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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Low power and privacy preserving sensor platform for occupancy detection

A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.

Velipasalar, Senem↗
DOE OSTI2024

Factors Influencing Grid-connected Heat Pump Water Heater Performance in the Southeast U.S.

Grid-connected heat pump water heaters (HPWH) can shift electrical load while minimizing impacts to hot water availability for occupants. This capability provides a flexible grid resource to utilities seeking to manage peak loads Such load control also can feasibly improve renewable utilization within the utility electric production mix, for instance using off-peak generation during periods with high renewable energy generation. It also offers lower electric bills to customers through increased energy efficiency of HPWH and cuts greenhouse gas emissions. In particular, the Southeast U.S. due to its high penetration of electric water heating presents a promising opportunity for grid-connected HPWHs. This paper builds upon the results of an extensive HPWH load shifting field study conducted in 51 occupied homes in Florida using EcoPort technology (Butzbaugh et al, 2022). In 2022, only an initial evaluation was available. Here, long term load results are available as well as examination of various control strategies and influences. Analysis is conducted for HPWH energy use and load shifting performance based on home occupancy (i.e., low and high) and water heater location (i.e., conditioned and unconditioned) across different temperature profiles. An unexpected outcome of this analysis was the poor performance of HPWHs located in conditioned spaces, possibly because of inadequate air volume from improper installation. We did find higher demand reductions from 2-hour load ups and slightly improved for critical peak signals in the afternoon control periods. As expected, higher occupancy households showed great load reductions.

Fenaughty, Karen↗
DOE OSTI2025

Spotlight: efficient automated global optimization in rietveld analysis of diffraction data

Performing reliable Rietveld analysis on tens or hundreds of powder diffraction datasets from parametric or time-resolved experiments often poses a bottleneck in extracting meaningful results from the data. While automated analysis of data has recently been demonstrated, high temperature annealing studies, during which phase transformations occur and lattice parameters may change due to repartitioning of elements, are prime examples where automation by a simple phase identification from a database of room temperature structures or automation by sequential refinements is likely to fail. To enable reliable, efficient, automated Rietveld analysis, we present a Python package named Spotlight , building on established Rietveld packages such as MAUD, GSAS , or GSAS-II , which extends the refinement of best fit parameters to a global optimization using an ensemble of optimizers leveraging hierarchical parallel execution on high-performance computing clusters. Spotlight further enables the efficient design of refinement plans through the iterative automated machine-learning of a surrogate for the refinement on which the global optimizations are performed until results from the surrogate converge to the response surface data. We demonstrate Spotlight with the analysis of uranium molybdenum and Ti–6Al–4V datasets, as well as in two open-source tutorials analyzing aluminium oxide and lead sulphate.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Model based co-simulation platform for integrated building system control and design optimization

Both steady-state and dynamic simulations have been widely used by HVAC&R industry to support product/equipment development for decades. Steady-state simulation focuses on the system mass, energy and momentum balance of an equilibrium state. It is based on high-fidelity components models, and thus is suitable for system and component design optimization. Dynamic simulation studies the system transient response and is generally used for controls development and verification. It usually does not require rigorous component models of high accuracy because 1) the commonly used PID control is feedback control whose control performance evaluation doesn’t require high fidelity system/plant model; 2) high-fidelity dynamic model significantly increases the number of equations and variables and creates tremendous challenge for math solver. For supervisory control, transactive control or optimization of an integrated building system, the HVAC&R equipment is often one of the sub-components to be controlled. High-fidelity equipment models are required for accurately evaluating control strategies. In addition, building equipment manufacturers have developed a lot of high-fidelity steady-state equipment/component models per their expertise. Thus, a platform that can integrate OEM high-fidelity steady-state model with dynamic building simulation and/or electric power system & grid simulation to support the development and verification of supervisory control for integrated building systems is necessary. In this study, ORNL’s heat pump design tool (HPDM) is utilized to develop a co-simulation platform for supervisory control and optimization in integrated building systems. It is based on a model that integrates high-fidelity steady-state simulation equipment models with dynamic building simulation. A practical case of using the proposed co-simulation platform to develop and evaluate the supervisory control and optimization is presented and discussed.

