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The Internal Active Thermal Control System (IATCS) aboard the International Space Station (ISS) contains an aqueous, alkaline fluid (pH 9.5±0.5) that aids in maintaining a habitable environment for the crew. Because microbes have significant potential to cause disease, adverse effects on astronaut health, and microbe-induced corrosion, the presence of both bacteria and viruses within IATCS fluids is of concern. This study sought to detect and identify viral populations in IATCS samples obtained from the Kennedy Space Center as a first step towards characterizing and understanding potential risks associated with them. Samples were concentrated and viral nucleic acids (NA) extracted providing solutions containing 8.87-22.67 μg NA per mL of heat transfer fluid. After further amplification viral DNA and cDNA were then pooled, fluorescently labeled, and hybridized onto a Combimatrix panvira 12K microarray containing probes for ∼1,000 known human viruses.

Understanding in-use fleet operating behavior is of paramount importance when evaluating the potential of advanced/alternative vehicle technologies. Accurately characterizing real world vehicle operation assists in properly allocating advanced technologies, playing a role in determining initial payback period and return on investment. In addition, this information contributes to the design and deployment of future technologies as the result of increased awareness regarding tractive power requirements associated with typical operating behavior. In this presentation, the concept of vehicle duty cycles and their relation to advanced technologies will be presented and explored. Additionally, current research attempts to characterize school bus operation will be examined, and existing computational analysis and evaluation tools associated with these efforts discussed. Presenter Adam Duran, National Renewable Energy Laboratory

In October, 1998, the first of the NASA New Millennium Spacecraft, DS1, was successfully launched into space. The objectives for this spacecraft are to test advanced technologies that can reduce the cost or risk of future missions. One of these technologies is the Solar Concentrator Array with Refractive Linear Element Technology (SCARLET). Although part of the advanced technology validation study, the array is also the spacecraft power source. Funded by BMDO, the SCARLET™ concentrator solar array is the first spaceflight application of a refractive lens concentrator. As part of the DS1 validation process, the amount of array diagnostics is very extensive. The data obtained includes temperature measurements at numerous locations on the 2-wing solar array. For each individual panel, a 5-cell module in one of the circuit strings is wired so that a complete I-V curve can be obtained. This data is used to verify sun pointing accuracy and array output performance.

The combined anticipated trends of vehicle sharing (ride-hailing), automated control, and powertrain electrification are poised to disrupt the current paradigm of predominately owner-driven gasoline vehicles with low levels of utilization. Shared, automated, electric vehicle (SAEV) fleets offer the potential for lower cost and emissions and have garnered significant interest among the research community. While promising, unmanaged operation of these fleets may lead to unintended negative consequences. One potentially unintended consequence is a high quantity of SAEVs charging during peak demand hours on the electric grid, potentially increasing the required generation capacity. This research explores the flexibility associated with charging loads demanded by SAEV fleets in response to servicing personal mobility travel demands. Travel demand is synthesized in four major United States metropolitan areas: Detroit, MI; Austin, TX; Washington, DC; and Miami, FL.

The Trace Gas Analyzer (TGA, Figure 1) is a self-contained, battery-powered mass spectrometer that is designed for use by astronauts during extravehicular activities (EVA) on the International Space Station (ISS). The TGA contains a miniature quadrupole mass spectrometer array (QMSA) that determines the partial pressures of ammonia, hydrazines, nitrogen, and oxygen. The QMSA ionizes the ambient gas mixture and analyzes the component species according to their charge-to-mass ratio. The QMSA and its electronics were designed, developed, and tested by the Jet Propulsion Laboratory (1,2). Oceaneering Space Systems supported JPL in QMSA detector development by performing 3D computer for optimal volumetric integration, and by performing stress and thermal analyses to parameterize environmental performance.

