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

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Low-Cost, Light Weight, Thin Film Solar Concentrator

This research addresses a cost barrier towards achieving a solar thermal collector system with an installed cost of $75/sq m and meet the Department of Energy's (DOE's) performance targets for optical errors, operations during windy conditions and lifetime. Current concentrators can cost as much as 40-50% of the total installed costs for a CSP plant. In order to reduce the costs from current $200-$250/sq m, it is important to focus on the overall system. The reflector surface is a key cost driver, and our film-based polymer reflector will help significantly in achieving DOE's cost target of $75/sq m. The ease of manufacturability, installation and replacement make this technology a compelling one to develop. This technology can be easily modified for a variety of CSP options including heliostats, parabolic dishes and parabolic troughs.

Concentrating Solar Power (CSP)↗
NASA NTRS2015

NASA Glenn Research Center Support of the Advanced Stirling Radioisotope Generator Project

A high-efficiency radioisotope power system was being developed for long-duration NASA space science missions. The U.S. Department of Energy (DOE) managed a flight contract with Lockheed Martin Space Systems Company to build Advanced Stirling Radioisotope Generators (ASRGs), with support from NASA Glenn Research Center. DOE initiated termination of that contract in late 2013, primarily due to budget constraints. Sunpower, Inc., held two parallel contracts to produce Advanced Stirling Convertors (ASCs), one with Lockheed Martin to produce ASC-F flight units, and one with Glenn for the production of ASC-E3 engineering unit "pathfinders" that are built to the flight design. In support of those contracts, Glenn provided testing, materials expertise, Government-furnished equipment, inspection capabilities, and related data products to Lockheed Martin and Sunpower. The technical support included material evaluations, component tests, convertor characterization, and technology transfer. Material evaluations and component tests were performed on various ASC components in order to assess potential life-limiting mechanisms and provide data for reliability models. Convertor level tests were conducted to characterize performance under operating conditions that are representative of various mission conditions. Despite termination of the ASRG flight development contract, NASA continues to recognize the importance of high-efficiency ASC power conversion for Radioisotope Power Systems (RPS) and continues investment in the technology, including the continuation of the ASC-E3 contract. This paper describes key Government support for the ASRG project and future tests to be used to provide data for ongoing reliability assessments.

Technology Transfer↗
NASA NTRS2003

Overview of NASA GRC Stirling Technology Development

The Stirling Radioisotope Generator (SRG) is currently being developed by Lockheed Martin Astronautics (LMA) under contract to the Depar1ment of Energy (DOE). The generator will be a high efficiency electric power source for NASA Space Science missions with the capability to operate in the vacuum of deep space or in an atmosphere such as on the surface of Mars. High system efficiency is obtained through the use of free-piston Stirling power conversion technology. Power output of the generator will be greater than 100 watts at the beginning of life with the decline in power being largely due to the decay of the plutonium heat source. In suppOl1 of the DOE SRG project, the NASA Glenn Research Center (GRC) has established a near-term technology effort to provide some of the critical data to ensure a successful transition to flight for what will be the first dynamic power system used in space. Initially, a limited number of technical areas were selected for the GRC effort, however this is now being expanded to more thoroughly cover a range of technical issues. The tasks include in-house testing of Stirling convertors and controllers, materials evaluation and heater head life assessment, structural dynamics, electromagnetic interference, organics evaluation, and reliability analysis. Most of these high-level tasks have several subtasks within. There is also an advanced technology effort that is complementary near-term technology effort. Many of the tests make use of the 55-We Technology Demonstration Convel10r (TDC). There have been multiple controller tests to support the LMA flight controller design effort. Preparation is continuing for a thermal/vacuum system demonstration. A pair of flight prototype TDC's have recently been placed on an extended test with unattended, continuous operation. Heater head life assessment efforts continue, with the material data being refined and the analysis moving toward the system perspective. Long-term magnet aging tests are continuing to characterize any possible aging in the strength or demagnetization resistance of the permanent magnets used in the linear alternator. In a parallel effort, higher performance magnets are also being evaluated. A reliability effort is being initiated that will help to guide the development activities with an increased focus on the necessary components and subsystems. Some other disciplines that are active in the GRC technology effort include structural dynamics, linear alternator analysis, EMI/EMC, controls, and mechanical design evaluation. This paper will provide an overview of some of the GRC technical efforts, including the current status, and a description of future efforts.

