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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 217 records · Page 12

Visual circuits of the avian telencephalon: evolutionary implications

Birds and primates are vertebrates that possess the most advanced, efficient visual systems. Although lineages leading to these two classes were separated about 300 million years ago, there are striking similarities in their underlying neural mechanisms for visual processing. This paper discusses such similarities with special emphasis on the visual circuits in the avian telencephalon. These similarities include: (1) the existence of two parallel visual pathways and their distinct telencephalic targets, (2) anatomical and functional segregation within the visual pathways, (3) laminar organization of the telencephalic targets of the pathways (e.g. striate cortex in primates), and (4) possible interactions between multiple visual areas. Additional extensive analyses are necessary to determine whether these similarities are due to inheritance from a common ancestral stock or the consequences of convergent evolution based on adaptive response to similar selective pressures. Nevertheless, such a comparison is important to identify the general and specific principles of visual processing in amniotes (reptiles, birds, and mammals). Furthermore, these principles in turn will provide a critical foundation for understanding the evolution of the brain in amniotes.

Review, Tutorial↗

Development of a Low-Power CO2 Removal and Compression System for Closed-Loop Air Revitalization in Future Spacecraft

The current CO2 removal technology of NASA is very energy intensive and contains many non-optimized subsystems. This paper discusses the design and prototype development of a two-stage CO2 removal and compression system that will utilize much less power than NASA s current CO2 removal technology. This integrated system contains a Nafion membrane followed by a residual water adsorber that performs the function of the desiccant beds in the four-bed molecular sieve (4BMS) system of the International Space Station (ISS). The membrane and the water adsorber are followed by a two-stage CO2 removal and compression subsystem that satisfies the operations of the CO2 adsorbent beds of the 4BMS aid the interface compressor for the Sabatier reactor connection. The two-stage compressor will utilize the principles of temperature-swing adsorption (TSA) compression technology for CO2 removal and compression. The similarities in operation and cycle times of the CO2 removal (first stage) and compression (second stage) operations will allow thermal coupling of the processes to maximize the efficiency of the system. In addition to the low-power advantage, this processor will maintain a lower CO2 concentration in the cabin than that can be achieved by the existing CO2 removal systems. The compact, consolidated, configuration of membrane gas dryer and CO2 separator and compressor will allow continuous recycling of humid air in the cabin and supply of compressed CO2 to the reduction unit for oxygen recovery. The device has potential application to the International Space Station and future, long duration, transit, and planetary missions.

Mulloth, Lila M.↗

Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size-dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first-principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size-dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well-trained NN potentials can provide high level of flexibility, transferability and accuracy on-par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities (similar to 10 4 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far-from-equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first-principles based and hence expensive. In this work, we introduce an active learning (AL) scheme that trains a NN model on-the-fly with minimal amount of first-principles based training data. Our AL workflow is initiated with a sparse training dataset (similar to 1 to 5 data points) and is updated on-the-fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with similar to 500 total reference calculations. Using an extensive DFT test set of similar to 1100 configurations, we show that our AL-NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/angstrom of the reference DFT calculations. Moreover, our AL-NN model also adequately captures the various size-dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL-NN model also captures bulk properties reasonably well, even though they were not included in the training data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

FAST.Farm Development and Validation of Structural Load Prediction Against Large Eddy Simulations

FAST.Farm is a mid-fidelity engineering tool developed by the National Renewable Energy Laboratory targeted at accurately and efficiently predicting wind turbine power production and structural loading in wind farm settings, including wake interactions between turbines. FAST.Farm is based on several principles of the dynamic wake meandering (DWM) model, but also addresses limitations of previous DWM implementations. Previous FAST.Farm studies have shown the similarities and differences between FAST.Farm and large eddy simulations for rigid turbine cases. The objective of this work is to quantify the ability of FAST.Farm to accurately predict turbine structural response in a small wind farm. This is done by comparing FAST.Farm structural response to SOWFA-OpenFAST results for three laterally-aligned turbines. The purpose of this study is to characterize the similarities and differences between FAST.Farm and a higher fidelity model for predicting turbine structural response in a wind farm for differing atmospheric inflows. Strong statistical agreement was found between FAST.Farm and SOWFA-OpenFAST structural response for the non-waked upstream turbine, and good agreement was found for the downstream turbines for most structural quantities. Higher differences were seen for downstream turbines with low ambient turbulence intensity or yawed turbines, suggesting areas for FAST.Farm wake dynamics modeling improvements. For all cases and turbines, small statistical differences were seen between blade deflections and bending moments, with larger differences for tower-top and tower-base bending moments. Overall, the results establish confidence for applying FAST.Farm to wind farm power and loads analyses and identify areas where further model validations and model improvements should be targeted.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

