Results for “personal mobility” — Page 10

DOE OSTI2023

Idaho National Laboratory’s FY 2021 Greenhouse Gas Report

A greenhouse gas (GHG) inventory is a systematic approach to account for the production and release of certain gases generated by an institution from various emission sources. The gases of interest are those that climate science has identified as related to anthropogenic global climate change. This document presents an inventory of GHGs generated during Fiscal Year (FY) 2021 by Idaho National Laboratory (INL)—a Department of Energy (DOE) sponsored entity located in southeastern Idaho. In recent years, concern has grown about the environmental impact of GHGs. This, together with a desire to decrease harmful environmental impacts, would be enough to encourage the calculation of an inventory of the total GHGs generated at INL. Additionally, INL has a desire to see how its emissions compare with similar institutions, including other DOE national laboratories. Executive Order 13834 requires that federal agencies and institutions track and report GHG emissions where required. INL’s GHG inventory was calculated according to methodologies identified in federal GHG guidance documents using operational control boundaries. It measures emissions generated in three scopes: (1) INL emissions produced directly by stationary or mobile combustion and by fugitive emissions, (2) the share of emissions generated by entities from which INL purchased electrical power, and (3) indirect or shared emissions generated by outsourced activities that benefit INL (occurring outside INL’s organizational boundaries but are a consequence of INL’s activities). This inventory found that INL generated 81,185.05 metric tons (MT) of CO 2 equivalent (CO 2 e) emissions during FY 2021. The following conclusions were made from looking at the results of the individual contributors to INL’s FY 2021 GHG inventory: Electricity (including the associated transmission and distribution losses) is the largest contributor to INL’s GHG inventory, with over 50% of the CO 2 e emissions; Other sources with high emissions were mobile combustion (fleet fuels), employee commuting, stationary combustion (facility fuels), and waste disposal (fugitive emissions from the onsite landfill); Sources with low emissions were waste disposal (contracted disposal), fugitive emissions from refrigerants, wastewater treatment (onsite and contracted), and business ground travel (in personal and rental vehicles). This report details the methods behind quantifying INL’s GHG inventory and discusses lessons learned on better practices by which information important to tracking GHGs can be tracked and recorded. It is important to note that because this report differentiates between those portions of INL that are managed and operated by Battelle Energy Alliance, LLC (BEA) and those managed by other contractors, it includes only INL’s activities overseen by BEA. It is assumed that other contractors will provide similar reporting for those activities they manage, where appropriate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Idaho National Laboratory’s FY 22 Greenhouse Gas Report

A greenhouse gas (GHG) inventory is a systematic approach to account for the production and release of certain gases generated by an institution from various emission sources. The gases of interest are those that climate science has identified as related to anthropogenic global climate change. This document presents an inventory of GHGs generated during fiscal year (FY) 2022 by Idaho National Laboratory (INL)—a Department of Energy (DOE) sponsored entity located in southeastern Idaho. In recent years, concern has grown about the environmental impact of GHGs. This, together with a desire to decrease harmful environmental impacts, would be enough to encourage the calculation of an inventory of the total GHGs generated at INL. Additionally, INL has a desire to see how its emissions compare with similar institutions, including other DOE national laboratories. Executive Order 14057 requires that federal agencies and institutions track and report GHG emissions where required. INL’s GHG inventory was calculated according to methodologies identified in federal GHG guidance documents using operational control boundaries. It measures emissions generated in three scopes: (1) INL emissions produced directly by stationary or mobile combustion and by fugitive emissions, (2) the share of emissions generated by entities from which INL purchased electrical power, and (3) indirect or shared emissions generated by outsourced activities that benefit INL (occurring outside INL’s organizational boundaries, but are a consequence of INL’s activities). This inventory found that INL generated 75,572.42 metric tons (MT) of CO2 equivalent (CO2e) emissions during FY 2022. The following conclusions were made from looking at the results of the individual contributors to INL’s FY 2022 GHG inventory: • Electricity (including the associated transmission and distribution losses) is the largest contributor to INL’s GHG inventory, with over 50% of the CO2e emissions. • Other sources with high emissions were mobile combustion (fleet fuels), employee commuting, stationary combustion (facility fuels), and waste disposal (fugitive emissions from the onsite landfill). • Sources with low emissions were waste disposal (contracted disposal), fugitive emissions from refrigerants, wastewater treatment (onsite and contracted), and business ground travel (in personal and rental vehicles). This report details the methods behind quantifying INL’s GHG inventory and discusses lessons learned on better practices by which information important to tracking GHGs can be tracked and recorded. It is important to note that because this report differentiates between those portions of INL that are managed and operated by Battelle Energy Alliance, LLC (BEA) and those managed by other contractors, it includes only INL’s activities overseen by BEA. It is assumed that other contractors will provide similar reporting for those activities they manage, where appropriate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Idaho National Laboratory’s FY 2020 Greenhouse Gas Report

