SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
U.S. Department of Energy national laboratories host world-class advanced computing and modeling capabilities. The National Renewable Energy Labowratory (NREL) inaugurated the Infrastructure Perception and Control Laboratory (IPC Lab) to advance mobility reach into real-world applications.
The analytical tools (analysis and computer simulation) needed to explain and predict the dynamic operation of air cushion landing systems (ACLS) is described. The following tasks were performed: the development of improved analytical models for the fan and the trunk; formulation of a heave pitch roll analysis for the complete ACLS; development of a general purpose computer simulation to evaluate landing and taxi performance of an ACLS equipped aircraft; and the verification and refinement of the analysis by comparison with test data obtained through lab testing of a prototype cushion. Demonstration of simulation capabilities through typical landing and taxi simulation of an ACLS aircraft are given. Initial results show that fan dynamics have a major effect on system performance. Comparison with lab test data (zero forward speed) indicates that the analysis can predict most of the key static and dynamic parameters (pressure, deflection, acceleration, etc.) within a margin of a 10 to 25 percent.
The Thermal Calculation Analysis Tool (TCAT), originally developed for the Space Systems Design Lab at the Georgia Institute of Technology, is a conceptual design tool capable of integrating aeroheating analysis into conceptual reusable launch vehicle design. It provides Thermal Protection System (TPS) unit thicknesses and acreage percentages based on the geometry of the vehicle and a reference trajectory to be used in calculation of the total cost and weight of the vehicle design. TCAT has proven to be reasonably accurate at calculating the TPS unit weights for in-flight trajectories; however, it does not have the capability of sizing TPS materials above cryogenic fuel tanks for ground hold operations. During ground hold operations, the vehicle is held for a brief period (generally about two hours) during which heat transfer from the TPS materials to the cryogenic fuel occurs. If too much heat is extracted from the TPS material, the surface temperature may fall below the freezing point of water, thereby freezing any condensation that may be present at the surface of the TPS. Condensation or ice on the surface of the vehicle is potentially hazardous to the mission and can also damage the TPS. It is questionable whether or not the TPS thicknesses provided by the aeroheating analysis would be sufficiently thick to insulate the surface of the TPS from the heat transfer to the fuel. Therefore, a design tool has been developed that is capable of sizing TPS materials at these cryogenic fuel tank locations to augment TCAT's TPS sizing capabilities.
The recent development of microcrystal electron diffraction (MicroED) in electron cryo-microscopy (cryo-EM) has addressed many of the challenges of X-ray crystallography [1], filling a long-standing need in organic chemistry for rapid and accurate high-resolution structural characterization [2]. Like X-ray crystallography, this is also a diffraction technique requiring crystalline specimens, but because high energy electrons have a more favorable ration of elastic to inelastic scattering [3], MicroED can acquire data using crystals that are orders of magnitude smaller than those needed for X-ray crystallography—even as small as 50 nm in length [4]. Additionally, the instrumentation required for MicroED is readily available, as many chemistry and biology labs already have access to cryo-capable transmission electron microscopes (TEM).
Ferrofluid microrobots have emerged as promising tools for minimally invasive medical procedures. Their unique properties to navigate complex fluids and reach otherwise inaccessible regions of the human body have enabled new applications in targeted drug delivery, tissue engineering, and diagnostics. Here, this paper proposes a model-predictive controller for the external magnetic manipulation of ferrofluid microrobots in three dimensions (3D). The internal optimization routine of the controller determines appropriate changes in the applied electromagnetic field to minimize the deviation between the actual and desired trajectories of the microrobot. A linear system governing locomotion is derived and used as the equality constraints of the optimization problems associated with the feedback index. In addition to ferrofluid droplets, the controller presented in this work may be applied to other magnetically-pulled microrobots. Several experiments are performed to validate the controller and showcase its ability to adapt to changes in system parameters such as the desired tracking trajectory and the size, orientation, deformation, and velocity of the microrobot. The accuracy of the controller is analyzed for each experiment, and the average error is found to be within 0.25 mm for small velocities. An additional experiment is performed to demonstrate significant improvement over a PID controller that is optimally tuned using Bayesian optimization. The results presented in this paper suggest that the proposed control algorithm could enable new microrobotic capabilities in minimally invasive medical procedures, lab-on-a-chip applications, and microfluidics.
Electron beam irradiation near 10 MeV is suitable for wastewater treatment. The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a CW superconducting linear accelerator capable of providing an electron beam of energy up to 10 MeV and up to 100 µA current. This contribution presents the beam transport simulations for a beamline to be used for the irradiation of wastewater samples at the UITF. The simulations were done using the code General Particle Tracer with the goal of obtaining an 8 MeV electron beam of radius (3-σ) of ~2.4 cm. The achieved energy spread is ~74.5 keV. The space charge effects were investigated when the bunch charge is varied to be up to 1000 times and the results showed that they do not affect the beam quality significantly.