Debris in the Orbiter crew compartment of early Shuttle missions created crew health concerns and physiological discomfort, and was the cause of some equipment malfunctions. Debris from Orbiters during flight and processing was analyzed, quantized, and evaluated to determine its source. Records were kept on the amount of debris vacuumed by the crew during on-orbit cleaning and the amount found on air-cooled avionics boxes during ground turnaround. After ground turnaround operations at Kennedy Space Center and Palmdale were reviewed from a facility, materials use, and materials control standpoint, the following remedial steps were taken.

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts.

Spark-ignition engine fuel standards have been put in place to ensure acceptable hot and cold weather driveability (HWD and CWD). Vehicle manufacturers and fuel suppliers have developed systems that meet our driveability requirements so effectively that drivers overwhelmingly find that their vehicles reliably start up and operate smoothly and consistently throughout the year. For HWD, fuels that are too volatile perform more poorly than those that are less volatile. Vapor lock is the apparent cause of poor HWD, but there is conflicting evidence in the literature as to where in the fuel system it occurs. Most studies have found a correlation between degraded driveability and higher dry vapor pressure equivalent or lower TV/L = 20, and less consistently with a minimum T50. For CWD, fuels with inadequate volatility can cause difficulty in starting and rough operation during engine warmup.

Driver behavior models play a significant role in representing different driving styles and the associated relationships with traffic patterns and vehicle energy consumption in simulation studies. The models often serve as a proxy for baseline human driving when assessing energy-saving strategies that alter vehicle velocity. Such models are especially important in connectivity-enabled energy-saving strategy research because they can easily adapt to changing driving conditions like posted speed limits or change in traffic light state. While numerous driver models exist, parametric driver models provide the flexibility required to represent variability in real-world driving through different combinations of model parameters. These model parameters must be informed by a representative set of parameter values for the driver model to adequately represent a real-world driver.

The disparate characteristics between conventional (CVs) and battery electric vehicles (BEVs) in terms of driving range, refill/recharge time, and availability of refuel/recharge infrastructure inherently limit the relative utility of BEVs when benchmarked against traditional driver travel patterns. However, given a high penetration of high-power public charging combined with driver tolerance for rerouting travel to facilitate charging on long-distance trips, the difference in utility between CVs and BEVs could be marginalized. We quantify the relationships between BEV utility, the deployment of fast chargers, and driver tolerance for rerouting travel and extending travel durations by simulating BEVs operated over real-world travel patterns using the National Renewable Energy Laboratory's Battery Lifetime Analysis and Simulation Tool for Vehicles (BLAST-V). With support from the U.S.

NASA-JSC is evaluating planetary mission scenarios where plants will provide the majority of the diet for the crew. The requirements of both plants and crew diet need to be integrated in the development of the final food system. Plant growth has limitations in type and quantity of crops to be produced while diets must meet palatability and nutritional requirements as well as limited processing labor, equipment and power. A plan is presented for the development of a food system based heavily on grown crops. Although the steps taken in the development are applicable to the design of any long duration flight food system. The process begins with the development of a food list, followed by preliminary menu design, nutritional analysis and finally menu testing.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

With support from the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory developed BLAST-V-the Battery Lifetime Analysis and Simulation Tool for Vehicles. The addition of high-resolution spatial-temporal travel histories enables BLAST-V to investigate user-defined infrastructure rollouts of publically accessible charging infrastructure, as well as quantify impacts on vehicle and station owners in terms of improved vehicle utility and station throughput. This paper presents simulation outputs from BLAST-V that quantify the utility improvements of multiple distinct rollouts of publically available Level 2 electric vehicle supply equipment (EVSE) in the Seattle, Washington, metropolitan area. Publically available data on existing Level 2 EVSE are also used as an input to BLAST-V. The resulting vehicle utility is compared to a number of mock rollout scenarios.