Sun, Jian↗
DOE OSTI2022

Zero Energy Ready Out of the Box

In this conference paper for the ASHRAE Buildings XV conference, December 2022, PNNL staff share key metrics for companies who are combining off site construction with certification to the U.S. Department of Energy’s Zero Energy Ready Home program to create homes that are both cost effective to construct and high performance to live in. Key performance metrics and measures for modular, panelized, and SIP homes are compared against all homes in the DOE Zero Energy Ready Home program and against new homes built to code. These metrics show that off-site construction homes got similar or better energy performance to site-built homes, and in the case of modular and panelized, at considerably lower average cost than site-built homes. The paper also highlights the experiences of two DOE Zero Energy Ready Home builders who employ offsite construction methods. One builder constructs production homes using pre-assembled panels. The other builder designs and ships kit homes all over the United States. The paper will focus on the optimum value engineering aspects of the panelized construction processes used by each builder to identify management and production practices employed by the builders to operate profitably while meeting high energy performance goals, despite a year marked by skyrocketing materials costs and supply chain and labor shortages. Cost and labor savings methods will be discussed in the context of these two unique business models.

U.S. Department of Energy Building Technologies Pr↗
DOE OSTI2021

xSDK: Building an ecosystem of highly efficient math libraries for exascale

Current efforts to build increasingly powerful computer architectures are opening up new avenues for more complex and higher fidelity simulations coupled with data analytics and learning, leading to new scientific insights and deeper understanding. At one extreme, exascale computers will be much faster than previous computer generations (performing 10 18 operations per second—that is, 1,000 times faster than petascale). To achieve these performance improvements, computer architectures are becoming increasingly complex, with deep memory hierarchies, very high node and core counts, and heterogeneous features such as graphics processing units (GPUs). Such architectural changes impact the full breadth of computing scales, as heterogeneity pervades even current-generation laptops, workstations, and moderate-sized clusters. While emerging advanced architectures provide unprecedented opportunities, they also present significant challenges for developers of scientific applications, such as multiphysics and multiscale codes, who must adapt their software to handle disruptive changes in architectures and new programming models that have not yet stabilized. Developers must consider increasing concurrency while reducing communication and synchronization, and other complexities such as the potential for using mixed precision to leverage the compute power available in low-precision tensor cores. On one hand, developers must implement new scientific capabilities, which in turn increase code complexity. On the other hand, the codes must be ported to new architectures, requiring the inclusion of new programming models and the restructuring of code to achieve good performance. Addressing these issues is beyond the capability of any single person or team—leading to the need for collaboration among many teams, who encapsulate their expertise in reusable software and work together to create sustainable software ecosystems.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Thermoplastic Aircraft Manufacturing Model

A manufacturing process to produce 80 single aisle sized thermoplastic composite aircraft a month is presented. Within the Hi-Rate Composite Aircraft Manufacturing (HiCAM) program a baseline aircraft fuselage was designed and modeled based on the Boeing 787 one-piece barrel build, but scaled down to a single aisle aircraft. Key Performance Parameters (KPPs) of recurring cost, non-recurring cost, and weight are calculated from the model. The outcome of this simulation indicated a prohibitively high non-recurring cost, so the thermoplastic team modeled the manufacturing flow for a panelized fuselage concept. The minimum success criteria is a 30% cost reduction and less than two percent increase in weight compared to the baseline aircraft. Automated Fiber Placement (AFP) is used to layup skins, stamp forming is used to make body frames, stringers, and passenger door surround parts. In the model, Co-Fusion was used to consolidate the skin while simultaneously welding on stringers to make stiffened panels. Additional welding technologies were considered to minimize fastener use and a vertical build concept was used for final assembly to reduce factory size. This manufacturing concept for a thermoplastic aircraft indicated a 39% cost and 12% weight savings compared to the baseline. Additional trade studies were conducted using Northrop Grumman’s Automated Stiffener Forming technology to make stringers and stamp forming instead of AFP to make skins.

thermoplastic composites↗
NASA NTRSDate not supplied

Thermoplastic Composite Aircraft Manufacturing Model

A manufacturing process to produce 80 single aisle sized thermoplastic composite aircraft a month is presented. Within the Hi-Rate Composite Aircraft Manufacturing (HiCAM) program a baseline aircraft fuselage was designed and modeled based on the Boeing 787 one-piece barrel build, but scaled down to a single aisle aircraft. Key Performance Parameters (KPPs) of recurring cost, non-recurring cost, and weight are calculated from the model. The outcome of this simulation indicated a prohibitively high non-recurring cost, so the thermoplastic team modeled the manufacturing flow for a panelized fuselage concept. The minimum success criteria is a 30% cost reduction and less than two percent increase in weight compared to the baseline aircraft. Automated Fiber Placement (AFP) is used to layup skins, stamp forming is used to make body frames, stringers, and passenger door surround parts. In the model, Co-Fusion was used to consolidate the skin while simultaneously welding on stringers to make stiffened panels. Additional welding technologies were considered to minimize fastener use and a vertical build concept was used for final assembly to reduce factory size. This manufacturing concept for a thermoplastic aircraft indicated a 39% cost and 12% weight savings compared to the baseline. Additional trade studies were conducted using Northrop Grumman’s Automated Stiffener Forming technology to make stringers and stamp forming instead of AFP to make skins.

