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NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boiloff Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗
NASA NTRS2019

Overexpression of Catalase in Mitochondria Mitigates the Effects of Simulated Microgravity and Social Isolation on Cytokine Expression in Mouse Hippocampus

Space flight missions are becoming longer and more common and evidence points to the physiological toll the missions have on the human body. Aging, sedentary lifestyle, and spaceflight have similar degenerative effects on almost every part of our body; both exposure to the space environment and aging result in cardiovascular deconditioning, bone loss, muscle atrophy, brain changes, and immune response impairment. We hypothesize that exposure to the space environment generates excessive Reactive Oxygen Species (ROS), which results in neuroinflammation and aging-like degenerative symptoms in the brain. We used the hindlimb unloading (HU) model to mimic microgravity with either paired or single housed animals (social isolation). Responses to 30d (30 days) of HU were compared in wildtype or transgenic MCAT (Mitochondrial-targeted CATalase) mice, in which mitochondrial ROS is quenched by over-expression of human catalase. Expression of 4-Hydroxynonenal (4HNE) and Park7 (redox-sensitive chaperone and sensor of oxidative stress) were measured by ELISA (Enzyme-Linked ImmunoSorbent Assay), a protein array quantified from the hippocampal cytokines and 8-hydroxy-2'-deoxyguanosine in serum was measured by ELISA to assess oxidative DNA damage. Preliminary analysis of cage behavior patterns from video collected at the end of the study showed that MCAT HU mice (socially housed) were more active and conducted more exploratory activities compared to NL (Normally Loaded). Our biochemical results showed simulated microgravity and/or social isolation caused changes in levels of cytokines related to immune responses. Two-way ANOVA (Analysis of Variance) revealed significant interaction effects of HU and genotype in expression levels of five cytokines (out of 35) in socially-housed animals. Elevation of these generally pro-inflammatory cytokines by HU in WT (Wild Type) mice was mitigated in MCAT mice, suggesting a role for mitochondrial ROS signaling in inflammatory CNS (Central Nervous System) responses to microgravity. Interestingly, some of these cytokines in the hippocampus displayed strong correlations to the 4HNE levels. We also found substantive cytokine responses to social isolation in the hippocampus; housing and genotype interaction effects were significant (by 2-Factor ANOVA) for 15 cytokines, most of which were mitigated in MCAT mice.Taken together, our results showed that both simulated microgravity and social isolation influenced cytokine levels in the hippocampus and MCAT mice were at least partially protected from these changes. These findings implicate a potentially important role for mitochondrial ROS in CNS responses to the challenges posed by long duration spaceflight.

MCAT↗
DOE OSTI2022

‪A Novel Methodology for Longitudinal Studies of Home Thermal Comfort Perception and Behavior

Human-building interactions significantly influence building energy consumption and affect peak energy demand. For example, heating and cooling contribute 46% of daily peak residential energy demand. Grid-interactive efficient buildings (GEBs) can potentially increase energy-demand flexibility and accelerate the adoption of renewables. However, traditional demand response (DR) programs focused on shedding peak loads disregard the human-building interactions leading to occupant thermal frustration. Specifically, they do not model occupants’ ability to override thermostat controls, nor how indoor environmental conditions and sociocultural factors affect the timing and magnitude of overrides. Studies have found 20% of occupants override thermostat setpoints during DR events longer than 6 hours yet lack detail about the motivation that might guide more successful efforts. Balancing energy-demand flexibility with occupant thermal comfort requires understanding dynamic occupant behavior, the underlying psychophysiological drivers, in the context of homes. This paper presents methods for scalable longitudinal studies of residential occupant behavior dynamics to inform the development of psychophysiological occupant-centric building models. Smart sensors were installed to measure local environmental conditions in 20 homes in two regions of the United States. Just-in-time ecological momentary assessments (EMAs) provided qualitative data on occupant thermal comfort and local environmental conditions not captured in Internet-of-Things (IoT) based studies or existing datasets. Participant interviews provided insight into environmental attitudes, mental models, and the role of economics in comfort and behavior, which in turn may affect thermostat interactions. Based on these data, interventions in the next phase of the study will collect data and monitor occupant behavior during simulated DR events.