A greenhouse gas (GHG) inventory is a systematic approach to account for the production and release of certain gases generated by an institution from various emission sources. The gases of interest are those that climate science has identified as related to anthropogenic global climate change. This document presents an inventory of GHGs generated during fiscal year (FY) 2020 by Idaho National Laboratory (INL)—a Department of Energy (DOE) sponsored entity located in southeastern Idaho. In recent years, concern has grown about the environmental impact of GHGs. This, together with a desire to decrease harmful environmental impacts, would be enough to encourage the calculation of an inventory of the total GHGs generated at INL. Additionally, INL has a desire to see how its emissions compare with similar institutions, including other DOE national laboratories. Executive Order 13834 requires that federal agencies and institutions track and report GHG emissions where required. INL’s GHG inventory was calculated according to methodologies identified in federal GHG Guidance documents using operational control boundaries. It measures emissions generated in three scopes: (1) INL emissions produced directly by stationary or mobile combustion and by fugitive emissions, (2) the share of emissions generated by entities from which INL purchased electrical power, and (3) indirect or shared emissions generated by outsourced activities that benefit INL (occurring outside INL’s organizational boundaries, but are a consequence of INL’s activities). This inventory found that INL generated 76,494.12 metric tons (MT) of CO2 equivalent (CO 2 e) emissions during FY 2020. The following conclusions were made from looking at the results of the individual contributors to INL’s FY 2020 GHG inventory: (1) Electricity (including the associated transmission and distribution losses) is the largest contributor to INL’s GHG inventory, with over 50% of the CO 2 e emissions; (2) Other sources with high emissions were employee commuting, mobile combustion (fleet fuels), stationary combustion (facility fuels), and waste disposal (fugitive emissions from the onsite landfill); and (3) Sources with low emissions were waste disposal (contracted disposal), fugitive emissions from refrigerants, wastewater treatment (onsite and contracted), and business ground travel (in personal and rental vehicles). This report details the methods behind quantifying INL’s GHG inventory and discusses lessons learned on better practices by which information important to tracking GHGs can be tracked and recorded. It is important to note that because this report differentiates between those portions of INL that are managed and operated by Battelle Energy Alliance, LLC (BEA) and those managed by other contractors, it includes only INL’s activities overseen by BEA. It is assumed that other contractors will provide similar reporting for those activities they manage, where appropriate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1993

A system architecture for an advanced Canadian wideband mobile satellite system

In this paper, the system architecture for an advanced Canadian ka-band geostationary mobile satellite system is described, utilizing hopping spot beams to support a 256 kbps wideband service for both N-ISDN and packet-switched interconnectivity to small briefcase-size portable and mobile terminals. An assessment is given of the technical feasibility of the satellite payload and terminal design in the post year 2000 timeframe. The satellite payload includes regeneration and on-board switching to permit single hop interconnectivity between mobile terminals. The mobile terminal requires antenna tracking and platform stabilization to ensure acquisition of the satellite signal. The potential user applications targeted for this wideband service includes: home-office, multimedia, desk-top (PC) videoconferencing, digital audio broadcasting, single and multi-user personal communications.

