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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Lipid-coated mesoporous silica nanoparticles for anti-viral applications via delivery of CRISPR-Cas9 ribonucleoproteins

Abstract Emerging and re-emerging viral pathogens present a unique challenge for anti-viral therapeutic development. Anti-viral approaches with high flexibility and rapid production times are essential for combating these high-pandemic risk viruses. CRISPR-Cas technologies have been extensively repurposed to treat a variety of diseases, with recent work expanding into potential applications against viral infections. However, delivery still presents a major challenge for these technologies. Lipid-coated mesoporous silica nanoparticles (LCMSNs) offer an attractive delivery vehicle for a variety of cargos due to their high biocompatibility, tractable synthesis, and amenability to chemical functionalization. Here, we report the use of LCMSNs to deliver CRISPR-Cas9 ribonucleoproteins (RNPs) that target the Niemann–Pick disease type C1 gene, an essential host factor required for entry of the high-pandemic risk pathogen Ebola virus, demonstrating an efficient reduction in viral infection. We further highlight successful in vivo delivery of the RNP-LCMSN platform to the mouse liver via systemic administration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Chemical clusters are relevant to many applications in catalysis, separations, materials, and energy sciences. Experimentally, the structure of clusters is difficult to determine, but it is very important in understanding their chemistry and properties. Computational methods can be used to examine cluster structure, however finding the most stable structure is not simple, particularly as the cluster size increases. Global optimization techniques have long been used to tackle the problem of the most stable structure, but such approaches would have to look for a global minimum, while sampling local minima over the whole potential energy surface as well. In this review, the state-of-the-art theory of global optimization theory is summarized. First, the definition, significance, relation to experiments, and a brief history of global optimization is presented. We then discuss, in more detail, three versatile global optimization methods: the basin hopping, the artificial bee colony algorithm, and the genetic algorithm. We close with some representative application examples of global optimization of clusters since 2016 and the challenges, open questions and opportunities in this field.

Global optimization, Chemical clusters, Artificial↗
DOE OSTI2025

Supporting New Advanced Nuclear Technologies for Commercial-Maritime Applications

The ANS summary doesn't require an abstract, but I will produce one for the purpose of LRS: The large demand for maritime nuclear power underscores the need for experimental campaigns and modeling and simulation of new advanced reactors, which offer numerous advantages in terms of safety, efficiency, and compactness. INL, through the work conducted by NRIC and ABS, has addressed some of the technical, regulatory, and economic aspects of potential nuclear commercial maritime applications. However, on the technical side, there remain important physical phenomena, particularly for advanced reactors, that are not yet fully understood. Addressing these knowledge gaps requires a combination of experiments and advanced modeling and simulation techniques. INL possesses significant expertise in Multiphysics modeling and simulation. By collaborating with INL, the maritime nuclear sector can leverage this expertise to advance the development and deployment of innovative nuclear technologies.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1996

Thirteenth Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion and Launch Vehicle Technology

This conference publication includes various abstracts and presentations given at the 13th Workshop for Computational Fluid Dynamic Applications in Rocket Propulsion and Launch Vehicle Technology held at the George C. Marshall Space Flight Center April 25-27 1995. The purpose of the workshop was to discuss experimental and computational fluid dynamic activities in rocket propulsion and launch vehicles. The workshop was an open meeting for government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.

Williams, R. W.↗
DOE OSTI2022

Phase-field framework with constraints and its applications to ductile fracture in polycrystals and fatigue

Abstract Modeling of ductile fracture in polycrystalline structures is challenging, since it requires integrated modeling of cracks, crystal plasticity, and grains. Here we extend the typical phase-field framework to the situations with constraints on the order parameters, and formulate two types of phase-field models on ductile fracture. The Type-I model incorporates three sets of order parameters, which describe the distributions of cracks, plastic strain, and grains, respectively. Crystal plasticity is employed within grain interiors accommodated by J 2 plasticity at grain boundaries. The applications of the Type-I model to single crystals and bicrystals demonstrate the influences of grain orientations and grain boundaries on crack growth. In the Type-II model, J 2 plasticity is assumed for the whole system and grain structures are neglected. Taking advantage of the efficiency of the fast Fourier transform, our Type-II model is employed to study low cycle fatigue. Crack closure and striation-like patterning of plastic strain are observed in the simulations. Crack growth rate is analyzed as a function of the J -integral, and the simulated fatigue life as a function of plastic strain agrees with the Coffin–Manson relation without a priori assumption.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

JPL Space Telecommunications Radio System Operating Environment

A flight-qualified implementation of a Software Defined Radio (SDR) Operating Environment for the JPL-SDR built for the CoNNeCT Project has been developed. It is compliant with the NASA Space Telecommunications Radio System (STRS) Architecture Standard, and provides the software infrastructure for STRS compliant waveform applications. This software provides a standards-compliant abstracted view of the JPL-SDR hardware platform. It uses industry standard POSIX interfaces for most functions, as well as exposing the STRS API (Application Programming In terface) required by the standard. This software includes a standardized interface for IP components instantiated within a Xilinx FPGA (Field Programmable Gate Array). The software provides a standardized abstracted interface to platform resources such as data converters, file system, etc., which can be used by STRS standards conformant waveform applications. It provides a generic SDR operating environment with a much smaller resource footprint than similar products such as SCA (Software Communications Architecture) compliant implementations, or the DoD Joint Tactical Radio Systems (JTRS).

Lux, James P.↗
DOE OSTI2021

An Algebra of Machine Learners with Applications

Machine learning (ML) methods are increasingly being applied to solve complex, data-driven problems in diverse areas, by exploiting the physical laws derived from first principles such as thermal hydraulics and the abstract laws developed recently for data and computing infrastructures. These physical and abstract laws encapsulate, typically in compact algebraic forms, the critical knowledge that complements data-driven ML models. We present a unified perspective of these laws and ML methods using an abstract algebra (A;⊕,Ⓧ), wherein the performance estimation and classification tasks are characterized by the additive ⊕ operations, and the diagnosis, reconstruction, and optimization tasks are characterized by the difference Ⓧ operations. This abstraction provides ML codes and their performance characterizations that are transferable across different areas. We describe practical applications of these abstract operations using examples of throughput profile estimation tasks in data transport infrastructures, and power-level and sensor error estimation tasks in nuclear reactor systems.

Rao, Nageswara↗