Kane, Michael↗
DOE OSTI2022

Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

Highly mutable infectious disease pathogens (hm-IDPs) such as HIV and influenza evolve faster than the human immune system can contain them, allowing them to circumvent traditional vaccination approaches and causing over one million deaths annually. Agent-based models can be used to simulate the complex interactions that occur between immune cells and hm-IDP-like proteins (antigens) during affinity maturation—the process by which antibodies evolve. Compared to existing experimental approaches, agent-based models offer a safe, low-cost, and rapid route to study the immune response to vaccines spanning a wide range of design variables. However, the highly stochastic nature of affinity maturation and vast sequence space of hm-IDPs render brute force searches intractable for exploring all pertinent vaccine design variables and the subset of immunization protocols encompassed therein. To address this challenge, we employed deep reinforcement learning to drive a recently developed agent-based model of affinity maturation to focus sampling on immunization protocols with greater potential to improve the chosen metrics of protection, namely the broadly neutralizing antibody (bnAb) titers or fraction of bnAbs produced. Using this approach, we were able to coarse-grain a wide range of vaccine design variables and explore the relevant design space. Our work offers new testable insights into how vaccines should be formulated to maximize protective immune responses to hm-IDPs and how they can be minimally tailored to account for major sources of heterogeneity in human immune responses and various socioeconomic factors. Our results indicate that the first 3 to 5 immunizations, depending on the metric of protection, should be specially tailored to achieve a robust protective immune response, but that beyond this point further immunizations require only subtle changes in formulation to sustain a durable bnAb response.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2013

Effects of Airport Tower Controller Decision Support Tool on Controllers Head-Up Time

Despite that aircraft positions and movements can be easily monitored on the radar displays at major airports nowadays, it is still important for the air traffic control tower (ATCT) controllers to look outside the window as much as possible to assure safe operations of traffic management. The present paper investigates whether an introduction of the NASA's proposed Spot and Runway Departure Advisor (SARDA), a decision support tool for the ATCT controller, would increase or decrease the controllers' head-up time. SARDA provides the controller departure-release schedule advisories, i.e., when to release each departure aircraft in order to minimize individual aircraft's fuel consumption on taxiways and simultaneously maximize the overall runway throughput. The SARDA advisories were presented on electronic flight strips (EFS). To investigate effects on the head-up time, a human-in-the-loop simulation experiment with two retired ATCT controller participants was conducted in a high-fidelity ATCT cab simulator with 360-degree computer-generated out-the-window view. Each controller participant wore a wearable video camera on a side of their head with the camera facing forward. The video data were later used to calculate their line of sight at each moment and eventually identify their head-up times. Four sessions were run with the SARDA advisories, and four sessions were run without (baseline). Traffic-load levels were varied in each session. The same set of user interface - EFS and the radar displays - were used in both the advisory and baseline sessions to make them directly comparable. The paper reports the findings and discusses their implications.

airport surface operations↗
DOE OSTI2022

Infrared-Fused Vision-Based Thermoregulation Performance Estimation for Personal Thermal Comfort-Driven HVAC System Controls

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Advanced Reactor Control and Operations (ARCO): A University Research Facility for Developing Optimized Digital Control Rooms