In this report computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO 2 -NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.

molecular fingerprint↗

Simulations of edge and SOL turbulence in diverted negative and positive triangularity plasmas

Optimizing the performance of magnetic confinement fusion devices is critical to achieving an attractive fusion reactor design. Negative triangularity (NT) scenarios have been shown to achieve excellent levels of energy confinement, while avoiding edge localized modes. Modeling turbulent transport in the edge and SOL is key in understanding the impact of NT on turbulence and extrapolating the results to future devices and regimes. Previous gyrokinetic turbulence studies have reported beneficial effects of NT across a broad range of parameters. However, most simulations have focused on the inner plasma region, neglecting the impact of NT on the outermost edge. In this work, we investigate the effect of NT in edge and scrape-off layer simulations, including the magnetic X-point and separatrix. For the first time, we employ a multi-fidelity approach, combining global, non-linear gyrokinetic simulations with drift-reduced fluid simulations, to gain a deeper understanding of the underlying physics at play. First-principles simulations using the GENE-X code demonstrate that in comparable NT and PT geometries, similar profiles are achieved, while the turbulent heat flux is reduced by more than 50% in NT. Comparisons with results from the drift-reduced fluid turbulence code GRILLIX suggest that the turbulence is driven by trapped electron modes. The parallel heat flux width on the divertor targets is reduced in NT, primarily due to a lower spreading factor S.

GENE-X↗

Certain aspects of the psychological analysis of programmer activity

The psychological analysis of programmer activity showed that one of its basic characteristics is the need to employ formal languages. In determining ways of effectively mastering the capacity to write algorithms in the algorithmic language, it is expedient to proceed from its similarity to the living languages and the language of mathematical formulas, and to choose certain psychological principles of mastering foreign languages and mathematical symbols in teaching the algorithmic language. General models of the input language significantly increase the effectiveness of its mastery and permit the development of thinking on the part of the students.

Bondarovskaya, V. M.↗

Aerodynamic preliminary analysis system 2. Part 1: Theory

A subsonic/supersonic/hypersonic aerodynamic analysis was developed by integrating the Aerodynamic Preliminary Analysis System (APAS), and the inviscid force calculation modules of the Hypersonic Arbitrary Body Program. APAS analysis was extended for nonlinear vortex forces using a generalization of the Polhamus analogy. The interactive system provides appropriate aerodynamic models for a single input geometry data base and has a run/output format similar to a wind tunnel test program. The user's manual was organized to cover the principle system activities of a typical application, geometric input/editing, aerodynamic evaluation, and post analysis review/display. Sample sessions are included to illustrate the specific task involved and are followed by a comprehensive command/subcommand dictionary used to operate the system.

Bonner, E.↗

Enhancement of multispectral thermal infrared images - Decorrelation contrast stretching

Decorrelation contrast stretching is an effective method for displaying information from multispectral thermal infrared (TIR) images. The technique involves transformation of the data to principle components ('decorrelation'), independent contrast 'stretching' of data from the new 'decorrelated' image bands, and retransformation of the stretched data back to the approximate original axes, based on the inverse of the principle component rotation. The enhancement is robust in that colors of the same scene components are similar in enhanced images of similar scenes, or the same scene imaged at different times. Decorrelation contrast stretching is reviewed in the context of other enhancements applied to TIR images.

Gillespie, Alan R.↗

A Case Study for Probabilistic Methods Validation (MSFC Center Director's Discretionary Fund, Project No. 94-26)

Probabilistic method is not a universally accepted approach for the design and analysis of aerospace structures. The validity of this approach must be demonstrated to encourage its acceptance as it viable design and analysis tool to estimate structural reliability. The objective of this Study is to develop a well characterized finite population of similar aerospace structures that can be used to (1) validate probabilistic codes, (2) demonstrate the basic principles behind probabilistic methods, (3) formulate general guidelines for characterization of material drivers (such as elastic modulus) when limited data is available, and (4) investigate how the drivers affect the results of sensitivity analysis at the component/failure mode level.

