Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “future buildings”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Towards an Agent-Based Blackboard System for Reactor Design Optimization

The field of reactor design is rich with opportunities for applications of computational optimization algorithms; these applications can range from preliminary core design to reactor shuffling patterns. Many of these schemes rely on sets of previously generated solutions (sometimes referred to as “generations”) to inform future decisions. While it is important to build upon prior knowledge, this process requires a full generation of solutions to be formed before future solutions can be examined. Rather than relying on a generational scheme to perform an optimization, we propose using an agent-based approach in conjunction with a blackboard framework for performing reactor design optimizations. Utilizing an agent-based approach allows agents to perform tasks independently, while retaining the ability to build off of previous solutions. We develop an agent-based blackboard system (ABBS) for determining the Pareto front (PF) in sodium fast reactor design optimization problems and compared this with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Our goal is to evaluate the viability of the ABBS in producing a PF that is comparable with the NSGA-II algorithm. The design space consists of the fuel height, fuel smear, and plutonium fraction in the core, and we seek to minimize the reactivity swing and plutonium mass, while maximizing the burnup. The diversity, coverage, and spread of the PFs generated by the two methods are examined, and the ABBS is able to converge to the same PF as the NSGA-II algorithm. These results show that the ABBS is able to find optimal designs that are similar to those found by the NSGA-II algorithm. We conclude our study by applying the ABBS to the design of a sodium-cooled fast reactor to dispose of weapons-grade plutonium. The ABBS finds a core design that can burn upwards of 17.5 kg of weapons-grade plutonium per year and degrade an additional 195 kg of weapons-grade plutonium per year into non-weapons-grade material.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2014

Precise Truss Assembly Using Commodity Parts and Low Precision Welding

Hardware and software design and system integration for an intelligent precision jigging robot (IPJR), which allows high precision assembly using commodity parts and low-precision bonding, is described. Preliminary 2D experiments that are motivated by the problem of assembling space telescope optical benches and very large manipulators on orbit using inexpensive, stock hardware and low-precision welding are also described. An IPJR is a robot that acts as the precise "jigging", holding parts of a local structure assembly site in place, while an external low precision assembly agent cuts and welds members. The prototype presented in this paper allows an assembly agent (for this prototype, a human using only low precision tools), to assemble a 2D truss made of wooden dowels to a precision on the order of millimeters over a span on the order of meters. The analysis of the assembly error and the results of building a square structure and a ring structure are discussed. Options for future work, to extend the IPJR paradigm to building in 3D structures at micron precision are also summarized.

Komendera, Erik↗
DOE OSTI2023

A Fusion of Geothermal and InSAR Data with Machine Learning for Enhanced Deformation Forecasting at the Geysers

The Geysers geothermal field in California is experiencing land subsidence due to the seismic and geothermal activities taking place. This poses a risk not only to the underlying infrastructure but also to the groundwater level which would reduce the water availability for the local community. Because of this, it is crucial to monitor and assess the surface deformation occurring and adjust geothermal operations accordingly. In this study, we examine the correlation between the geothermal injection and production rates as well as the seismic activity in the area, and we show the high correlation between the injection rate and the number of earthquakes. This motivates the use of this data in a machine learning model that would predict future deformation maps. First, we build a model that uses interferometric synthetic aperture radar (InSAR) images that have been processed and turned into a deformation time series using LiCSBAS, an open-source InSAR time series package, and evaluate the performance against a linear baseline model. The model includes both convolutional neural network (CNN) layers as well as long short-term memory (LSTM) layers and is able to improve upon the baseline model based on a mean squared error metric. Then, after getting preprocessed, we incorporate the geothermal data by adding them as additional inputs to the model. This new model was able to outperform both the baseline and the previous version of the model that uses only InSAR data, motivating the use of machine learning models as well as geothermal data in assessing and predicting future deformation at The Geysers as part of hazard mitigation models which would then be used as fundamental tools for informed decision making when it comes to adjusting geothermal operations.

