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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 235 records · Page 13

Efficient hybrid explicit-implicit learning for multiscale problems

Splitting method is a powerful method to handle application problems by splitting physics, scales, domain, and so on. Many splitting algorithms have been designed for efficient temporal discretization. Here, in this paper, our goal is to use temporal splitting concepts in designing machine learning algorithms and, at the same time, help splitting algorithms by incorporating data and speeding them up. We propose a machine learning assisted splitting scheme which improves the efficiency of the scheme meanwhile preserves the accuracy. We consider a recently introduced multiscale splitting algorithms, where the multiscale problem is solved on a coarse grid. To approximate the dynamics, only a few degrees of freedom are solved implicitly, while others explicitly. This splitting concept allows identifying degrees of freedom that need implicit treatment. In this paper, we use this splitting concept in machine learning and propose several strategies. First, the implicit part of the solution can be learned as it is more difficult to solve, while the explicit part can be computed. This provides a speed-up and data incorporation for splitting approaches. Secondly, one can design a hybrid neural network architecture because handling explicit parts requires much fewer communications among neurons and can be done efficiently. Thirdly, one can solve the coarse grid component via PDEs or other approximation methods and construct simpler neural networks for the explicit part of the solutions. We discuss these options and implement one of them by interpreting it as a machine translation task. This interpretation of the splitting scheme successfully enables us using the Transformer since it can perform model reduction for multiple time series and learn the connection between them. We also find that the splitting scheme is a great platform to predict the coarse solution with insufficient information of the target model: the target problem is partially given and we need to solve it through a known problem which approximates the target. Our machine learning model can incorporate and encode the given information from two different problems and then solve the target problems. We conduct four numerical examples and the results show that our method is stable and accurate.

97 MATHEMATICS AND COMPUTING↗

Kinetic Analysis of Proton-Coupled Electron Transfer at an Electrode-Immobilized Complex

An alkyne-terminated cobalt complex, [Co(Cp)(dppe ≡H )(Cl)] + (Cp = cyclopentadienyl; dppe ≡H = 1,2-bis-(di-(4-ethynyl-phenyl)phosphino)ethane), (Co ≡H ) was immobilized onto a glassy carbon electrode using two attachment strategies: Cu(I) catalyzed azide–alkyne click chemistry and reductive electropolymerization. The modified electrodes prepared through reductive electropolymerization exhibit current densities and peak resolutions for the electrochemical reduction of the cobalt species, which are amenable to electroanalytical quantification of coupled chemical reactions. Through peak shift analysis of cyclic voltammograms recorded in the presence of 4-chloroanilinium tetrafluoroborate, we quantified the proton transfer rate constant for the stepwise proton-coupled electron transfer reaction that reduces the electrode-immobilized [Co(Cp)(dppe ≡H )(Cl)] + to [H–Co(Cp)(dppe ≡H )] + (k PT app = (9.3 ± 1.8) × 10 5 M –1 s –1 ). In conclusion, the extraction of kinetic parameters for an elementary proton-coupled electron transfer reaction of an electrode-immobilized complex represents the first experimental measurement of its type and lays crucial groundwork for kinetic analyses of hybrid catalyst–electrode architectures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗

GRAPH — an readout ASIC for large MCP based detectors

We present a programmable 16 channel, mixed signal, low power readout ASIC, having the project historically named Gigasample Recorder of Analog waveforms from a PHotodetector (GRAPH). It is designed to read large aperture single photon imaging detectors using micro channel plates for charge multiplication, and measuring the detector's response on crossed strips anodes to extrapolate the incoming photon position. Each channel consists of a fast, low power and low noise charge sensitive amplifier, which provides a myriad of coarse and fine programmable options for gain and shaping settings. Further, the amplified signal is recorded using, to our knowledge novel, the Hybrid Universal sampLing Architecture (HULA) ADC. A kind of mixed signal double buffer memory, that enables concurrent waveform recording, and selected event digitized data extraction. The sampling frequency is freely adjustable between few kHz up to 125 MHz, while the chip's internal digital memory holds a history 2048 samples for each channel, with a digital headroom of 12 bits. An optimized region of interest sample-read algorithm allows to extract the information just around the event pulse peak, while selecting the next event, thus substantially reducing the operational dead time. The chip is designed in 130 nm TSMC CMOS technology, and its power consumption is around 47 mW per channel.