The Advanced Reactor Control and Operations (ARCO) facility was constructed in January 2018 to serve as a test bed for advanced reactor control rooms and operator support systems. Since then, it has supported human-machine interface user experience research, fault detection and mitigation technology development, control room concept of operations development, and remote operations research. ARCO serves as the control room for the Compact Integral Effects Test (CIET) facility, which replicates the primary-side flow paths and thermal-hydraulic behavior of a fluoride-salt-cooled high-temperature reactor (FHR) using simulant fluids and scaling principles. New reactor designs feature different operating conditions and scenarios than those in existing reactors. ARCO supports the research and development of digital tools for operator communications, intuitive real-time data analysis, online health monitoring and prognostics, and control room cybersecurity. By integrating these different technologies, ARCO acts as a prototypical control system to iteratively develop methods and tools of operation in advanced small modular nuclear reactors. This paper describes the features of and challenges to operating advanced small modular reactors underlying the design basis for ARCO and its operator support systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1989

Communication as group process media of aircrew performance

This study of group process was motivated by a high-fidelity flight simulator project in which aircrew performance was found to be better when the crew had recently flown together. Considering recent operating experience as a group-level input factor, aspects of the communication process between crewmembers (Captain and First Officer), were explored as a possible mediator to performance. Communication patterns were defined by a speech act typology adapted for the flightdeck setting and distinguished crews that had previously flown together (FT) from those that had not flown together (NFT). A more open communication channel with respect to information exchange and validation and greater First Officer participation in task-related topics was shown by FT crews while NFT crews engaged in more non-task discourse, a speech mode less structured by roles and probably serving a more interpersonal function. Relationships between the speech categories themselves, representing linguistic, and role-related interdependencies provide guidelines for interpreting the primary findings.

Aviation↗
NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗
DOE OSTI2024

Cellular response of keratinocytes to the entry and accumulation of nanoplastic particles

Plastic accumulation in the environment is rapidly increasing, and nanoplastics (NP), byproducts of environmental weathering of bulk plastic waste, pose a significant public health risk. Particles may enter the human body through many possible routes such as ingestion, inhalation, and skin absorption. However, studies on NP penetration and accumulation in human skin are limited. Loss or reduction of the keratinized skin barrier may enhance the skin penetration of NPs. The present study investigated the entry of NPs into a human skin system modeling skin with compromised barrier functions and cellular responses to the intracellular accumulations of NPs. Two in vitro models were employed to simulate human skin lacking keratinized barriers. The first model was an ex vivo human skin culture with the keratinized dermal layer (stratum corneum) removed. The second model was a 3D keratinocyte/dermal fibroblast cell co-culture model with stratified keratinocytes on the top and a monolayer of skin fibroblast cells co-cultured at the bottom. The penetration and accumulation of the NPs in different cell types were observed using fluorescent microscopy, confocal microscopy, and cryogenic electron microscopy (cryo-EM). The cellular responses of keratinocytes and dermal fibroblast cells to stress induced by NPs stress were measured. The genetic regulatory pathway of keratinocytes to the intracellular NPs was identified using transcript analyses and KEGG pathway analysis. The cellular uptake of NPs by skin cells was confirmed by imaging analyses. Transepidermal transport and penetration of NPs through the skin epidermis were observed. According to the gene expression and pathway analyses, an IL-17 signaling pathway was identified as the trigger for cellular responses to internal NP accumulation in the keratinocytes. The transepidermal NPs were also found in co-cultured dermal fibroblast cells and resulted in a large-scale transition from fibroblast cells to myofibroblast cells with enhanced production of α-smooth muscle actin and pro-Collagen Ia. The upregulation of inflammatory factors and cell activation may result in skin inflammation and ultimately trigger immune responses.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information.

