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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

The Nimbus F Random Access Measurement System /RAMS/

In 1974, the Random Access Measurement System (RAMS) will be launched aboard the Nimbus F satellite as part of the Tropical Wind, Energy Conversion, and Reference Level Experiment (TWERLE). This paper describes operation and performance of the RAMS instrument, which will provide a means of tracking and collecting data from a large number of instrumented platforms. In operation, the RAMS will perform satellite onboard processing of up to eight simultaneous platform transmissions, following search and detection of the randomly received platform transmissions in a compressed-time expanded-frequency domain. The processed data is stored aboard the satellite for readout every 108 minutes (orbital period), and platform locational coordinates and/or velocity components are determined in a central ground data processing facility.

Coates, J. L.↗
DOE OSTI2021

β-decay feeding intensity distributions of 71,73 Ni

This paper presents the β-decay feeding intensity distribution and Gamow-Teller transition strength distribution of 71,73 Ni. These quantities were measured using the technique of total absorption spectroscopy at the National Superconducting Cyclotron Laboratory with the Summing NaI(Tl) detector. These measurements provide sensitive constraints to theoretical models used to predict β-decay properties far from stability for astrophysical applications. Specifically, for the astrophysical r process, the majority of the involved nuclei are not accessible by current facilities, and the nuclear input is mainly provided by theory. Furthermore, the present work reports on two neutron-rich nickel isotopes in the region where the weak r process is expected to be relevant in stellar nucleosynthesis. The experimental results are compared to two theoretical models, namely the shell model and the quasiparticle random-phase approximation, to help further refine theoretical calculations and aid in future r-process studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1993

A statistical analysis of mesoscale rainfall as a random cascade

The paper considers the random cascade theory for spatial rainfall. Particular attention was given to the following four areas: (1) the relationship of the random cascade theory of rainfall to the simple scaling and the hierarchical cluster-point-process theories, (2) the mathematical foundations for some of the formalisms commonly applied in the develpment of statistical cascade theory, (3) the empirical evidence for a random cascade theory of rainfall, and (4) the way of using data for making estimates of parameters and for making statistical inference within this theoretical framework. An analysis of space-time rainfall data is presented. Cascade simulations are carried out to provide a comparison with methods of analysis that are applied to the rainfall data.

Gupta, Vijay K.↗
DOE OSTI2018

Random Alloyed versus Intermetallic Nanoparticles: A Comparison of Electrocatalytic Performance

As synthetic methods advance for metal nanoparticles, more rigorous studies of structure–function relationships can be made. Many electrocatalytic processes depend on the size, shape, and composition of the nanocatalysts. Here, the properties and electrocatalytic behavior of random alloyed and intermetallic nanoparticles are compared. Beginning with an introduction of metallic nanoparticles for catalysis and the unique features of bimetallic compositions, the discussion transitions to case studies of nanoscale electrocatalysts where direct comparisons of alloy and intermetallic compositions are undertaken for methanol electrooxidation, formic acid electrooxidation, the oxygen reduction reaction, and the electroreduction of carbon dioxide (CO2). Design and synthesis strategies for random alloyed and intermetallic nanoparticles are discussed, with an emphasis on Pt–M and Cu–M compositions as model systems. The differences in catalytic performance between alloys and intermetallic nanoparticles are highlighted in order to provide an outlook for future electrocatalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

COWALKER:EFFECTIVE TRANSPORT PROPERTIES OF COMPOSITE MATERIALS

SF-23-026 This software computes effective transport properties of composite materials involving fibers and nanoparticles using a random-walk algorithm that efficiently scales to an arbitrary number of processes and cores. Effective transport properties (thermal, electrical) are key to bridge the microstructure of complex materials with its macroscopic behavior. Traditional approaches either use effective medium approximations (closed mathematical expressions that are approximation for certain conditions) or continuum simulation models such as finite element or finite volume, which require the generation of a mesh for each configuration explored. cowalker leverages the equivalence between laplacian or heat equation-based models and random walks to compute the asymptotic transport properties from an ensemble of first sojourn times of a random walker moving through the composite material. This allows us to directly define a composite material as a collection of particles and use algorithms developed for molecular dynamics to quickly compute the intersection of the walker with the different interfaces in the material. cowalker is developed in C++, and it relies on the GNU Scientific Library for random generation. cowalker is currently delivered as source code, so the GSL library is not included in cowalker's distribution. A more userfriendly version, cowalker.jl is currently in development and will be released as part of cowalker.