Schreiber, Jeffrey↗
NASA NTRS2016

Radioisotope Power Systems Program: A Program Overview

NASA's Radioisotope Power Systems (RPS) Program continues to plan, mature research in energy conversion, and partners with the Department of Energy (DOE) to make RPS ready and available to support the exploration of the solar system in environments where the use of conventional solar or chemical power generation is impractical or impossible to meet potential future mission needs. Recent programs responsibilities include providing investment recommendations to NASA stakeholders on emerging thermoelectric and Stirling energy conversion technologies and insight on NASA investments at DOE in readying a generator for the Mars 2020 mission. This presentation provides an overview of the RPS Program content and status and the approach used to maintain the readiness of RPS to support potential future NASA missions.

Nuclear↗
NASA NTRS2014

Aerosol, Cloud and Trace Gas Observations Derived from Airborne Hyperspectral Radiance and Direct Beam Measurements in Recent Field Campaigns

The AERONET (AErosol RObotic NETwork) ground-based suite of sunphotometers provides measurements of spectral aerosol optical depth (AOD), precipitable water and spectral sky radiance, which can be inverted to retrieve aerosol microphysical properties that are critical to assessments of aerosol-climate interactions. Because of data quality criteria and sampling constraints, there are significant limitations to the temporal and spatial coverage of AERONET data and their representativeness for global aerosol conditions. The 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) instrument, jointly developed by NASA Ames and PNNL with NASA Goddard collaboration, combines airborne sun tracking and AERONET-like sky scanning with spectroscopic detection. Being an airborne instrument, 4STAR has the potential to fill gaps in the AERONET data set. Dunagan et al. [2013] present results establishing the performance of the instrument, along with calibration, engineering flight test, and preliminary scientific field data. The 4STAR instrument operated successfully in the SEAC4RS [Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys] experiment in Aug./Sep. 2013 aboard the NASA DC-8 and in the DoE [Department of Energy]-sponsored TCAP [Two Column Aerosol Project, July 2012 & Feb. 2013] experiment aboard the DoE G-1 aircraft (Shinozuka et al., 2013), and acquired a wealth of data in support of mission objectives on all SEAC4RS and TCAP research flights. 4STAR provided direct beam measurements of hyperspectral AOD, columnar trace gas retrievals (H2O, O3, NO2; Segal-Rosenheimer et al., 2014), and the first ever airborne hyperspectral sky radiance scans, which can be inverted to yield the same products as AERONET ground-based observations. In addition, 4STAR measured zenith radiances underneath cloud decks for retrievals of cloud optical depth and effective diameter. In this presentation, we provide an overview of the new 4STAR capabilities for airborne field campaigns, with an emphasis on comparisons between 4STAR and AERONET sky radiances, and retrievals of aerosol microphysical properties based on sky radiance measurements, column trace gas amounts from spectral direct beam measurements and cloud property retrievals from zenith mode observations for a few select case studies in the SEAC4RS and TCAP experiments. We summarize the aerosol, trace gas, cloud and airmass characterization studies made possible by the combined 4STAR direct beam, and sky/zenith radiance observations.

Aerosol-Climate↗
NASA NTRS2016

Improved Test Planning and Analysis Through the Use of Advanced Statistical Methods

The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.

Green, Lawrence L.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases [1]. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission [2]. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions.

Zwack, Mathew R.↗
NASA NTRS2017

Studies of Short Time Response Options for Potentially Hazardous Objects: Current and Forthcoming Results

NASA's Goddard Space Flight Center (GSFC) and the National Nuclear Security Administration (NNSA), Department of Energy (DOE) National Laboratories, Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory(LANL), and Sandia National Laboratory (SNL) are collaborating on Planetary Defense Research. The research program is organized around three case studies: 1. Deflection of the Potentially Hazardous Asteroid (PHA) 101955 Bennu (1999 RQ36)[OSIRIS-REx mission target], 2. Deflection of the secondary member of the PHA 65803 Didymos (1996 GT) [DART mission target], 3. Deflection of a scaled-down version of the comet 67PChuryumov-Gerasimenko [Rosetta mission target]. NASAGSFC is providing astrodynamics and spacecraft mission design expertise, while NNSA, DOE, LLNL, LANL and SNL are providing expertise in modeling the effects of kinetic impactor spacecraft and nuclear explosive devices on the target objects.