Price J. M.↗

Electrolyte design for Li-ion batteries under extreme operating conditions

The ideal electrolyte for the widely used LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811)||graphite lithium-ion batteries is expected to have the capability of supporting higher voltages (≥4.5 volts), fast charging (≤15 minutes), charging/discharging over a wide temperature range (±60 degrees Celsius) without lithium plating, and non-flammability. No existing electrolyte simultaneously meets all these requirements and electrolyte design is hindered by the absence of an effective guiding principle that addresses the relationships between battery performance, solvation structure and solid-electrolyte-interphase chemistry. Here we report and validate an electrolyte design strategy based on a group of soft solvents that strikes a balance between weak Li+-solvent interactions, sufficient salt dissociation and desired electrochemistry to fulfil all the aforementioned requirements. Remarkably, the 4.5-volt NMC811||graphite coin cells with areal capacities of more than 2.5 milliampere hours per square centimetre retain 75 per cent (54 per cent) of their room-temperature capacity when these cells are charged and discharged at -50 degrees Celsius (-60 degrees Celsius) at a C rate of 0.1C, and the NMC811||graphite pouch cells with lean electrolyte (2.5 grams per ampere hour) achieve stable cycling with an average Coulombic efficiency of more than 99.9 per cent at -30 degrees Celsius. The comprehensive analysis further reveals an impedance matching between the NMC811 cathode and the graphite anode owing to the formation of similar lithium-fluoride-rich interphases, thus effectively avoiding lithium plating at low temperatures. Additionally, this electrolyte design principle can be extended to other alkali-metal-ion batteries operating under extreme conditions.

25 ENERGY STORAGE↗

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗

V. The X-15 flight research program in relation to the development of advanced military aircraft

The design, construction, and development of advanced military aircraft undoubtedly will involve the utilization of many relatively new concepts in several deafferent, but related, areas. In many cases, these new concepts will require new flight techniques or involve development problems inherent in the use of relatively unproven methods, materials, structures, systems, and configurations. Moreover, flight verification will be required of aircraft aerodynamics and flight behavior because of the usual uncertainties associated with predicted data. Solutions to these problems obviously would require flight testing involving extensive time and effort before the aircraft could become operationally acceptable. Upon the inception of the X-15 research airplane project, many new and far-reaching techniques and principles were studied and applied in the aircraft design and are currently being demonstrated and investigated. Because similar concepts are likely to be used in advanced military aircraft, the X-15 flight program will provide significant information, over a broad flight environment, pertinent to the development of these vehicles. This paper discusses the research objectives of the X-15 flight program, some of the flight aerodynamic characteristics currently being obtained, some development problems encountered, and the experience obtained with the advanced systems investigated.

Military aircraft↗

An exact plane-stress solution for a class of problems in orthotropic elasticity

An exact solution for the stress field within a rectangular slab of orthotropic material is found using a two dimensional Fourier series formulation. The material is required to be in plane stress, with general stress boundary conditions, and the principle axes of the material must be parallel to the sides of the rectangle. Two load cases similar to those encountered in materials testing are investigated using the solution. The solution method has potential uses in stress analysis of composite structures.

Erb, D. A.↗

TALON and CRADLE: Systems for the rescue of tumbling spacecraft and astronauts

Advanced pressure suit and tool designs are beginning to allow extravehicular astronauts to repair space vehicles and so increase mission life and system reliability. A common spacecraft failure that is a severe challenge to the rescue mission planner is loss of attitude control resulting in tumbling motion. If an extravehicular astronaut flying the Manned Maneuvering Unit (MMU) 'falls' into a tumble, the result could be loss of life. TALON (Tumble Arresting Large Oscillation Nullifier) is a device capable of capturing a target in an uncontrolled three-axis tumble. CRADLE (Concentric Rotating Astronaut Detumble Lifesaving Equipment) is a similar device sized to rescue a suited astronaut. The two rescue vehicles work on the same basic principle. They are structural shells with articulated limbs which can surround a tumbling target and thus align both the chaser and target centers of mass (CM).

