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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Contributing to Net Zero Building: High Energy Efficient EIFS Wall Systems

The team led by Dow Corning collaborated to increase the thermal performance of exterior insulation and finishing systems (EIFS) to reach R-40 performance meeting the needs for high efficiency insulated walls. Additionally, the project helped remove barriers to using EIFS on retrofit commercial buildings desiring high insulated walls. The three wall systems developed within the scope of this project provide the thermal performance of R-24 to R-40 by incorporating vacuum insulation panels (VIPs) into an expanded polystyrene (EPS) encapsulated vacuum insulated sandwich element (VISE). The VISE was incorporated into an EIFS as pre-engineered insulation boards. The VISE is installed using typical EIFS details and network of trained installers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Machine learning framework for assessment of microbial factory performance

Metabolic models can estimate intrinsic product yields for microbial factories, but such frameworks struggle to predict cell performance (including product titer or rate) under suboptimal metabolism and complex bioprocess conditions. On the other hand, machine learning, complementary to metabolic modeling necessitates large amounts of data. Building such a database for metabolic engineering designs requires significant manpower and is prone to human errors and bias. We propose an approach to integrate data-driven methods with genome scale metabolic model for assessment of microbial bio-production (yield, titer and rate). Using engineered E. coli as an example, we manually extracted and curated a data set comprising about 1200 experimentally realized cell factories from ~100 papers. We furthermore augmented the key design features (e.g., genetic modifications and bioprocess variables) extracted from literature with additional features derived from running the genome-scale metabolic model iML1515 simulations with constraints that match the experimental data. Then, data augmentation and ensemble learning (e.g., support vector machines, gradient boosted trees, and neural networks in a stacked regressor model) are employed to alleviate the challenges of sparse, non-standardized, and incomplete data sets, while multiple correspondence analysis/principal component analysis are used to rank influential factors on bio-production. The hybrid framework demonstrates a reasonably high cross-validation accuracy for prediction of E.coli factory performance metrics under presumed bioprocess and pathway conditions (Pearson correlation coefficients between 0.8 and 0.93 on new data not seen by the model).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

PSB27: A thylakoid protein enabling Arabidopsis to adapt to changing light intensity

Project Title: Immunophilins in the assembly and maintenance of photosynthetic electron transport chain in Arabidopsis Applicant: The Regents of the University of California PI: Sheng Luan, University of California at Berkeley Photosynthetic light energy conversion entails coordinated function of complex molecular machines that capture and convert light energy into chemical forms through photosynthetic electron transport chain. Each molecular machine, such as photosystem II (PSII), may consist of dozens of protein subunits and small molecule cofactors. Despite advanced understanding of the structure and function of these complexes, little is known about “How individual proteins and cofactors assemble into a functional machine and how do these molecular machines maintain their structure and function under a highly hazardous lumenal environment.” Our studies on immunophilins have unexpectedly contributed to the understanding of this question. Originally defined as cellular receptors for immunosuppressants, immunophilins have been discovered in a wide range of organisms from bacteria, fungi, plants, to animals. Immunophilins function in protein folding processes as chaperones and foldases. Arabidopsis genome encodes ca. 50 immunophilins. The most striking finding is that 16 immunophilin members are targeted to chloroplast thylakoid lumen, by far the largest group in the lumenal proteome. What is the function of immunophilins in the thylakoid lumen? Our studies have demonstrated critical roles for several immunophilins in the biogenesis and maintenance of photosynthetic complexes such as PSII. These studies have made a critical link between immunophilins and the assembly of photosynthetic machines and thus opened up a new area of research in photosynthesis. Our goal is to dissect the roles of immunophilins and their partners in the assembly and maintenance of the photosynthetic electron transport chain. The specific objectives for this funding period will be: 1. To dissect the mechanism of action for CYP38. Our studies suggest an “autoinhibitory” model for CYP38 action. We plan to test this model and determine the mechanistic details for CYP38 function by biochemical and genetic procedures. 2. To determine the mechanism of action of FKBP20-2/FKBP16-3. Studies on FBKP16-3 and FKBP20-2 suggest that they may work together as a redox-dependent duo in PSII assembly. We will test this hypothesis using biochemical and genetic tools. 3. To determine the function of lumenal FKBPs by multi-gene mutagenesis approach. Using new genetic knockout (KO) procedures especially CRISPR/CAS9 system, we plan to generate multi-gene KO models that will likely provide vital information on those FKBPs with functional redundancy. 4. Functional analysis of thylakoid lumen network. We will focus our effort on objectives 1-3. Objective 4 represents a long-term goal to establish a more comprehensive network of proteins in the thylakoid lumen and their function in the assembly and maintenance of photosynthetic complexes. I hypothesize that the 16 immunophilin-type chaperones and foldases in the thylakoid lumen constitute an “assembly line” for the various components in the photosynthetic electron transport chain. Understanding this assembly line will enable further engineering of more efficient photosynthetic machines to capture more light energy and enhance plant productivity. Furthermore, it is envisioned that such molecular “assembly line” may be rebuilt to produce artificial photosynthesis in vitro or in non-photosynthetic organisms. These are highly relevant to the mission of “Photosynthetic Systems” and “Physical Biosciences” programs to “enhance our understanding of energy capture, conversion, and/or storage.” During the funding period, we have performed a number of experiments under the proposed objectives and obtained several new results. We have found in Objective 1 that CYP38 is associated to the thylokoid membrane with a larger complex that might be PSII supercomplex. Under objective 2, we have found that FKBP16-2 interacted with PSB27 that was further pursuited and published a research article in PNAS (attached). Under Objective 3, we have identified several mutants of other FKBPs in the thyalkoid lumen that should be further studied if future funding is available. Under Objective 4, we have started to build a network of lumenal proteins that play a number of roles in photosynthesis. For example, the CYP37 and CYP28 are linked to chloroplast signaling to nucleus, critical for controlling plant response to high light and adaptation to climate change. Unfortunately these studies have been terminated due to funding shortage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Coal-derived conductive pavement for winter de-icing: prototype, modeling, and simulation

