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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a: system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The "gap" that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the ciasses of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS1975

The transformation of aerodynamic stability derivatives by symbolic mathematical computation

The formulation of mathematical models of aeronautical systems for simulation or other purposes, involves the transformation of aerodynamic stability derivatives. It is shown that these derivatives transform like the components of a second order tensor having one index of covariance and one index of contravariance. Moreover, due to the equivalence of covariant and contravariant transformations in orthogonal Cartesian systems of coordinates, the transformations can be treated as doubly covariant or doubly contravariant, if this simplifies the formulation. It is shown that the tensor properties of these derivatives can be used to facilitate their transformation by symbolic mathematical computation, and the use of digital computers equipped with formula manipulation compilers. When the tensor transformations are mechanised in the manner described, man-hours are saved and the errors to which human operators are prone can be avoided.

Howard, J. C.↗
DOE OSTI2020

2‐Aminobenzenethiol‐Functionalized Silver‐Decorated Nanoporous Silicon Photoelectrodes for Selective CO 2 Reduction

Abstract A molecularly thin layer of 2‐aminobenzenethiol (2‐ABT) was adsorbed onto nanoporous p‐type silicon (b‐Si) photocathodes decorated with Ag nanoparticles (Ag NPs). The addition of 2‐ABT alters the balance of the CO 2 reduction and hydrogen evolution reactions, resulting in more selective and efficient reduction of CO 2 to CO. The 2‐ABT adsorbate layer was characterized by Fourier transform infrared (FTIR) spectroscopy and modeled by density functional theory calculations. Ex situ X‐ray photoelectron spectroscopy (XPS) of the 2‐ABT modified electrodes suggests that surface Ag atoms are in the +1 oxidation state and coordinated to 2‐ABT via Ag−S bonds. Under visible light illumination, the onset potential for CO 2 reduction was −50 mV vs. RHE, an anodic shift of about 150 mV relative to a sample without 2‐ABT. The adsorption of 2‐ABT lowers the overpotentials for both CO 2 reduction and hydrogen evolution. A comparison of electrodes functionalized with different aromatic thiols and amines suggests that the primary role of the thiol group in 2‐ABT is to anchor the NH 2 group near the Ag surface, where it serves to bind CO 2 and also to assist in proton transfer.

Kan, Miao↗
DOE OSTI2022

Time reversal and CP invariance in Calabi-Yau compactifications

We revisit the question of time reversal and CP invariance in Calabi-Yau compactifications. We show that time reversal invariance is respected by quantum corrections to the prepotential. In particular, field independent θ angles whose presence is dictated by requiring integrality of relevant monodromy transformations can take precisely the quantized values compatible with time reversal invariance. Furthermore, monodromy symmetry enlarges the region on moduli space on which time reversal is not spontaneously broken. We define the action of the CP transformation for multi-parameter models and argue that on the slice of moduli space where it is defined, CP> is trivially a symmetry of the theory. For supersymmetric vacua that lie in this slice, we derive a condition on the third cohomology of the compactification manifold which determines whether CPpreserving fluxes exist that stabilize the moduli to such points. In the case of one-parameter models, the condition is always satisfied.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Nuclear and chloroplast genome engineering of a productive non-model alga Desmodesmus armatus: Insights into unusual and selective acquisition mechanisms for foreign DNA

Despite the tremendous potential of algae to contribute to a future bioeconomy, there are practical and theoretical limitations to how well naturally sourced species and strains can perform in an outdoor setting. The application of biotechnology to modulate and engineer algae metabolism or to increase performance, resilience, or produce novel compounds, offers opportunities to overcome some of the major commercialization barriers. There are numerous approaches reported in the literature having variable success on genetic engineering of algae with non-model algae often presenting unique challenges to genetic engineering. We report here on successful nuclear and chloroplast genomic integration of selection marker resistance in the non-model alga Desmodesmus armatus. Nuclear transformation was accomplished using both electroporation and Agrobacterium-mediated approaches. However, in all surviving transformants, DNA integration was accompanied by excision and/or rearrangement of the gene of interest and fluorescence reporter coding sequences. Similarly, chloroplast transformation was successfully accomplished using a biolistic DNA delivery method. For these transformants, we also observed off-target mutations in the chloroplast genome, not previously observed in other, more routinely used, algae species. Finally, we present insights into potential mechanisms for these observed truncations, rearrangements, and mutations in D. armatus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The impact of energy-efficiency upgrades and other distributed energy resources on a residential neighborhood-scale electrification retrofit