Yazbeck, Joe (ORCID:0000000302235260)↗
DOE OSTI2022

Stable salt hydrate-based thermal energy storage materials

Heating and cooling systems in building infrastructure utilize conventional materials that account for a considerable amount of energy usage and waste. Phase change material (PCM) is considered a promising candidate for thermal energy storage that can improve energy efficiency in building systems. In this work, a novel salt hydrate-based PCM composite with high energy storage capacity, relatively higher thermal conductivity, and excellent thermal cycling stability was designed and developed. The thermal cycling stability of the PCM composite was enhanced by using dextran sulfate sodium (DSS) salt as a polyelectrolyte additive, which significantly reduced the phase segregation of salt hydrate. The energy storage capacity and the thermal conductivity of the composite were enhanced by the addition of various graphitic materials along with Borax nucleator. A significant increase in thermal cycling stability was observed for the DSS-modified composite, with over 100 thermal cycles without degradation. The final PCM composite exhibited as much as 290% increase in energy storage capacity relative to the pure salt hydrate, and approximately 20% increase in thermal conductivity. In addition, the PCM composite developed can be produced at larger scale, and can potentially change the future of heating/cooling system in building infrastructure.

42 ENGINEERING↗
NASA NTRSDate not supplied

NASA’s Role in Putting Earth Science Information to Use in Decision-Making

The demand for decision-relevant climate information is continually growing, both because of the severity and intensity of climate change impacts and because of the growing recognition that people can take actions today that will mitigate the risk from future climate impacts. Agencies across the federal government are responding to this need by working together to support the Nation in becoming more resilient to climate change. NASA Earth Science has been a key engine of understanding global climate change and how it impacts people around the world. Additionally, for over two decades, the Agency has worked, via partnerships, to connect NASA Earth Science knowledge with decision support needs, often taking a service-oriented approach by working with boundary organizations and climate service providers. In this presentation, we describe NASA’s unique role in enabling new and practical uses of Earth science information, and how the Agency supports public and private sector partners. We highlight several key examples of NASA projects and activities that support local to regional climate resilience efforts. In addition, we discuss how NASA’s new Earth Information Center, a partnership among several federal agencies, enables in-person and virtual visitors to see how our planet is changing in ways that affect their lives and livelihoods. Lastly, we discuss plans for how NASA Earth Science will continue to help build a more climate resilient future.

earth science↗
DOE OSTI2023

Connecting Electric Vehicle Charging Infrastructure to Commercial Buildings

Electric vehicles (EVs) are growing in popularity and gaining meaningful market share with record sales year over year in the last decade. EV charging equipment, also known as EV chargers (EVC) or EV supply equipment (EVSE), must proportionally match the growing number of new EVs on the road for a comparable experience to gas-powered vehicles. The majority of EV charging currently happens at residential buildings. However, demand for EV charging at commercial buildings will significantly increase with wider mainstream EV adoption and as businesses return to more normal operation following COVID-19 pandemic disruptions. Charging equipment can include various sub-systems like power conditioning module, control software, safety devices, metering, communication, cooling, connectors, and its wiring. EV charging at commercial buildings could be used for public, workplace, and commercial fleet charging. This document aims to describe how EVC can be connected to commercial buildings, including considerations for facility managers, and the effects that charging will have on the buildings electrical distribution system. More specifically, this resource provides an overview of: understanding EV charging basics: how charging equipment connects to the building and to EVs; required infrastructure updates needed at the building site to connect EVC to existing distribution systems; network strategies for cost-effective operation; metering and utility considerations for billing and incentives; charging equipment ownership options; future trends in EVC connection to buildings.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2022

Best practice reporting guideline for building stock energy models

Buildings are responsible for 38% of global greenhouse gas (GHG) emissions and, therefore, pathways to reduce their impact are crucial to achieve climate targets. Building stock energy models (BSEMs) have long been used as a tool to assess the current and future energy demand and environmental impact of building stocks. BSEMs have become more and more complex and are often tailored to case-specific datasets, which results in a high degree of heterogeneity among models. This heterogeneity, together with a lack of consistency in the reporting hinders the understanding of these models and, thereby, an accurate interpretation and comparison of results. In this paper we present a reporting guideline in order to improve reporting practices of BSEMs. The guideline was developed by experts as part of the IEA's Annex 70 and builds upon reporting guidelines from other fields. It consists of five topics (Overview, Model Components, Input and Output, Quality Assurance and Additional Information), which are further subdivided into subtopics. We explain which model aspects should be described in each subtopic, and provide illustrative examples on how to apply the guideline. In closing, the reporting guideline is consistent with the model classification framework and online model registry also developed in the Annex.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