Dollar, Robert Austin↗
NASA NTRS1998

A Cognitive-System Model for En Route Air Traffic Management

NASA Ames Research Center has been engaged in the development of advanced air traffic management technologies whose basic form is cognitive aiding systems for air traffic controller and flight deck operations. In the design and evaluation of such systems the dynamic interaction between the airborne aiding system and the ground-based aiding systems forms a critical coupling for control. The human operator is an integral control element in the system and the optimal integration of human decision and performance parameters with those of the automation aiding systems offers a significant challenge to cognitive engineering. This paper presents a study in full mission simulation and the development of a predictive computational model of human performance. We have found that this combination of methodologies provide a powerful design-aiding process. We have extended the computational model Man Machine Integrated Design and Analysis System (N13DAS) to include representation of multiple cognitive agents (both human operators and intelligent aiding systems), operating aircraft airline operations centers and air traffic control centers in the evolving airspace. The demands of this application require the representation of many intelligent agents sharing world-models, and coordinating action/intention with cooperative scheduling of goals and actions in a potentially unpredictable world of operations. The operator's activity structures have been developed to include prioritization and interruption of multiple parallel activities among multiple operators, to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. We have exercised this model in a multi-air traffic sector scenario with potential conflict among aircraft at and across sector boundaries. We have modeled the control situation as a multiple closed loop system. The inner and outer loop alerting structure of air traffic management has many implications that need to be investigated to assure adequate design. First, there are control and stability factors implicit in the design. As the inner loop response time approaches that of the outer loop, system stability may be compromised in that controllers may be solving a problem the nature of which has already been changed by pilot action. Second, information exchange and information presentation for both air and ground must be designed to complement as opposed to compete with each other. Third, the level of individual and shared awareness in trajectory modification and flight conformance needs to be defined. Fourth, the level of required awareness and performance impact of mixed fleet operations and failed-mode recovery must be explored.

Corker, Kevin M.↗
NASA NTRS1993

Programmable display pushbuttons on the Space Station's telerobot control panel

The Man-Systems Telerobotics Laboratory at NASA's Johnson Space Center and supported by Lockheed, is working to ensure that the Flight Telerobotic Servicer (FTS) to be used on the Space Shuttle (Orbiter) and the Space Station has a well designed user interface from a Human Factors perspective. The FTS, which is a project led by NASA's Goddard Space Flight Center, will be a telerobot used for Space Station construction, maintenance, and satellite repair. It will be directly controlled from workstations on the Orbiter and the Space Station and monitored from a ground workstation. The FTS will eventually evolve into a more autonomous system, but in the short-term the system will be manually operated (teleoperated) for many tasks. This emphasizes the importance of the human/telerobot interface on this system. This phase of the FTS workstation evaluation covers a preliminary study of programmable display pushbuttons (PDP's). The PDP is constructed of a matrix of directly addressable electroluminescent (EL) pixels which can be used to form dot-matrix characters. PDP's can be used to display more than one message and to control more than one function. Since the PDP's have these features, then a single PDP may possibly replace the use of many single-function pushbuttons, rotary switches, and toggle switches, thus using less panel space. It is of interest to determine if PDP's can be used to adequately perform complex hierarchically structured task sequences. The objective of this investigation was to compare the performance of experienced and inexperienced Remote Manipulator System (RMS) operators while performing an RMS like task on simulated PDP and non-PDP computer prototypes so that guidelines governing the use of programmable display pushbuttons on the FTS workstation could be created. The functionality of the RMS on the Orbiter was used as a model for this evaluation since the functionality of the FTS at the time of this writing has not been solidified.

Stuart, Mark A.↗
NASA NTRS2009

Feasibility of Mixed Equipage Operations in the Same Airspace

This study used a human-in-the-loop simulation to examine the feasibility of mixed equipage operations in an automated separation assurance environment under higher traffic densities. The study involved two aircraft equipage alternatives with and without data link and four traffic conditions. In all traffic conditions the unequipped traffic count was increased linearly throughout the scenario from approximately 5 to 20 aircraft. Condition One consisted solely of this unequipped traffic, while the remaining three conditions also included a constant number of equipped aircraft operating within the same airspace: 15 equipped aircraft in condition two, 30 in condition three, and 45 in condition four. If traffic load became excessive during any run, participants were instructed to refuse sector entry to inbound unequipped aircraft until sector load became manageable. Results showed a progressively higher number of unequipped aircraft turned away under the second, third, and fourth scenario conditions. Controller workload also increased progressively. Participants rated the mixed operations concept as acceptable, with some qualifications about procedures and information displays. These results showed that mixed operations might be feasible in the same airspace, if unequipped aircraft count is held to a workable level. This level will decrease with increasing complexity. The results imply that integrated airspace configuration is feasible to a limit. The results also indicate that the conflict detection and resolution automation, equipage, and traffic density are important factors that will need to be considered for airspace configuration.

