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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
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Evolution of ʟ–DOPA 4,5–dioxygenase activity allows for recurrent specialisation to betalain pigmentation in Caryophyllales

The evolution of ʟ-DOPA 4,5-dioxygenase activity, encoded by the gene DODA, was a key step in the origin of betalain biosynthesis in Caryophyllales. We previously proposed that ʟ-DOPA 4,5-dioxygenase activity evolved via a single Caryophyllales-specific neofunctionalisation event within the DODA gene lineage. However, this neofunctionalisation event has not been confirmed and the DODA gene lineage exhibits numerous gene duplication events, whose evolutionary significance is unclear. To address this, we functionally characterised 23 distinct DODA proteins for ʟ-DOPA 4,5-dioxygenase activity, from four betalain-pigmented and five anthocyanin-pigmented species, representing key evolutionary transitions across Caryophyllales. By mapping these functional data to an updated DODA phylogeny, we then explored the evolution of ʟ-DOPA 4,5-dioxygenase activity. We find that low ʟ-DOPA 4,5-dioxygenase activity is distributed across the DODA gene lineage. In this context, repeated gene duplication events within the DODA gene lineage give rise to polyphyletic occurrences of elevated ʟ-DOPA 4,5-dioxygenase activity, accompanied by convergent shifts in key functional residues and distinct genomic patterns of micro-synteny. In the context of an updated organismal phylogeny and newly inferred pigment reconstructions, we argue that repeated convergent acquisition of elevated ʟ-DOPA 4,5-dioxygenase activity is consistent with recurrent specialisation to betalain synthesis in Caryophyllales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

An Atom-Precise Approach to Damp First-Order Phase Transitions and Its Implications for Neuromorphic Signal Processing

Neuromorphic computing inspired by mammalian intelligence aims to emulate the nonlinear dynamics of biological neurons and synapses to achieve fast, low-energy, and highly efficient information processing. Brain-inspired computing relies on the design and discovery of materials exhibiting nonlinear current–voltage profiles, frequently underpinned by electronic state transitions, to achieve spiking neurons and dynamically tunable synapses. A signature challenge in the design of artificial neurons is controlling the steepness of first-order transitions in active elements, as abrupt transitions are at risk of driving unstable voltage and temperature oscillations, which result in catastrophic device failure. A critical knowledge gap is the lack of structure–function correlations mapping the composition and atomistic structure of crystalline solids to nonlinear dynamical response characteristics. Here, we address the key question of how modification of atomistic structure correlates with alteration of neuron-like functionality. Constructing oscillator circuits from millimeter-scale single crystals enables high-resolution atomic structure solutions, which we use to demonstrate that the selective positioning of Pb cations modifies charge ordering along a one-dimensional CuxV2O5 framework even at low insertion stoichiometries, thereby providing an atom-precise design parameter for damping first-order transitions. We use temperature-variant X-ray diffraction and X-ray spectroscopy to elucidate the suppression of Cu-ion shuttling based on the precise positioning of Pb ions in seven-coordinated tunnel interstitial sites as the mechanistic basis for transition broadening, thus bridging a critical gap between statistical mechanics and quantum chemical descriptions of phase transitions. Such mechanistic understanding thus paves the way to site-selective modification strategies for modulating the sharpness of first-order transitions, with an exemplary demonstration here in tuning neuronal signal processing.

Crystal structure↗
DOE OSTI2019

Brønsted–Evans–Polanyi relation for CO oxidation on metal oxides following the Mars–van Krevelen mechanism

Scaling relations are widely used to model catalytic reactions on metal surfaces, but they are less commonly applied to metal oxides. Oxygen vacancy formation energies have been suggested as a descriptor for the activity toward oxidation reactions via the Mars-van Krevelen mechanism. However, there is currently no function that maps oxygen vacancy formation energies to CO oxidation barriers. We have compiled a data set for CO oxidation on doped and pristine metal oxide surfaces as well as metal/metal oxide interface sites using density functional theory. Based on this data, we can predict CO oxidation barriers from oxygen vacancy formation energies using a Brønsted–Evans–Polanyi relation with a mean absolute error of 14 kJ/mol. Contrary to what is known for reactions on metal surfaces, we find that the scaling parameter α that describes the lateness of the transition state for CO oxidation on metal oxides is not constant.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Genetic variation at transcription factor binding sites largely explains phenotypic heritability in maize

Abstract Comprehensive maps of functional variation at transcription factor (TF) binding sites (cis-elements) are crucial for elucidating how genotype shapes phenotype. Here, we report the construction of a pan-cistrome of the maize leaf under well-watered and drought conditions. We quantified haplotype-specific TF footprints across a pan-genome of 25 maize hybrids and mapped over 200,000 variants, genetic, epigenetic, or both (termed binding quantitative trait loci (bQTL)), linked tocis-element occupancy. Three lines of evidence support the functional significance of bQTL: (1) coincidence with causative loci that regulate traits, includingvgt1,ZmTRE1and the MITE transposon nearZmNAC111under drought; (2) bQTL allelic bias is shared between inbred parents and matches chromatin immunoprecipitation sequencing results; and (3) partitioning genetic variation across genomic regions demonstrates that bQTL capture the majority of heritable trait variation across ~72% of 143 phenotypes. Our study provides an auspicious approach to make functionalcis-variation accessible at scale for genetic studies and targeted engineering of complex traits.

