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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

TChem-atm (v2.0.0): scalable performance-portable multiphase atmospheric chemistry

We present TChem-atm, a performance-portable approach that enables efficient simulation of chemically detailed and multiphase atmospheric chemistry on modern heterogeneous computing architectures. Unlike previous efforts that rely on architecture-specific code or focus exclusively on gas-phase chemistry, TChem-atm supports fully coupled gas–aerosol systems with execution across CPUs, NVIDIA GPUs, and AMD GPUs through the Kokkos programming model. It integrates the flexible multiphase capabilities of the Community Atmospheric Model Chemistry Package (CAMP) with the high-performance kinetic routines of TChem, and includes automatic Jacobian construction with support for a range of stiff ODE solvers. In a proof-of-concept integration with the particle-resolved model PartMC, TChem-atm reproduces the existing PartMC–CAMP implementation within solver tolerances and delivers substantial GPU speedups, especially for large particle populations. Performance benchmarks reveal substantial speedups on GPU platforms, particularly for large particle populations, with consistent results across hardware backends. TChem-atm enables performance-portable execution across CPUs and GPUs, though optimal efficiency may require modest architecture-specific tuning (e.g., team and vector sizes), with up to a twofold improvement on the NVIDIA H100. It directly supports sectional and particle-resolved host models, while modal aerosol schemes require minor adaptation to provide particle-scale quantities such as representative diameters. By enabling chemically detailed, multiphase simulations with performance portability and host-model flexibility, TChem-atm facilitates the incorporation of advanced chemistry into atmospheric models.

Díaz-Ibarra, Oscar Homero [Sandia National Laborat↗

The NASA Sounding Rocket Program and space sciences

High altitude suborbital rockets (sounding rockets) have been extensively used for space science research in the post-World War II period; the NASA Sounding Rocket Program has been on-going since the inception of the Agency and supports all space science disciplines. In recent years, sounding rockets have been utilized to provide a low gravity environment for materials processing research, particularly in the commercial sector. Sounding rockets offer unique features as a low gravity flight platform. Quick response and low cost combine to provide more frequent spaceflight opportunities. Suborbital spacecraft design practice has achieved a high level of sophistication which optimizes the limited available flight times. High data-rate telemetry, real-time ground up-link command and down-link video data are routinely used in sounding rocket payloads. Standard, off-the-shelf, active control systems are available which limit payload body rates such that the gravitational environment remains less than 10(-4) g during the control period. Operational launch vehicles are available which can provide up to 7 minutes of experiment time for experiment weights up to 270 kg. Standard payload recovery systems allow soft impact retrieval of payloads. When launched from White Sands Missile Range, New Mexico, payloads can be retrieved and returned to the launch site within hours.

Flight Experiment↗

Fabrication of a Composite Tow-Steered Structure for Air-Launch Vehicle Applications

Generation Orbit (GO) is developing the GO Launcher-1 (GO-1), a single stage liquid rocket that is launched from a Gulfstream III carrier aircraft platform. The vehicle is designed as the next generation platform for hypersonic flight testing and suborbital microgravity research. To reduce mass and increase payload, GO partnered with NASA Langley Research Center to design, analyze, optimize, and fabricate a tow-steered manufacturing development unit of a cylindrical section of a liquid oxygen tank. The fabrication process of using the ISAAC (Integrated Structural Assembly of Advanced Composites) system is described. The structural design challenges and the concept design solutions are also presented to provide the context for the fabrication process. The in-situ nondestructive evaluation supporting the effort is also described.

Grenoble, Ray↗

Development of High-Performance Graphene-HgCdTe Detector Technology for Mid-Wave Infrared Applications

A high-performance graphene-based HgCdTe detector technology is being developed for sensing over the mid-wave infrared (MWIR) band for NASA Earth Science, defense, and commercial applications. This technology involves the integration of graphene into HgCdTe photodetectors that combines the best of both materials and allows for higher MWIR(2-5 m) detection performance compared to photodetectors using only HgCdTe material. The interfacial barrier between the HgCdTe-based absorber and the graphene layer reduces recombination of photogenerated carriers in the detector. The graphene layer also acts as high mobility channel that whisks away carriers before they recombine, further enhancing the detector performance. Likewise, HgCdTe has shown promise for the development of MWIR detectors with improvements in carrier mobility and lifetime. The room temperature operational capability of HgCdTe-based detectors and arrays can help minimize size, weight, power and cost for MWIR sensing applications such as remote sensing and earth observation, e.g., in smaller satellite platforms. The objective of this work is to demonstrate graphene-based HgCdTe room temperature MWIR detectors and arrays through modeling, material development, and device optimization. The primary driver for this technology development is the enablement of a scalable, low cost, low power, and small footprint infrared technology component that offers high performance, while opening doors for new earth observation measurement capabilities.

