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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Life Support Systems for Lunar Landers

Engineers designing life support systems for NASA s next Lunar Landers face unique challenges. As with any vehicle that enables human spaceflight, the needs of the crew drive most of the lander requirements. The lander is also a key element of the architecture NASA will implement in the Constellation program. Many requirements, constraints, or optimization goals will be driven by interfaces with other projects, like the Crew Exploration Vehicle, the Lunar Surface Systems, and the Extravehicular Activity project. Other challenges in the life support system will be driven by the unique location of the vehicle in the environments encountered throughout the mission. This paper examines several topics that may be major design drivers for the lunar lander life support system. There are several functional requirements for the lander that may be different from previous vehicles or programs and recent experience. Some of the requirements or design drivers will change depending on the overall Lander configuration. While the configuration for a lander design is not fixed, designers can examine how these issues would impact their design and be prepared for the quick design iterations required to optimize a spacecraft.

Anderson, Molly↗
DOE OSTI2019

FLUKA-MARS15 simulations to optimize the Fermilab PIP-II movable beam absorber

PIP-II is the Fermilab's flagship project to provide powerful, high-intensity proton beams to the laboratory's experiments. The heart of the PIP-II project is an H- 800 MeV superconducting linear accelerator. In order to commission the beam and operate safely the linac, several constraints were evaluated. The design of a movable 5 kW beam absorber was finalized to allow staged beam commissioning in different linac locations. Prompt and residual radiation levels were calculated, and radiation shields were optimized to keep those values within the acceptable levels in the areas surrounding beam absorber. Monte Carlo calculations with FLUKA and MARS15 codes are presented in the paper to support these studies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2013

Improved sliced velocity map imaging apparatus optimized for H photofragments

Time-sliced velocity map imaging (SVMI), a high-resolution method for measuring kinetic energy distributions of products in scattering and photodissociation reactions, is challenging to implement for atomic hydrogen products. We describe an ion optics design aimed at achieving SVMI of H fragments in a broad range of kinetic energies (KE), from a fraction of an electronvolt to a few electronvolts. In order to enable consistently thin slicing for any imaged KE range, an additional electrostatic lens is introduced in the drift region for radial magnification control without affecting temporal stretching of the ion cloud. Time slices of {approx}5 ns out of a cloud stretched to Greater-Than-Or-Slanted-Equal-To 50 ns are used. An accelerator region with variable dimensions (using multiple electrodes) is employed for better optimization of radial and temporal space focusing characteristics at each magnification level. The implemented system was successfully tested by recording images of H fragments from the photodissociation of HBr, H{sub 2}S, and the CH{sub 2}OH radical, with kinetic energies ranging from <0.4 eV to >3 eV. It demonstrated KE resolution Less-Than-Or-Equivalent-To 1%-2%, similar to that obtained in traditional velocity map imaging followed by reconstruction, and to KE resolution achieved previously in SVMI of heavier products. We expect it to perform just as well up to at least 6 eV of kinetic energy. The tests showed that numerical simulations of the electric fields and ion trajectories in the system, used for optimization of the design and operating parameters, provide an accurate and reliable description of all aspects of system performance. This offers the advantage of selecting the best operating conditions in each measurement without the need for additional calibration experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

RF design of APEX2 two-cell continuous-wave normal conducting photoelectron gun cavity based on multi-objective genetic algorithm

High brightness, high repetition rate electron beams are key components for optimizing the performance of next generation scientific instruments, such as MHz-class X-ray Free Electron Laser (XFEL) and Ultra-fast Electron Diffraction/Microscopy (UED/UEM). In the Advanced Photo-injector EXperiment (APEX)at Berkeley Lab, a photoelectron gun based on a 185.7 MHz normal conducting re-entrant RF cavity, has been proven to be a feasible solution to provide high brightness, high repetition rate electron beam for both XFEL and UED/UEM. Based on the success of APEX, a new electron gun system, named APEX2, has been under development to further improve the electron beam brightness. For APEX2, we have designed a new 162.5 MHz two-cell photoelectron gun and achieved a significant increase on the cathode launching field and the beam exit energy. For a fixed charge per bunch, these improvements will allow for the emittance reduction and hence to an increased beam brightness. The design of APEX2 gun cavity is a complex problem with multiple design goals and restrictions, some even competing each other. In conclusion, for a systematic and comprehensive search for the optimized cavity geometry, we have developed and implemented a novel optimization method based on the Multi-Objective Genetic Algorithm (MOGA).