Genetics & Heredity↗
NASA NTRS1998

Design of Neural Networks for Fast Convergence and Accuracy

A novel procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed to provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component spacecraft design changes and measures of its performance. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The design algorithm attempts to avoid the local minima phenomenon that hampers the traditional network training. A numerical example is performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
DOE OSTI2011

THE FIRST PUBLIC RELEASE OF SOUTH POLE TELESCOPE DATA: MAPS OF A 95 deg{sup 2} FIELD FROM 2008 OBSERVATIONS

The South Pole Telescope (SPT) has nearly completed a 2500 deg{sup 2} survey of the southern sky in three frequency bands. Here, we present the first public release of SPT maps and associated data products. We present arcminute-resolution maps at 150 GHz and 220 GHz of an approximately 95 deg{sup 2} field centered at R.A. 82.{sup 0}7, decl. -55 Degree-Sign . The field was observed to a depth of approximately 17 {mu}K arcmin at 150 GHz and 41 {mu}K arcmin at 220 GHz during the 2008 austral winter season. Two variations on map filtering and map projection are presented, one tailored for producing catalogs of galaxy clusters detected through their Sunyaev-Zel'dovich effect signature and one tailored for producing catalogs of emissive sources. We describe the data processing pipeline, and we present instrument response functions, filter transfer functions, and map noise properties. All data products described in this paper are available for download at http://pole.uchicago.edu/public/data/maps/ra5h30dec-55 and from the NASA Legacy Archive for Microwave Background Data Analysis server. This is the first step in the eventual release of data from the full 2500 deg{sup 2} SPT survey.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

An unconstrained Lagrangian formulation and conservation laws for the Schrödinger map system

We consider energy-critical Schrödinger maps from R{sup 2} into S{sup 2} and H{sup 2}. Viewing such maps with respect to orthonormal frames on the pullback bundle provides a gauge field formulation of the evolution. We show that this gauge field system is the set of Euler-Lagrange equations corresponding to an action that includes a Chern-Simons term. We also introduce the stress-energy tensor and derive conservation laws. In conclusion we offer comparisons between Schrödinger maps and the closely related Chern-Simons-Schrödinger system.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Neurodegenerative disease mutations in TREM2 reveal a functional surface and distinct loss-of-function mechanisms

Genetic variations in the myeloid immune receptor TREM2 are linked to several neurodegenerative diseases. To determine how TREM2 variants contribute to these diseases, we performed structural and functional studies of wild-type and variant proteins. Our 3.1 Å TREM2 crystal structure revealed that mutations found in Nasu-Hakola disease are buried whereas Alzheimer’s disease risk variants are found on the surface, suggesting that these mutations have distinct effects on TREM2 function. Biophysical and cellular methods indicate that Nasu-Hakola mutations impact protein stability and decrease folded TREM2 surface expression, whereas Alzheimer’s risk variants impact binding to a TREM2 ligand. Additionally, the Alzheimer’s risk variants appear to epitope map a functional surface on TREM2 that is unique within the larger TREM family. These findings provide a guide to structural and functional differences among genetic variants of TREM2, indicating that therapies targeting the TREM2 pathway should be tailored to these genetic and functional differences with patient-specific medicine approaches for neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Maghami, Peiman G.↗
DOE OSTI2010

Relativistic energy density functionals: Low-energy collective states of {sup 240}Pu and {sup 166}Er

The empirical relativistic density-dependent, point-coupling energy density functional, adjusted exclusively to experimental binding energies of a large set of deformed nuclei with Aapprox =150-180 and Aapprox =230-250, is tested with spectroscopic data for {sup 166}Er and {sup 240}Pu. Starting from constrained self-consistent triaxial relativistic Hartree-Bogoliubov calculations of binding energy maps as functions of the quadrupole deformation in the beta-gamma plane, excitation spectra and E2 transition probabilities are calculated as solutions of the corresponding microscopic collective Hamiltonian in five dimensions for quadrupole vibrational and rotational degrees of freedom and compared with available data on low-energy collective states.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

TH-EF-207A-04: A Dynamic Contrast Enhanced Cone Beam CT Technique for Evaluation of Renal Functions