Takats, P.↗

NASA NTRS2003

The Cam Shell: An Innovative Design With Materials and Manufacturing

Most of the personal audio and video recording devices currently sold on the open market all require hands to operate. Little consideration was given to designing a hands-free unit. Such a system once designed and made available to the public could greatly benefit mobile police officers, bicyclists, adventurers, street and dirt motorcyclists, horseback riders and many others. With a few design changes water sports and skiing activities could be another large area of application. The cam shell is an innovative design in which an audio and video recording device (such as palm camcorder) is housed in a body-mounted protection system. This system is based on the concept of viewing and recording at the same time. A view cam is attached to a helmet wired to a recording unit encased in a transparent body-mounted protection system. The helmet can also be controlled by remote. The operator will have full control in recording everything. However, the recording unit will be operated completely hands-free. This project will address the design considerations and their effects on material selection and manufacturing. It will enhance the understanding of the structure of materials, and how the structure affects the behavior of the material, and the role that processing play in linking the relationship between structure and properties. A systematic approach to design feasibility study, cost analysis and problem solving will also be discussed.

Chung, W. Richard↗

NASA NTRSDate not supplied

Novel Tilting Ducted-Fan Aerial Vehicle Configurations

There has been recent renewed interested in ducted-fan, powered-lift vertical takeoff and landing aircraft vehicles for uninhabited aerial vehicle and urban aerial mobility aerial applications. Early work was conducted in the 1950’s to the late 1980’s into ducted-fan vehicles, especially at the time for military missions. The focus of ducted-fan research began to shift to UAV’s and personal air transportation in the 2000’s. Work in the early 2000’s fostered interest into ducted-fan aerial vehicles for vertical lift planetary aerial vehicle missions, especially for missions to Titan, a moon of Saturn. For example, early work at NASA Ames conducted the first known investigation into oval ducts (with internal tandem fans) to be used for these aerial vehicles. The use of twin oval ducts (with internal tandem fans) allowed the use of quadrotor-style rotor rpm control for vehicle trim (in hover, for transition to high-speed forward flight the addition of a tilt-actuator to pitch the twin oval ducts forward to be aligned with the freestream needed to be added to the control approach. This current study explores open-rotor, single isolated ducted-fan, and full vehicle configuration aerodynamic performance for hover, transition, and cruise regimes for novel tilting ducted-fan aerial vehicle configurations. The objective of this study is to better understand the design aerodynamics trade space for noncircular tilting ducted-fans. The primary analysis tool used in this paper is the mid-fidelity computational fluid dynamics RotCFD tool. Several interesting and largely unexplored rotor-on-rotor (or fan-on-fan), duct-on-rotor, rotor- on-duct, and duct-on-airframe are examined in this paper. Many of these aerodynamic interactions are the key to overall aerial vehicle performance. Limited parametric sweeps of rotor-to-rotor spacing (and, therefore, sizing and shape of the enveloping ducts) for the various duct configurations will also be performed. The trade-offs between duct thrust augmentation in hover versus improved transition controllability versus improved cruise L/D will be discussed for various novel duct configurations. These ducted-fan configurations include oval ducts, ‘figure-eight’ ducts, and ‘B-shaped’ ducts, among others.

Ducted-Fan↗

DOE OSTI2024

Shared Use Travel Behavior for Improving Rural Mobility: Insights from Greene County, Pennsylvania

