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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 37 records · Page 2

Innovative Strategies for Long-Term Monitoring of Complex Groundwater Plumes at DOE’s Legacy Sites (Workshop Report)

Most remaining Department of Energy (DOE) sites will require extended periods of institutional control, especially at complex groundwater sites where attenuation-based strategies have been implemented to facilitate closure. The current practice of monitoring—obtaining and analyzing contaminant concentration in groundwater samples at numerous wells—will account for a large portion of the projected life-cycle at these DOE sites unless a new approach is adopted. State-of-the-art technologies are being developed, including in situ sensors, geophysics, radiation mapping, numerical modeling and AI/ML. These technologies can optimize monitoring strategies in space and time, provide spatially extensive information at vulnerable regions and/or provide more continuous monitoring at lower cost. As part of DOE’s Office of Environmental Management (DOE-EM’s) efforts to advance long-term monitoring systems, an in-person/virtual hybrid workshop was hosted by Savannah River National Laboratory (SRNL) on January 24 and 25, 2023, in Augusta, Georgia. Because DOE-EM’s complex sites will eventually be transferred to DOE’s Office of Legacy Management (DOE-LM), representatives of DOE-LM were important participants in the workshop. The purpose of the workshop was to identify challenges and opportunities for deploying advanced technologies for long-term monitoring at DOE sites. The key questions during the workshop were: 1) the regulatory acceptance of replacing a process that traditionally has used laboratory sampling and analysis of groundwater samples, and 2) the application of this strategy to the southwestern arid sites that include many of the remaining DOE-EM and DOE-LM complex groundwater plumes. Characteristics common to most arid sites present both limitations and opportunities for advanced technologies. DOE-EM has funded a National Laboratory team from SRNL, Lawrence Berkeley National Laboratory (LBNL), and Pacific Northwest National Laboratory (PNNL) to establish the overarching framework of long-term monitoring by systematically combining advanced hardware and software technologies. This project is titled “Advanced Long-Term Environmental Monitoring Systems (ALTEMIS)” and is sponsored by the DOE-EM Technology Development Program. The multi-laboratory team is currently developing and testing innovative monitoring strategies, including the use of in situ groundwater sensors, geophysics, drone/satellite-based remote sensing, reactive transport modeling, and artificial intelligence/machine learning (AI/ML). The project’s demonstration testbed is at the Savannah River Site (SRS) F-Area Seepage Basins, where a well-characterized complex groundwater plume composed of uranium and other radionuclides is in the latter stages of remediation. The workshop included more than 70 participants, presentations, a field visit to F-Area, breakout working groups, and large group discussion. Participants developed recommendations on five topics: in situ sensors, spatially integrative tools, challenges to regulatory acceptance, AI/ML strategies, and transitioning sites to DOE-LM.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Autonomy Verification & Validation Roadmap and Vision 2045

Advanced capabilities planned for the next generation of autonomous and increasingly autonomous air vehicles will include non-traditional components based on artificial intelligence, machine learning, and complex optimization and planning algorithms. These complex components will be used to provide enhanced safety and high-level decision-making functions. However, there are serious barriers to the deployment of autonomous aircraft in the National Airspace System (NAS). Current civil aviation certification processes are based on the concept that the correct behavior of a system or a component must be completely specified and verified prior to operation. This report from the Autonomy Verification and Validation (V&V) Roadmap and Vision 2045 project presents the most recent effort to build a comprehensive list of verification challenges and needs for autonomous aircraft, a roadmap to meet those autonomy V&V needs, the services they can enable, and point to the certification gaps they fill. To accomplish these goals, we assembled a team of world-class researchers from the aerospace industry (Boeing, Collins Aerospace, and GeneralElectric) and academia (University of Michigan, University of Texas, and Massachusetts Institute of Technology) with deep expertise in autonomy, aerospace systems, and assurance of Artificial Intelligence/machine learning systems.

Software Assurance↗

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗

TechEdSat-13: The First Flight of an AI/ML Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/ Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗

TechEdSat-13: The First Flight of a Neuromorphic Processor

Neuromorphic processors, inspired by the wiring of the brain, permit certain classes of Artificial Intelligence/Machine Learning (AI/ML) algorithms to run far more efficiently. Ultimately, such systems will make the small- and nano-satellite platforms even more useful in terms of greatly improved power, communication and internal data management. In addition, on-board processing of images and data will help to not only rapidly interpret the information, but also reduce the amount of data that needs to be transmitted to ground stations. This initial flight experiment uses the Intel/Loihi processor combined with a custom interface board and three communication channels to run the AI/ML scripts. These will vary with increasing length and complexity during the course of the mission. The algorithms will use the (at first) limited sensor data to ‘learn’ - with comparisons to similar architecture in comparable ground experiments. Some of the applications for successor flights in the TES-n flight series include Cognitive Communications, whereby the overall communication system is optimized per overflight – by optimizing timing and data transmission functions. Lastly, the performance of the Loihi 14nm process technology will be monitored for performance in the LEO radiation environment, thus looking for induced hardware and software errors. This information will help guide future radiation protection techniques to extend the lifetime and overall utility. The TES-13 is a 3U nanosat successfully launched by the Virgin Orbit Launcher-1 on January 13, 2022, and will presage more flights and AI/ML applications to come.