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

X-ray micro computed tomography characterization of cellular SiC foams for their applications in chemical engineering

Open-cell SiC foams clearly are promising materials for continuous-flow chemical applications such as heterogeneous catalysis and distillation. X-ray micro computed tomography characterization of cellular β-SiC foams at a spatial voxel size of 13.6{sup 3} μm{sup 3} and the interpretation of morphological properties of SiC open-cell foams with implications to their transport properties are presented. Static liquid hold-up in SiC foams was investigated through in-situ draining experiments for the first time using the μ-CT technique providing thorough 3D information about the amount and distribution of liquid hold-up inside the foam. This will enable better modeling and design of structured reactors based on SiC foams in the future. In order to see more practical uses, μ-CT data of cellular foams must be exploited to optimize the design of the morphology of foams for a specific application. - Highlights: •Characterization of SiC foams using novel X-ray micro computed tomography. •Interpretation of structural properties of SiC foams regarding to their transport properties. •Static liquid hold-up analysis of SiC foams through in-situ draining experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Active and Durable PGM-free Cathodic Electrocatalysts for Fuel Cell Application

Platinum Group Metal-free (PGM-free) Oxygen Reduction Reaction (ORR) electrocatalysts possess high intrinsic activity measured by a Rotating Disk Electrode (RDE) method. However, under fuel cell operating conditions, PGM-free ORR electrocatalysts have underperformed compared to platinum catalysts. PGM-free ORR electrocatalyst’s fuel cell performance can be improved by designing and optimizing the cathodic catalyst layer (CL) and Membrane Electrode Assembly (MEA) construction such that: 1) it efficiently provides oxygen access to ORR active sites (through catalyst morphology control); 2) it removes water from the CL (by tuning the hydrophobicity of the PGM-free catalysts and the catalyst layer structure); and 3) it increases proton conductivity (by homogeneous mixing of catalysts and ionomer). Studying the CL is complex due to the absence of well-established protocols of MEA activation, especially compared to platinum-containing catalysts. PGM-free fuel cell testing protocols would need to optimize potentiostatic vs galvanostatic measurements, scan rates, parameters of Electrochemical Impedance Spectroscopy (EIS) and Beginning of Experiment (BOE) criteria. To make the PGM-free catalyst’s fuel cell performance comparable to platinum, the synergistic effort of materials design, fine tuning of the catalyst layer and comprehensive electrochemical analysis is required.

08 HYDROGEN↗
DOE OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗
DOE OSTI2025

Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications

In this work we introduce a novel two-level overlapping additive Schwarz preconditioner for accelerating the training of scientific machine learning applications. The design of the proposed preconditioner is motivated by the nonlinear two-level overlapping additive Schwarz preconditioner. The neural network parameters are decomposed into groups (subdomains) with overlapping regions. In addition, the network’s feed-forward structure is indirectly imposed through a novel subdomain-wise synchronization strategy and a coarse-level training step. Through a series of numerical experiments, which consider physicsinformed neural networks and operator learning approaches, we demonstrate that the proposed two-level preconditioner significantly speeds up the convergence of the standard (LBFGS) optimizer while also yielding more accurate machine learning models. Moreover, the devised preconditioner is designed to take advantage of model-parallel computations, which can further reduce the training time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Improved MPI collectives for MPI processes in shared address spaces

