conducted in collaboration with the Medical Training Group at JSC and with Wyle that provides medical training to crew members, biomedical engineers (BMEs), and flight surgeons under the Bioastronautics contract. Human factors personnel at Johnson Space Center have investigated medical performance support tools for CMOs and flight surgeons.

Byrne, Vicky↗

NASA NTRS2010

Time-Gating Processes in Intra-Cavity Mode-Locking Devices Like Saturable Absorbers and Kerr Cells

Photons are non-interacting entities. Light beams do not interfere by themselves. Light beams constituting different laser modes (frequencies) are not capable of re-arranging their energies from extended time-domain to ultra-short time-domain by themselves without the aid of light-matter interactions with suitable intra-cavity devices. In this paper we will discuss the time-gating properties of intra-cavity "mode-locking" devices that actually help generate a regular train of high energy wave packets.

Prasad, Narasimha↗

NASA NTRS2014

Co-op Essay - Tour 1

The Mission Operations Directorate (MOD) is responsible for the training, planning and performance of all U.S. manned operations in space. Within this directorate all responsibilities are divided up into divisions. The EVA, Robotics & Crew Systems Operations Division performs ground operations and trains astronauts to carry out some of the more "high action" procedures in space. For example they orchestrate procedures like EVAs, or ExtraVehicular Activities (spacewalks), and robotics operations external to the International Space Station (ISS). The robotics branch of this division is responsible for the use of the Mobile Servicing System (MSS). This system is a combination of two robotic mechanisms and a series of equipment used to transport them on the ISS. The MSS is used to capture and position visiting vehicles, transport astronauts during EVAs, and perform external maintenance tasks on the ISS. This branch consists of two groups which are responsible for crew training and flight controlling, respectively. My first co-op tour took place Fall 2013. During this time I was given the opportunity to work in the robotics operations branch of the Mission Operations Directorate at NASA's Johnson Space Center. I was given a variety of tasks that encompassed, at a base level, all the aspects of the branch.

Porter, Derrick↗

NASA NTRS2012

Evaluating the Use of Remote Sensing Data in the U.S. Agency for International Development Famine Early Warning Systems Network

The U.S. Agency for International Development (USAID)'s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods, and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. We analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000 to 2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices, and food access parameters in their analysis of food security problems. The reports display large-scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data were used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10% of the time, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

USAID↗

NASA NTRS2015

Assessing and Promoting Functional Resilience in Flight Crews During Exploration Missions

NASA plans to send humans to Mars in about 20 years. The NASA Human Research Program supports research to mitigate the major risks to human health and performance on extended missions. However, there will undoubtedly be unforeseen events on any mission of this nature - thus mitigation of known risks alone is not sufficient to ensure optimal crew health and performance. Research should be directed not only to mitigating known risks, but also to providing crews with the tools to assess and enhance resilience, as a group and individually. We can draw on ideas from complexity theory and network theory to assess crew and individual resilience. The entire crew or the individual crewmember can be viewed as a complex system that is composed of subsystems (individual crewmembers or physiological subsystems), and the interactions between subsystems are of crucial importance for overall health and performance. An understanding of the structure of the interactions can provide important information even in the absence of complete information on the component subsystems. This is critical in human spaceflight, since insufficient flight opportunities exist to elucidate the details of each subsystem. Enabled by recent advances in noninvasive measurement of physiological and behavioral parameters, subsystem monitoring can be implemented within a mission and also during preflight training to establish baseline values and ranges. Coupled with appropriate mathematical modeling, this can provide real-time assessment of health and function, and detect early indications of imminent breakdown. Since the interconnected web of physiological systems (and crewmembers) can be interpreted as a network in mathematical terms, we can draw on recent work that relates the structure of such networks to their resilience (ability to self-organize in the face of perturbation). There are many parameters and interactions to choose from. Normal variability is an established characteristic of a healthy physiological response. Healthy coupling has been investigated less extensively, but there are cases in which too tight or too loose coupling can be problematic. This might be in inter-individual behaviors, such as sleep cycles, coordination of work and meal times, and coupled motions during communication. Less apparent are couplings of physiological systems, nevertheless examples abound of coupled systems which might be monitored: cardio-respiratory rhythms; circadian rhythms, body temperature, and sleep; stress markers and cognition, sleep, and performance; profiles of biochemical markers related to immune function and nutritional status; sensorimotor aspects such as motion sickness, ataxia, reaction time, and manual control. Tools for resilience are then the means to measure and analyze these parameters, incorporate them into appropriate models of normal variability and interconnectedness, and recognize when parameters or their couplings are outside of normal limits. What to do when a problem is identified depends on its nature. Changes can be made to crew procedures, work pacing, interpersonal interactions, sleep cycles, meal timing and content, as guided by the model. Use and continued development of these methods could not only provide tools for resilience, but also meaningful autonomous work for the crew on an extended flight.

Shelhamer, Mark↗

NASA NTRS2018

Leveraging Commercial Software Defined Radio for Low Cost Deep Space Testing

In a typical space mission development life cycle, there is a stage where the spacecraft needs to test against the ground station for interface compatibility to ensure that the spacecraft will be properly tracked after launch. This testing normally requires the spacecraft team to bring their flight equipment to the ground station facility. While recognizing that testing with actual flight or engineering module is the most preferred option because of maximum fidelity, there are occasion when the use of actual flight hardware is a logistically challenge because of spacecraft development. Having another test tool that can emulate the spacecraft signal – by recording the signal transmitted by the spacecraft and regenerate an RF signal for ground system testing - would be very useful. It is even more an attractive option if such spacecraft emulator is inexpensive and highly portable. In this paper, we describe a low-cost, light-weight recorder/playback assembly (RPA) that supports deep space missions testing. The equipment leverages on commercially available software defined radios (SDR) and public-domain software. The RPA has been used to support two missions. One effort is to validate that the Uchinoura 34-m tracking station of the Japanese Aerospace Exploration Agency (JAXA) would be able to track the upcoming NASA Exploration Mission 1 (EM-1) spacecraft, scheduled for launch in 2019. The second effort is to help with the testing and certification of the 21-m antenna ground station at the Morehead State University (MSU) in Kentucky, United States, prior to the time when the Lunar IceCube spacecraft is ready for actual compatibility testing. The RPA also enables students/staff training of the new ground station, using the RPA signal as test input into the system. This low-cost test signal allows the MSU team to save money on not having to develop a full-scale self-generated telemetry test signal source.

