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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Lixisenatide reduces amyloid plaques, neurofibrillary tangles and neuroinflammation in an APP/PS1/tau mouse model of Alzheimer's disease

Highlights: • Lixisenatide reduced amyloid plaques, neurofibrillary tangles and neuroinflammation. • Activiation of PKA-CREB and inhibition of p38-MAPK were the protective mechanisms. • Lixisenatide might have the potential to be developed as a novel therapy for AD. Type 2 diabetes mellitus (T2DM) has been identified as a high risk factor for Alzheimer's disease (AD). The impairment of insulin signaling has been found in AD brain. Glucagon-like peptide-1 (GLP-1) is an incretin hormone, normalises insulin signaling and acts as a neuroprotective growth factor. We have previously shown that the long-lasting GLP-1 receptor (GLP-1R) agonist lixisenatide plays an important role in memory formation, synaptic plasticity and cell proliferation of rats. In the follow-up study, we analysed the neuroprotective effect and mechanism of lixisenatide, injected for 60 days at 10 nmol/kg i.p. once daily in APP/PS1/tau female mice and C57BL/6J female mice (as control) aged 12 month. The results showed that lixisenatide could reduce amyloid plaques, neurofibrillary tangles and neuroinflammation in the hippocampi of 12-month-old APP/PS1/tau female mice; activation of PKA-CREB signaling pathway and inhibition of p38-MAPK might be the important mechanisms in the neuroprotective function of lixisenatide. The study demonstrated that GLP-1R agonists such as lixisenatide might have the potential to be developed as a novel therapy for AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

A tensor train-based isogeometric solver for large-scale 3D poisson problems

We introduce a three-dimensional (3D), fully tensor train (TT) assembled isogeometric analysis (IGA) framework, TT-IGA, for solving partial differential equations (PDEs). Our method reformulates IGA discrete operators into TT format, enabling efficient compression and computation. Geometry evaluations use the original NURBS description at sampling points and TT approximation is applied to geometry-derived coefficient fields and discrete operators. We demonstrate the effectiveness of the proposed TT-IGA framework on the three-dimensional Poisson equation, achieving substantial reductions in memory and computational cost without compromising solution quality.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Using Machine Learning Tools to Predict Compressor Stall

Clean energy has become an increasingly important consideration in today’s power systems. As the push for clean energy continues, many coal-fired power plants are being decommissioned in favor of renewable power sources such as wind and solar. However, the intermittent nature of renewables means that dynamic load following traditional power systems is crucial to grid stability. With high flexibility and fast response at a wide range of operating conditions, gas turbine systems are poised to become the main load following component in the power grid. Yet, rapid changes in load can lead to fluid flow instabilities in gas turbine power systems. These instabilities often lead to compressor surge and stall, which are some of the most critical problems facing the safe and efficient operation of compressors in turbomachinery today. Although the topic of compressor surge and stall has been extensively researched, no methods for early prediction have been proven effective. This study explores the utilization of machine learning tools to predict compressor stall. The long short-term memory (LSTM) model, a form of recurrent neural network (RNN), was trained using real compressor stall datasets from a 100 kW recuperated gas turbine power system designed for hybrid configuration. Two variations of the LSTM model, classification and regression, were tested to determine optimal performance. The regression scheme was determined to be the most accurate approach, and a tool for predicting compressor stall was developed using this configuration. Overall, results show that the tool is capable of predicting stalls 5–20 ms before they occur. With a high-speed controller capable of 5 ms time-steps, mitigating action could be taken to prevent compressor stall before it occurs.

42 ENGINEERING↗
DOE OSTI2014

SU-E-T-258: Parallel Optimization of Beam Configurations for CyberKnife Treatments

Purpose: The CyberKnife delivers a large number of beams originating at different non-planar positions and with different orientation. We study how much the quality of treatment plans depends on the beams considered during plan optimization. Particularly, we evaluate a new approach to search for optimal treatment plans in parallel by running optimization steps concurrently. Methods: So far, no deterministic, complete and efficient method to select the optimal beam configuration for robotic SRS/SBRT is known. Considering a large candidate beam set increases the likelihood to achieve a good plan, but the optimization problem becomes large and impractical to solve. We have implemented an approach that parallelizes the search by solving multiple linear programming problems concurrently while iteratively resampling zero weighted beams. Each optimization problem contains the same set of constraints but different variables representing candidate beams. The search is synchronized by sharing the resulting basis variables among the parallel optimizations. We demonstrate the utility of the approach based on an actual spinal case with the objective to improve the coverage. Results: The objective function is falling and reaches a value of 5000 after 49, 31, 25 and 15 iterations for 1, 2, 4, and 8 parallel processes. This corresponds to approximately 97% coverage in 77%, 59%, and 36% of the mean number of iterations with one process for 2, 4, and 8 parallel processes, respectively. Overall, coverage increases from approximately 91.5% to approximately 98.5%. Conclusion: While on our current computer with uniform memory access the reduced number of iterations does not translate into a similar speedup, the approach illustrates how to effectively parallelize the search for the optimal beam configuration. The experimental results also indicate that for complex geometries the beam selection is critical for further plan optimization.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2018

