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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

FPGA Sequencer for Radar Altimeter Applications

A sequencer for a radar altimeter provides accurate attitude information for a reliable soft landing of the Mars Science Laboratory (MSL). This is a field-programmable- gate-array (FPGA)-only implementation. A table loaded externally into the FPGA controls timing, processing, and decision structures. Radar is memory-less and does not use previous acquisitions to assist in the current acquisition. All cycles complete in exactly 50 milliseconds, regardless of range or whether a target was found. A RAM (random access memory) within the FPGA holds instructions for up to 15 sets. For each set, timing is run, echoes are processed, and a comparison is made. If a target is seen, more detailed processing is run on that set. If no target is seen, the next set is tried. When all sets have been run, the FPGA terminates and waits for the next 50-millisecond event. This setup simplifies testing and improves reliability. A single vertex chip does the work of an entire assembly. Output products require minor processing to become range and velocity. This technology is the heart of the Terminal Descent Sensor, which is an integral part of the Entry Decent and Landing system for MSL. In addition, it is a strong candidate for manned landings on Mars or the Moon.

Berkun, Andrew C.↗
NASA NTRS1978

Processing laser velocimeter high-speed burst counter data

The statistical calculations of the mean and potential bias errors are considered with respect to their applicability to the randomly sampled velocity data obtained from a laser velocimeter. Techniques for displaying the resulting data in the form of arrow plots, flow streamline plots, and contour maps are also discussed. A description is presented of the operation of a new hardware interface between the signal processing electronics and the computer. The interface has been constructed to make it possible to use recently developed data processing techniques for obtaining turbulent power spectra from laser velocimeter data. Additional capabilities which were built into the interface allow the measurement of the velocity vector for each particle passing through the laser velocimeter sample window. By processing the data with extended versions of the turbulent power spectra techniques, cross spectra, interactions between velocity magnitude fluctuations, and flow angle fluctuations may be investigated.

Meyers, J. F.↗
DOE OSTI2016

3D Representative Volume Element Reconstruction of Fiber Composites via Orientation Tensor and Substructure Features

To provide a seamless integration of manufacturing processing simulation and fiber microstructure modeling, two new stochastic 3D microstructure reconstruction methods are proposed for two types of random fiber composites: random short fiber composites, and Sheet Molding Compounds (SMC) chopped fiber composites. A Random Sequential Adsorption (RSA) algorithm is first developed to embed statistical orientation information into 3D RVE reconstruction of random short fiber composites. For the SMC composites, an optimized Voronoi diagram based approach is developed for capturing the substructure features of SMC chopped fiber composites. The proposed methods are distinguished from other reconstruction works by providing a way of integrating statistical information (fiber orientation tensor) obtained from material processing simulation, as well as capturing the multiscale substructures of the SMC composites.

Li, Yi↗
DOE OSTI2014

Uniform bipolar resistive switching properties with self-compliance effect of Pt/TiO{sub 2}/p-Si devices

We report uniform bipolar resistive switching characteristic with self-compliance effect of Pt/TiO{sub 2}/p-Si devices in which TiO{sub 2} thin films were prepared directly on p-Si substrates by chemical solution deposition method. The resistive switching parameters of the Pt/TiO{sub 2}/p-Si cell obtained, such as distribution of threshold voltages, retention time, as well as resistance variation of high resistance state (HRS) and low resistance state (LRS), were investigated, and the conduction mechanisms of HRS and LRS were analyzed. The conductive mechanism at LRS and low voltage region of HRS was dominated by Ohmic law. At the high voltage region of HRS, the conductive mechanism followed the space charge limited current theory. The resistive switching phenomenon can be explained by electron trapping and de-trapping process, in which the defects (most probably oxygen vacancies) act as electron traps. Our study suggests that using p-type silicon as bottom electrode can provide a simple method for fabricating a resistive random access memory with self-compliance function. In addition, the Pt/TiO{sub 2}/p-Si configuration is compatible with complementary metal oxide semiconductor process.

36 MATERIALS SCIENCE↗
NASA NTRS1981

Search strategy effects on PN acquisition performance

The present paper focusses on 'random' and 'expanding window' PN acquisition search strategies and analytically develops the PN acquisition time statistics as functions of salient system parameters such as prediction SNR, detection and false alarm probabilities and a priori information on epoch location. The significance of this analysis is its general applicability to arbitrary postdetection processing schemes. Computed performance results account for the above salient parameters, wherein sequential detection is employed in conjunction with random and selected expanding window search strategies.