We report ambitious targets for carbon emissions reductions are highlighting new challenges for electrification strategies, leading to an increased focus on building load flexibility and energy management to complement the variability inherent in renewable energy generation. Over the next decade millions of existing homes could undergo electrification retrofits, and there is an urgent need to understand the potential impacts of electrifying major residential loads such as water and space heating on community load characteristics, resident energy bills, and the utility's distribution system. Behind-the-meter distributed energy resources (DERs), including efficiency measures, photovoltaics (PV), battery storage, managed electric vehicle (EV) charging, and controls such as home energy management systems (HEMS), can significantly alter a neighborhood's load profile and provide benefits to both the residents and the grid. We present a novel approach to characterizing the impact of a hypothetical neighborhood-scale residential retrofit program on individual homes' energy use profiles, associated utility bills, and the local distribution system. We modeled a mixed-fuel community of 30 single-family homes in Denver, Colorado, and compared the effects of retrofit scenarios ranging from conventional energy-efficiency upgrades to full electrification with and without more advanced DER technologies. We analyzed which packages of DERs most reliably enable demand flexibility in response to a time-of-use (TOU) rate for this and similar neighborhoods. Our buildings-to-grid co-simulation framework includes a generic secondary distribution feeder model to capture voltage profiles, transformer loading, and other grid impacts in each case. We also calculated the carbon emissions associated with energy use in the community. The methodology developed here can be broadly applied to community-scale beneficial electrification studies in other regions, climates, utility infrastructures, and building typologies to make specific, targeted recommendations based on quantified projections of energy demand in any given community. Our findings indicate that residential electrification can be achieved without negatively impacting the monthly utility bill, and that a combination of conventional energy-efficiency measures, PV, battery, controls, and managed EV charging to maximize a community's demand flexibility is a promising strategy. Adding DERs (especially PV) as part of efficient electrification produces much bigger savings than efficient electrification without DERs. A key barrier is that upgrades require upfront costs, and modest utility bill savings result in long payback periods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

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↗
DOE OSTI2021

The effects of free surfaces on deformation twinning in HCP metals

Deformation twinning is a predominant mode of plastic deformation for hexagonal close packed metals, like Mg and Ti. The heterogenous microstructure and the local stresses associated with twinning play a key role in their mechanical response and fracture. Surface analyses, like electron microscopy, are frequently employed to spatially map microstructure and micromechanical fields in order to study twinning behavior. However, these measurements are inherently influenced by the vicinity of the free surface. In this work, an elasto-visco-plastic fast-Fourier-transform (EVP-FFT) polycrystal modeling approach is employed to investigate the effects of free surfaces on twin development before and after loading. We compare calculated micromechanical fields on free surfaces with those calculated inside the bulk and, in some cases, experimental surface measurements. The results indicate that the creation of free surfaces can promote twin propagation and growth and can influence twin morphology by causing a twin lamella to become larger, more blunted and irregular. The structure along the twin boundaries are also affected, due to the higher driving stresses that extend prismatic-basal and basal-prismatic facets. Furthermore, free surfaces invoke different slip activities in the twin and the surrounding parent crystal by enhancing basal, prismatic and pyramidal slip in some localized regions, while reducing slip in others. We demonstrate that the simulated free-surface effects lead to better qualitative and quantitative agreement with experimental measurements from scanning electron microscopy and digital image correlation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Manipulating Aromaticity to Redirect Topochemical Polymerization Pathways

Topochemical polymerization (TCP) represents an essential route to create regio- and stereoregular polymers through solid-state transformations. Herein, we present an innovative strategy for controlling topochemical polymerization pathways by tailoring the terminal group aromaticity in the para-azaquinodimethane (AQM) ring system. Substituting phenyl groups with less aromatic furyl units extends significant spin density delocalization across the conjugated core upon thermal activation, inducing significant diradicaloid characters at furyl positions and enabling unconventional reactivities in both solution and solid states. Thermal treatment in toluene yields a unique cyclophane dimer formed via furyl-methine C-C coupling, confirmed by X-ray crystallography, while solid-state reactions produce polymers formed via both intercolumnar furyl-methine coupling and intracolumnar methine-methine coupling. The spin-center-directed mechanism underlying these transformations is validated through theoretical modeling and isotopic labeling experiments. This study highlights the prowess of aromaticity modulation in functional pro-aromatic systems, which enables the synthesis of polymers with main chain structures that are otherwise difficult to access.

Zhang, Qingsong↗
DOE OSTI2024

Role of dislocations on martensitic transformation temperatures and microstructure: A molecular dynamics study

Microstructure and defects strongly affect martensitic transformations in metallic alloys. Significant progress has been made in understanding the atomic-level processes that control the role of grain boundaries and precipitates in these solid-to-solid phase transformations. Yet, the role of dislocations and their structures on martensitic transformation temperature and the resulting microstructure remains unclear. Therefore, we used large-scale molecular dynamics simulations to study the forward and reverse transformation of a martensitic material modeled after Ni63Al37 under cyclic thermal loading. The simulations reveal that dislocations in the austenite phase act as one-dimensional seeds for the martensite phase, which is present at temperatures significantly above the martensite start value. We find a reduction in the dislocation density during cyclic thermal loading, which results in the increase in martensite and austenite transition temperatures, in agreement with experiments. Importantly, we extracted a critical martensitic nuclei size for developing stable domains and found that relatively low dislocation densities are needed to grow independent martensitic variants resulting in a multi-domain structure.