A Low-Rank Multigrid Method for the Stochastic Steady-State Diffusion Problem

We study a multigrid method for solving large linear systems of equations with tensor product structure here. Such systems are obtained from stochastic finite element discretization of stochastic partial differential equations such as the steady-state diffusion problem with random coefficients. When the variance in the problem is not too large, the solution can be well approximated by a low-rank object. In the proposed multigrid algorithm, the matrix iterates are truncated to low rank to reduce memory requirements and computational effort. The technique is proved convergent with an analytic error bound. Numerical experiments show its effectiveness in solving the Galerkin systems compared to the original multigrid solver, especially when the number of degrees of freedom associated with the spatial discretization is large.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

AMReX

AMReX is a software framework for the development and implementation of massively parallel algorithms using block-structured adaptive mesh refinement (AMR). Block-structured AMR provides the basis for the temporal and spatial discretization strategy for a large variety of applications of interest to the DOE. AMReX provides a unified infrastructure with the functionality needed for applications to be able to effectively and efficiently utilize machines from laptops to exascale accelerator-based architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multi-physics algorithms. AMReX supports algorithms that solve systems of partial differential equations (PDEs) in simple or complex geometries, and those that use particles and/or particle-mesh operations to represent component physical processes.

Zhang, Weiqun↗
NASA NTRS1973

Models of man as a suboptimal predictor

Models of man making predictions of future states of discrete linear dynamic systems are considered. The task is forced-pace, but the pace is slow enough to eliminate the effects of reaction time and neuromuscular lag. The best of the several models considered includes the constraints of limited memory and observation noise.

Rouse, W. B.↗
NASA NTRS1981

Flight test of a full authority Digital Electronic Engine Control system in an F-15 aircraft

The Digital Electronic Engine Control (DEEC) system considered is a relatively low cost digital full authority control system containing selectively redundant components and fault detection logic with capability for accommodating faults to various levels of operational capability. The DEEC digital control system is built around a 16-bit, 1.2 microsecond cycle time, CMOS microprocessor, microcomputer system with approximately 14 K of available memory. Attention is given to the control mode, component bench testing, closed loop bench testing, a failure mode and effects analysis, sea-level engine testing, simulated altitude engine testing, flight testing, the data system, cockpit, and real time display.

Barrett, W. J.↗
NASA NTRS1923

A Study of Taking off and Landing an Airplane

This report covers the results of an investigation carried on at the Langley Memorial Aeronautical Laboratory of the National Advisory Committee for Aeronautics for the purpose of discussing the various methods of effecting the take-off and the landing of an airplane, and to make a direct analysis of the control movements, the accelerations, and air speeds during these the maneuvers. The recording instruments developed at the laboratory were used in this test and the records obtained by them were made the basis for a comparative study of the two extreme methods of taking off (the tail-high and tail-low methods) and of various types of landings. The records should be of considerable value to a student pilot in enabling him to visualize the movements of the controls and the consequent effect upon the air speed and acceleration.

Carroll, T↗
NASA NTRS2011

Developmental Testing of Habitability and Human Factors Tools and Methods During Neemo 15

Currently, no established methods exist to collect real-time human factors and habitability data while crewmembers are living aboard the International Space Station (ISS), traveling aboard other space vehicles, or living in remote habitats. Currently, human factors and habitability data regarding space vehicles and habitats are acquired at the end of missions during postflight crew debriefs. These debriefs occur weeks or often longer after events have occurred, which forces a significant reliance on incomplete human memory, which is imperfect. Without a means to collect real-time data, small issues may have a cumulative effect and continue to cause crew frustration and inefficiencies. Without timely and appropriate reporting methodologies, issues may be repeated or lost. TOOL DEVELOPMENT AND EVALUATION: As part of a directed research project (DRP) aiming to develop and validate tools and methods for collecting near real-time human factors and habitability data, a preliminary set of tools and methods was developed. These tools and methods were evaluated during the NASA Extreme Environments Mission Operations (NEEMO) 15 mission in October 2011. Two versions of a software tool were used to collect observational data from NEEMO crewmembers that also used targeted strategies for using video cameras to collect observations. Space habitability observation reporting tool (SHORT) was created based on a tool previously developed by NASA to capture human factors and habitability issues during spaceflight. SHORT uses a web-based interface that allows users to enter a text description of any observations they wish to report and assign a priority level if changes are needed. In addition to the web-based format, a mobile Apple (iOS) format was implemented, referred to as iSHORT. iSHORT allows users to provide text, audio, photograph, and video data to report observations. iSHORT can be deployed on an iPod Touch, iPhone, or iPad; for NEEMO 15, the app was provided on an iPad2.