47 OTHER INSTRUMENTATION↗

Design and Optimization of a Fast-Switching Electromagnetic Valve Actuator for Off-highway Vehicle Hydraulic Systems

Electrification of off-highway vehicles offers improved efficiency and reduction in greenhouse gas emissions. However, the extremely low power densities of electric machines relative to the hydraulics used in conventional off-highway vehicles makes electrification challenging. Although the recently proposed Hybrid Hydraulic Electric Architecture (HHEA) can overcome this limitation, it requires frequent switching between the pressure rails using switching valves resulting in losses. Fast switching minimizes the switching losses in these valves, which otherwise significantly reduce the overall efficiency of the system. The primary contribution of this paper is the design and optimization of a linear electromagnetic actuator for a fast-switching valve. This paper identifies the actuator requirements to enable fast switching and presents a design and optimization technique to realize actuators that meet the design objectives. In addition, a multi-physics approach is proposed to model the performance of the complete switching valve by considering the electromagnetic, fluidic, and mechanical physics. An optimal actuator design is obtained using the developed optimization framework, and the multi-physics model demonstrates that the selected design achieves a 30% reduction in throttling-related switching losses by switching 70 % faster than comparable commercial valve designs.

Linear actuators, off-highway vehicle electrificat↗

MAPLE v.1.0

SAND2025-00659O MAPLE is a software tool that uses epigenomic data to predict gene expression. MAPLE uses a set of epigenomic modifications to determine the effect on gene expression in a specific subset of species. The algorithm can be trained on additional species and epigenomic modifications, enhancing its predictive capabilities. EAGLE employs a hybrid neural network architecture, featuring a convolutional front-end and a multi-head attention layer, to process pre-processed signal data as input. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Davis IV, Warren↗

Pairing Dynamics in Low-energy Nuclear Collisions

Superfluidity is a generic feature of various quantum systems at low temperatures and it is in particular important for the description of dynam- ics of low energy nuclear reactions. The Time-Dependent Density Func- tional Theory (TDDFT) is, to date, the only microscopic method which takes into account in a consistent way far-from-equilibrium dynamics of pairing field and single-particle degrees of freedom. Here, the local version of TDDFT, so-called TDSLDA, is particularly useful for the description of nuclear reactions and is well-suited for leadership class computers of hybrid (CPU+GPU) architecture. The preliminary results obtained for collisions involving both medium-mass and heavy nuclei at the energies around the Coulomb barrier are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Wide field and diffraction limited array camera for SIRTF

The Infrared Array Camera for the space Infrared Telescope Facility (SIRTF/IRAC) is capable of two-dimensional photometry in either a wide field or diffraction-limited mode over the wavelength interval from 2 to 30 microns. Three different two-dimensional direct readout (DRO) array detectors will be used: Band 1-InSb or Si:In (2-5 microns) 128 x 128 pixels, Band 2-Si:Ga (5-18 microns) 64 x 64 pixels, and Band 3-Si:Sb (18-30 microns) 64 x 64 pixels. The hybrid DRO readout architecture has the advantages of low read noise, random pixel access with individual readout rates, and nondestructive readout. The scientific goals of IRAC are discussed, which are the basis for several important requirements and capabilities of the array camera: (1) diffraction-limited resolution from 2-30 microns, (2) use of the maximum unvignetted field of view of SIRTF, (3) simultaneous observations within the three infrared spectral bands, and (4) the capability for broad and narrow bandwidth spectral resolution. A strategy has been developed to minimize the total electronic and environmental noise sources to satisfy the scientific requirements.