YANGUAS-GIL, ANGEL↗
DOE OSTI2018

Kinetics of State Switching in Quasi-One-Dimensional Nanosystems. Statistical Model of the Influence of Defects

The role of native and radiation-induced defects in the state switching kinetics for one-dimensional systems (nanowires, molecular single-chain magnets, biological macromolecules, and many others) has been studied. An analytical approach to the description of the influence of randomly located defects on the dynamics of new-phase domains is developed using an analogy with stochastic processes in queueing theory. This method makes it possible to calculate threshold phenomena and the dependence of switching fields on material parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Robust Online Sequential RVFLNs for Data Modeling of Dynamic Time-Varying Systems with Application of an Ironmaking Blast Furnace

In a world where the increasing complexity of modern industrial processes brings difficulties for accurate mathematical modeling, taking advantage of data has become an efficient solution to complex dynamic process modeling issue. In this paper, we develop a novel robust online sequential version of random vector functional-link networks (RVFLNs) for data-driven modeling of dynamic time-varying system and applied it in a blast furnace (BF) ironmaking process. First, to overcome the time-varying dynamics of process and to enable the RVFLNs to learn online with avoiding data saturation, an improved online sequential version of RVFLNs (OS-RFVLNs) is first presented by online sequential learning with forgetting factor. This improved OS-RVFLNs algorithm is not only suitable for the real-time and large data transfer situation, but also can adjust the sensitivity of the algorithm to different samples with the help of the introduced forgetting factor. Second, since the output weights of the improved OS-RVFLNs as well as other RVFLNs algorithms are obtained by the least squares approach, a robustness problem may occur when the training dataset is contaminated with various outliers. To solve this problem, a Cauchy distribution weighted M-estimator is introduced to improve the robustness of the improved OS- RVFLNs. For this proposed robust OS-RVFLNs (R-OS- RVFLNs), since the weights of different outlier data are properly determined by the Cauchy distribution function, their corresponding contribution on modeling can be properly distinguished. Thus robust and better modeling results can be achieved. Experiments using actual industrial data of BF ironmaking process and comparative studies have demonstrated that the proposed method produces a better estimation accuracy and stronger robustness than other methods.

Blast furnace (BF), Modelling, Dynamic systems↗
DOE OSTI2025

Accelerating Neutrino Event Generation in MARLEY Using CUDA-Based RNG and GPU Parallelization

MARLEY is a simulation tool that helps scientists study how low-energy neutrinos interact with matter. To work properly, MARLEY uses random numbers thousands of times in each simulation. These random numbers are important for modeling things like how neutrinos collide with atoms and what particles they produce. Right now, MARLEY runs on a regular computer processor (CPU) and uses a built-in random number generator called the Mersenne Twister. This setup works, but it can be slow, especially when trying to simulate many events. This research focuses on making MARLEY run faster by moving the random number generation and some of the repetitive calculations from the CPU to a graphics processing unit (GPU), which can handle many tasks at the same time. We use CUDA (a tool for programming NVIDIA GPUs) and cuRAND (a GPU-based random number library) to test faster alternatives to the current random number system. We compare different GPU-based generators, like curand_mtgp32, xorwow, and philox, to see which ones are the quickest and still give reliable results. Early tests show that using the GPU can make MARLEY simulations much faster. This project not only helps improve current simulation performance but also moves closer to a full simulation chain where all stages can run on modern GPU hardware.

Dunkley, Kimieka [Florida A-M]↗
DOE OSTI2017

How Far Is Quasar UV/Optical Variability from a Damped Random Walk at Low Frequency?