Idle, Dunning, V↗

Electro-optic Imaging Fourier Transform Spectrometer

JPL is developing an innovative compact, low mass, Electro-Optic Imaging Fourier Transform Spectrometer (E-0IFTS) for hyperspectral imaging applications. The spectral region of this spectrometer will be 1 - 2.5 pm (1000 -4000 cm-') to allow high-resolution, high-speed hyperspectral imaging applications [l-51. One application will be theremote sensing of the measurement of a large number of different atmospheric gases simultaneously in the sameairmass. Due to the use of a combination of birefiingent phase retarders and multiple achromatic phase switches toachieve phase delay, this spectrometer is capable of hyperspectral measurements similar to that of the conventionalFourier transform spectrometer but without any moving parts. In this paper, the principle of operations, systemarchitecture and recent experimental progress will be presen.

solild state imaging Fourier tranform spectrometer↗

Benefits of Hybrid-Electric Propulsion to Achieve 4x Increase in Cruise Efficiency for a VTOL Aircraft

Electric propulsion enables radical new vehicle concepts, particularly for Vertical Takeoff and Landing (VTOL) aircraft because of their significant mismatch between takeoff and cruise power conditions. However, electric propulsion does not merely provide the ability to normalize the power required across the phases of flight, in the way that automobiles also use hybrid electric technologies. The ability to distribute the thrust across the airframe, without mechanical complexity and with a scale-free propulsion system, is a new degree of freedom for aircraft designers. Electric propulsion is scale-free in terms of being able to achieve highly similar levels of motor power to weight and efficiency across a dramatic scaling range. Applying these combined principles of electric propulsion across a VTOL aircraft permits an improvement in aerodynamic efficiency that is approximately four times the state of the art of conventional helicopter configurations. Helicopters typically achieve a lift to drag ratio (L/D) of between 4 and 5, while the VTOL aircraft designed and developed in this research were designed to achieve an L/D of approximately 20. Fundamentally, the ability to eliminate the problem of advancing and retreating rotor blades is shown, without resorting to unacceptable prior solutions such as tail-sitters. This combination of concept and technology also enables a four times increase in range and endurance while maintaining the full VTOL and hover capability provided by a helicopter. Also important is the ability to achieve low disc-loading for low ground impingement velocities, low noise and hover power minimization (thus reducing energy consumption in VTOL phases). This combination of low noise and electric propulsion (i.e. zero emissions) will produce a much more community-friendly class of vehicles. This research provides a review of the concept brainstorming, configuration aerodynamic and mission analysis, as well as subscale prototype construction and flight testing that verifies transition flight control. A final down-selected vehicle is also presented.

Fredericks, William J.↗

Site heterogeneity and broad surface-binding isotherms in modern catalysis: Building intuition beyond the Sabatier principle

Learning the science of heterogeneous catalysis and electrocatalysis always starts with the simple case of a flat, uniform surface with an ideal adsorbate. It has of course been recognized for a century that real catalysts are more complicated. For the increasingly complex catalysts of the 21st century, this Perspective argues that surface heterogeneity and non-ideal binding isotherms are central features, and their implications need to be incorporated in current thinking. A variety of systems are described herein where catalyst complexity leads to broad, non-Langmuirian surface isotherms for the binding of hydrogen atoms – and this occurs even for ideal, flat Pt(111) surfaces. Modern catalysis employs nanoscale materials whose surfaces have substantial step, edge, corner, impurity, and other defect sites, and they increasingly have both metallic and non-metallic elements M n X m , including metal oxides, chalcogenides, pnictides, carbides, doped carbons, etc. The surfaces of such catalysts are often not crystal facets of the bulk phase underneath, and they typically have a variety of potential active sites. Catalytic surfaces in operando are often non-stoichiometric, amorphous, dynamic, and impure, and often vary from one part of the surface to another. Understanding of the issues that arise at such nanoscale, multi-element catalysts is just beginning to emerge. Yet these catalysts are widely discussed using Brønsted/Bell-Evans-Polanyi (BEP) relations, volcano plots, Tafel slopes, the Butler-Volmer equation, and other linear free energy relations (LFERs), which all depend on the implicit assumption that the active sites are “similar” and that surface adsorption is close to ideal. These assumptions underly the ubiquitous intuition based on the Sabatier Principle, that the fastest catalysis will occur when key intermediates have free energies of adsorption that are not too strong nor too weak. Current catalysis research often aims to minimize the complexity of non-ideal isotherms through experimental and computational design (e.g., the use of single crystal surfaces), and these studies are the foundation of the field. In contrast, this Perspective argues that the heterogeneity of binding sites and binding energies is an inherent strength of these catalysts. Here, this diversity makes many nanoscale catalysts inherently a high-throughput screen wrapped in a tiny package. Only by making the heterogeneity part of the foundation of catalysis models, sorting the types of active sites and dissecting non-ideal binding isotherms, will modern catalysis learn to harness the inherent diversity of real catalysts. Controlling and exploiting diversity rather than avoiding it will help to optimize complex modern catalysts and catalytic conditions.

Mayer, James M.↗