Since the first detailed compositional analysis of dissolved organic matter (DOM) in 1995, high resolution mass spectrometry (HRMS) has become a vital tool for DOM characterization. While the upward trend in HRMS for molecular level analysis of DOM continues, so do the challenges of data comparison and interpretation between laboratories operating instruments of differing performance and user operating conditions. It is therefore essential that the community establishes whether data and trends can be compared robustly between research groups. To this end, 4 identically prepared DOM samples were each studied by 16 laboratories, representing 17 commercially-purchased instruments located across 8 countries, using positive- and negative-mode electrospray ionization (ESI) HRMS analysis. Despite the widely reported sensitivity of ESI experiments to sample matrix, preparation, ion source operation and instrument tuning parameters, the instruments used in this study showed relatively low variability between results for the same samples, particularly H/C and AI metrics. The variation in O/C and m/z metrics was relatively higher, although z-score graphs and %BCD demonstrated that the relative difference in general chemical composition between samples was the same for each instrument included in the study. As such, these metrics can be used for fingerprinting of DOM samples and as a benchmark for quality control in participating laboratories, with future strides in environmental science building upon these efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Structural Diversity in Dimension-Controlled Assemblies of Tetrahedral Gold Nanocrystals

Polyhedron packings have fascinated humans for centuries and continue to inspire scientists of modern disciplines. Despite extensive computer simulations and a handful of experimental investigations, understanding of the phase behaviors of synthetic tetrahedra has remained fragmentary largely due to the lack of tetrahedral building blocks with tunable size and versatile surface chemistry. Here, in this study, we report the remarkable richness of and complexity in dimension-controlled assemblies of gold nanotetrahedra. By tailoring nanocrystal interactions from long-range repulsive to hard-particle-like or to systems with short-ranged directional attractions through control of surface ligands and assembly conditions, nearly a dozen of two-dimensional and three-dimensional superstructures including the cubic diamond and hexagonal diamond polymorphs are selectively assembled. We further demonstrate multiply twinned icosahedral supracrystals by drying aqueous gold nanotetrahedra on a hydrophobic substrate. This study expands the toolbox of the superstructure by design using tetrahedral building blocks and could spur future computational and experimental work on self-assembly and phase behavior of anisotropic colloidal particles with tunable interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SOMAS: a platform for data-driven material discovery in redox flow battery development

Abstract Aqueous organic redox flow batteries offer an environmentally benign, tunable, and safe route to large-scale energy storage. The energy density is one of the key performance parameters of organic redox flow batteries, which critically depends on the solubility of the redox-active molecule in water. Prediction of aqueous solubility remains a challenge in chemistry. Recently, machine learning models have been developed for molecular properties prediction in chemistry and material science. The fidelity of a machine learning model critically depends on the diversity, accuracy, and abundancy of the training datasets. We build a comprehensive open access organic molecular database “Solubility of Organic Molecules in Aqueous Solution” (SOMAS) containing about 12,000 molecules that covers wider chemical and solubility regimes suitable for aqueous organic redox flow battery development efforts. In addition to experimental solubility, we also provide eight distinctive quantum descriptors including optimized geometry derived from high-throughput density functional theory calculations along with six molecular descriptors for each molecule. SOMAS builds a critical foundation for future efforts in artificial intelligence-based solubility prediction models.

25 ENERGY STORAGE↗
DOE OSTI2024

Supermassive primordial black holes from inflation

There is controversy surrounding the origin and evolution of our universe's largest supermassive black holes (SMBHs). In this study, we consider the possibility that some of these black holes formed from the direct collapse of primordial density perturbations. Since the mass of a primordial black hole is limited by the size of the cosmological horizon at the time of collapse, these SMBHs must form rather late, and are naively in conflict with constraints from CMB spectral distortions. These limits can be avoided, however, if the distribution of primordial curvature perturbations is highly non-Gaussian. After quantifying the departure from Gaussianity needed to evade these bounds, we explore a model of multi-field inflation — a non-minimal, self-interacting curvaton model — which has all the necessary ingredients to yield such dramatic non-Gaussianities. We leave the detailed model building and numerics to a future study, however, as our goal is to highlight the challenges associated with forming SMBHs from direct collapse and to identify features that a successful model would need to have. This study is particularly timely in light of recent observations of high-redshift massive galaxy candidates by the James Webb Space Telescope as well as evidence from the NANOGrav experiment for a stochastic gravitational wave background consistent with SMBH mergers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Complementing model species with model clades