Sood, Ashok K.↗

DIVA/DeviceEditor v6.1.2

DIVA is an end-to-end DNA design and construction management platform that streamlines how researchers design, build, and receive sequence-verified DNA constructs. Through a web-based BioCAD interface (DeviceEditor), researchers independently design DNA constructs and submit them to a centralized queue with a single action. Designs progress transparently through standardized states which allow researchers to track status and access finished constructs via a central DNA repository. Submitted designs are reviewed by dedicated staff for feasibility and optimization, reducing costly failures and improving downstream execution. Automated DNA assembly software optimizes construction strategies by reusing existing parts where possible and sourcing synthetic DNA only when needed. Standardized, sequence-agnostic assembly methods enable many independent constructs to be built in parallel using lab automation, dramatically increasing throughput. High-throughput next-generation sequencing is used to verify construct accuracy, with flexible platforms selected based on task requirements. Throughout the process, detailed success and failure data are captured and analyzed, enabling continuous improvement of assembly protocols. Compared to traditional, manual DNA construction workflows, DIVA offers higher scalability, transparency, reproducibility, and data-driven optimization.

Plahar, Hector [Lawrence Berkeley National Laborat↗

Highly interactive, steered scientific workflows on HPC systems: optimizing design solutions

Scientific workflows are becoming increasingly important in high performance computing (HPC) settings, as the feasibility and appeal of many simultaneous heterogeneous tasks increases with increasing hardware capabilities. Currently no HPC-based workflow platform supports a dynamically adaptable workflow with interactive steering and analysis at run-time. Furthermore, for most workflow programs, compute resources are fixed for a given instance, resulting in a possible waste of expensive allocation resources when tasks are spawned and killed. Here we describe the design and testing of a run-time-interactive, adaptable, steered workflow tool capable of executing thousands of parallel tasks without an MPI programming model, using a database management system to facilitate task management through multiple live connections. We find that on the Oak Ridge Leadership Computing Facility pre-exascale Summit supercomputer it is possible to launch and interactively steer workflows with thousands of simultaneous tasks with negligible latency. For the case of particle simulation and analysis tasks that run for minutes to hours, this paradigm offers the prospect of a robust and efficient means to perform simulation-space exploration with on-the-fly analysis and adaptation.

Ossyra, John R↗

Applying the ATOM drug discovery platform to small-molecule antivirals (Annual Report)

The objective of this project is to advance our understanding of the ML approaches needed to find the most potent inhibitor through an AI guided search that simultaneously optimizes for limited off-target safety activity and desirable pharmacokinetics (PK) properties. The methods will be tested for inhibiting SARS-CoV-2 activity through inhibition of the main protease as a demonstration, but the methodology will be developed to apply to any biothreat target using a small-molecule protein binding-based intervention, with limited experimental data. Computational experiments are conducted using our ATOM generative molecular design (GMD) loop software, which is the implementation of our AI/ML drug design pipeline.

59 BASIC BIOLOGICAL SCIENCES↗

Field Validation of a Building Operating System Platform

The U.S General Services Administration's (GSA's) Green Proving Ground program, in partnership with the National Renewable Energy Laboratory. completed a large pilot study of an Energy Management Information Systems (EMIS) with Automated System Optimization (ASO). Four test bed facilities, each with different building characteristics and systems, were chosen for the implementation of cloud-based EMIS with ASO. Depending on functionality, this tool can be extremely effective in energy management and energy optimization in buildings. The capabilities evaluated in the pilot ranged from energy savings and energy consumption predictions to evaluations of user acceptance, operability, and ease of installation. This report presents the methodology, lessons learned and best practices, and deployment recommendations for the GSA's portfolio of commercial office space, comprising more than 8,500 properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The TASAR Project: Launching Aviation on an Optimized Route Toward Aircraft Autonomy

The Traffic Aware Strategic Aircrew Request (TASAR) concept applies onboard automation for the purpose of advising the pilot of route modifications that would be beneficial to the flight. Leveraging onboard computing platforms with connectivity to avionics and diverse data sources on and off the aircraft, TASAR introduces a new, powerful capability for in-flight trajectory management to the cockpit and its flight crew that is anticipated to induce a significant culture change in airspace operations. Flight crews empowered by TASAR and its derivative technologies could transform from today’s flight plan followers to proactive trajectory managers, taking an initial critical step towards increasing autonomy in the airspace system. TASAR was developed as a catalyst for operational autonomy, a future vision where the responsibilities and authorities of trajectory management reside with the aircraft operator and are distributed among participating aircraft, thus fulfilling a vision dating back decades and enabling a fully scalable airspace system. This NASA Technical Paper maps TASAR to its foundational vision and traces its research and development from initial concept generation to an operational evaluation by a U.S. airline in revenue service, the final stage before technology transfer and commercialization.