Weinberg, A.↗
DOE OSTI2019

Vehicle energy consumption estimation using large scale simulations and machine learning methods

Vehicle energy consumption and powertrain operations for future vehicle powertrain technologies are predicted through full-vehicle simulations using Autonomie, a simulation tool developed at Argonne National Laboratory. Over 1 million vehicles, with different powertrain types (conventional and numerous electrified powertrains) and component technologies (advanced engines, batteries, electrical machines, light weighting) are simulated for outputs including vehicle fuel economy and cost. Despite the ability of Autonomic to predict fuel economy given various combination of vehicle inputs, the full-vehicle simulation is very time-consuming. It takes approximately 84 h to simulate for 33,060 vehicles even using distributed computing on a cluster of 128 worker nodes. Moreover, Autonomic is unable to populate a continuous output space through simulation since it only yields outputs with discrete values instead of an input-output relation. This paper proposes a novel large-scale learning and prediction process (LSLPP) via machine learning approaches to answer the need for a continuous space of vehicle fuel economy and a more efficient simulation process. By learning in a supervised fashion to discover structures in data, the machine learning approaches seek to train a model that can explain the data with which we can perform prediction over unseen data. As such, we are able to accomplish analytic continuation on the current discrete space of simulation results. In addition, we present a random sampling -based numerosity reduction algorithm incorporated in LSLPP that reduces simulation runs and improves efficiency of data procurement. We analytically derive a theoretical bound in order to guarantee the effectiveness of our algorithm. We also compare random sampling to stratified sampling and suggest sampling strategies. A user-friendly tool is developed to support the process. Our experimental results confirm that the proposed LSLPP is able to greatly accelerate prediction and analysis of vehicle fuel economy. The output of this study is used by the US government to evaluate the impact of its R&D funding on energy security and CO 2 emission as well as for the setting of Corporate Average Fuel Economy (CAFE) standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Radio frequency mixing modules for superconducting qubit room temperature control systems

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost, and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing boards to address this challenge by integrating I/Q quadrature mixing, intermediate frequency/LO (local oscillator)/RF power level adjustments, and direct current bias fine tuning on a 40 × 80 mm 2 four-layer printed circuit board with electromagnetic interference shielding. The RF mixing module is designed to work with RF and LO frequencies between 2.5 and 8.5 GHz. The typical image rejection and adjacent channel isolation are measured to be ~27 dBc and ~50 dB. By scanning the drive phase in a loopback test, the module short-term amplitude and phase linearity are typically measured to be 5 ×10 -4 (V pp /V mean ) and 1 ×10 -3 radian (pk-pk). The operation of the RF mixing board was validated by integrating it into the room temperature control system of a superconducting quantum processor and executing randomized benchmarking characterization of single and two qubit gates. We measured a single-qubit process infidelity of 9.3(3) × 10 -4 and a two-qubit process infidelity of 2.7(1) × 10 -2 .

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A modified electrolyte non-random two-liquid model with analytical expression for excess enthalpy: Application to the MEA-H 2 O-CO 2 system

We report accurate thermodynamic properties of electrolyte systems are critical for the design and operation of many chemical processes. A comprehensive description of the thermodynamic framework for multi-electrolyte mixed solvent systems is presented, where the parameter structure of the symmetric electrolyte-Non-Random Two Liquid (e-NRTL) model is reformulated and a thermodynamically consistent and analytically derived formulation for the excess enthalpy is developed from the e-NRTL model. The refined parameter structure of the e-NRTL model avoids numerical singularities of the analytical formulation for the excess enthalpy in the absence of ionic species and extends the derived excess enthalpy formulation to non-electrolyte systems. The thermodynamic framework is demonstrated for the MEA-H 2 O-CO 2 case study using experimental data on thermodynamic quantities for the binary MEA-H 2 O system and the ternary MEA-H 2 O-CO 2 system. The model is implemented in Pyomo and will be available for release in the Institute for the Design of Advanced Energy Systems (IDAES) computational platform.

Monoethanolamine↗
NASA NTRS1974

Design, processing, and testing of LSI arrays for space station

The development of a beam-leaded low power, high performance metal-oxide-semiconductor (MOS), 256-bit random access memory (RAM) was reported. Previous success with the aluminum-gate current-sense version and a silicon-gate voltage-sense version led to the present effort to make a beam-leaded silicon-gate RAM. Some problems unique to the silicon-on-sapphire beam-lead process development are presented. Beam-leaded SOS TA5388 devices using a Si3N4 passivation layer were shown to have good electrical parameters.

