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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 91 records · Page 5

Carbon-Based Nano-Electro-Mechanical-Systems

We provide an overview of our work where carbon-based nanostructures have been applied to two-dimensional (2D) planar and three-dimensional (3D) vertically-oriented nano-electro-mechanical (NEM) switches. In the first configuration, laterally oriented single-walled nanotubes (SWNTs) synthesized using thermal chemical vapor deposition (CVD) were implemented for forming bridge-type 2D NEMS switches, where switching voltages were on the order of a few volts. In the second configuration, vertically oriented carbon nanofibers (CNFs) synthesized using plasma-enhanced (PE) CVD have been explored for their potential application in 3D NEMS. We have performed nanomechanical measurements on such vertically oriented tubes using nanoindentation to determine the mechanical properties of the CNFs. Electrostatic switching was demonstrated in the CNFs synthesized on refractory metallic nitride substrates, where a nanoprobe was used as the actuating electrode inside a scanning-electron-microscope. The switching voltages were determined to be in the tens of volts range and van der Waals interactions at these length scales appeared significant, suggesting such structures are promising for nonvolatile memory applications. A finite element model was also developed to determine a theoretical pull-in voltage which was compared to experimental results.

Complementary-Metal-Oxide-Semiconductor (CMOS)↗

Graphene Based Reversible Nano-Switch/Sensor Schottky Diode (NANOSSSD) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one layer of graphene formed on the electrode. The at least one layer of graphene provides an electrical connection between the electrode and the substrate and is the electroactive element in the device.

Miranda, Felix A.↗

Nano-ADEPT Aeroloads Wind Tunnel Test

Analysis completed since the test suggests that all test objectives were met– This claim will be verified in the coming weeks as the data is examined further– Final disposition of test objective success will be documented in a final reportsubmitted to NASA stakeholders (early August 2015)– Expect conference paper in early 2016• Data products and observations made during testing will be used to refinecomputational models of Nano-ADEPT• Carbon fabric relaxed from its pre-test state during the test– System-level tolerance for relaxation will be driven by destination-specific andmission-specific aerothermal and aerodynamic requirements• Bonus experiment of asymmetric shape demonstrates that an asymmetricdeployable blunt body can be used to generate measureable lift– With a strut actuation system and a robust GN&C algorithm, this effect could beused to steer a blunt body at hypersonic speeds to aid precision landing

atmospheric entry↗

Nano-ADEPT Aeroloads Wind Tunnel Test

A wind tunnel test of the Adaptable Deployable Entry and Placement Technology (ADEPT) was conducted in April 2015 at the US Army's 7 by10 Foot Wind Tunnel located at NASA Ames Research Center. Key geometric features of the fabric test article were a 0.7 meter deployed base diameter, a 70 degree half-angle forebody cone angle, eight ribs, and a nose-to-base radius ratio of 0.7. The primary objective of this wind tunnel test was to obtain static deflected shape and pressure distributions while varying pretension at dynamic pressures and angles of attack relevant to entry conditions at Earth, Mars, and Venus. Other objectives included obtaining aerodynamic force and moment data and determining the presence and magnitude of any dynamic aeroelastic behavior (buzz/flutter) in the fabric trailing edge. All instrumentation systems worked as planned and a rich data set was obtained. This paper describes the test articles, instrumentation systems, data products, and test results. Four notable conclusions are drawn. First, test data support adopting a pre-tension lower bound of 10 foot pounds per inch for Nano-ADEPT mission applications in order to minimize the impact of static deflection. Second, test results indicate that the fabric conditioning process needs to be reevaluated. Third, no flutter/buzz of the fabric was observed for any test condition and should also not occur at hypersonic speeds. Fourth, translating one of the gores caused ADEPT to generate lift without the need for a center of gravity offset. At hypersonic speeds, the lift generated by actuating ADEPT gores could be used for vehicle control.

system engineering↗

Polymer Nanofiber Based Reversible Nano-Switch/Sensor Diode (Nanosssd) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one polymer nanofiber deposited on the electrode. The at least one polymer nanofiber provides an electrical connection between the electrode and the substrate and is the electroactive element in the device.

Miranda, Felix A.↗

Polymer Nanofiber Based Reversible Nano-Switch/Sensor Schottky Diode (Nanosssd) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one polymer nanofiber deposited on the electrode. The at least one polymer nanofiber provides an electrical connection between the electrode and the substrate and is the electroactive element in the device.