Fazio, G. G.↗

Wide field and diffraction limited array camera for SIRTF

The Infrared Array Camera for the Space Infrared Telescope Facility (SIRTF/IRAC) is capable of two-dimensional photometry in either a wide field or diffraction-limited mode over the wavelength interval from 2 to 30 microns. Three different two-dimensional direct readout (DRO) array detectors are being considered: Band 1-InSb or Si:In (2-5 microns) 128 x 128 pixels, Band 2-Si:Ga (5-18 microns) 64 x 64 pixels, and Band 3-Si:Sb (18-30 microns) 64 x 64 pixels. The hybrid DRO readout architecture has the advantages of low read noise, random pixel access with individual readout rates, and nondestructive readout. The scientific goals of IRAC are discussed, which are the basis for several important requirements and capabilities of the array camera: (1) diffraction-limited resolution from 2-30 microns, (2) use of the maximum unvignetted field of view of SIRTF, (3) simultaneous observations within the three infrared spectral bands, and (4) the capability for broad and narrow bandwidth spectral resolution. A strategy has been developed to minimize the total electronic and environmental noise sources to satisfy the scientific requirements.

Fazio, G. G.↗

Adaptive fuzzy system for 3-D vision

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Mitra, Sunanda↗

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

A modular, unsupervised neural network architecture for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns on-line in a stable and efficient manner. The initial classification is performed in two stages: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from fuzzy C-means system equations for the centroids and the membership values. The AFLC algorithm is applied to the Anderson Iris data and laser-luminescent fingerprint image data. It is concluded that the AFLC algorithm successfully classifies features extracted from real data, discrete or continuous.

Newton, Scott C.↗

The time keeping system for GPS block IIR

The precision time keeping system (TKS) in the Global Positioning System (GPS), Block IIR satellites is designed to operate under severe natural and man made environmental conditions. The Block IIR TKS provides precise, autonomous time keeping for periods of up to seven months, without the intervention of the GPS Control Segment. The TKS is implemented using both linear and non-linear controls. The resulting TKS architecture uses a hybrid analog/digital phase locked loop (PLL). The paper provides details of the design and analysis of the TKS. The simulation techniques and the test bed activities used in performing the TKS design trade-offs are described. The effects of non-linear controls are analyzed using a TKS computer simulation of the PLL. The results from a hardware test bed are provided that verify desired TKS operation. The design criteria for the TKS computer simulation and the hardware test bed are indicated. The concepts for verification and testing of the TKS computer simulation and hardware test bed are presented.

Rawicz, H. C.↗

Bioelectric Control of a 757 Class High Fidelity Aircraft Simulation

This paper presents results of a recent experiment in fine grain Electromyographic (EMG) signal recognition, We demonstrate bioelectric flight control of 757 class simulation aircraft landing at San Francisco International Airport. The physical instrumentality of a pilot control stick is not used. A pilot closes a fist in empty air and performs control movements which are captured by a dry electrode array on the arm, analyzed and routed through a flight director permitting full pilot outer loop control of the simulation. A Vision Dome immersive display is used to create a VR world for the aircraft body mechanics and flight changes to pilot movements. Inner loop surfaces and differential aircraft thrust is controlled using a hybrid neural network architecture that combines a damage adaptive controller (Jorgensen 1998, Totah 1998) with a propulsion only based control system (Bull & Kaneshige 1997). Thus the 757 aircraft is not only being flown bioelectrically at the pilot level but also demonstrates damage adaptive neural network control permitting adaptation to severe changes in the physical flight characteristics of the aircraft at the inner loop level. To compensate for accident scenarios, the aircraft uses remaining control surface authority and differential thrust from the engines. To the best of our knowledge this is the first time real time bioelectric fine-grained control, differential thrust based control, and neural network damage adaptive control have been integrated into a single flight demonstration. The paper describes the EMG pattern recognition system and the bioelectric pattern recognition methodology.

Jorgensen, Charles↗

Effects of Compression, Staging, and Braid Angle on Braided Rope Seal Performance

This viewgraph presentation describes the effects of compression, staging and braid angle on braided rope seals. The contents include: 1) Test Fixture Schematics; 2) Comparison of Hybrid Seal Braid Architecture; 3) Residual Interference After Compression Cycling; 4) Effect of Compression, Braid, and Staging on Seal Flow; 5) Effect of Staging on Seal Pressure Drop; 6) Three Stag Seal Durability; 7) P&W Turbine Vane Seal Requirements; and 8) Next Generation Fighter F-22 P&W F119 Engines.