Wing, David J.↗

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

ABSTRACT Effective selection of the potential candidates that meet certain conditions in a tremendously large search space has been one of the major concerns in many real-world applications. In addition to the nearly infinitely large search space, rigorous evaluation of a sample based on the reliable experimental or computational platform is often prohibitively expensive, making the screening problem more challenging. In such a case, constructing a high-throughput screening (HTS) pipeline that pre-sifts the samples expected to be potential candidates through the efficient earlier stages, results in a significant amount of savings in resources. However, to the best of our knowledge, despite many successful applications, no one has studied optimal pipeline design or optimal pipeline operations. In this study, we propose two optimization frameworks, applying to most (if not all) screening campaigns involving experimental or/and computational evaluations, for optimally determining the screening thresholds of an HTS pipeline. We validate the proposed frameworks on both analytic and practical scenarios. In particular, we consider the optimal computational campaign for the long non-coding RNA (lncRNA) classification as a practical example. To accomplish this, we built the high-throughput virtual screening (HTVS) pipeline for classifying the lncRNA. The simulation results demonstrate that the proposed frameworks significantly reduce the effective selection cost per potential candidate and make the HTS pipelines less sensitive to their structural variations. In addition to the validation, we provide insights on constructing a better HTS pipeline based on the simulation results.

97 MATHEMATICS AND COMPUTING↗

CRADA Final Report: Open Microgrid Platform

Current microgrid control technology is mostly proprietary, expensive, and inflexible. An open-source, vendor-neutral, publish-subscribe controller will significantly lower the barrier to entry for new technologies and market entrants: inverter, battery, and load control manufacturers; cloud and services providers; and energy aggregators. An OpenFMB controller can significantly reduce the acquisition, integration, and ongoing operating and maintenance costs and increase revenue opportunities for energy producers and consumers. It allows distributed energy resource (DER) owners to replace individual DER components as richer-function, lower-cost devices become available or to incorporate new forecast and optimization algorithms as they are developed. A standard, secure, full-function DER field controller will also provide a low-cost means for grid operators to effectively manage the variability and uncertainty of solar photovoltaics (PV). Under the terms of this CRADA, ORNL has worked with Open Energy Solutions (OES) to advance the transition of ORNL’s existing open-source microgrid controller into a platform geared toward mainstream implementation. Using a more prevalent and memory-safe programming language, Rust, OES has converted ORNL’s current generation on-grid optimization into an application more suitable for industry use. The results of the ported optimization were analyzed by inputting the same arguments in both the current-generation and next-generation optimizers and verifying that the results were equivalent.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Agent-Based Distributed Energy Resources for Supporting Intelligence at the Grid Edge

This article proposes a novel multi-agent framework that can link various forms of resources and power electronic systems into distributed energy resources (DER). The proposed multiagent architecture can also integrate DERs to a central controller for optimization and control to support the grid. To demonstrate the flexibility of this novel framework, the developed agent system is applied to a set of end-use systems. Furthermore, the agent framework is validated in hardware using controller-hardware-in-the-loop simulation platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗

High level in vivo mucin-type glycosylation in Escherichia coli

Background::Increasing efforts have been made to assess the potential of Escherichia coli strains for the production of complex recombinant proteins. Since a considerable part of therapeutic proteins are glycoproteins, the lack of the post-translational attachment of sugar moieties in standard E. coli expression strains represents a major caveat, thus limiting the use of E. coli based cell factories. The establishment of an E. coli expression system capable of protein glycosylation could potentially facilitate the production of therapeutics with a putative concomitant reduction of production costs. Results:The previously established E. coli strain expressing the soluble form of the functional human-derived glycosyltransferase polypeptide N-acetylgalactosaminyltransferase 2 (GalNAc-T2) was further modified by co-expressing the UDP-GlcNAc 4-epimerase WbgU derived from Plesiomonas shigelloides. This enables the conversion of uridine 5'-diphospho-N-acetylglucosamine (UDP-GlcNAc) to the sugar donor uridine 5'-diphospho-N-acetylgalactosamine (UDP-GalNAc) in the bacterial cytoplasm. Initially, the codon-optimised gene wbgU was inserted into a pET-derived vector and a Tobacco Etch Virus (TEV) protease cleavable polyhistidine-tag was translationally fused to the C- terminus of the amino acid sequence. The 4-epimerase was subsequently expressed and purified. Following the removal of the polyhistidine-tag, WbgU was analysed by circular dichroism spectroscopy to determine folding state and thermal transitions of the protein. The in vitro activity of WbgU was validated by employing a modified glycosyltransferase assay. The conversion of UDP-GlcNAc to UDP-GalNAc was shown by capillary electrophoresis analysis. Using a previously established chaperone pre-/co- expression platform, the in vivo activity of both glycosyltransferase GalNAc-T2 and 4-epimerase WbgU was assessed in E. coli, in combination with a mucin 10-derived target protein. Monitoring glycosylation by liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS), the results clearly indicated the in vivo glycosylation of the mucin-derived acceptor peptide. Conclusion:In the present work, the previously established E. coli- based expression system was further optimized and the potential for in vivo O-glycosylation was shown by demonstrating the transfer of sugar moieties to a mucin-derived acceptor protein. The results offer the possibility to assess the practical use of the described expression platform for in vivo glycosylations of important biopharmaceutical compounds in E. coli.

59 BASIC BIOLOGICAL SCIENCES↗

Giant Topological Hall Effect in van der Waals Heterostructures of CrTe 2 /Bi 2 Te 3

Discoveries of the interfacial topological Hall effect (THE) provide an ideal platform for exploring the physics arising from the interplay between topology and magnetism. The interfacial topological Hall effect is closely related to the Dzyaloshinskii-Moriya interaction (DMI) at an interface and topological spin textures. However, it is difficult to achieve a sizable THE in heterostructures due to the stringent constraints on the constituents of THE heterostructures, such as strong spin-orbit coupling (SOC). Here, we report the observation of a giant THE signal of 1.39 mu Omega.cm in the van der Waals heterostructures of CrTe 2 /Bi 2 Te 3 fabricated by molecular beam epitaxy, a prototype of two-dimensional (2D) ferromagnet (FM)/topological insulator (TI). This large magnitude of THE is attributed to an optimized combination of 2D ferromagnetism in CrTe 2 , strong SOC in Bi 2 Te 3 , and an atomically sharp interface. Furthermore, our work reveals CrTe 2 /Bi 2 Te 3 as a convenient platform for achieving large interfacial THE in hybrid systems, which could be utilized to develop quantum science and high-density information storage devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Auxetic metamaterials from disordered networks

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as Poisson's ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruningbased theoretical ideas have not been successful. In this study, we introduce a more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and in the nonlinear regimes. In particular, it is shown that Poisson's ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting 2D sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks' susceptibility to tuning to design networks that possess a distribution of stiffer and more compliant bonds and whose auxetic behavior is even greater than that of homogeneous networks. Finally, taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of unique mechanical metamaterials.

36 MATERIALS SCIENCE↗

Photoacoustic flow cytometry for nanomaterial research

Conventional flow cytometry is a versatile tool for drug research and cell characterization. However, it is poorly suited for quantification of non-fluorescent proteins and artificial nanomaterials without the use of additional labeling. The rapid growth of biomedical applications for small non-fluorescent nanoparticles (NPs) for drug delivery, image contrast and therapy enhancement, as well as research focused on natural cell pigments and chromophores, demands high-throughput quantification methods for the non-fluorescent components. In this work, we present an advanced novel photoacoustic (PA) fluorescence flow cytometry (PAFFC) platform that integrates NP quantification though PA detection with conventional sample characterization using fluorescence labeling. PAFFC simplifies high-throughput analysis of cell-NP interactions, optimization of targeted nanodrugs, and NP toxicity assessment by providing a direct correlation between NP uptake and characterization of toxicity markers for every cell.

Acoustics↗

Evaluating Nonuniform Reduction in HIP and SYCL on GPUs

Motivated by maturing programming models and portability for heterogeneous computing, we describe the challenges posed by hardware architectures and programming models when migrating an optimized implementation of nonuniform reduction from CUDA to HIP and SYCL. We explain the migration experience, evaluate the performance of the reduction on GPU -based computing platforms, and provide feedback on improving portability for the development of the SYCL programming model.

Jin, Zheming↗