Thaxton, S. S.↗
DOE OSTI2016

Treatments for Neutron Resonance Elastic Scattering Using the Multipole Formalism in Monte Carlo Codes

The elastic scattering cross sections of resonant nuclides can vary drastically within small energy intervals. In reactor physics simulations, accurate treatment of neutron resonance elastic scattering is vital, as the phenomenon can have a strong effect on scattering kinematics which, in turn, affect macroscopic values, such as the fuel temperature reactivity coefficient. To reduce memory requirements and facilitate coupling with heat transfer, a new representation for cross sections was recently adopted in OpenMC based on the multipole formalism. However, this new approach invalidates the previous implementations of correct resonance treatments, including the Doppler Broadening Rejection Correction (DBRC) method. This work thus developed a modified DBRC algorithm, called DBRC-MP, compatible with the new data representation. This new method is also verified against the previous DBRC method. While slightly more computationally costly than the traditional DBRC approach using ACE format cross sections, the use of the multipole representation in treating resonance scattering enables the reduction of memory requirements by a hundred-fold and facilitates the representation of temperature dependent cross sections. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

New-Sum: A Novel Online ABFT Scheme For General Iterative Methods

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recover from errors when combined with a checkpoint/rollback scheme.

Instantaneous Footprint↗
DOE OSTI2024

Effect of boron concentration on local structure and spontaneous polarization in AlBN thin films

The discovery of ferroelectricity in polar wurtzite-based ternary materials, such as Al 1–x B x N, has attracted attention due to their compatibility with complementary metal–oxide–semiconductor processes and potential use in integrated non-volatile memory devices. However, the origin of ferroelectricity and the fundamental control of the polarization switching in these materials are still under intensive investigation but appear to be related to local disorder induced from the alloying. In this work, we report the effect of boron alloying on the local structure of Al 1–x B x N films deposited by magnetron sputtering. Our results reveal a diminished crystalline order as a function of boron concentration, accompanied by a reduction in the spontaneous polarization. The film disorder is primarily associated with the dissimilar bond lengths between Al–N and B–N and the formation of threading dislocations induced by B incorporation in the structure.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Second-Order Josephson Effect in Excitonic Insulators

We show that in electron-hole bilayers with excitonic orders arising from conduction and valence bands formed by atomic orbitals that have different parities, nonzero interlayer tunneling leads to a second-order Josephson effect. This means the interlayer electrical current is related to the phase of the excitonic order parameter as J = J c sin 2θ instead of J = J c sin θ and that the system has two degenerate ground states at θ = 0, π that can be switched by an interlayer voltage pulse. When generalized to a three dimensional stack of alternating electron-hole planes or a two dimensional stack of chains, the ac Josephson effect implies that electric field pulses perpendicular to the layers and chains can steer the order parameter phase between the two degenerate ground states, making these devices ultrafast memories. Here, the order parameter steering also applies to the excitonic insulator candidate Ta 2 NiSe 5 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2004

The effects of heavy particle irradiation on exploration and response to environmental change

Free radicals produced by exposure to heavy particles have been found to produce motor and cognitive behavioral toxicity effects in rats similar to those found during aging. The present research was designed to investigate the effects of exposure to 56Fe particles on the ability of male Sprague-Dawley rats to detect novel arrangements in a given environment. Using a test of spatial memory previously demonstrated to be sensitive to aging, open field activity and reaction to spatial and non-spatial changes were measured in a group that received a dose of 1.5 Gy (n=10) of 56Fe heavy particle radiation or in non-radiated controls (n=10). Animals irradiated with 1.5 Gy of 56Fe particles exhibited some age-like effects in rats tested, even though they were, for the most part, subtle. Animals took longer to enter, visited less and spent significantly less time in the middle and the center portions of the open field, independently of total frequency and duration of activity of both groups. Likewise, irradiated subjects spend significantly more time exploring novel objects placed in the open field than did controls. However, irradiated subjects did not vary from controls in their exploration patterns when objects in the open field were spatially rearranged. Thus, irradiation with a dose of 1.5 Gy of 56Fe high-energy particle radiation elicited age-like effects in general open field exploratory behavior, but did not elicit age-like effects during the spatial and non-spatial rearrangement tasks. Published by Elsevier Ltd on behalf of COSPAR.

NASA Program Biomedical Research and Countermeasur↗
DOE OSTI2020

Time-series learning of latent-space dynamics for reduced-order model closure

In this work, we study the performance of long short-term memory networks (LSTMs) and neural ordinary differential equations (NODEs) in learning latent-space representations of dynamical equations for an advection-dominated problem given by the viscous Burgers equation. Our formulation is devised in a nonintrusive manner with an equation-free evolution of dynamics in a reduced space with the latter being obtained through a proper orthogonal decomposition. In addition, we leverage the sequential nature of learning for both LSTMs and NODEs to demonstrate their capability for closure in systems that are not completely resolved in the reduced space. We assess our hypothesis for two advection-dominated problems given by the viscous Burgers equation. We observe that both LSTMs and NODEs are able to reproduce the effects of the absent scales for our test cases more effectively than does intrusive dynamics evolution through a Galerkin projection. This result empirically suggests that time-series learning techniques implicitly leverage a memory kernel for coarse-grained system closure as is suggested through the Mori–Zwanzig formalism.

97 MATHEMATICS AND COMPUTING↗