Mission Design↗
NASA NTRS2017

Improvement of Automated POST Case Success Rate Using Support Vector Machines

During early conceptual design of complex systems, concept down selection can have a large impact upon program life-cycle cost. Therefore, any concepts selected during early design will inherently commit program costs and affect the overall probability of program success. For this reason it is important to consider as large a design space as possible in order to better inform the down selection process. For conceptual design of launch vehicles, trajectory analysis and optimization often presents the largest obstacle to evaluating large trade spaces. This is due to the sensitivity of the trajectory discipline to changes in all other aspects of the vehicle design. Small deltas in the performance of other subsystems can result in relatively large fluctuations in the ascent trajectory because the solution space is non-linear and multi-modal. In order to help capture large design spaces for new launch vehicles, the authors have performed previous work seeking to automate the execution of the industry standard tool, Program to Optimize Simulated Trajectories (POST). This work initially focused on implementation of analyst heuristics to enable closure of cases in an automated fashion, with the goal of applying the concepts of design of experiments (DOE) and surrogate modeling to enable near instantaneous throughput of vehicle cases.3 As noted in [4] work was then completed to improve the DOE process by utilizing a graph theory based approach to connect similar design points.

Zwack, Mathew R.↗
NASA NTRS2019

Implementation of Cross-Agency Nuclear Applications

The Radioisotope Power System (RPS) Program was established in 2009 to manage RPS investments for NASA to ensure the availability of RPS for the exploration of the solar system in environments where conventional solar or chemical power generation is impractical or impossible. The RPS Program is a multi-center and multi-agency program. NASA is in partnership with the Department of Energy (DOE) Office of Nuclear Energy to provide technologically robust nuclear power system solutions to robotic spacecraft and exploration missions. During the last decade, the RPS Program and DOE have supported missions, developed technologies and initiated new power system developments. These technical areas, as all technical areas, have challenges and standard engineering solutions; however, clearing the path to enable the technical work requires agreements to be established. This paper describes a process by which two governmental agencies have established a successful basis to accomplish the needed work.

Zakrajsek, June F.↗
NASA NTRS2016

Risk Reduction for the System Design of an Enhanced MMRTG

Engineering of an enhanced Multi-Mission Radioisotope Thermoelectric Generator (eMMRTG) began 3 years ago with agreement between the U.S. National Aeronautics and Space Administration (NASA) and the U.S. Department of Energy (DOE). The Jet Propulsion Laboratory is leading the transfer of skutterudite thermoelectric couple technology to industry and leading the systems engineering of the proposed eMMRTG. Should NASA fund the flight development of an eMMRTG, the DOE would lead the flight system development. The analytical models and design of the MMRTG have been enhanced to use skutterudite couples developed at Jet Propulsion Laboratory over the last two decades. This required a thorough evaluation of the MMRTG design, and concurrent engineering of an enhanced MMRTG to shed light on potential design issues or risks. At the end of the U.S. fiscal year (FY) 2015, a complete catalog of risks was produced. Tests, hardware development, and analyses have now been put in place to mitigate those risks to acceptable levels. Few risks can ever be truly eliminated unless a design is modified to eliminate specific risks and not introduce more severe risks. The design imperative for the eMMRTG is to change only a few MMRTG features; therefore, risks can largely only be mitigated, not eliminated, through design changes. However, many risks can be effectively - though not totally - eliminated without design changes. This paper briefly describes some of the documented risks and mitigations or reduction approaches to be applied in the coming 3 years, on the way to completing the eMMRTG concept.