Schneider, W. C.↗
DOE OSTI2021

Transcranial Direct Current Stimulation (tDCS) Improves Empathy and Recognition of Facial Emotions Conveying Threat in Adults with Autism Spectrum Disorder (ASD): A Randomized Controlled Pilot Study

Introduction: Empathy is critical for human interactions to become shared and meaningful, and it is facilitated by the expression and processing of facial emotions. Deficits in empathy and facial emotion recognition are associated with individuals with autism spectrum disorder (ASD), with specific concerns over inaccurate recognition of facial emotion expressions conveying a threat. Yet, the number of evidenced interventions for facial emotion recognition and processing (FERP), emotion, and empathy remains limited, particularly for adults with ASD. Transcranial direct current stimulation (tDCS), a noninvasive brain stimulation, may be a promising treatment modality to safely accelerate and/or enhance treatment interventions to increase their efficacy. Methods: This study investigates the effectiveness of FERP, emotion, and empathy treatment interventions paired with tDCS for adults with ASD. Differences in scores on the Empathy Quotient (EQ) and on a FERP test were predicted for verum tDCS when compared to sham. Verum/sham tDCS was randomly assigned in a within-subjects, double-blinded design in adults with ASD without intellectual disability. Results: Verum tDCS significantly improved EQ scores and FERP scores for emotions that conveyed threat. Conclusions: These results suggest the potential for increasing the efficacy of treatment interventions by pairing them with tDCS for individuals with ASD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

From inanimate matter to living systems

Since the early part of this century, the Genesis account of the origin and evolution of life has been explained as an extrapolation of astronomical and geochemical processes. The essence of the answer to date is a protoreproductive protocell of much biochemical and cytophysical competance. The processes of its origin, molecular ordering, and its functions are described. A crucial understanding is that of the nonrandomness of evolutionary processes at all stages (with perhaps a minor statistical component). In this way, evolution conflicts with statistical randomness; the latter is a favorite assumption of both scientific and creationistic critics of the proteinoid theory. The principle contribution of the proteinoid theory to the understanding of general biology is to particularize the view that evolutionary direction is rooted in the shapes of molecules, in stereochemistry. After molecules of the right kind first assembled to protocells, life in its various stages of evolution was an inevitable consequence. It is molecules that continue to assemble as part of living process and, in the role of enzymes, continue to direct life cycle of the cell.

Fox, S. W.↗
DOE OSTI2014

Sensitivity Gains, Linearity, and Spectral Reproducibility in Nonuniformly Sampled Multidimensional MAS NMR Spectra of High Dynamic Range.

Recently, we have demonstrated that considerable inherent sensitivity gains are attained in MAS NMR spectra acquired by nonuniform sampling (NUS) and introduced maximum entropy interpolation (MINT) processing that assures the linearity of transformation between the time and frequency domains. In this report, we examine the utility of the NUS/MINT approach in multidimensional datasets possessing high dynamic range, such as homonuclear 13C–13C correlation spectra. We demonstrate on model compounds and on 1–73-(U-13C,15N)/74–108-(U-15N) E. coli thioredoxin reassembly, that with appropriately constructed 50 % NUS schedules inherent sensitivity gains of 1.7–2.1-fold are readily reached in such datasets. We show that both linearity and line width are retained under these experimental conditions throughout the entire dynamic range of the signals. Furthermore, we demonstrate that the reproducibility of the peak intensities is excellent in the NUS/MINT approach when experiments are repeated multiple times and identical experimental and processing conditions are employed. Finally, we discuss the principles for design and implementation of random exponentially biased NUS sampling schedules for homonuclear 13C–13C MAS correlation experiments that yield high quality artifact-free datasets.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗
DOE OSTI2013