Existing pavement de-icing methods result in high installation and maintenance costs, traffic delays, excessive weariness and corrosion, and negative environmental and safety impacts. To overcome these challenges, this paper presents an innovative pathway to designing and constructing smart self-heating pavements using a low-cost coal-char bearing asphalt material. The conductive asphalt incorporates coal char, i.e., a key byproduct of the coal pyrolysis process, into the Stone Mastic Asphalt (SMA) mixture. This asphalt containing coal-derived solid carbon exhibits highly tailorable electrical conductivity, satisfactory mechanical and thermophysical properties, and superior cost-efficiency as compared to other conductive pavement materials. The de-icing performance was also demonstrated by laboratory experiments on a bench-scale prototype. Furthermore, an efficient thermal network model was developed and validated by experiments to investigate the transient thermal behavior and energy performance of the Ohmic heating pavement system. Furthermore, the whole-year energy simulation case studies were conducted on a bridge pavement with an annual energy use of 24.6–1444.8 kWh/m 2 , showcasing its potential in field applications across cool humid, cold humid, and subarctic/arctic climate zones.

42 ENGINEERING↗
DOE OSTI2017

Overall Water Splitting with Room-Temperature Synthesized NiFe Oxyfluoride Nanoporous Films

Freestanding and lightweight thin-films were rationally designed to serve as robust electrodes for renewable energy applications. A facile and scalable nanomanufacturing process was developed to fabricate these transformative thin-film electrodes (iron group mixed oxides) that exhibit a nanoporous structure and controllable composition. More specifically, electrodeposition and anodic treatments were employed to produce freestanding and lightweight metal oxides nanoporous layers (NPL). These NPL can be directly used as flexible and additive-free electrodes for renewable energy generation (water splitting) and storage (supercapacitor) applications without requiring binders and current collector and other additives. Significantly enhanced electrochemical performance was achieved due to the unique merits of the NPL: i) highly porous structure considerably increases the electrode/electrolyte interface, which facilitate electrochemical reactions; ii) NPL substantially increase the number of active sites that are favorable in electrochemical reactions; iii) residual metal network within the NPL forms a conductive framework, drastically improving electrode strength, flexibility and conductivity.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The National Solar Radiation Database (NSRDB) for CSP Applications

This study examines the differences in direct normal irradiance (DNI) in two versions of the National Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory (NREL). NSRDB V3 of the NSRDB includes significant changes to various parts of the radiative transfer model and inputs to the model compared to NSRDB V2. The changes in NSRDB V3 resulted in lower uncertainty than NSRDB V2. The study quantified the uncertainty and the spatial and temporal variability under clear-sky conditions. The uncertainty estimation was performed using a standardized method, The Guide to the Expression of Uncertainty in Measurement (GUM). The evaluation of the accuracy of the NSRDB was conducted using high-quality ground-measurements for seven National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) stations for 1998-2015.

14 SOLAR ENERGY↗
DOE OSTI2019

Thermal tensor renormalization group simulations of square-lattice quantum spin models

In this work, we benchmark the well-controlled and numerically accurate exponential thermal tensor renormalization group (XTRG) in the simulation of interacting spin models in two dimensions. Finite temperature introduces a finite thermal correlation length ξ, such that for system sizes L >> ξ finite-size calculations actually simulate the thermodynamic limit. Here in this paper, we focus on the square lattice Heisenberg antiferromagnet (SLH) and quantum Ising models (QIM) on open and cylindrical geometries up to width W = 10 . We explore various one-dimensional mapping paths in the matrix product operator (MPO) representation, whose performance is clearly shown to be geometry dependent. We benchmark against quantum Monte Carlo (QMC) data, yet also the series-expansion thermal tensor network results. Thermal properties including the internal energy, specific heat, and spin structure factors, etc. are computed with high precision, obtaining excellent agreement with QMC results. XTRG also allows us to reach remarkably low temperatures. For SLH, we obtain an energy per site u$^*_g$ ≃ -0.6694 (4) and a spontaneous magnetization m$^*_S$ ≃ 0.30 ( 1 ) already consistent with the ground-state properties, which is obtained from extrapolated low-T thermal data on W ≤ 8 cylinders and W ≤ 10 open strips, respectively. We extract an exponential divergence versus T of the structure factor S (M), as well as the correlation length ξ, at the ordering wave vector M = ($π,π$) , which represents the renormalized classical behavior and can be observed over a narrow but appreciable temperature window, by analyzing the finite-size data by XTRG simulations. For the QIM with a finite-temperature phase transition, we employ several thermal quantities, including the specific heat, Binder ratio, as well as the MPO entanglement to determine the critical temperature $T_c$.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

