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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Assured Performance of Additively Manufactured and Composite Parts through Embedded Sensing and Life Cycle Monitoring: An Enabler for in-Space Use

Lightweight composites have a range of applications in aerospace where operational costs and mission (payload) capabilities depend significantly on component weight. Additive manufacturing of the composite components expands the design and production space allowing for complex part geometries. A limiting factor to the utilization of the composites in high value applications, such as aerospace generally, and particularly for in-space applications, is the ability to determine the part quality both at the time of production as well as through the lifetime of the component. Work on monitoring the performance of additively manufactured and autoclaved composites that encompasses the design and manufacturing as well as use in operational environments will be discussed. Optimization of the component design for manufacturability, the use of process monitoring sensors during the component build as well as embedding of carbon nanotube yarn based structural sensors will be described.

Additive Manufacturing↗
DOE OSTI2024

Development of Low-Cost, High-Performance, Easy-To-Apply, Non-Flammable, Inorganic Phase Change Material (PCM) Technology (Project Final Report)

This report describes a 45-months long research program focused on the development of novel, easy-to-apply, non-flammable, and high-performance inorganic phase change materials (PCMs) for building and industrial applications. The University of Massachusetts Lowell (UML) formed a world-class team consisting of researchers form InsolCorp (only N. American manufacturer of inorganic PCM systems for building applications), and a group of industrial advisors, to develop a universal/multipurpose, simple-to-manufacture and cost-effective PCM technology. The project team expects that the results of this work will spur in the future the adoption of thermal storage materials – a key building energy saving technology as identified by DOE BTO – for a variety of building envelope applications. The main goal of this project was to demonstrate a suite of low-cost, multipurpose, and durable inorganic PCM formulations with phase transition temperatures encompassing typical building applications (between +5 o C and +55 o C). The first objective was to design, fabricate, and experimentally validate a performance of inexpensive, durable, highly efficient, non-flammable, and easy to manufacture PCMs. To allow a variety of building applications, the project team focused on formulations that exhibit repeatable phase transitions between +5 o C and +55 o C. To follow the DOE BTO cost efficiency target without compromising thermal performance, our work was based on inorganic compounds (mostly salt hydrates) and their blends, which represent a fraction of the cost of most of organic PCMs with about twice as high density as well as significantly higher thermal conductivity and phase change enthalpy. The second objective was to develop easy-to-manufacture and -install packaging/encapsulation designs that are 1) a superior barrier to current state-of-the-art macro-packaging, which significantly reduces the risk of loss of hydration water and PCM leak, and 2) optimal in enhancing the heat exchange rates with the surroundings and within the PCM core to ensure complete charging/discharging of the entire PCM within the product. Finally, the project’s intend was to scale-up the fabrication process to demonstrate installation on system-scale applications, and to validate the performance under field conditions. This work aimed at developing low-cost, high-energy storage, and reliable latent heat storage technology for building applications. This development was realized by formulating and integrating the following two technology components: 1) inorganic salt hydrate based PCMs that have high latent enthalpies and are low-cost and durable, and 2) PCM encapsulation (packaging) technology that maximizes PCM concentration and enhances heat transport characteristics in the product and with the external environment/materials. High thermal storage capacity, low cost and fire resistance are key to the building market entry for PCM technology. Therefore, the project’s focus was on salt-hydrate-based formulations which satisfy all these criteria. Packaging and/or encapsulation of PCM is a key processing step. The project team recognized that a low-cost and simple-to-manufacture salt hydrate-based PCM technology holds the best chance to be successful in the building construction market, a market which is traditionally extremely sensitive to cost and where commodity thermal insulations are the benchmark for envelope-related energy saving measures. That is why, in this project, the main intention was to minimize the production cost and maximize the product energy storage density without sacrificing the PCM performance. It was achieved through: 1. Minimizing the non-PCM components (plastics, additives, packaging/encapsulation materials, etc.) because they are significantly more expensive than salt hydrates, 2. Using highly thermally conductive and lightweight PCM carrier (packaging material) to facilitate more complete phase cycling, and 3. Optimizing the thickness and minimizing air spaces in product design (such as in pouched PCM). For this purpose, our approach was to enable an easy system design, including selection of the PCM operating temperatures, optimizing the necessary heat storage capacity (by stacking together several layers of PCM products), and if needed, a synchronized usage of PCM products of different temperatures. A specially designed, robust, highly thermally conducting and highly impermeable packaging (to retain salt hydrate water during phase transition cycles) was designed and tested to increase the overall system thermal performance and durability. All PCM products developed during this project were tested in both lab scale and in full scale field conditions. It is expected that, after further developments and commercialization, the developed PCM technologies may be also applied in space conditioning, energy storage technologies, and heat transfer applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Generic Discretization Library