Subcell resolution in simplex stochastic collocation for spatial discontinuities

Subcell resolution has been used in the Finite Volume Method (FVM) to obtain accurate approximations of discontinuities in the physical space. Stochastic methods are usually based on local adaptivity for resolving discontinuities in the stochastic dimensions. However, the adaptive refinement in the probability space is ineffective in the non-intrusive uncertainty quantification framework, if the stochastic discontinuity is caused by a discontinuity in the physical space with a random location. The dependence of the discontinuity location in the probability space on the spatial coordinates then results in a staircase approximation of the statistics, which leads to first-order error convergence and an underprediction of the maximum standard deviation. To avoid these problems, we introduce subcell resolution into the Simplex Stochastic Collocation (SSC) method for obtaining a truly discontinuous representation of random spatial discontinuities in the interior of the cells discretizing the probability space. The presented SSC–SR method is based on resolving the discontinuity location in the probability space explicitly as function of the spatial coordinates and extending the stochastic response surface approximations up to the predicted discontinuity location. The applications to a linear advection problem, the inviscid Burgers’ equation, a shock tube problem, and the transonic flow over the RAE 2822 airfoil show that SSC–SR resolves random spatial discontinuities with multiple stochastic and spatial dimensions accurately using a minimal number of samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Spatial and temporal prediction of radiation dose rates near Fukushima Daiichi Nuclear Power Plant

In this paper, we have developed a methodology to estimate the spatiotemporal distribution of radiation air dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP). In our exploratory data analysis, we found that (1) the temporal evolution of dose rates is composed of a log-linear decay trend and fluctuations of air dose rates that are spatially correlated among adjacent monitoring posts; and (2) the slope of the log-linear environmental decay trend can be represented as a function of the apparent initial dose rates, coordinate position, land-use type, and soil type. From these observations, we first estimated the log-linear decay trend at each location based on these predictors, using the random forest method. We then developed a modified Kalman filter coupled with a Gaussian process model to estimate the dose-rate time series at a given location and time. We applied this method to the Fukushima evacuation zone (as of March 2017), which included 17 monitoring post locations (with monitoring datasets collected between 2014 and 2018) and generated a time series of dose-rate maps. Our results show that this approach allows us to produce accurate spatial and temporal predictions of radiation dose-rate maps using limited spatiotemporal measurements.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Superstructure Optimization of Waste Plastic Pyrolysis, Integrating Thermal, Catalytic, and Plasma Technologies with Machine Learning

Global plastic waste generation exceeds 430 million tonnes per year, yet fewer than 9% are recycled in the United States. Pyrolysis offers a chemical recycling route at scale, but existing techno-economic and life cycle assessments fix product yields to single pure polymers, producing economic and environmental outputs that break down when the feed composition changes. Here, we present a superstructure optimization framework that addresses this by embedding a composition-aware random forest yield predictor, trained on 566 pyrolysis experiments, within a full-scale process simulation. Product distributions update automatically as feed allocation shifts across four reactor chemistries: conventional thermal, catalytic (HZSM-5), thermal oxo-degradation, and nonequilibrium CO2 plasma. The optimal superstructure achieves minimum selling prices of −0.56 to −0.76/kg feed and global warming potentials of −0.276 to −0.322 kg CO2-eq/kg feed across four commodity price scenarios, confirming profitable, carbon-negative operation without tipping fees. Carbon abatement costs of $\$$0.46 to $\$$1.25/kg CO2-eq are competitive with direct air capture. Sensitivity analysis shows that the catalytic-plasma split fraction is the single largest driver of both economic and climate performance, while hydrocracking allocation in the wax upgrading stage is emission-neutral across the full variable range. Mixed plastic waste streams, evaluated as composition-variable feedstocks rather than pure resins, are profitable and carbon-negative across realistic market conditions. These results give a quantitative basis for reactor selection, circular economy investment, and policy design targeting chemical recycling on a large scale.

Life cycle assessment↗
DOE OSTI2016

Heterogeneous silicon mesostructures for lipid-supported bioelectric interfaces

Silicon-based materials have widespread application as biophysical tools and biomedical devices. Here we introduce a biocompatible and degradable mesostructured form of silicon with multi-scale structural and chemical heterogeneities. The material was synthesized using mesoporous silica as a template through a chemical vapour deposition process. It has an amorphous atomic structure, an ordered nanowire-based framework and random submicrometre voids, and shows an average Young’s modulus that is 2–3 orders of magnitude smaller than that of single-crystalline silicon. In addition, we used the heterogeneous silicon mesostructures to design a lipid-bilayer-supported bioelectric interface that is remotely controlled and temporally transient, and that permits non-genetic and subcellular optical modulation of the electrophysiology dynamics in single dorsal root ganglia neurons. Our findings suggest that the biomimetic expansion of silicon into heterogeneous and deformable forms can open up opportunities in extracellular biomaterial or bioelectric systems.

Jiang, Yuanwen↗