As the number of cores per node keeps increasing, it becomes increasingly important for MPI to leverage shared memory for intranode communication. This paper investigates the design and optimization of MPI collectives for clusters of NUMA nodes. We develop performance models for collective communication using shared memory and we demonstrate several algorithms for various collectives. Experiments are conducted on both Xeon X5650 and Opteron 6100 InfiniBand clusters. The measurements agree with the model and indicate that different algorithms dominate for short vectors and long vectors. We compare our shared-memory allreduce with several MPI implementations-Open MPI, MPICH2, and MVAPICH2-that utilize system shared memory to facilitate interprocess communication. On a 16-node Xeon cluster and 8-node Opteron cluster, our implementation achieves on geometric average 2.3X and 2.1X speedup over the best MPI implementation, respectively. Our techniques enable an efficient implementation of collective operations on future multi- and manycore systems.

Collective communication↗
DOE OSTI2025

Active learning path-dependent properties using a cloud-based materials acceleration platform

Solid state materials are central to many modern technologies in which a given material may be exposed to a variety of environments. The material properties often vary with the sequence of environments in an irreversible manner, resulting in a quintessential path-dependency in experimental observables. While sequential learning techniques have been effectively deployed for accelerating learning of state properties of materials, they often use a consistent environment path in all experiments. To elevate such techniques for making optimal decisions in experimental investigations of path-dependent properties, we introduce an iterated expected information gain acquisition function that optimizes over entire experimental trajectories. This approach is implemented within a cloud-based Materials Acceleration Platform architecture utilizing an event-driven stateful broker coupled with remote HELAO (Hierarchical Experimental Laboratory Automation and Orchestration) instances and an AI science manager. The platform's efficacy was demonstrated through a case study optimizing multi-step spectro-electrochemical experiments to identify optically stable potential windows in (Co–Ni–Sb)O z metal oxides. The system successfully integrated AI-driven experiment design, remote laboratory automation, and cloud-based data infrastructure, validating the platform's capability for managing complex, adaptive, path-dependent workflows in materials discovery.

Guevarra, Dan [California Institute of Technology ↗
DOE OSTI2014

Analysis of optical near-field energy transfer by stochastic model unifying architectural dependencies

We theoretically and experimentally demonstrate energy transfer mediated by optical near-field interactions in a multi-layer InAs quantum dot (QD) structure composed of a single layer of larger dots and N layers of smaller ones. We construct a stochastic model in which optical near-field interactions that follow a Yukawa potential, QD size fluctuations, and temperature-dependent energy level broadening are unified, enabling us to examine device-architecture-dependent energy transfer efficiencies. The model results are consistent with the experiments. This study provides an insight into optical energy transfer involving inherent disorders in materials and paves the way to systematic design principles of nanophotonic devices that will allow optimized performance and the realization of designated functions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Cavallo's multiplier for in situ generation of high voltage

Here, a classic electrostatic induction machine, Cavallo's multiplier, is suggested for in situ production of very high voltage in cryogenic environments. The device is suitable for generating a large electrostatic field under conditions of very small load current. Operation of the Cavallo multiplier is analyzed, with quantitative description in terms of mutual capacitances between electrodes in the system. A demonstration apparatus was constructed, and measured voltages are compared to predictions based on measured capacitances in the system. The simplicity of the Cavallo multiplier makes it amenable to electrostatic analysis using finite element software, and electrode shapes can be optimized to take advantage of a high dielectric strength medium such as liquid helium. A design study is presented for a Cavallo multiplier in a large-scale, cryogenic experiment to measure the neutron electric dipole moment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Computational Materials Design for Ceramic Nuclear Waste Forms Using Machine Learning, First-Principles Calculations, and Kinetics Rate Theory