Abstract Model species continue to underpin groundbreaking plant science research. At the same time, the phylogenetic resolution of the land plant tree of life continues to improve. The intersection of these 2 research paths creates a unique opportunity to further extend the usefulness of model species across larger taxonomic groups. Here we promote the utility of the Arabidopsis thaliana model species, especially the ability to connect its genetic and functional resources, to species across the entire Brassicales order. We focus on the utility of using genomics and phylogenomics to bridge the evolution and diversification of several traits across the Brassicales to the resources in Arabidopsis, thereby extending scope from a model species by establishing a “model clade.” These Brassicales-wide traits are discussed in the context of both the model species Arabidopsis and the family Brassicaceae. We promote the utility of such a “model clade” and make suggestions for building global networks to support future studies in the model order Brassicales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Partnership and Pipeline Office Overview [Slides]

Partnerships ensure a strong Laboratory and support our missions. Partnerships are critical to our success as a Laboratory: community relations, colleges and universities and technical communities, regional and national economic development through industrial partners, DOE Laboratories, and other federal agencies. The Laboratory has many tools to build and enhance partnerships. The Community Programs Office in our Northern New Mexico Region has K-12 educational programs, supports teacher professional development and retention in regional elementary, middle and high schools, and provides community college engagement. The Partnerships and Pipeline Office works with the National Security Education Center (NSEC) which has broad university engagement including New Mexico universities. The Feynman Center for Innovation is involved in 'tech transfer' to innovation asset stewardship. The External Assignments Office shares LANL's technical expertise. The Student and Postdoc Program Offices build our workforce of the future.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

NTESS Savings and Income Plan Summary Plan Description

National Technology & Engineering Solutions of Sandia, LLC (NTESS) is pleased to sponsor the NTESS Savings and Income Plan (401(k) Plan), formerly known as the Sandia Corporation Savings and Income Plan, which is designed to help you build financial resources for the future. The 401(k) Plan can be an important part of saving for your retirement. This Summary Plan Description (SPD) explains how to determine if you are eligible to participate in the 401(k) Plan by saving directly from your wages, if you are eligible to receive employer contributions, when you may make withdrawals from your 401(k) Plan Account, and other important information about the 401(k) Plan. More detailed information is contained in the official NTESS Savings and Income Plan document, which governs the operation of the 401(k) Plan. In the event there is or appears to be any discrepancy between the terms of the 401(k) Plan document and this SPD, the terms of the 401(k) Plan document control.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

FY22 Laboratory Directed Research and Development Annual Report

The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites; 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in actives impacting national security; 3) Lead ST&E as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits; 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications; 5) Build a workforce for the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Investigating Hydrogen Isotope Exchange Reactions on Lithium Aluminate Pellets in TPBAR (FY22 Report)

We developed a novel operando Raman spectroscopy method for investigation of hydrogen (H) isotope exchange reactions in the lithium aluminate (γ-LiAlO 2 ) that allows modeling of tritium behaviors in high temperature and in an irradiated environment. The lithium aluminate pellet is a main component in the Tritium-Producing Burnable Absorber Rod (TPBAR). We used deuterium ( 2 H or D) as a surrogate to simulate tritium ( 3 H or T). We used a surface analysis tools in situ/operando Raman spectroscopy to observe the transformation OH and OD compositional changes. We also used ToF-SIMS to analyze the lithium aluminate pellet control sample to build the base line for future in situ/operando analysis. To conduct operando Raman spectroscopy, we developed a custom reaction cell with a detachable micro heater using microelectromechanical systems (MEMS) and 3D printing techniques. Multiple versions were developed and tested. Using the new reaction cell, we demonstrated operando Raman spectroscopy of water (H 2 O) and deuterated water (D 2 O) with nitrogen (N 2 ) exposure onto the lithium aluminate (LiAlO2) pellet specimen, respectively, using a wet gas injection setup. We also successfully developed a detachable microheater that can heat up to ~250°C for ~90 mins. The Raman spectra did not show clear H 2 O and D 2 O characteristic peaks, which indicates that introducing H 2 O and D 2 O onto the surface of the pellet is challenging due to its dense structure nature. Our efforts suggest that various improvements are needed, such as increasing reaction cell operating gas pressure and thinning pellet sample thickness, to obtain meaningful measurements.

07 ISOTOPE AND RADIATION SOURCES↗