An Application Development Platform for Neuromorphic Computing

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Dean, Mark↗
DOE OSTI2024

Reconfigurable Network Slicing Orchestration in Network Function Virtualization Compatible Operational Technology Environment

The ongoing transition to Industry 4.0, which is characterized by increased inter-connectivity of cyber-physical systems, requires having time-sensitive, high throughput, and secure transfer of critical data in industrial sites. In this context, network slicing emerges as a critical tool to ensure timely data delivery by provisioning the network resources to cater to specific applications’ requirements and mitigating potential cyber attacks. To address these challenges, this paper aims to tackle two key questions essential for the successful implementation of network slicing in industrial environments. First, it investigates architectural considerations for developing a network infrastructure capable of supporting network slicing functionalities effectively. The proposed approach significantly improves deployment efficiency over traditional manual configurations. Second, it delves into the automated orchestration process, elucidating the steps and components involved in transitioning from a static network management approach to dynamically leverage network function virtualization schemes for creating network slices in ad-hoc manner. The system demonstrates high throughput suitable for production-level solutions and maintains exceptionally low latency, making it ideal for ultra-reliable low-latency communications. Even with increased network demands, the system remains stable, with effective Quality of Service (QoS) management, ensuring reliable performance under varying conditions. The proposed architecture outlines the necessary components, services, and communication protocols required for a production-level orchestrator for network segmentation in SCADA environments.

Rodiles Delgado, Brian G.↗
DOE OSTI2021

The PetscSF Scalable Communication Layer

PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable for exascale computers that utilize GPUs and other accelerators. PetscSF provides a simple application programming interface (API) for managing common communication patterns in scientific computations by using a star-forest graph representation. PetscSF supports several implementations based on MPI and NVSHMEM, whose selection is based on the characteristics of the application or the target architecture. An efficient and portable model for network and intra-node communication is essential for implementing large-scale applications. The Message Passing Interface, which has been the de facto standard for distributed memory systems, has developed into a large complex API that does not yet provide high performance on the emerging heterogeneous CPU-GPU-based exascale systems. Here, we discuss the design of PetscSF, how it can overcome some difficulties of working directly with MPI on GPUs, and we demonstrate its performance, scalability, and novel features.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Synchronized Phasor Measurement Unit-based Central Controller for Normal and Contingent Operation of a High-Capacity HVDC Macrogrid for the Continental US

This paper presents a Synchronized Phasor Measurement Unit (PMU) based controller to support the coordination and operation of a high-capacity high-voltage direct current (HVDC) macrogrid transmission for the continental US. The proposed controller allows for optimal scheduling of each segment of the multi-terminal HVDC network under normal and contingent conditions, while considering minimum levels of frequency response requirements. The performance of the proposed controller is tested on a 100,222-bus representation of the US Eastern and Western interconnections. Results show that a centralized PMU-based controller improves the frequency nadir of the HVDC macrogrid under contingency conditions by 25.9%. Furthermore, the primary frequency response (PFR) of the HVDC macrogrid is increased by 33.4%. The performance of each interconnection under the largest credible contingency conditions is also discussed.

Mohan, Nihal↗
DOE OSTI2016

GridPACK™ : A Framework for Developing Power Grid Simulations on High-Performance Computing Platforms

This paper describes the GridPACK™ framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework makes extensive use of software templates to provide high level functionality while at the same time allowing developers the freedom to express whatever models and algorithms they are using. GridPACK™ contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors and using parallel linear and non-linear solvers to solve algebraic equations. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support IO and to mana

Palmer, Bruce J.↗
DOE OSTI2024

High Linearity RF Circuits in CMOS Platforms (CRADA Final Report)

The goal of this CRADA was to understand high linearity silicon-on-insulator (SOI) radio frequency switches. The first direction investigated was high linearity RF mixers as these components are composed of a network of switches. During the course of the investigation, it was determined that while the SOI RF switches were effective in the application in mixers and produced state of the art performance, the use of SOI for mixers is not economically feasible because the technology only worked with advanced CMOS nodes <65nm which can have an NRE cost (from lithography masks) in excess of $\$$500,000.

42 ENGINEERING↗
DOE OSTI2022

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Severino, Joseph↗
DOE OSTI2021

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates

Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In this study, we used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). We then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, we found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.

54 ENVIRONMENTAL SCIENCES↗