Rural communities are considered disadvantaged communities as they suffer from a lack of transport options. Thus, rural regionsprovide less accessibility for commuters to reach their destination as opposed to urban regions. However, the issues of transport disadvantageand shared use mobility in rural areas within the United States (US) have not been well investigated. Furthermore, transport disadvantagediffers between communities and regions across the globe; thus, there is a need to study the behavioral choices of rural commuters within theUS context. This study contributes by analyzing the behavioral choices of rural communities within the US through a case study site ofWaynesburg, Pennsylvania, for adopting a shared use shuttle service. K-means clusters showed that trips from the survey data were a goodrepresentation of real trips from Ecolane. Furthermore, random parameter-based binary logit models were calibrated using data collected fromstudents, faculty, and residents in Waynesburg, Greene County, to study the behavioral choices of commuters. The findings for the faculty andstudents group revealed that prior experience with shared services increases the likelihood of using a shared shuttle. An important personalcharacteristic of inconvenience showed a higher propensity toward using existing modes as opposed to a shared shuttle. Such commutersvalue personal vehicles as more convenient as they have childcare responsibilities and varying schedules for work that require them to moveback and forth across locations, thus making a shared shuttle less attractive for them. The socioeconomic factors of age and gender show ahigher propensity for using shared shuttles. Furthermore, the findings from this study could be helpful for agencies in improving rural mobility andconsidering such shared mobility services for rural communities

42 ENGINEERING↗

DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

1994 SEMCOG Household-Based Person Trip Survey

The Southeast Michigan Council of Governments contracted with the Applied Management & Planning Group to conduct a travel behavior survey of 7,361 households from March 28, 1994, to June 10, 1994. The primary purpose of this study was to provide the council with a new database of travel behavior to assist in updating the region's transportation models. This data collection includes demographic, socioeconomic, travel, and mobile source emissions models for projecting future patterns of development, travel, congestion, and air pollution. Information about household characteristics and travel was collected using a one-day, activity-focused diary and a separate household survey. In an activity diary, respondents recorded 65,535 trips during the assigned the day.

1Hz data↗

NASA NTRS2003

Detecting the Use of Intentionally Transmitting Personal Electronic Devices Onboard Commercial Aircraft

The need to detect unauthorized usage of intentionally transmitting portable electronic devices (PEDs) onboard commercial aircraft is growing, while still allowing passengers to use selected unintentionally transmitting devices, such as laptop computers and CD players during non-critical stages of flight. The following paper presents an installed system for detecting PEDs over multiple frequency bands. Additionally, the advantages of a fixed verses mobile system are discussed. While data is presented to cover the frequency range of 20 MHz to 6.5 GHz, special attention was given to the Cellular/PCS bands as well as Bluetooth and the FRS radio bands. Measurement data from both the semi-anechoic and reverberation chambers are then analyzed and correlated with data collected onboard a commercial aircraft to determine the dominant mode of coupling inside the passenger cabin of the aircraft versus distance from the source. As a final check of system feasibility, several PEDs transmission signatures were recorded and compared with the expected levels.

Woods, Randy↗

NASA NTRS1991

Antennas for mobile satellite communications

A NASA sponsored program, called the Mobile Satellite (MSAT) system, has prompted the development of several innovative antennas at L-band frequencies. In the space segment of the MSAT system, an efficient, light weight, circularly polarized microstrip array that uses linearly polarized elements was developed as a multiple beam reflector feed system. In the ground segment, a low-cost, low-profile, and very efficient microstrip Yagi array was developed as a medium-gain mechanically steered vehicle antenna. Circularly shaped microstrip patches excited at higher-order modes were also developed as low-gain vehicle antennas. A more recent effort called for the development of a 20/30 GHz mobile terminal antenna for future-generation mobile satellite communications. To combat the high insertion loss encountered at 20/30 GHz, series-fed Monolithic Microwave Integrated Circuit (MMIC) microstrip array antennas are currently being developed. These MMIC arrays may lead to the development of several small but high-gain Ka-band antennas for the Personal Access Satellite Service planned for the 2000s.