The GenDiL library is a collection of C++ software abstractions designed to discretize and solve partial differential equations (PDEs) for high-performance computing (HPC) applications. Its primary focus is on modern C++ generic programming, which helps ensure portability across various hardware architectures. The central idea behind the library is to provide building blocks for numerical algorithms-such as discretization methods and iteration patterns-so that domain experts can focus on the math, rather than the low-level details of hardware or implementation. By defining abstractions for data types, iteration over computational grids, and scheduling of operations, the library isolates the high-level PDE algorithms from the platform-specific optimizations needed to achieve efficient performance.

Dudouit, Yohann [Lawrence Livermore National Labor↗
DOE OSTI2020

Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Durden, David J.↗
DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗
DOE OSTI2021

Managing Army Plug Load Equipment Energy Use: Vending Machines

Vending machines consume the most energy of individual plug load devices common to Army buildings. As a group, they rank fourth in total electricity consumption, behind only clothes dryers, laptop computers, and telecom loads. Upgrading 80% of the estimated 22,000 refrigerated vending machines on Army installations to the latest ENERGY STAR ® performance and optimizing their efficiency settings could save 31 million kWh and $2 million per year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2003

A Tabletop Tool for Modeling Life Support Systems

This paper describes the development plan for a comprehensive research and diagnostic tool for aspects of advanced life support systems in space-based laboratories. Specifically it aims to build a high fidelity tabletop model that can be used for the purpose of risk mitigation, failure mode analysis, contamination tracking, and testing reliability. We envision a comprehensive approach involving experimental work coupled with numerical simulation to develop this diagnostic tool. It envisions a 10% scale transparent model of a space platform such as the International Space Station that operates with water or a specific matched index of refraction liquid as the working fluid. This allows the scaling of a 10 ft x 10 ft x 10 ft room with air flow to 1 ft x 1 ft x 1 ft tabletop model with water/liquid flow. Dynamic similitude for this length scale dictates model velocities to be 67% of full-scale and thereby the time scale of the model to represent 15% of the full- scale system; meaning identical processes in the model are completed in 15% of the full- scale time. The use of an index matching fluid (fluid that matches the refractive index of cast acrylic, the model material) allows making the entire model (with complex internal geometry) transparent and hence conducive to non-intrusive optical diagnostics. So using such a system one can test environment control parameters such as core flows (axial flows), cross flows (from registers and diffusers), potential problem areas such as flow short circuits, inadequate oxygen content, build up of other gases beyond desirable levels, test mixing processes within the system at local nodes or compartments and assess the overall system performance. The system allows quantitative measurements of contaminants introduced in the system and allows testing and optimizing the tracking process and removal of contaminants. The envisaged system will be modular and hence flexible for quick configuration change and subsequent testing. The data and inferences from the tests will allow for improvements in the development and design of next generation life support systems and configurations.

Ramachandran, N.↗
NASA NTRS2005

Space Laboratory on a Table Top: A Next Generative ECLSS design and diagnostic tool

This paper describes the development plan for a comprehensive research and diagnostic tool for aspects of advanced life support systems in space-based laboratories. Specifically it aims to build a high fidelity tabletop model that can be used for the purpose of risk mitigation, failure mode analysis, contamination tracking, and testing reliability. We envision a comprehensive approach involving experimental work coupled with numerical simulation to develop this diagnostic tool. It envisions a 10% scale transparent model of a space platform such as the International Space Station that operates with water or a specific matched index of refraction liquid as the working fluid. This allows the scaling of a 10 ft x 10 ft x 10 ft room with air flow to 1 ft x 1 ft x 1 ft tabletop model with water/liquid flow. Dynamic similitude for this length scale dictates model velocities to be 67% of full-scale and thereby the time scale of the model to represent 15% of the full- scale system; meaning identical processes in the model are completed in 15% of the full- scale-time. The use of an index matching fluid (fluid that matches the refractive index of cast acrylic, the model material) allows making the entire model (with complex internal geometry) transparent and hence conducive to non-intrusive optical diagnostics. So using such a system one can test environment control parameters such as core flows (axial flows), cross flows (from registers and diffusers), potential problem areas such as flow short circuits, inadequate oxygen content, build up of other gases beyond desirable levels, test mixing processes within the system at local nodes or compartments and assess the overall system performance. The system allows quantitative measurements of contaminants introduced in the system and allows testing and optimizing the tracking process and removal of contaminants. The envisaged system will be modular and hence flexible for quick configuration change and subsequent testing. The data and inferences from the tests will allow for improvements in the development and design of next generation life support systems and configurations. Preliminary experimental and modeling work in this area will be presented. This involves testing of a single inlet-exit model with detailed 3-D flow visualization and quantitative diagnostics and computational modeling of the system.