Kopardekar, Parimal↗
NASA NTRS2009

Feasibility of Mixed Equipage Operations in the Same Airspace

This study used a human-in-the-loop simulation to examine the feasibility of mixed equipage operations in an automated separation assurance environment under higher traffic densities. The study involved two aircraft equipage alternatives - with and without data link- and four traffic conditions. In all traffic conditions the unequipped traffic count was increased linearly throughout the scenario from approximately 5 to 20 aircraft. Condition One consisted solely of this unequipped traffic, while the remaining three conditions also included a constant number of equipped aircraft operating within the same airspace: 15 equipped aircraft in condition two, 30 in condition three, and 45 in condition four. If traffic load became excessive during any run, participants were instructed to refuse sector entry to inbound unequipped aircraft until sector load became manageable. Results showed a progressively higher number of unequipped aircraft turned away under the second, third, and fourth scenario conditions. Controller workload also increased progressively. Participants rated the mixed operations concept as acceptable, with some qualifications about procedures and information displays. These results showed that mixed operations might be feasible in the same airspace, if unequipped aircraft count is held to a workable level. This level will decrease with increasing complexity. The results imply that integrated airspace configuration is feasible to a limit. The results also indicate that the conflict detection and resolution automation, equipage, and traffic density are important factors that will need to be considered for airspace configuration.

Kopardekar, Parimal↗
NASA NTRSDate not supplied

Combining Disparate Measures of Metabolic Rate During Simulated Spacewalks

Scientists from NASA's Extravehicular Activities (EVA) Physiology Systems and Performance Project help design space suits for future missions, during which astronauts are expected to perform EVA activities on the Lunar or Martian surface. During an EVA, an astronaut's integrated metabolic rate is used to predict how much longer the activity can continue and still provide a safe margin of remaining consumables. For EVAs in the Apollo era, NASA physicians monitored live data feeds of heart rate, O 2 consumption, and liquid cooled garment (LCG) temperatures, which were subjectively combined or compared to produce an estimate of metabolic rate. But these multiple data feeds sometimes provided conflicting estimates of metabolic rate, making real-time calculations of remaining time difficult for physician/monitors. Currently, designs planned for the Constellation Program EVAs utilize an automated, but largely heuristic methodology for incorporating the above three measurements, plus an additional one - CO 2 production, ignoring data that appears in conflict; however a more rigorous model-based approach is desirable. In this study, we show how principal axis factor analysis, in combination with OLS regression and LOWESS smoothing can be used to estimate metabolic rate as a data-driven weighted average of heart rate, O 2 consumption, LCG temperature data, and CO 2 production. Preliminary results suggest less sensitivity to occasional spikes in observed data feeds, and reasonable within-subject reproducibility when applied to subsequent tasks. These methods do not require physician monitoring and as such can be automated in the electronic components of future space suits. With additional validation, our models show promise for increasing astronaut safety, while reducing the need for and potential errors associated with human monitoring of multiple systems.