Studies have shown that UV/optical light curves of quasars can be described using the prevalent damped random walk (DRW) model, also known as the Ornstein–Uhlenbeck process. A white noise power spectral density (PSD) is expected at low frequency in this model; however, a direct observational constraint to the low-frequency PSD slope is difficult due to the limited lengths of the light curves available. Meanwhile, quasars show scatter in their DRW parameters that is too large to be attributed to uncertainties in the measurements and dependence on the variation of known physical factors. In this work we present simulations showing that, if the low-frequency PSD deviates from the DRW, the red noise leakage can naturally produce large scatter in the variation parameters measured from simulated light curves. The steeper the low-frequency PSD slope, the larger scatter we expect. Based on observations of SDSS Stripe 82 quasars, we find that the low-frequency PSD slope should be no steeper than −1.3. The actual slope could be flatter, which consequently requires that the quasar variabilities should be influenced by other unknown factors. We speculate that the magnetic field and/or metallicity could be such additional factors.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1990

Dynamic load-sharing using predicted process resource requirements

Heuristics which use predicted process resource requirements to make scheduling decisions are proposed. Four heuristics are presented. The first two, MINQ and SMPL, employ centralized scheduling and the remaining two, DMINQ and FDMINQ, use distributed scheduling. These heuristics are first compared against random scheduling and then against two conventional heuristics, CENTEX and DISTED, which schedule processes solely based on system state information. Results based on trace-driven simulations show that the proposed centralized heuristics offer significantly improved mean response time and they require fewer status update messages. In experiments using the same status update rates, SMPL response times were, on the average, 22 percent lower than those for CENTEX; MINQ response times were, on the average, 18 percent lower. The simulations also showed that MINQ and SMPL can perform as well as, or better than, CENTEX while using up to 70 percent fewer status update messages. The use of fewer status update messages imposes less overhead on the system. The use of prediction for distributed scheduling produced similar results. When prediction was used to filter small processes and execute them locally a 50 percent improvement in response times was obtained.

Goswami, Kumar K.↗
NASA NTRS2010

Application of Ensemble Detection and Analysis to Modeling Uncertainty in Non Stationary Process

Characterization of non stationary and nonlinear processes is a challenge in many engineering and scientific disciplines. Climate change modeling and projection, retrieving information from Doppler measurements of hydrometeors, and modeling calibration architectures and algorithms in microwave radiometers are example applications that can benefit from improvements in the modeling and analysis of non stationary processes. Analyses of measured signals have traditionally been limited to a single measurement series. Ensemble Detection is a technique whereby mixing calibrated noise produces an ensemble measurement set. The collection of ensemble data sets enables new methods for analyzing random signals and offers powerful new approaches to studying and analyzing non stationary processes. Derived information contained in the dynamic stochastic moments of a process will enable many novel applications.

Racette, Paul↗
DOE OSTI2024

Zero-Power Analog Optical Processing

The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Statistical model of the stimulated forward Brillouin scattering driven by a randomized laser beam in plasma

The modeling of a spatially incoherent laser beam remains a central problem of the parametric instabilities in the context of inertial confinement fusion. This letter gives a simplified and comprehensive overview of the recent analytical developments regarding the modeling of these laser beams and a comparison with a dedicated experiment. Our model accounts for the first time for the statistical standard deviation of the gain and accurately captures the entanglement between wave mixing processes and the speckle correlations thus resolving the longstanding contradictions between the random phase approximation and the model of independent speckles. It is successfully compared to a recent laser beam spray experiment and the associated paraxial simulations, demonstrating that backscattering predictions require accounting for the beam spray. Furthermore, our framework thus provides a way to evaluate and guide the analysis of parametric instabilities in high laser energy experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1981

Reliability-based optimization under random vibration environment

A methodology of formulating the optimum design problem for structural systems with random parameters and subjected to random vibration as a mathematical programming problem is presented. The proposed method is applied to the optimum design of a cantilever beam with a tip mass and a truss structure supporting a water tank. The excitations are assumed to be Gaussian processes and the geometric and material properties are taken to be normally distributed random variables. The probabilistic constraints are specified for individual failure modes since it is easier to specify the reliability level for each failure mode keeping in view the consequences of failure in that particular mode. The time parameter appearing in the random vibration based constraints is eliminated by replacing the probabilities of failure by suitable upper bounds. The numerical results demonstrate the feasibility and effectiveness of applying the reliability-based design concepts to structures with random parameters and operating in random vibration environment.