Ramachandran, N.↗
NASA NTRS2013

Silicon Micromachining for Terahertz Component Development

Waveguide component technology at terahertz frequencies has come of age in recent years. Essential components such as ortho-mode transducers (OMT), quadrature hybrids, filters, and others for high performance system development were either impossible to build or too difficult to fabricate with traditional machining techniques. With micromachining of silicon wafers coated with sputtered gold it is now possible to fabricate and test these waveguide components. Using a highly optimized Deep Reactive Ion Etching (DRIE) process, we are now able to fabricate silicon micromachined waveguide structures working beyond 1 THz. In this paper, we describe in detail our approach of design, fabrication, and measurement of silicon micromachined waveguide components and report the results of a 1 THz canonical E-plane filter.

Filter↗
DOE OSTI2022

Building and Executing Aggressive Research Plans in a Large National Laboratory Consortium: Insights from the Co-Optimization of Fuels and Engines Initiative

This report describes lessons learned in the establishment, execution and termination of a large, multi-institutional consortium, derived from the Co-Optimization of Fuels and Engines experience. The decision to form a consortium comes with benefits (in advancing challenging multidisciplinary research) and costs (in time and additional management funds). Once the decision is made, key elements to a strong start include establishing a shared vision and goals; engaging an experienced project manager early; instituting feedback and oversight mechanisms to ensure relevance, strong performance, and situational awareness. Once a consortium is up and running, DOE and leadership should strike the right balance between competition and collaboration; foster an environment that builds trust; and adjust the organizational structure as needed to maintain collaboration. Finally, DOE and the labs can plan effectively for a smooth transition as a consortium winds down. This report provides some additional lessons and details on these lessons that we hope future DOE and lab leaders will find useful as they contemplate standing up new consortia.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Dynamic Facade Dashboard v0.1.0

The dashboard is a useful tool for early-stage building design decision-making and communication, as it can help users quickly compare the energy and non-energy related performance of various automated, integrated facade systems using a library of pre-computed data. Users can explore the impacts of various design choices by selecting different facade glazing and shading systems, facade control strategies, and lighting control strategies across multiple climate zones. The dashboard instantly visualizes key metrics, including energy usage in HVAC and lighting, peak cooling and heating load, and daylight availability, allowing immediate trade-off analysis to optimize building efficiency and comfort.

Yu, Tammie [Lawrence Berkeley National Laboratory ↗
DOE OSTI2025

If We Build Them, They Will Run: Automated HPC Apps Deployment and Profiling with eBPF in Cloud

The high performance computing (HPC) community is in a period of transition. The rise of AI/ML coupled with a changing landscape of resources deems portability a new metric of performance, and methods to move between on-premises and cloud environments and assess compatibility are paramount. Here we design and test a strategy for bridging the gap between traditional HPC and Kubernetes environments – first containerizing applications, providing automated orchestration to run studies, and packaging the setup with automated means to assess performance using low overhead eXtended Berkeley Packet Filter (eBPF) programs. We first assess different designs for eBPF collection, demonstrating a tradeoff between number of programs deployed on a node and overhead added. We develop 5 low overhead eBPF programs that combine with streaming ML models to assess CPU, futex, TCP, shared memory, and file access across four different builds of an HPC application for CPU and GPU. We use eBPF data to generate insights into the possible underlying etiology of scaling issues. We then assess compatibility of a well-known benchmark, HPCG, across matrices of micro-architectures and optimization levels (217 containers across 24 instance types and over 7500 runs). We provide to the community 30 applications to deploy in our automated setup and perform a scaling study from 4 to a maximum of 256 nodes for both CPU and GPU applications. Finally, we use our gained knowledge about performance to generate compatibility artifacts that are used by a newly developed Kubernetes controller to intelligently select instance type based on optimizing a figure of merit. Along with insights to scaling in this environment with a collection of applications and templates to work from, we provide an overall strategy for approaching HPC application deployment and image selection based on compatibility in cloud.