Alan H Feiveson↗
NASA NTRS1992

Integration mockup and process material management system

Work to define and develop a full scale Space Station Freedom (SSF) mockup with the flexibility to evolve into future designs, to validate techniques for maintenance and logistics and verify human task allocations and support trade studies is described. This work began in early 1985 and ended in August, 1991. The mockups are presently being used at MSFC in Building 4755 as a technology and design testbed, as well as for public display. Micro Craft also began work on the Process Material Management System (PMMS) under this contract. The PMMS simulator was a sealed enclosure for testing to identify liquids, gaseous, particulate samples, and specimen including, urine, waste water, condensate, hazardous gases, surrogate gasses, liquids, and solids. The SSF would require many trade studies to validate techniques for maintenance and logistics and verify system task allocations; it was necessary to develop a full scale mockup which would be representative of current SSF design with the ease of changing those designs as the SSF design evolved and changed. The tasks defined for Micro Craft were to provide the personnel, services, tools, and materials for the SSF mockup which would consist of four modules, nodes, interior components, and part task mockups of MSFC responsible engineering systems. This included the Engineering Control and Life Support Systems (ECLSS) testbed. For the initial study, the mockups were low fidelity, soft mockups of graphics art bottle, and other low cost materials, which evolved into higher fidelity mockups as the R&D design evolved, by modifying or rebuilding, an important cost saving factor in the design process. We designed, fabricated, and maintained the full size mockup shells and support stands. The shells consisted of cylinders, end cones, rings, longerons, docking ports, crew airlocks, and windows. The ECLSS required a heavier cylinder to support the ECLSS systems test program. Details of this activity will be covered. Support stands were designed and built for each module to sufficiently move and rotate each module. Secondary structures such as floors, ceilings, bulkheads, standoffs, racks, etc. were developed and built.

Verble, Adas James, Jr.↗
NASA NTRS2007

Pharmacotherapeutics of Intranasal Scopolamine: FDA Regulations and Procedures for Clinical Applications

Space Motion Sickness (SMS) is commonly experienced by astronauts and often requires treatment with medications during the early flight days of a space mission. Bioavailability of oral (PO) SMS medications is often low and highly variable; additionally, physiological changes in a microgravity environment exacerbate variability and decrease bioavailability. These factors prompted NASA to develop an intranasal dosage form of scopolamine (INSCOP) suitable for the treatment of SMS. However, to assure safety and efficacy of treatment in space, NASA physicians prescribe commercially available pharmaceutical products only. Development of a pharmaceutical preparation for clinical use must follow distinct clinical phases of testing, phase I through IV to be exact, before it can be approved by the FDA for approval for clinical use. After a physician sponsored Investigative New Drug (IND) application was approved by the FDA, a phase I clinical trial of INSCOP formulation was completed in normal human subjects and results published. The current project includes three phase II clinical protocols for the assessment of pharmacokinetics and pharmacodynamics (PK/PD), efficacy, and safety of INSCOP. Three clinical protocols that were submitted to FDA to accomplish the project objectives: 1) 002-A, a FDA Phase II dose ranging study with four dose levels between 0.1 and 0.4 mg in 12 subjects to assess PK/PD, 2) 002-B, a phase II clinical efficacy study in eighteen healthy subjects to compare efficacy of 0.2 (low dose) and 0.4 mg (high dose) INSCOP for prophylactic treatment of motion-induces (off-axis vertical rotation) symptoms, and (3) 002-C, a phase II clinical study with twelve subjects to determine bioavailability and pharmacodynamics of two doses (0.2 and 0.4 mg) of INSCOP in simulated microgravity, antiorthostatic bedrest. All regulatory procedures were competed that include certification for Good laboratory Procedures by Theradex , clinical documentation, personnel training, selection of clinical research operations contractor, data capturing and management, and annual reporting of results to FDA were successfully completed. Protocol 002-A was completed and sample and data analysis is currently in progress. Protocol 002-B is currently in progress at Dartmouth Hitchcock Medical Center and Protocol 002-C has been submitted to the FDA and will be implemented at the same contractor site as 002-A. An annual report was filed as required by FDA on the results of Protocol 002-A. Once all the three Phase II protocols are completed, a New Drug Administration application will be filed with FDA for Phase III clinical assessment and approval for marketing of the formulation. A commercial vendor will be identified for this phase. This is critical for making this available for treatment of SMS in astronauts and military personnel on duty. Once approved by FDA, INSCOP can be also used by civilian population for motion sickness associated with recreational travel and other ailments that require treatment with anticholinergic drugs.

Das, H.↗