Purpose: To develop a simple but robust method for the early detection and evaluation of renal functions using dynamic contrast enhanced cone beam CT technique. Methods: Experiments were performed on an integrated imaging and radiation research platform developed by our lab. Animals (n=3) were anesthetized with 20uL Ketamine/Xylazine cocktail, and then received 200uL injection of iodinated contrast agent Iopamidol via tail vein. Cone beam CT was acquired following contrast injection once per minute and up to 25 minutes. The cone beam CT was reconstructed with a dimension of 300×300×800 voxels of 130×130×130um voxel resolution. The middle kidney slices in the transvers and coronal planes were selected for image analysis. A double exponential function was used to fit the contrast enhanced signal intensity versus the time after contrast injection. Both pixel-based and region of interest (ROI)-based curve fitting were performed. Four parameters obtained from the curve fitting, namely the amplitude and flow constant for both contrast wash in and wash out phases, were investigated for further analysis. Results: Robust curve fitting was demonstrated for both pixel based (with R{sup 2}>0.8 for >85% pixels within the kidney contour) and ROI based (R{sup 2}>0.9 for all regions) analysis. Three different functional regions: renal pelvis, medulla and cortex, were clearly differentiated in the functional parameter map in the pixel based analysis. ROI based analysis showed the half-life T1/2 for contrast wash in and wash out phases were 0.98±0.15 and 17.04±7.16, 0.63±0.07 and 17.88±4.51, and 1.48±0.40 and 10.79±3.88 minutes for the renal pelvis, medulla and cortex, respectively. Conclusion: A robust method based on dynamic contrast enhanced cone beam CT and double exponential curve fitting has been developed to analyze the renal functions for different functional regions. Future study will be performed to investigate the sensitivity of this technique in the detection of radiation induced kidney dysfunction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

From Machine Learning to Machine Reasoning: A Model-based Approach to Analyze Equipment Reliability Data

In current nuclear power plants (NPPs) a large amount of condition-based data which can be used to assess and monitor component health and performance. Assessing component health from such data can be performed with a large variety of methods. While the analysis of numeric data can be performed with several methods, the extraction of information from textual data remains a challenge. Currently employed natural language processing (NLP) methods do not really provide quantitative information that might be contained in IRs. In addition, the integration of numeric and textual data to identify possible causal relationships between data elements is still an unresolved challenge. This paper presents an approach to extract information from textual (e.g., incident or maintenance reports) and numeric data that relies on model based system engineer (MBSE) models. MBSE are diagrams designed to represent system and component dependencies (from both a form and functional point of view). In our approach, MBSE models emulate system engineer knowledge about component/system architecture. NLP methods are employed to perform syntactic and semantic analyses. Syntactic analysis analyzes the grammatical structure of a sentence while semantic analysis is designed to analyze the logic structure of a sentence. An innovative element of our approach is that semantic analysis uses MBSE models to identify links between textual elements. Similarly, numeric data is directly linked to elements of the MBSE models in order to map which functions are being monitored.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids

Density functional theory (DFT)-based simulations of materials have first-principles accuracy, but are very computationally expensive. For simulating various properties of multi-component alloys, the cluster expansion (CE) technique has served as the standard workaround to improve computational efficiency. However, the standard CE technique is difficult to extend to exotic and/or low-symmetry lattices, often implemented via iteration over particular cluster types, which must be enumerated per lattice structure. In this work, we introduce the tensor cluster expansion (TCE), implemented in the open-source code tce-lib, which maps correlation functions to mixed tensor contractions, eliminating the need to iterate over cluster types and additionally making the calculation of correlation functions well-suited for massively parallel architectures like GPUs. We show that local interaction energies are an immediate consequence of the TCE formalism, yielding nearly $\mathcal{O}$(1) energy difference calculations. We then use this formalism to fit CE models for the TaW and CoNiCrFeMn systems, and use these models to respectively compute the enthalpy of mixing curve and Cowley short-range order parameters, showing excellent agreement with ground truth data.

Cluster expansion↗
DOE OSTI2013

TWO MASS DISTRIBUTIONS IN THE L 1641 MOLECULAR CLOUDS: THE HERSCHEL CONNECTION OF DENSE CORES AND FILAMENTS IN ORION A

We present Herschel survey maps of the L 1641 molecular clouds in Orion A. We extracted both the filaments and dense cores in the region. We identified which of the dense sources are proto- or pre-stellar, and studied their association with the identified filaments. We find that although most (71%) of the pre-stellar sources are located on filaments there, is still a significant fraction of sources not associated with such structures. We find that these two populations (on and off the identified filaments) have distinctly different mass distributions. The mass distribution of the sources on the filaments is found to peak at 4 M {sub ☉} and drives the shape of the core mass function (CMF) at higher masses, which we fit with a power law of the form dN/dlogM∝M {sup –1.4±0.4}. The mass distribution of the sources off the filaments, on the other hand, peaks at 0.8 M {sub ☉} and leads to a flattening of the CMF at masses lower than ∼4 M {sub ☉}. We postulate that this difference between the mass distributions is due to the higher proportion of gas that is available in the filaments, rather than in the diffuse cloud.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Architecture of autonomous systems

Automation of Space Station functions and activities, particularly those involving robotic capabilities with interactive or supervisory human control, is a complex, multi-disciplinary systems design problem. A wide variety of applications using autonomous control can be found in the literature, but none of them seem to address the problem in general. All of them are designed with a specific application in mind. In this report, an abstract model is described which unifies the key concepts underlying the design of automated systems such as those studied by the aerospace contractors. The model has been kept as general as possible. The attempt is to capture all the key components of autonomous systems. With a little effort, it should be possible to map the functions of any specific autonomous system application to the model presented here.

Dikshit, Piyush↗