thermoplastic composites↗
NASA NTRSDate not supplied

Lightweight Surveillance and Target Acquisition Radar Characterization for High Density Vertiplex Beyond Visual Line of Sight Operations

Abstract- with the growth of the National Aeronautics and Space Administration (NASA) National Campaign, it is becoming increasingly important for government and enforcement agencies to be able to monitor and safely track urban air traffic that will take place in the coming years. The performance of the Lightweight Surveillance and Target Acquisition Radar (LSTAR) is investigated and statistically characterized. The compact LSTAR is mounted on a building at NASA Langley Research Center (LaRC) and operated to track a SR-22 aircraft. Performance data of of the LSTAR tracking is presented by comparing the LSTAR-tracked flight against the on-board flight records and statistical characteristics are presented. Implications of findings to operations within High Density Vertiplex (HDV) at LaRC are discussed. The LSTAR ultimately proves to be a compact method to safely track and monitor small aircraft and other unmanned aerial vehicles.

Nicholas Gaug↗
DOE OSTI2025

Thermophilic Chassis-Enabled High-Throughput Selection of a Thermostable Fluorogenic Reporter

Thermostable proteins show increased shelf life and performance at elevated temperatures and under harsh conditions, resulting in lower costs for various industrial and biotechnological applications. However, due to a limited understanding of the relationship between stability and function, protein stabilization remains primarily a trial-and-error approach. Therefore, building a combinatorial library of mutations predicted to improve stability, followed by experimental testing, represents a markedly improved methodology. However, the lack of high-throughput approaches to screen even a moderately sized library presents a major bottleneck in the field. Here, in this study, we use a thermophile, Parageobacillus thermoglucosidasius (Ptherm) to rapidly screen combinatorial libraries consisting of rationally designed thermostabilizing mutations (∼10 3 –10 4 ) of a mesophilic fluorescent reporter, Y-FAST. On a Petri dish, microbial growth at an elevated temperature and exposure to fluorogen yielded several colonies of Ptherm that showed distinct fluorescence at 55 and 68 °C in our two sequentially generated libraries using Rosetta and ProteinMPNN, respectively. The Y-FAST variants isolated from fluorescent colonies were brighter than Y-FAST and showed higher resistance to thermal and chemical denaturation. AlphaFold-predicted structures and MD simulations revealed stability-enhancing salt bridges and hydrogen bond networks in the isolated FAST variants. The moderately thermostable FAST (tsFAST) and hyperstable FAST (hsFAST) were then demonstrated as translation reporters for protein expression and folding at elevated temperatures, such as 55 and 68 °C. Our approach of combinatorial library generation and high-throughput screening in a thermophilic chassis could, in principle, be extended to other proteins fused to these translation reporters. Furthermore, the hsFAST protein is small─half the size of the green fluorescent protein─and does not require oxygen for maturation, making it ideal for engineering extremophilic anaerobes for biosensing and bioconversion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Wire arc additive manufactured A36 steel performance for marine renewable energy systems

Additive manufacturing has established itself to be advantageous beyond small-scale prototyping, now supporting full-scale production of components for a variety of applications. Despite its integration across industries, marine renewable energy technology is one largely untapped application with potential to bolster clean energy production on the global scale. Wave energy converters (WEC) are one specific facet within this realm that could benefit from AM. As such, wire arc additive manufacturing (WAAM) has been identified as a practical method to produce larger scale marine energy components by leveraging cost-effective and readily available A36 steel feedstock material. The flexibility associated with WAAM can benefit production of WEC by producing more complex structural geometries that are challenging to produce traditionally. Additionally, for large components where fine details are less critical, the high deposition rate of WAAM in comparison to traditional wrought techniques could reduce build times by an order of magnitude. In this context of building and supporting WEC, which experience harsh marine environments, an understanding of performance under large loads and corrosive environments must be understood. Hence, WAAM and wrought A36 steel tensile samples were manufactured, and mechanical properties compared under both dry and corroded conditions. Here, the unique microstructure created via the WAAM process was found to directly correlate to the increased ultimate tensile and yield strength compared to the wrought condition. Static corrosion testing in a simulated saltwater environment in parallel with electrochemical testing highlighted an outperformance of corroded WAAM A36 steel than wrought, despite having a slighter higher corrosion rate. Ultimately, this study shows how marine energy systems may benefit from additive manufacturing components and provides a foundation for future applications of WAAM A36 steel.