TechEdSat↗

Insight into molecular basis and dynamics of full-length CRaf kinase in cellular signaling mechanisms

Raf kinases play key roles in signal transduction in cells for regulating proliferation, differentiation, and survival. Despite decades of research into functions and dynamics of Raf kinases with respect to other cytosolic proteins, understanding Raf kinases is limited by the lack of their full-length structures at the atomic resolution. Here, we present the first model of the full-length CRaf kinase obtained from artificial intelligence/machine learning algorithms with a converging ensemble of structures simulated by large-scale temperature replica exchange simulations. Our model is validated by comparing simulated structures with the latest cryo-EM structure detailing close contacts among three key domains and regions of the CRaf. Our simulations identify potentially new epitopes of intramolecule interactions within the CRaf and reveal a dynamical nature of CRaf kinases, in which the three domains can move back and forth relative to each other for regulatory dynamics. The dynamic conformations are then used in a docking algorithm to shed insight into the paradoxical effect caused by vemurafenib in comparison with a paradox breaker PLX7904. In this study, we propose a model of Raf-heterodimer/KRas-dimer as a signalosome based on the dynamics of the full-length CRaf.

59 BASIC BIOLOGICAL SCIENCES↗

From bench to biofactory: high-throughput technologies and automated workflows to accelerate biomanufacturing

Microbial production of target molecules has advanced significantly in recent years driven by innovations in enzyme engineering, DNA synthesis, and genomic editing. However, to access the massive potential of microbial production, a vast parametric space remains to be investigated to optimize these biobased processes for a robust bioeconomy. Here, we review the current state of the art, some key challenges and possible solutions. We see a critical role of automation, high-throughput technologies, self-driving and cloud labs, and data management to enable Artificial Intelligence/Machine Learning and mechanistic models to overcome the design space challenges and accelerate the development of novel bio-based solutions. Accurate models will expedite the development and scale-up of engineered microbes for a range of final products from many starting materials.

Petzold, Christopher J↗

Delivering real-time multi-modal materials analysis with enterprise beamlines

Contemporary advancements in low-cost automation and computation, reduced barrier to entry in developing artificial intelligence/machine learning (AI/ML), and increased ability to represent complex materials in digital form have led to a number of accelerated materials discovery platforms. However, many of these approaches operate with completely rigid vertical integration in an isolated feedback loop using limited modalities. In order to make a substantial impact on discovering new energy materials, AI-driven experiments must operate collaboratively with each other and researchers and over multiple measurement modalities. Herein, we describe the potential for an “internet of things” approach to self-driving enterprise beamlines that merges core information technologies, robotics, and multi-modal AI. The approach will enable full utility of light sources, collaborate effectively with other remote materials acceleration platforms, and help stride toward the world’s energy future.

36 MATERIALS SCIENCE↗

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗

mzapy : An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format

We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of a core library with standard mass spectrometry utilities greatly facilitates fast software development. Here, we present a Python library, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python with minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multi-omics domain. The free and open source mzapy is built with extensibility in mind, and future development will support cloud computing and artificial intelligence/machine learning applications. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Lai, Jhih-Siang (ORCID:0000000156775890)↗

A Review of Cyber-Physical Security for Photovoltaic Systems

In this paper, the challenges and a future vision of the cyber-physical security of photovoltaic (PV) systems are discussed from a firmware, network, PV converter controls, and grid security perspective. The vulnerabilities of PV systems are investigated under a variety of cyber-attacks, ranging from data integrity attacks to software-based attacks. A success rate metric is designed to evaluate the impact and facilitate decision making. Model-based and data-driven methods for threat detection and mitigation are summarized. In addition, the blockchain technology that addresses cyber-attacks in software and cyber networks is described. Simulation and experimental results that show the impact of cyber-attacks at the converter (device) and grid (system) levels are presented. Finally, potential research opportunities are discussed for next-generation, cyber-secure power electronics systems. These opportunities include multi-scale controllability, self-/event-triggering control, artificial intelligence/machine learning, hot patching, and online security. As of today, this study will be one of the few comprehensive studies in this emerging and fast-growing area.

14 SOLAR ENERGY↗

Nitrogen limitation causes a seismic shift in redox state and phosphorylation of proteins implicated in carbon flux and lipidome remodeling in Rhodotorula toruloides