Rao, S. S.↗
DOE OSTI2025

A GPU Accelerated Mixed‐Precision Finite Difference Informed Random Walker (FDiRW) Solver for Strongly Inhomogeneous Diffusion Problems

In nature, many complex multi‐physics coupling problems exhibit significant diffusivity inhomogeneity, where one process occurs several orders of magnitude faster than others temporally. Simulating rapid diffusion alongside slower processes demands intensive computational resources due to the necessity for small time steps. To address these computational challenges, we have developed an efficient numerical solver named Finite Difference informed Random Walker (FDiRW). In this study, we propose a GPU‐accelerated, mixed‐precision configuration for the FDiRW solver to maximize efficiency through GPU multi‐threaded parallel computation and lower precision computation. Numerical evaluation results reveal that the proposed GPU‐accelerated mixed‐precision FDiRW solver can achieve a 117× speedup over the CPU baseline, while an additional 1.75× speedup is achieved by employing lower precision GPU computation. Notably, for large model sizes, the GPU‐accelerated mixed‐precision FDiRW solver demonstrates strong scaling with the number of nodes used in simulation. When simulating radionuclide absorption processes by porous wasteform particles with a medium‐sized model of 192 × 192 × 192, this approach reduces the total computational time to 10 min, enabling the simulation of larger systems with strongly inhomogeneous diffusivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Machine learning maximized Anderson localization of phonons in aperiodic superlattices

Nanostructuring materials to achieve ultra-low lattice thermal conductivity has proven to be extremely attractive for numerous applications such as thermoelectric energy conversion. Anderson localization of phonons due to aperiodicity can reduce thermal conductivity in superlattices, but the lower limit of thermal conductivity remains elusive due to the prohibitively large design space. In this work, we demonstrate that an intuition-based manual search for aperiodic superlattice structures (random multilayers or RMLs) with the lowest thermal conductivity yields only a local minimum, while a genetic algorithm (GA) based approach can efficiently identify the globally minimum thermal conductivity by only exploring a small fraction of the design space. Our results show that this minimum value occurs at an average RML period that is, surprisingly, smaller than the period corresponding to the minimum SL thermal conductivity. Interestingly, above this critical period, scattering of incoherent phonons at interfaces is less, whereas below this period, the room for randomization becomes less, thus putting more coherent phonons out of Anderson localization and causing increased thermal conductivity. Moreover, the lower limit of the thermal conductivity occurs at a moderate rather than maximum randomness of the layer thickness. Our machine learning approach demonstrates a general process of exploring an otherwise prohibitively large design space to gain non-intuitive physical insights.

42 ENGINEERING↗
DOE OSTI2022

Isochronic Evolution and the Radioactive Decay of r-process Nuclei

Abstract We report on the creation and application of a novel decay network that uses the latest data from experiment and evaluation. We use the network to simulate the late-time phase of the rapid neutron capture ( r ) process. In this epoch, the bulk of nuclear reactions, such as radiative capture, have ceased, and nuclear decays are the dominant transmutation channels. We find that the decay from short-lived to long-lived species naturally leads to an isochronic evolution in which nuclei with similar half-lives are populated at the same time. We consider random perturbations along each isobaric chain to initial solar-like r -process compositions to demonstrate the isochronic nature of the late-time phase of the r -process. Our analysis shows that detailed knowledge of the final isotopic composition allows for the prediction of late-time evolution with a high degree of confidence despite uncertainties that exist in astrophysical conditions and the nuclear physics properties of the most neutron-rich nuclei. We provide the time-dependent nuclear composition in the Appendix as supplemental material.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not presented for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens' sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