Steinetz, Bruce M.↗

A Brief Survey of Media Access Control, Data Link Layer, and Protocol Technologies for Lunar Surface Communications

This paper surveys and describes some of the existing media access control and data link layer technologies for possible application in lunar surface communications and the advanced wideband Direct Sequence Code Division Multiple Access (DSCDMA) conceptual systems utilizing phased-array technology that will evolve in the next decade. Time Domain Multiple Access (TDMA) and Code Division Multiple Access (CDMA) are standard Media Access Control (MAC) techniques that can be incorporated into lunar surface communications architectures. Another novel hybrid technique that is recently being developed for use with smart antenna technology combines the advantages of CDMA with those of TDMA. The relatively new and sundry wireless LAN data link layer protocols that are continually under development offer distinct advantages for lunar surface applications over the legacy protocols which are not wireless. Also several communication transport and routing protocols can be chosen with characteristics commensurate with smart antenna systems to provide spacecraft communications for links exhibiting high capacity on the surface of the Moon. The proper choices depend on the specific communication requirements.

Wallett, Thomas M.↗

A Study on the Use of Solid-Oxide Fuel Cells for Increased Power Generation on Small Aircraft

A growing demand for higher-power electrical equipment on aircraft is driving the exploration of alternative electrical power generation devices. “Hybrid-electric” aircraft architectures that leverage multiple or alternative power sources are also becoming more popular with rising concerns over efficiency. Fuel cells are particularly interesting as auxiliary or secondary electrical power generation devices, due to their relative efficiency and longevity, and could be used as a “power pod” for vehicles without enough on-board electrical generation capability. This paper examines the feasibility and potential value of using an externally-mounted, hybridelectric, solid-oxide fuel cell (SOFC) power system on a Class IV unmanned aerial vehicle (UAV) for the purpose of power generation in excess of the baseline electrical load. Moreover, the paper will demonstrate that a SOFC auxiliary power unit (APU) provides significant value to an aircraft by producing a relatively large amount of additional electrical power for a relatively low initial investment when compared to producing additional power by scaling the engine.

Litherland, Brandon L.↗

Strategy for Developing Technologies for Megawatt-class Nuclear Electric Propulsion Systems