Miranda, Felix A.↗

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As space missions continue to become more ambitious, complex, and distant to Earth, the need for advanced on-board intelligent decision making to guide everything from mission operations to fault detection and recovery has become a major front of space research. While the prevalence of research on such Artificial Intelligence / Machine Learning (AI/ML) modules has exploded, the capacity to experimentally validate such modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available AI/ML computational platforms via the TechEdSat (TES-n) flight series as part of what is programmatically referred to as the BRAINSTACK. BRAINSTACK will provide an orbital AI/ML evaluation laboratory where computational experiments are pre-loaded into memory prior to launch, and then executed as desired during the mission, with results reported back and program tweaks or new data sets uploaded as needed. Processors selected as part of the BRAINSTACK are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and more recently, neuromorphic processors in LEO operations. Neuromorphic processors are of particular interest due to their superior computational power efficiency over GPUs. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on January 13, 2022 and continues to operate in orbit despite almost no space environment modifications. The Intel Loihi Gen-1 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. This experiment utilized a Loihi packaged in the ‘Kapoho Bay’ USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by a host Intel Pentium single-board computer to handle scheduling of the AI/ML application payloads, and communications with the satellite vehicle manager. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the basis of the evolving BRAINSTACK in the upcoming three TES-n/NOW flights. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space AI/ML applications may be initially tested.

Artificial Intelligence↗

BRAINSTACK – A Platform for Artificial Intelligence & Machine Learning Collaborative Experiments on a Nano-Satellite

As the space economy continues to expand through increasingly easy access to advanced and inexpensive technology, space missions themselves have become more ambitious with exploration targets growing ever distant while simultaneously requiring larger guidance and communication budgets. These conflicting desires of distance and control drive the need for advanced on-board intelligent decision making to reduce communication and control limitations by automating as many mission functions as possible in-situ. While the amount of research on such Artificial Intelligence and Machine Learning (AI/ML) software modules has grown exponentially, the capacity to experimentally validate such software modules in space in a rapid and inexpensive format has not. To this end, the Nano Orbital Workshop (NOW) group at NASA Ames Research Center has been at the forefront of performing initial flight evaluation tests of ‘commercially’ available bleeding-edge computational platforms via what is programmatically referred to as the BrainStack on the TechEdSat (TES-n) flight series. This on-orbit computational platform provides an evaluation laboratory where advanced software experiments are pre-loaded into memory prior to launch, then executed as payloads during mission operations with results reported back and program tweaks or new training sets uploaded as needed. Processors selected as part of the BrainStack are of ideal size, packaging, and power consumption for easy integration into a cube satellite structure. These experiments have included the evaluation of small, high-performance GPUs and, more recently, neuromorphic processors, in LEO operations. Neuromorphic processors are of particular interest due to their superior power efficiency over GPUs in intelligent automation applications. The first TES-n flight test of an Intel first-generation Loihi neuromorphic processor launched on TES-13, January 13, 2022, and continues to operate in orbit despite no significant modifications to harden the processor against the space environment. The Intel Loihi Gen-1 on TES-13 is characterized by a 14nm 128-core Spiking Neural Network (SNN) able to support on-chip training. The processor is packaged in the Kapoho Bay USB module, providing a relatively straight-forward interface to the bus avionics system. The Kapoho Bay was in turn managed by an Intel Pentium single-board computer to handle scheduling of the software application payloads and communications with the satellite’s primary computer. The recently released Intel Loihi Gen-2, able to support integer-valued spike payloads and produced using 7nm process, will form part of the continually evolving BrainStack in the upcoming three TES-n/NOW flights. The Kapoho Point unit will incorporate eight Loihi-2 processors, enabling neural networks of up to one million neurons and one billion synapsis. Additionally, it is planned to measure the radiation environment these processors experience to understand any degradation or computational artifacts caused by long term space radiation exposure on these novel architectures. This evolving flexible and collaborative environment involving various research teams across NASA and other organizations is intended to be a convenient orbital test platform from which many anticipated future space automation applications may be initially tested.

Artificial Intelligence↗

An overview of NASA's activities in micro-nano technologies

An examination of how mass is used in spacecraft design indicates that technology efforts directed only to reduce the mass of electronics, both digital and analog will not significantly reduce the mass of a spacecraft, regardless of how much success those efforts achieve.

micro-nano technology↗

Terrestrial Applications of a Nano-g Accelerometer

Multi-axis compact seismometers designs that have twelve decades of dynamic range will be described. Density profilometers, precision gradiometers, gyros and vibration isolation designs and applications will be discussed.

terrestrial nano-g accelerometer↗