The LANSCE accelerator is an important tool in the assessment and certification of the nation’s nuclear stockpile. LANSCE provides critical materials and nuclear data needed to develop a predictive capability for the performance of nuclear weapons. As the stockpile ages, materials are replaced, and manufacturing processes change, it is critical that we understand how these changes impact device performance. Dynamic proton radiography and neutron diffraction are ideally suited to contribute to this need. These capabilities are essential to enabling the NNSA labs to become more responsive and agile in advancing the deterrent of the future.
Transit electrification is difficult, requiring the consideration of many factors including route dynamics, vehicle utilization, and local ambient temperatures. Incumbent planning tools typically rely upon static vehicle efficiency numbers which do not consider local factors specific to each fleet. This paper summarizes a collaboration between NREL and Microgrid Labs, which produced an automated sizing tool capable of producing vehicle battery and motor sizing for drop-in electrification. Furthermore, the workflow also exports the electric-vehicle supply equipment needed to support fleet electrification. The paper summarizes the model theory and includes a sample pilot study performed in support of the Via Mobility transit fleet.
The overall objective of this project is the development of a lab-on-a-chip (LOC) electrochemical sensor capable of accurately measuring heavy metal (lead (Pb), cadmium (Cd), and arsenic (As)) concentrations in complex aqueous streams such as wastewater. The specific goals include: i) identify how heavy metal speciate and transform during the different stages of the wastewater treatment train, and explore pre-treatment methods that will de-complex heavy metals, making them available to ASV, ii) develop appropriate electrodes for ASV using organic/inorganic nanomaterials, and iii) integrate pre-treatment steps and specialized electrodes into an LOC sensor device capable of autonomously determining heavy metal concentrations in complex wastewater streams.
Electron beam irradiation near 10 MeV is suitable for wastewater treatment. The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a CW superconducting linear accelerator capable of providing an electron beam of energy up to 10 MeV and up to 100 µA current. This contribution presents the beam transport simulations for a beamline to be used for the irradiation of wastewater samples at the UITF. The simulations were done using the code General Particle Tracer with the goal of obtaining an 8 MeV electron beam of radius (3-σ) of ~2.4 cm. The achieved energy spread is ~74.5 keV. The space charge effects were investigated when the bunch charge is varied to be up to 1000 times and the results showed that they do not affect the beam quality significantly.
LANL appears competitive with peer NNSA labs, below most DOE Science labs, but trending down - why? Publication rates or counts are an indicator of productivity. Field-weighted citation impact (FWCI) is an indicator of impact.
By 1997, the Thomas Jefferson National Accelerator Facility will collect over one Terabyte of raw information per day of Accelerator operation from three concurrently operating Experimental Halls. When post-processing is included, roughly 250 TB of raw and formatted experimental data will be generated each year. By the year 2000, a total of one Petabyte will be stored on-line. Critical to the experimental program at Jefferson Lab (JLab) is the networking and computational capability to collect, store, retrieve, and reconstruct data on this scale. The design criteria include support of a raw data stream of 10-12 MB/second from Experimental Hall B, which will operate the CEBAF (Continuous Electron Beam Accelerator Facility) Large Acceptance Spectrometer (CLAS). Keeping up with this data stream implies design strategies that provide storage guarantees during accelerator operation, minimize the number of times data is buffered allow seamless access to specific data sets for the researcher, synchronize data retrievals with the scheduling of postprocessing calculations on the data reconstruction CPU farms, as well as support the site capability to perform data reconstruction and reduction at the same overall rate at which new data is being collected. The current implementation employs state-of-the-art StorageTek Redwood tape drives and robotics library integrated with the Open Storage Manager (OSM) Hierarchical Storage Management software (Computer Associates, International), the use of Fibre Channel RAID disks dual-ported between Sun Microsystems SMP servers, and a network-based interface to a 10,000 SPECint92 data processing CPU farm. Issues of efficiency, scalability, and manageability will become critical to meet the year 2000 requirements for a Petabyte of near-line storage interfaced to over 30,000 SPECint92 of data processing power.
Reuse greatly reduces development costs. Savings can be invested in new/improved capabilities Or returned to sponsor Worth the extra time to "do it right" Operator training greatly reduced. MIPL MER personnel can step into MSL easily because the programs are familiar. Application programs much easier to write. Can assume core capabilities exist already. Multimission Instrument (Image) Processing Lab at MIPL Responsible for the ground-based instrument data processing for (among other things) all recent in-situ Mars missions: Mars Pathfinder Mars Polar Lander (MPL) Mars Exploration Rovers (MER) Phoenix Mars Science Lab (MSL) Responsibilities for in-situ missions Reconstruction of instrument data from telemetry Systematic creation of Reduced Data Records (RDRs) for images Creation of special products for operations, science, and public outreach In the critical path for operations MIPL products required for planning the next Sol s activities
Mars Organic Molecule Analyzer (MOMA) is an instrument suite on the ESA ExoMars 2018 Rover, and the Mass Spectrometer (MOMA-MS) is being built at Goddard Space Flight Center (GSFC). As MOMA-MS is a life-detection instrument and it thus falls in the most stringent category of Planetary Protection (PP) biological cleanliness requirements. Less than 0.03 sporem2 is allowed in the instrument sample path. In order to meet these PP requirements, MOMA-MS must be built and maintained in a low bioburden environment. The MOMA-MS project at GSFC maintains three cleanrooms with varying levels of bioburden control. The Aseptic Assembly Cleanroom has the highest level of control, applying three different bioburden reducing methods: 70 IPA, 7.5 Hydrogen Peroxide, and Ultra-Violet C light. The three methods are used in rotation and each kills microbes by a different mechanism, reducing the likelihood of microorganisms developing resistance to all three. The Integration and Mars Chamber Cleanrooms use less biocidal cleaning, with the option to deploy extra techniques as necessary. To support the monitoring of cleanrooms and verification that MOMA-MS hardware meets PP requirements, a new Planetary Protection lab was established that currently has the capabilities of standard growth assays for spore or vegetative bacteria, rapid bioburden analysis that detects Adenosine Triphosphate (ATP), plus autoclave and DHMR verification. The cleanrooms are monitored both for vegetative microorganisms and by rapid ATP assay, and a clear difference in bioburden is observed between the aseptic the other cleanroom.