In late fiscal year 2020, the Space Nuclear Propulsion (SNP) project began the process of formulating an investment strategy to support development of the technologies required for a high-power (megawatt-class) nuclear electric propulsion (NEP) system capable of performing human-scale missions. This activity was initiated concurrent with several high-level studies and assessments were either under way or had just concluded. Studies of human-scale Mars missions have been performed several times over the past two decades. One of the most recent studies examined opposition-class human Mars missions to occur in the late 2030s timeframe [1,2]. The mission architecture assumed a hybrid NEP/chem-propelled vehicle that used a high specific impulse (Isp) NEP-system and a liquid oxygen (LOx)-liquid methane high thrust chemical stage (two 110 kN (25 klbf) thrust, 365 s Isp engines) for maneuvers performed to enter and exit gravity wells. Trajectory analyses performed in this study showed that such a mission could be performed with 2-4 MWe directed into the electric propulsion system (operating for 20,000+ hours), with the large range representing different opposition-class Mars mission opportunities and permutations on the trajectory design, concept of operations, and technology choices. In 2020, the NASA Engineering and Safety Center (NESC) performed a study to evaluate the maturity of the different technologies required for nuclear propulsion systems [3]. The executive summary of this report provided the following top-level conclusions: • “The majority of critical technologies for… NEP/Chem… systems are relatively immature” • “TRLs [technology readiness levels] in the literature are often overestimated” • “The majority of critical technologies… for NEP/Chem… systems are at a relatively high level of advancement degree of difficulty (AD2 > 4) for maturation, requiring a dual development approach” • “The proper assessment of baseline TRL and AD2 values and the estimation of requirements and resources required for advancement have been consistent issues for NEP,” • “Non-advocate reviews should occur at the start of a technology program and at all key milestones.” In 2021, the National Academies of Science, Engineering, and Medicine (NASEM) issued a separate report [4] identifying the “primary technical and programmatic challenges, merits, and risks for maturing space nuclear propulsion technologies of interest to a future human Mars exploration mission.” That work contained several important findings, including: • “Developing a MWe-class NEP system for the baseline mission would require increasing power by orders of magnitude relative to NEP system flight- or ground-based technology demonstrations completed to date.” • “Subscale in-space flight testing of NEP systems cannot address many of the risks and potential failure modes associated with the baseline mission NEP system. With sufficient M&S [modeling & simulation] and ground testing, including modular subsystem tests at full scale and power, flight qualification requirements can be met by the cargo missions that will precede the first crewed mission to Mars. Fully integrated ground testing may not be required.” • “As a result of low and intermittent investment over the past several decades, it is unclear if even an aggressive program would be able to develop an NEP system capable of executing the baseline mission in 2039.” These efforts motivated the SNP project to investigate the technologies available for a megawatt-class high power nuclear electric propulsion system. That system is illustrated schematically in Figure 1 and is comprised of five separate top-level critical technology elements (CTEs). 1. Nuclear Reactor – Thermal power source for the system, utilizing high-assay low enriched uranium (HALEU) as the nuclear fuel. Reactor radiation shielding is also included in this CTE. 2. Power Conversion – Operates as a thermodynamic cycle, accepting nuclear reactor thermal power as an input and converting it to mechanical power. 3. Power Management and Distribution (PMAD) – Accepts as an input mechanical power from the power conversion system, which is used to generate electrical power. The PMAD system also distributes the generated electrical power to all other parts of the spacecraft, including the high-power EP system. The PMAD system may also perform duties such as isolation, fault detection, and power transformation/rectification for different spacecraft systems, including the thrusters. 4. Electric Propulsion (EP) – Accepts as an input electrical power, which is used to accelerate a propellant to high speeds to produce thrust. This system includes the power processing unit (PPU), which converts the power it receives to the correct current and voltage required by the thrusters, and the propellant storage and feed systems, which contain and meter the flow of propellant to the thrusters. 5. Thermal Management (Radiators/Heat Rejection) – The cold side of the thermodynamic power conversion cycle, accepts thermal power from the power conversion system and radiatively rejects that heat to space. In this paper, we describe the SNP project formulation and investment strategy that aims to accomplish the research and development required to advance the technology readiness for each CTE. The strategy relies heavily upon experimental testing supported by modeling and simulation to yield realistic assessments of the technologies, which in turn will be used to inform future NEP system-level design decisions and any potential technology downselects.

Kurt A Polzin↗

High-Fidelity Aerodynamic Analysis and Optimization of the SUSAN Electrofan Concept

The Subsonic Single Aft Engine (SUSAN) Electrofan is a new single-aisle regional jet class transport aircraft being studied by NASA, which benefits from reduced fuel consumption through a reduction in the number of hydrocarbon fuel-burning engines from two to one. This is achieved through a hybrid-electric system architecture, where a series of generators are used to extract power from a fuel-consuming tail cone thruster to drive electric propulsors mounted on the wings. To support the development of these propulsion systems, which involve high levels of propulsion-airframe integration, this paper presents high-fidelity analyses through computational fluid dynamics (CFD) simulations provided by the Launch Ascent and Vehicle Aerodynamics (LAVA) framework, which also includes capabilities for modeling the effect of active propulsors. A trade space exploration is performed to investigate the advantages of several wing mounted distributed electric propulsion system configurations, including over-wing, under-wing, and trailing-edge arrangements. For the aft fuselage propulsor, focus is on the sizing of the inlet, core, and bypass ducts. High-fidelity aerodynamic shape optimization is also used to help minimize the inlet distortion intensity experienced by the aft propulsor fan through a reshaping of the fuselage.

CAS↗