Physics↗
DOE OSTI2026

Zero-day Attack Detection in Digital Substations Using In-Context Learning

In this paper, we address the critical challenge of detecting zero-day attacks in digital substations that employ the IEC-61850 communication protocol to ensure the security and reliability of modern power systems. While many heuristic and machine learning (ML)-based methods have been proposed for attack detection in IEC-61850 digital substations, generalization to unknown or zero-day attacks remains a challenge. We propose an approach that leverages the in-context learning ability of transformer architecture, which enables the model to learn from a few examples of a new task without explicit retraining. Our experiments on the IEC-61850 dataset demonstrate that the proposed method achieves more than 87% detection accuracy on zero-day attacks while the existing baselines fail. We believe this work has the potential to enhance the security of digital substations by enabling the effective detection of zero-day attacks.

LIu, Chen-Ching [Virginia Tech] (ORCID:00000002894↗
DOE OSTI2023

Development of a Machine-Learned Cruise Guide Indicator for Rotorcraft

This paper presents a machine-learned virtual cruise guide indicator (vCGI) for Chinook helicopters. Two temporal neural networks were trained and evaluated on measured data from 55 flight tests, one for the fore rotor and another for the aft rotor, to predict a vCGI value, which protects 23 components from fatigue damage during steady-state conditions. Three different classes of machine learning architectures were evaluated for prediction of the vCGI from time sequences: a temporal convolutional neural network with 1D dilated causal convolutions, a long short-term memory recurrent neural network, and an attention-based transformer architecture. The final average model accuracy on unseen flight data is currently greater than 93% for CGI values which could result in fatigue damage and 90% for normal operation CGI values. Model accuracy was improved through a series of advancements in:(1) selection of optimal training data using temporal collective variables and unsupervised learning, (2) dataset augmentation with maximum-entropy temporal collective variables, and (3) implementation of a mixture-of-experts classification- regression approach using an adversarial classification approach to assign maneuver labels. The results are presented for each advancement in model development along with lessons learned in training machine learning models on real- world, time-dependent rotorcraft data.

Boyer, Mathew↗
DOE OSTI2022

A new large-scale suspended sediment model and its application over the United States

Abstract. Suspended sediment plays a vital role in the regional and global cycling of carbon and nutrients by carrying carbon and nutrients from headwaters into lowland rivers and the oceans. Sediment transport through river systems is often fundamentally modified by human activities such as reservoir management. However, a physically based representation of sediment transport is still missing in most existing Earth system models (ESMs), which are essential tools for modeling and predicting Earth system changes. Here, we introduce a multi-process river sediment module for ESMs, which includes (1) hillslope soil erosion and sediment discharge into streams, (2) sediment transport processes through river networks, (3) reservoir operation based on the inflows from upstream areas and water demand from downstream areas, and (4) sediment trapping by reservoirs. All model parameters are estimated a priori without calibration. We apply this new sediment modeling framework to the contiguous United States and validate it against historical observations of monthly streamflow and sediment discharges at 35 river gauges. The model reasonably well captures the long-term balance and seasonal variations of suspended sediment in large river systems. Furthermore, our model results show that suspended sediment discharge in managed rivers is affected more by reservoirs' direct trapping of sediment particles than by their flow regulation. This new sediment module enables future modeling of the transportation and transformation of carbon and nutrients carried by the fine sediment along the river–ocean continuum to close the global carbon and nutrient cycles.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Design study of a low bit rate transmitter and simulator

The results achieved toward developing a baseline communications channel model for studying low bit rate telemetry transmissions from Venus Planetary Explorer spacecraft are presented. The overall communications systems model, discrete channel model, input signal generation, and signal acquisition model are discussed. Discrete Fourier transform considerations, sampling routines, and flow charts are included.

Zusman, F. S.↗
NASA NTRS1977

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a LANDSAT scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data.

Abotteen, R. A.↗
NASA NTRS1977

Performance tests of signature extension algorithms

Comparative tests were performed on seven signature extension algorithms to evaluate their effectiveness in correcting for changes in atmospheric haze and sun angle in a Landsat scene. Four of the algorithms were cluster matching, and two were maximum likelihood algorithms. The seventh algorithm determined the haze level in both training and recognition segments and used a set of tables calculated from an atmospheric model to determine the affine transformation that corrects the training signatures for changes in sun angle and haze level. Three of the algorithms were tested on a simulated data set, and all of the algorithms were tested on consecutive-day data. The classification performance on the data sets using the algorithms is presented, along with results of statistical tests on the accuracy and proportion estimates. The three algorithms tested on the simulated data produced significant improvements over the results obtained using untransformed signatures. For the consecutive-day data, the tested algorithms produced improvements in most but not all cases. The tests indicated also that no statistically significant differences were noted among the algorithms.

Abotteen, R.↗