Mars Organic Molecule Analyzer (MOMA) is an instrument suite on the European Space Agency (ESA) ExoMars 2020 Rover, and the Mass Spectrometer (MOMA-MS) is being built at Goddard Space Flight Center (GSFC). MOMA-MS is a life-detection instrument and thus falls in the most stringent category of Planetary Protection (PP) biological cleanliness requirements. Less than 0.03 spore/m2 are allowed in the instrument sample path. In order to meet these PP requirements, MOMA-MS must be built and maintained in a low bioburden environment. The MOMA-MS project at GSFC maintains three clean rooms with varying levels of bioburden control. The Aseptic Assembly Clean room has the highest level of control, applying three different bioburden reducing methods: 70% Isopropyl Alcohol (IPA), 7.5% Hydrogen Peroxide, and Ultra-Violet C (UVC) light. The three methods are used in rotation and each kills microorganisms by a different mechanism, reducing the likelihood of microorganisms developing resistance to all three. The Integration and Mars Chamber Clean rooms use less biocidal cleaning, with the option to deploy extra techniques as necessary. To support the monitoring of clean rooms and verification that MOMA-MS hardware meets PP requirements, a new Planetary Protection lab was established that currently has the capabilities of standard growth assays for spore or vegetative bacteria, rapid bioburden analysis that detects Adenosine Triphosphate (ATP), plus autoclave and Dry Heat microbial Reduction (DHMR) verification. The clean rooms are monitored for vegetative microorganisms and by rapid ATP assay, and a clear difference in bioburden is observed between the aseptic and other clean room.
Wildfires are an important source of reactive nitrogen species including nitrogen oxides (NOx = NO + NO2) and nitrous acid (HONO), as well as products such as nitric acid (HNO3) and particulate nitrate (p-NO3-). These species have strongly influence air quality and climate. In summer of 2019 (July 25 —August 21), as part of the FIREX-AQ ground mission, we drove ~3000 miles and sampled smoke of different ages in both daytime and nighttime from 5 different wildfires in the western US. Using recently developed methods that have been validated in both laboratory and field settings, we conducted high time resolution collection of NOx, NO2, HONO, HNO3, and p-NO3- for off-line characterization of isotopic composition δ15N, δ18O and Δ17O (=δ17O−0.52 δ18O). δ15N serves as a potential tracer for tracking sources and reactive nitrogen cycling pathways, δ18O and Δ17O provide key information about oxidation pathways (i.e. O3 versus RO2). Our measured δ15N for HONO ranges from -4.9‰ to +5.2‰ and -8‰ to +5.2‰ respectively, in the majority overlapping with our prior lab controlled burning results, which suggests the capability of δ15N for tracing the biomass burning emissions against other sources including vehicular emissions and soil emissions. In addition, our measurements show δ18O and Δ17O for NO2 and HONO vary significantly day vs night, as well as in young smoke vs aged smoke, likely reflecting different oxidizing mechanisms under different smoke conditions. Preliminary results of isotopic signatures of all the above reactive nitrogen species will be presented, and their relationships will be explored to identify wildfire derived reactive nitrogen source signatures and to constrain atmospheric processing pathways (e.g. secondary formation of HONO) for different fires under various smoke conditions depending on fuel types, fire conditions and meteorological conditions.
The presentation includes general description on the following test facilities: Fuel Cell Testing Lab, Structural Dynamics Lab, Thermal Vacuum Test Facilities - including a description of the proposed Kinetic High Altitude Simulator concept, EMI Test Lab, and the Creek Road Cryogenic Complex - specifically the Small Multi-purpose Research Facility (SMiRF) and the Cryogenics Components Lab 7 (CCL-7).
Mega AI, an internal investment at Pacific Northwest National Laboratory, aims to develop next-generation artificial intelligence (AI) capabilities unique to the Department of Energy national lab complex to address research gaps in large-scale multimodal representation learning, multitask inferences, and the need for increased generalizability, rapid adaptivity, and usability of AI technologies. In this newsletter, we highlight recent developments in the research community on next-gen AI technologies focusing on massive-scale model development, deployment and evaluation, data and code availability, model interactions, and new features and capabilities that are relevant to Mega AI’s goals and science and security applications.