eMMRTG↗
NASA NTRS2019

Fusion-Enabled Pluto Orbiter and Lander

Direct Fusion Drive (DFD) is a unique fusion engine concept based on the Princeton Field-Reversed Configuration (PFRC) fusion reactor conceived by Dr. Sam Cohen of the Princeton Plasma Physics Laboratory. DFD would enable the Pluto orbiter and lander context mission and more broadly enable true "rapid transit" to outer-planet and near interstellar space. The truly game-changing levels of thrust and power in a modestly sized package could integrate with our current launch infrastructure while radically expanding the science capability of these missions. Our Phase I was our first funded work on the DFD, with previous work at PSS occurring only under internal R&D. We established the feasibility of our Pluto mission trajectories using straight-line and planar models, including a departure spiral from Earth and insertion at Pluto. We developed our first thrust and specific impulse model using the results of the UEDGE multi-fluid code. Our specific power model was improved. During this Phase II effort, we continued our efforts to increase the fidelity of the designs for the RF, magnet, and shielding subsystems. Dedicated thrust augmentation experiments were run on the PFRC experiment, using a supersonic gas puffing valve. For the first time, we analyzed the design of a closed-loop operation mode and estimated the hardware that would be required for a dual-mode engine. In an exciting new development, we have invented a new thermophotovoltaic thermal conversion method that has the potential to have efficiencies of a Brayton or Stirling system. Our report presents details of these analyses. Our roadmap to bringing DFD to flight predicts that with sufficient support, a first flight unit could be built by 2040. We anticipate that three machine generations are required before this point: a ~1 T PFRC-3 machine hitting new plasma temperature and density levels, a ~5 T PFRC-4 machine with first demonstration of D-3He fusion, and a flight. (The current experiment, PFRC-2, is limited to about 0.1 T). In order to achieve a flight in 2035-2040, the TRL of the supporting systems must be increased in parallel, including low mass radiators, cryogenic propellant storage, and large (>100 kW) thermal conversion systems. Fortunately, many of these systems are dual-use and are required for other technologies including fission systems, so DFD would contribute to and benefit from those programs. This NIAC support and the results of our work have led to multiple follow-on contracts. We won two NASA STTRs focused on DFD subsystems, one on the RF system and one on the superconducting magnets, and our magnet STTR is now midway through a Phase II. We will be receiving a superconducting test magnet for experiments at PPPL this summer. In addition, we won an ARPA-E OPEN grant; this is the first time that OPEN has included fusion technologies, and we are part of a cohort of three alternative fusion companies now supported directly by DOE. We are very optimistic that if we are able to meet our experimental milestones in the next 12-18 months, we will be competitive for additional DOE grants to build PFRC-3.

Propulsion↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
NASA NTRS2022

Chapter 7: Integrative Science

A key part of DOE's mission is addressing energy and environmental problems through transformative science and technology, which includes discovering and developing new energy systems and understanding and predicting their consequences at local to global scales. Enabling these advances are the fundamental scientific discoveries and tools delivered by the Office of Science. Implicit in DOE's mission is the need to understand the complex, multiscale interactions between energy systems and the environment. Attaining this knowledge requires an integrated research portfolio that promotes understanding and discovery across different subsystems of the overall energy system and enables sustainable prosperity through a vibrant bioeconomy.

Biological and Environmental Research Advisory Com↗
NASA NTRSDate not supplied

The NASA Space Radiation Laboratory

The Space Radiation Element (SRE) of the Human Research Program (HRP) aims to establish a robust biospecimen and tissue sharing collection (BTSC) to improve sample collection, tracking, access, distribution, and usability to maximize scientific return with intention. Future missions to the Moon, Mars, and beyond will require the appropriate characterization and mitigation of the many of the health risks associated with cosmic radiation exposure. An ongoing partnership between the National Aeronautics and Space Agency (NASA) and the Office of Science for the U.S. Department of Energy (DOE) has established the NASA Space Radiation Laboratory (NSRL) at the DOE Brookhaven National Laboratory (BNL) to create a space radiation ground-analog. NSRL provides the capability to use simulated space radiation with high energy ion beams for assessment of biological outcomes relevant space radiation risks as well as electronics testing for avionics safety. Current NSRL irradiation capabilities include a variety of exposure variables available for experimental needs: - Up to approximately 10 Gy/min dose-rates with a 20 × 20 cm 2 beam area. - Down to several hundred particles within a 60 × 60 cm 2 beam area. - Dose fractionation across multiple days is also possible. - For most radiobiology experiments the exposure structure, referred to as “spills”, has a 4 second repetition time where ions are extracted uniformly during a 0.3-0.4 second spill, followed by a ~3.6 second recovery time when the beam is off. - Both single or mixed-beam field irradiations are possible including a full 33-beam GCRSim and 6-beam SimGCRSim. Biological experiment capabilities: Two labs for Cell/tissue culture procedures can be conducted in two lab spaces with a separate lab available for sample processing that contains two chemical fume hoods. Sixteen fully equipped cell culture laboratories are available in the NSRL’s Long Term Support area in the Medical Building. The current vivarium configuration can support approximately 8,000 rodents/year for animal-based experiments. The NSRL area animal husbandry system includes five caging racks with 48 spaces in each coupled to a ventilation/filtration system in largest room, and two smaller rooms with conventional caging systems. Schedule: The NSRL is nominally open for experiment scheduling during three runs per year; Spring (March – May), Summer (May - June), and Fall (Sept – Nov). NASA funding is not required for biological and electronic testing experiments at the NSRL.

Jason M Weeks↗