36 MATERIALS SCIENCE↗
NASA NTRS2002

High-Power, High-Frequency Si-Based (SiGe) Transistors Developed

Future NASA, DOD, and commercial products will require electronic circuits that have greater functionality and versatility but occupy less space and cost less money to build and integrate than current products. System on a Chip (SOAC), a single semiconductor substrate containing circuits that perform many functions or containing an entire system, is widely recognized as the best technology for achieving low-cost, small-sized systems. Thus, a circuit technology is required that can gather, process, store, and transmit data or communications. Since silicon-integrated circuits are already used for data processing and storage and the infrastructure that supports silicon circuit fabrication is very large, it is sensible to develop communication circuits on silicon so that all the system functions can be integrated onto a single wafer. Until recently, silicon integrated circuits did not function well at the frequencies required for wireless or microwave communications, but with the introduction of small amounts of germanium into the silicon to make silicon-germanium (SiGe) transistors, silicon-based communication circuits are possible. Although microwavefrequency SiGe circuits have been demonstrated, there has been difficulty in obtaining the high power from their transistors that is required for the amplifiers of a transmitter, and many researchers have thought that this could not be done. The NASA Glenn Research Center and collaborators at the University of Michigan have developed SiGe transistors and amplifiers with state-of-the-art output power at microwave frequencies from 8 to 20 GHz. These transistors are fabricated using standard silicon processing and may be integrated with CMOS integrated circuits on a single chip. A scanning electron microscope image of a typical SiGe heterojunction bipolar transistor is shown in the preceding photomicrograph. This transistor achieved a record output power of 550 mW and an associated power-added efficiency of 33 percent at 8.4 GHz, as shown. Record performance was also demonstrated at 12.6 and 18 GHz. Developers have combined these state-of-the-art transistors with transmission lines and micromachined passive circuit components, such as inductors and capacitors, to build multistage amplifiers. Currently, a 1-W, 8.4-GHz power amplifier is being built for NASA deep space communication architectures.

Ponchak, George E.↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Affordable, Robust Ceramic Joining Technology (ARCJoinT) Given 1999 R and D 100 Award

Advanced ceramics and fiber-reinforced ceramic matrix composites with high strength and toughness, good thermal conductivity, thermal shock resistance, and oxidation resistance are needed for high-temperature structural applications in advanced high-efficiency and high-performance engines, space propulsion components, and land-based systems. The engineering designs of these systems require the manufacturing of large parts with complex shapes, which are either quite expensive or impossible to fabricate. In many instances, it is more economical to build complex shapes by joining together simple geometrical shapes. Thus, joining has been recognized as an enabling technology for the successful utilization of advanced ceramics and fiber-reinforced composite components in high-temperature applications. However, such joints must retain their structural integrity at high temperatures and must have mechanical strength and environmental stability comparable to those of the bulk materials. In addition, the joining technique should be robust, practical, and reliable. ARCJoinT, which is based on the reaction-forming approach, is unique in terms of producing joints with tailorable microstructures. The formation of joints by this approach is attractive since the thermomechanical properties of the joint interlayer can be tailored to be very close to those of the base materials. In addition, high-temperature fixturing is not needed to hold the parts at the infiltration temperature. The joining process begins with the application of a carbonaceous mixture in the joint area, holding the items to be joined in a fixture, and curing at 110 to 120 C for 10 to 20 min. This step fastens the pieces together. Then, silicon or a silicon alloy in tape, paste, or slurry form is applied around the joint region and heated to 1250 to 1425 C (depending on the type of infiltrant) for 10 to 15 min. The molten silicon or silicon-refractory metal alloy reacts with carbon to form silicon carbide with controllable amounts of silicon and other phases as determined by the alloy composition. Joint thickness can be readily controlled through adjustments of the properties of the carbonaceous paste and the applied fixturing force. The photograph shows various shapes of silicon-carbide-based ceramics and fiberreinforced composites that have been joined using ARCJoinT. Thermomechanical and thermochemical characterization of joints is underway for a wide variety of silicon-carbidebased advanced ceramics and fiber-reinforced composites under the hostile environments that will be encountered in engine applications. ARCJoinT, which was developed by researchers at the NASA Glenn Research Center at Lewis Field, received R&D Magazine's prestigious R&D 100 Award in 1999.