White, Leslie↗

NASA NTRSDate not supplied

Comparison of Physical Workload Across Eva-Simulation Analog Environments: Hybrid Space Suit Simulator and Pressurized Suit Testing

NASA conducts research, testing, and training across a variety of analog environments to support characterization of human performance during Extravehicular Activities (EVAs). Time utilizing pressurized suits in an offloaded environment is both limited and expensive, making it challenging to carry out extensive research with these suits. The Human Physiology, Performance, Protection, and Operations (H-3PO) Laboratory at NASA Johnson Space Center designed a Hybrid Space Suit Simulator (HS3) as a low-cost, workload approximator and easy access research tool to provide relevant physical and cognitive workloads during simulated EVAs. A pilot study was conducted in a 1g analog environment where six healthy subjects (3 male, 3 female) underwent simulated 5-hour EVAs in the HS3. This study used a COSMED K5 portable metabolic analyzer and a Polar H10 heart rate monitor to evaluate physical workload during the EVAs. For direct comparison, EVA tasks and timelines were modeled after a similar study conducted in pressurized suits (Mark-III spacesuit, n=3 male; small xPGS spacesuit, n=3 female) at NASA’s Active Response Gravity Offload System (ARGOS) offloaded to Lunar gravity (1/6 g). The 1g HS3 data demonstrated increased metabolic rate when compared to pressurized, Lunar-offloaded suited simulated EVAs for certain tasks including the kneeling scoop (HS3: 1194±199 BTU/hr, ARGOS: 860±170 BTU/hr, p < 0.05) and 20 lb object relocation (HS3: 1651±136 BTU/hr, ARGOS: 1048±252.8 BTU/hr, p < 0.05), but it did not demonstrate any significant differences in heart rate (p > 0.05). Other simulated EVA tasks, such as a 500 m traverse (HS3: 1431±177 BTU/hr, ARGOS: 1212±294 BTU/hr, p > 0.05), showed similar workload profiles with no significant workload differences between HS3 and ARGOS. The HS3 is a useful research tool for increasing workloads in EVA research without pressurized suits, though it may overestimate workload during certain EVA tasks when used in 1g environments.

Zachary Wusk↗

NASA NTRSDate not supplied

Utilizing Advanced Air Mobility Rotorcraft Tools for Wildfire Applications

Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadcopter reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles’ translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.

Rotorcraft↗

NASA NTRSDate not supplied

Utilizing Advanced Air Mobility Rotorcraft Tools for Wildfire Applications

Rotorcraft↗

NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗

NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

predictive model↗

DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint

Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

The Effect of Antagonistic Behavior in Reinforcement Learning

The significant achievements of deep reinforcement learning (RL) have motivated researchers to also investigate its shortcomings. Such work has shown that typical methods in deep RL tend to produce brittle policies that overfit to the training environment. In this paper, we introduce the notion of purely antagonistic behavior in value-based agents, where the objective is not to maximize reward but to minimize the victim’s value over time. This notion is motivated by the scenario in which an antagonistic human architect, without access to the environment’s reward function, wants to build an RL agent that can impede another well-trained RL victim agent. First, we formalize a notion of antagonistic behavior in RL. Then, we provide experiments that show how a purely antagonistic agent performs compared to a well-trained victim that learns directly from the game’s rewards. Our results suggest that if one’s goal is to find vulnerabilities in well-trained agents, direct access to the environment’s rewards is not necessary, and antagonistic behavior can be measured independently from environment wins and losses.

Fujimoto, Ted C.↗

DOE OSTI2024

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

42 ENGINEERING↗

DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

NASA NTRS1996

Development of a low-cost virtual reality workstation for training and education

Virtual Reality (VR) is a set of breakthrough technologies that allow a human being to enter and fully experience a 3-dimensional, computer simulated environment. A true virtual reality experience meets three criteria: (1) it involves 3-dimensional computer graphics; (2) it includes real-time feedback and response to user actions; and (3) it must provide a sense of immersion. Good examples of a virtual reality simulator are the flight simulators used by all branches of the military to train pilots for combat in high performance jet fighters. The fidelity of such simulators is extremely high -- but so is the price tag, typically millions of dollars. Virtual reality teaching and training methods are manifestly effective, but the high cost of VR technology has limited its practical application to fields with big budgets, such as military combat simulation, commercial pilot training, and certain projects within the space program. However, in the last year there has been a revolution in the cost of VR technology. The speed of inexpensive personal computers has increased dramatically, especially with the introduction of the Pentium processor and the PCI bus for IBM-compatibles, and the cost of high-quality virtual reality peripherals has plummeted. The result is that many public schools, colleges, and universities can afford a PC-based workstation capable of running immersive virtual reality applications. My goal this summer was to assemble and evaluate such a system.

Phillips, James A.↗

DOE OSTI2020

Accelerating geostatistical modeling using geostatistics-informed machine Learning

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.

58 GEOSCIENCES↗