Computer science↗
DOE OSTI2021

Hierarchical Power Flow Control in Smart Grids: Enhancing Rotor Angle and Frequency Stability with Demand-Side Flexibility

Large-scale integration of renewables in power systems gives rise to new challenges for keeping synchronization and frequency stability in volatile and uncertain power flow states. To ensure the safety of operation, the system must maintain adequate disturbance rejection capability at the time scales of both rotor angle and system frequency dynamics. This calls for flexibility to be exploited on both the generation and demand sides, compensating volatility and ensuring stability at the two separate time scales. This article proposes a hierarchical power flow control architecture that involves both transmission and distribution networks as well as individual buildings to enhance both small-signal rotor angle stability and frequency stability of the transmission network. The proposed architecture consists of a transmission-level optimizer enhancing system damping ratios, a distribution-level controller following transmission commands and providing frequency support, and a building-level scheduler accounting for quality of service and following the distribution-level targets. Furthermore, we validate the feasibility and performance of the whole control architecture through real-time hardware-in-loop tests involving real-world transmission and distribution network models along with real devices at the Stone Edge Farm Microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Recent development and manufacturing of coal-derived building materials

Coal has been challenged because coal combustion generates large quantities of carbon dioxide (CO2), which contributes to global warming. Pyrolysis char (PC), a valuable byproduct of coal that does not liberate CO2 when produced in an inert environment, can be used as a high carbon content fuel in combustion and gasification. However, using PC as a fuel is not an eco-friendly and cost-efficient way to make energy. Contrastingly using PC to make engineered products does offer an eco-friendly, attractive and value-added use. This paper introduces a novel integrated coal pyrolysis and solvent extraction process invented by the University of Wyoming (UW) to convert as-mined coal from the Powder River Basin Coal Field, Wyoming, to functional carbon products, which together liberates very little CO2. PC and coal deposit, extract and residual (CDER) are two functional carbon products that can be used in the development and manufacture of high value coal-derived building materials. The novel development of three building products known as coal-derived char bricks (CCB), carbon structural unit (CSU), and char-based insulation foam (CIF) are introduced. The approaches to manufacture them are investigated, including consideration of the eco-friendly approaches, optimization of engineering performance, and cost effectiveness relative to traditional products. CCBs show advantages in terms of their light weight (< 1 g/cm3) and good compressive strength (> 14 MPa). CSUs possess high compressive strength (> 30 MPa) and low thermal conductivity (< 0.3 W/mK), while CIFs can achieve a low thermal conductivity of 0.06 W/mK. Two demonstration houses have been built on UW campus, one using CCBs and another using commercial clay bricks. The performances of these houses are being monitored for temperature control, humidity uptake, noise reduction, inside air volatile organic content, indoor air quality and weatherability. The performance of the CCBs in terms of these criteria is discussed.

Ng, Kam↗
DOE OSTI2021

PD21A680: Additive Manufacturing Process Optimization and Qualification FY21 Year-end Report

This project builds upon knowledge gained from previous projects on Additive Manufacturing (AM) operation, process control and monitoring, repeatability, and process improvement. This project will exclusively focus on metal powder bed additive manufacturing (PBAM). Objective 1: Advance metal powder bed AM R&D capabilities. Perform enhanced R&D on increased build volumes and throughput, safety and handling controls, process monitoring, and diagnostics with a focus on production end-states. Objective 2: Build upon knowledge gained from previous PDRD projects to develop deeper understanding of process controls, essential for transitioning metal AM processes for future applications. Objective 3: Maintain business development and research advantages through collaborations with external industry and university partners.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Understanding the Molecular‐level Interactions Between Ionic Liquids and Molecular Species to Design and Develop Novel Solvent Systems for Environmental and Energy Applications (DOE-EPSCoR – DE‐SC0020282 Final Technical Report)

The central theme of the work is to develop an understanding of the molecular-level interactions between ionic liquids and molecular species that govern a specific set of separations (aliphatic/aromatic, Topic I), reactions (SNAr, high-temp reactions, Topic II) and polymer (ultrahigh performance polymers, Topic III) synthesis processes through the synergistic application of synthesis, characterization and simulation. Understanding these interactions will provide significant insight into how to optimize these and other processes that are influenced by these interactions. An important additional goal is to build the collaborative infrastructure to enable the cluster researchers to develop professional relationships to enhance this and future research endeavors. This collaboration, the Alabama Advanced Solvents Cluster (AASC) is on ongoing collaborative effort between many of the investigators.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Cybersecurity Testbed for Smart Buildings

Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.

cyber-physical systems↗