Singh, Mrityunjay↗
DOE OSTI2026

Machine learning-enhanced MPC for demand flexibility in small commercial buildings: An experimental study

Small- and medium-sized commercial buildings (SMCBs) represent the majority of U.S. commercial building stock and a significant share of peak electricity demand, yet they often lack centralized building automation systems, representing a significant untapped resource for urban energy management. This infrastructure gap makes advanced control implementation challenging, limiting the potential for widespread demand flexibility. Model Predictive Control (MPC) has shown strong potential for load shifting, peak demand reduction, and cost savings, but its effectiveness is hindered by unmeasured disturbances such as internal heat gains. This paper presents a Hybrid MPC framework that integrates a physics-based gray-box building thermal model, identified using a lumped disturbance (LD) approach, with a machine learning (ML) model for forecasting unmeasured disturbances. The hybrid approach is designed for buildings with multiple individually controlled heat pump and thermostat pairs, common in SMCBs, and aims to optimize coordinated scheduling of multiple heat pumps under dynamic electricity pricing while respecting comfort constraints. The methodology is validated through both simulations of case study buildings and experimental studies at a highly-instrumented test facility. Simulation results show that the Hybrid MPC achieves substantial load shifting and peak demand reduction, approaching the performance of an ideal MPC with perfect disturbance knowledge, and outperforming a conventional MPC without disturbance forecasting. In experiments, the Hybrid MPC reduced daily HVAC energy costs by 8.7%, peak-price time load (load shifting) by 41.7%, and peak demand by 29.2% compared to baseline control, demonstrating comparable benefits to the 11.6% cost savings, 42.9% load shifting, and 23.2% peak reduction of the ideal MPC. These results demonstrate that the proposed hybrid modeling approach can significantly improve MPC performance in real-world SMCB applications without requiring additional disturbance measurements.

Demand Flexibility↗
DOE OSTI2022

Amine-functionalized porous organic polymers for carbon dioxide capture

Recent developments in CO 2 capture using porous organic polymers (POPs) have received accrescent attention due to their sorbent properties such as high CO 2 uptake capacity and selectivity, tunable chemical structure and permanent porosity. POPs are constructed using two and/or three-dimensional organic monomers (building blocks) linked to each other through covalent bonding, creating high porosity. The pore structure in POPs is exceptionally stable, which leads to their cyclable CO 2 adsorption performance. However, POPs generally suffer from low CO 2 uptake and selectivity due to their interaction with CO 2 in physisorption limits (20–40 kJ mol -1 ). Similar to that in other physisorbents, the CO 2 uptake capacity of POPs further decreases under humid conditions. Pursuant to these limitations, amine functionalization in POPs has resulted in enhanced CO 2 uptake performance with improved CO 2 selectivity over non-polar gases such as N 2 . More importantly, several types of amine-functionalized POPs showed that the CO 2 uptake could remain intact under humid conditions such as in post-combustion flue gas. This review article covers recent developments in amine-functionalized porous organic polymers. Three main categories of amine functionalization, such as direct amine synthesis, amine impregnation and amine grafting, were investigated in detail by considering the effect of amines on the sorbent properties and CO 2 capture performance of POPs. The recent findings in amine-functionalized POPs were investigated including porous polymeric networks (PPNs), covalent organic frameworks (COFs), amine linked POPs, hyper-crosslinked polymers (HCPs), conjugated microporous polymers (CMPs), benzimidazole linked polymers (BILPs), porous aromatic frameworks (PAFs) and polymers of intrinsic microporosity (PIMs).

36 MATERIALS SCIENCE↗
DOE OSTI2022

VTK-m User's Guide (V.1.8)

Extreme scale systems require a new programming model and a fundamental change in how we design algorithms. To address these issues we created VTK-m: the visualization toolkit for multi-/many-core architectures. VTK-m supports a number of algorithms and the ability to design further algorithms through a top-down design with an emphasis on extreme parallelism. VTK-m also provides support for finding and building links across topologies, making it possible to perform operations that determine manifold surfaces, interpolate generated values, and find adjacencies. Although VTK-m provides a simplified high-level interface for programming, its template-based code removes the overhead of abstraction. VTK-m simplifies the development of parallel scientific visualization algorithms by providing a framework of supporting functionality that allows developers to focus on visualization operations. Additionally, VTK-m provides data abstractions not provided by other libraries that make code written in VTK-m more versatile.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Study of micrometeroid orbital debris

Knowing the impact effects of the space environment on engineering materials is critical if we are to build space hardware which will last for the duration of the mission. This study will perform measurements on impacted materials in order to determine the effects of the high velocity impacts on structures which are planned for long duration in space.

Workman, Gary L.↗