Background: Oleaginous yeast are prodigious producers of oleochemicals, offering alternative and secure sources for applications in foodstuff, skincare, biofuels, and bioplastics. Nitrogen starvation is the primary strategy used to induce oil accumulation in oleaginous yeast as part of a global stress response. While research has demonstrated that post-translational modifications (PTMs), including phosphorylation and protein cysteine thiol oxidation (redox PTMs), are involved in signaling pathways that regulate stress responses in metazoa and algae, their role in oleaginous yeast remain understudied and unexplored. Results: Towards linking the yeast oleaginous phenotype to protein function, we integrated lipidomics, redox proteomics, and phosphoproteomics to investigate Rhodotorula toruloides under nitrogen-rich and starved conditions over time. Our lipidomics results unearthed interactions involving sphingolipids and cardiolipins with ER stress and mitophagy. Our redox and phosphoproteomics data highlighted the roles of the AMPK, TOR, and calcium signaling pathways in regulation of lipogenesis, autophagy, and oxidative stress response. As a first, we also demonstrated that lipogenic enzymes including fatty acid synthase are modified as a consequence of shifts in cellular redox states due to nutrient availability. Conclusions: We conclude that lipid accumulation is largely a consequence of carbon rerouting and autophagy governed by changes to PTMs, and not increases in the abundance of enzymes involved in central carbon metabolism and fatty acid biosynthesis. Our systems-level approach sets the stage for acquiring multidimensional data sets for protein structural modeling and predicting the functional relevance of PTMs using Artificial Intelligence/Machine Learning (AI/ML). Coupled to those bioinformatics approaches, the putative PTM switches that we delineate will enable advanced metabolic engineering strategies to decouple lipid accumulation from nitrogen limitation.

Lipid Signalling↗

Advances in additive manufacturing, materials, and applications with AI/ML

There is high interest in making digital manufacturing a central facet of the new manufacturing landscape. However, in the materials science world, there is much work and opportunity to realize the full potential of artificial intelligence/machine learning (AI/ML) with regard to the structure–composition–processing–property (SCPP) relationship. For polymers (thermoplastics, thermosets, elastomers) and composites (nanocomposites), the origin of their high performance and even recyclability starts with design and formulation. Processing methods enable more property development based on curing, shape-factor forming, and anisotropic directionality. In subtractive manufacturing, high-performance and engineering polymers can be shaped and milled to very high tolerance and specifications and used as replacements for metals and alloys. In conclusion, this typically relies on digital manufacturing methods but tends to be wasteful in materials.

Lara-Ceniceros, Tania E. [Centro de Investigación ↗

FAIR data infrastructure and tools for AI-assisted streamflow prediction

Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..

54 ENVIRONMENTAL SCIENCES↗

November 2021 Operational Highlights

Completed: 1. the initial tests of document workflows for Titan on the Red, which included standing up a software encryption capability to test data ingestion; 2. a software encryption capability in order to ingest the first data source by the end of the month; 3. a stand-alone computer for classified ontology data entry. Titan on the Red is an artificial intelligence/machine learning system to make digitizing, cataloging, and searching NSRC collections easier and more efficient. Created Online Vault backups, with one copy stored at LANL and one shipped to Lawrence Livermore National Laboratory. The Online Vault is a classified, searchable library of LANL’s nuclear weapons design and test history. Imported new Laboratory Directed Research and Development (LDRD) documents based on revised access categories. Bulk ingested nearly 10,000 documents via java-based PowerLoader. This application ingests metadata and content into the Online Vault to meet the requirements for the NSRC collections. Rehoused 300 linear feet of weapons physics documents in archival, acid-free storage folders and boxes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

RTDP: Streaming Readout Real-Time Development and Testing Platform

The Thomas Jefferson National Accelerator Facility (JLab) has created and is currently working on various tools to facilitate streaming readout (SRO) for upcoming experiments. These include reconstruction frameworks with support for Artificial Intelligence/Machine Learning, distributed High Throughput Computing (HTC), and heterogeneous computing which all contribute significantly to swift data processing and analysis. Designing SRO systems that combine such components for new experiments would benefit from a platform that would combine both simulation and execution components for simulation, testing, and validation before large investments are made. The Real-Time Development Platform (RTDP) is being developed as part of an LDRD funded project at JLab. RTDP aims to establish a seamless connection between algorithms, facilitating the seamless processing of data from SRO to analysis, as well as enabling the execution of these algorithms in various configurations on compute and data centers. Individual software components simulating specific hardware can be replaced with actual hardware when it is available.

Gyurjyan, Vardan↗

Abstract for CRADA between National Energy Technology Laboratory and Shell International Exploration & Production, Inc

Introducing CO₂ into geothermal systems as a working fluid in reservoirs can enhance geothermal conductivity, production, and pressure maintenance. A cross-disciplinary interaction of geothermal reservoir stimulation and CO₂ utilization satisfies renewable energy demands and operations that support sustainable energy infrastructure. Challenges to implementing CO₂-stimulated geothermal enhancement (CS-GE) include (1) accurately characterizing and imaging CO₂-stimulated geothermal reservoirs; (2) quantitatively inferring CS-GE evolution under current and future engineered conditions for cost effective operations; and (3) monitoring resources by improving observational methods to advance the understanding of complex geothermal systems for sweep efficiency and, ultimately, cost effectiveness. NETL and Shell will collaborate under this CRADA to develop software to image key features, including CO₂-stimulated fracture imaging, CO₂ fluid sweep imaging and heat transfer and exchange imaging by leveraging available datasets and applying advanced Artificial Intelligence/Machine Learning (AI/ML), multi-level data analytics and data/information fusion to better understand the comprehensive mechanisms of CO₂-stimulated geothermal systems.

15 GEOTHERMAL ENERGY↗