Ceramic waste forms are designed to immobilize radionuclides for permanent disposal in geological repositories. One of the principal criteria for the effective incorporation of waste elements is their compatibility with the host material. In terms of performance under environmental conditions, the resistance of the waste forms to degradation over long periods of time is a critical concern when they are exposed to natural environments. Due to their unique crystallographic features and behavior in nature environment as exemplified by their natural analogues, ceramic waste forms are capable of incorporating problematic nuclear waste elements while showing promising chemical durability in aqueous environments. Recent studies of apatite- and hollandite-structured waste forms demonstrated an approach that can predict the compositions of ceramic waste forms and their long-term dissolution rate by a combination of computational techniques including machine learning, first-principles thermodynamics calculations, and modeling using kinetic rate equations based on critical laboratory experiments. By integrating the predictions of elemental incorporation and degradation kinetics in a holistic framework, the approach could be promising for the design of advanced ceramic waste forms with optimized incorporation capacity and environmental degradation performance. Such an approach could provide a path for accelerated ceramic waste form development and performance prediction for problematic nuclear waste elements.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1990

High energy, high resolution X-ray optics

The scientific goals of X-ray astronomy are considered to evaluate the relative advantages of using classical Wolter-1 optics or using a different approach. The portion of the X-ray band over 10 keV is unexploited in the present X-ray optics technology, and focussing in this portion of the band is crucial because nonfocussed experiments are background limited. The basic design of 'hard' X-ray optics is described theoretically emphasizing the very small angles of incidence in the grazing-incidence optics. Optimization of the signal-to-noise ratio is found to occur at a finite angular resolution. In real applications, the effective area reduced by the efficiency of the two reflections is 80 percent at energies up to 40 keV, and the quality of the reflecting surface can be monitored to minimize scattering. Focussing optics are found to offer improvements in signal-to-noise as well as more effective scientific return because microelectronic focal-plane technology is employed.

Weisskopf, Martin C.↗
NASA NTRS1992

Flexible structure experiments at JPL and WPAFB - H-infinity controller designs

The authors describe some control system experiments which were carried out on flexible structure facilities at the Jet Propulsion Laboratory (JPL) and the Wright Patterson Air Force Base (WPAFB). They document some recent design studies for two contrasting flexible structure facilities. The first facility was the JPL/AFAL flexible antenna in Pasadena, California. The second was the WPAFB 12-meter cantilever truss in Dayton, Ohio. Each structure possessed a number of flexible modes of vibration which were lightly damped. The purpose of the experiments was to achieve active damping of the flexible modes via a digital control system. The feedback controllers were designed using the H-infinity technique of weighted gap optimization to increase the structural damping and achieve robust control.

Buddie, Scott A.↗
NASA NTRS2017

Quantum Approximate Optimization with Hard and Soft Constraints

Challenging computational problems arising in the practical world are frequently tackled by heuristic algorithms. Small universal quantum computers will emerge in the next year or two, enabling a substantial broadening of the types of quantum heuristics that can be investigated beyond quantum annealing. The immediate question is What experiments should we prioritize that will give us insight into quantum heuristics? One leading candidate is the quantum approximate optimization algorithm (QAOA) metaheuristic. Here, we provide a framework for designing QAOA circuits for a variety of combinatorial optimization problems with both hard constraints that must be met and soft constraints whose violation we wish to minimize. We work through a number of examples, and discuss design principles and implementation considerations.

Hadfield, Stuart↗
NASA NTRS2018

Numerical and Experimental Study of Local Resin Pressure for the Manufacturing of Composite Structures and Their Effect on Porosity

Porosity can have significant impact on the mechanical performance of composite structures. The primary sources of voids during the cure of composites include air entrapped during lay-up, bag or tool leaks, and the off gassing of volatiles. Capturing the physics of void evolution during composite processing is a challenge due to the number of sources and changing phenomena that give rise to voids as the resin cures. Local changes in resin pressure due to geometric features or cure shrinkage have been experimentally linked to a higher likelihood of void formation. In this work, a preliminary model was developed to predict local resin pressure which can be used to identify regions more susceptible to porosity. Experiments were conducted to validate the model accuracy. Ultimately, this model can be used as a tool to minimize the likelihood of porosity by optimizing the material selection, part design, layup, and cure process.

Bedayat, Houman↗