This work details two approaches for evaluating transmission warming technology: experimental dynamometer testing and development of a simplified transmission efficiency model to quantify effects under varied real world ambient and driving conditions. Two vehicles were used for this investigation: a 2013 Ford Taurus and a highly instrumented 2011 Ford Fusion (Taurus and Fusion). The Taurus included a production transmission warming system and was tested over hot and cold ambient temperatures with the transmission warming system enabled and disabled. A robot driver was used to minimize driver variability and increase repeatability. Additionally the instrumented Fusion was tested cold and with the transmission pre-heated prior to completing the test cycles. These data were used to develop a simplified thermally responsive transmission model to estimate effects of transmission warming in real world conditions.

A technology demonstration propylene Loop Heat Pipe (LHP) has been tested extensively in support of the implementation of this two-phase thermal control technology on NASA’s Earth Observing System (EOS) Tropospheric Emission Spectrometer (TES) instrument. This cryogenic instrument is being developed at the Jet Propulsion Laboratory (JPL) for NASA. This paper reports on the transient characterization testing results showing low frequency temperature oscillations. Steady state performance and model correlation results can be found elsewhere. Results for transient startup and shutdown are also reported elsewhere. In space applications, when LHPs are used for thermal control, the power dissipation components are typically of large mass and may operate over a wide range of power dissipations; there is a concern that the LHP evaporator may see temperature oscillations at low powers and over some temperature range.

Conceptual designs for a space suit Personal Life Support Subsystem (PLSS) were developed and assessed to determine if upgrading the system using new, emerging, or projected technologies to fulfill basic functions would result in mass, volume, or performance improvements. Technologies were identified to satisfy each of the functions of the PLSS in three environments (zero-g, Lunar, and Martian) and in three time frames (2006, 2010, and 2020). The viability of candidate technologies was evaluated using evaluation criteria such as safety, technology readiness, and reliability. System concepts (schematics) were developed for combinations of time frame and environment by assigning specific technologies to each of four key functions of the PLSS -- oxygen supply, waste removal, thermal control, and power. The PLSS concepts were evaluated using the ExtraVehicular Activity System Sizing Analysis Tool, software created by NASA to analyze integrated system mass, volume, power and thermal loads.

Current vehicle climate control systems are dramatically overpowered because they are designed to condition the cabin air mass in a specified period of time. A more effective and energy efficient objective is to directly achieve thermal comfort of the passengers. NREL is developing numerical and experimental tools to predict human thermal comfort in non-uniform transient thermal environments. These tools include a finite element model of human thermal physiology, a psychological model that predicts both local and global thermal comfort, and a high spatial resolution sweating thermal manikin for testing in actual vehicles.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Wheat is a candidate crop for the Advanced Life Support (ALS) system, and cereal grains and their products will be included on long-term space missions beyond low earth orbit. While the exact supply scenario has yet to be determined, some type of post-processing of these grains must occur if they are shipped as bulk ingredients or grown on site for use in foods. Understanding the requirements for processing grains in space is essential for incorporating the process into the ALS food system. The ESM metric developed by NASA describes and compares individual system impact on a closed system in terms of a single parameter, mass. The objective of this study was to compare the impact of grain mill type on the ESM of producing yeast and flat breads. Hard red spring wheat berries were ground using a Brabender Quadrumat Jr. or the Kitchen-Aid grain mill attachment (both are proposed post-harvest technologies for the ALS system) to produce white and whole wheat flour, respectively.

The capabilities of the JPL Electronic Nose have been expanded to include characteristics required for a Technology Demonstration schedule on the International Space Station (ISS) in 2008-2009 [1,2]. Concurrently, to accommodate specific needs on ISS, the processes, tools and analyses which influence all aspects of development of the device have also been expanded. The Third Generation ENose developed for this program uses two types of sensor substrates, newly developed inorganic and organic sensor materials, redesigned electronics, onboard near real-time data analysis and power and data interfaces specifically for ISS. This paper will discuss the Third Generation ENose with a focus on detection of mercury in the parts-per-billion range.