Huang, John↗

NASA NTRS2007

Automating CapCom Using Mobile Agents and Robotic Assistants

Mobile Agents (MA) is an advanced Extra-Vehicular Activity (EVA) communications and computing system to increase astronaut self-reliance and safety, reducing dependence on continuous monitoring and advising from mission control on Earth. MA is voice controlled and provides information verbally to the astronauts through programs called "personal agents." The system partly automates the role of CapCom in Apollo-including monitoring and managing navigation, scheduling, equipment deployment, telemetry, health tracking, and scientific data collection. Data are stored automatically in a shared database in the habitat/vehicle and mirrored to a site accessible by a remote science team. The program has been developed iteratively in authentic work contexts, including six years of ethnographic observation of field geology. Analog field experiments in Utah enabled empirically discovering requirements and testing alternative technologies and protocols. We report on the 2004 system configuration, experiments, and results, in which an EVA robotic assistant (ERA) followed geologists approximately 150 m through a winding, narrow canyon. On voice command, the ERA took photographs and panoramas and was directed to serve as a relay on the wireless network.

Clancey, William J.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2004

Personal Cooling for Extra-Vehicular Activities on Mars

Extra-vehicular activities (EVA) on Mars will require suits with sophisticated thermal control systems so that astronauts can work comfortably for extended periods of time. Any use of consumables such as water that cannot be easily replaced should be of particular concern. In this aspect the EVA suits for Mars environment need to be different from the current Space Shuttle Extra Vehicular Mobility Units (EMU) that depend on water sublimation into space for removing heat from suits. Moreover, Mars environment is quite different from what a typical EMU may be exposed to. These variations call for careful analysis and innovative engineering for design and fabrication of an appropriate thermal control system. This paper presents a thermal analysis of astronaut suits for EVA with medium metabolic intensity under a typical hot and a nominal cold environment on Mars. The paper also describes possible options that would allow conservation of water with low usage of electrical power. The paper then presents the conceptual design of a portable cooling unit for one such solution.

Pu, Zhengxiang↗

NASA NTRS2004

Personal Cooling for Extra-Vehicular Activities on Mars

Extra-vehicular activities (EVA) on Mars will require suits with sophisticated thermal control systems so that astronauts can work comfortably for extended periods of time. Any use of consumables such as water that cannot be easily replaced should be of particular concern. In this aspect the EVA suits for Mars environment need to be different from the current Space Shuttle Extra Vehicular Mobility Units (EMU) that depend on water sublimation into space for removing heat from suits. Moreover, Mars environment is quite different from what a typical EMU may be exposed to. These variations call for careful analysis and innovative engineering for design and fabrication of an appropriate thermal control system. This paper presents a thermal analysis of astronaut suits for EVA with medium metabolic intensity under a typical hot and a nominal cold environment on Mars. The paper also describes possible options that would allow conservation of water with low usage of electrical power. The paper then presents the conceptual design of a portable cooling unit for one such solution.

Pu, Zhengxiang↗

NASA NTRS2006

NASA Helps Design the 'Cockpit of the Future'

One of the initiatives at Langley involved the research and development of a Small Aircraft Transportation System Network (SATS-Net), which was the communications infrastructure for NASA s SATS program.The primary goal of SATS-Net was to develop secure virtual travel portals for the Nation s general aviation airports and to offer an alternative to commercial air and ground transportation through general aviation. The secondary goals were to increase mobility, reduce door-to- door travel times, and provide air transportation to underserved markets at an affordable cost. One of the specific plans was to create a network of information about the 2,000-plus non-tower, non-radar airports in the Nation. This information would be accessible from a person s home, office, PDA, or local airport. This network provides real-time information and would be a "one-stop shop" portal with dynamic access to real-time Web cameras for weather conditions, flight planning information, local lodging, restaurants, and attraction information. SATS-Net would provide membership services much like that of AAA.

Source record↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) DATASCOPE Technical Interchange Meeting 21st August 2023: Background and Summary of Discussion

An aspiration for EIMO datascope is to realize artificial intelligence-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A vision proposed to the meeting participants was that of a “system of systems,” whereby EIMO will utilize AI-supported natural language processing and machine learning techniques to synthesize embedded reference databases and real-time data streams [input vectors] from multiple data sources to continuously and seamlessly assess crew health & performance. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will ideally have a degree of mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats.

Artificial Intelligence↗