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At least 469 records · Page 26

Novel Carbon (C)-Boron (B)-Nitrogen (N)-Containing H2 Storage Materials

The following summarizes the research conducted for DOE project DE-EE0005658 “Novel Carbon(C)-Boron(B)-Nitrogen(N)-Containing H2 Storage Materials”. This work focused in part on the continued study of two materials identified from the preceding project DE-FG360GO18143 (“Hydrogen Storage by Novel CBN Heterocycle Materials”) as lead candidates to meet the DOE technical targets for either vehicular or non-automotive hydrogen storage applications. Specifically, a room-temperature liquid, 3-methyl-1,2-cyclopentane (B), and a high H2 capacity solid, 1,2-BN-cyclohexane (J), were selected for further characterization and performance optimization. In addition to these compounds, the current project also aimed to prepare several new materials predicted to be disposed towards direct reversibility of H2 release and uptake, a feature deemed critical to achieving efficient recycling of spent fuel end products. To assist in the rational design of these and other next-generation materials, this project undertook to investigate the mechanism of hydrogen release from established compounds (mainly B and J) using a combined experimental/computational approach. Among this project’s signature accomplishments, the preliminary synthetic route to B was optimized for production on decagram scale. With such quantities of material available, its performance in powering an actual 30 W proton exchange membrane (PEM) fuel cell stack was tested and found to be identical to that of facility H2. Despite this positive proof-of-concept achievement, however, further consideration of neat B as a potential hydrogen storage material was abandoned due to evidence of thermal instability. Specifically, mass spectrometry-coupled thermogravimetric analysis (TGA-MS) revealed significant H2 release from B to initiate at 50 °C, well below the 60 °C minimum threshold set by the DOE. This result prompted a more extensive investigation in the decomposition mechanism of B vis-à-vis that of J, which exhibited in neat form a substantially higher onset temperature for spontaneous H2 release (70 °C). Solution-phase kinetic experiments using ReactIR established a second-order dependence for the initial loss of H2 from both B and J; Arrhenius analysis, however, revealed the activation barrier for this reaction was lower for B than for J, which presumably contributes to the diminished thermal stability of the former. On the basis of these and other experimental results, extensive computational efforts yielded a reasonable mechanistic model for the dehydrogenation of 1,2-BN-cycloalkane materials. While the prospect of neat B as a suitable hydrogen storage material was discarded, it was proposed that the combination of B with more thermally stable amine-borane-based materials might afford mixtures with improved properties. Indeed, when B was combined with ammonia borane (AB) in a 2:1 molar ratio, the two materials formed a liquid. More significantly, this mixture remained liquid even after complete dehydrogenation, thus establishing the potential for a single-phase fuel cycle. (In contrast, the dehydrogenation product of neat B is a low melting solid (mp = 28-30 °C).) Another advantage conferred by the blend formulation was a dramatic reduction in the amount of borazine produced by AB. Borazine is a well-known contaminant of H2 produced by the thermal decomposition of neat AB, and exerts deleterious effects on fuel cell performance. Residual gas analysis (RGA) of the gas stream generated from the B-AB blend, however, detected just 0.01% borazine content when a Pt-Ni nanoparticle dehydrogenation catalyst was used. In all ii then, the 2:1 B-AB blend marks a major achievement in the effort to develop a suitable liquid amine-borane hydrogen storage material, and merits further investigation into the optimization for practical adoption. Similar realization of the potential of J as a high % wt. H2 material required a method to dehydrogenate the carbonaceous components of the molecule without the use of a sacrificial hydrogen acceptor, as had been reported in the previous project. Ultimately, this reaction was achieved for a B,N-disubstituted BN-cyclohexene model substrate using a gas flow system with a fixed Pd/C catalyst bed. Considerable work remains, however, to translate these initial results into a general protocol for complete dehydrogenation of fully saturated BN-cycloalkane materials such as J. With concrete confirmation of the possibility to perform both BN and CC dehydrogenation on a single theoretical substrate, COMSOL modeling was used to evaluate the effects of thermodynamically coupling the two reactions. It was hypothesized that the heat generated from exothermic BN dehydrogenation would partially drive the endothermic CC dehydrogenation reaction; this additional heat consumption was expected to in turn confer the benefit of lowering the maximum reactor temperature. A two-dimensional model of an axisymmetric reactor including experimental kinetic and calculated thermodynamic parameters for both reactions did indeed predict these outcomes. The extent to which the effects of thermodynamic coupling actually manifested, however, were also revealed to depend strongly on the relative rates of the two reactions, as well as the magnitude of the equilibrium constant governing the progress of the endothermic process. Given the evident complexity of attaining high effective % wt. H2 capacity with J, alternative systems were investigated for greater facility of extensive H2 release. Among those studied, 1,2,4,5-bis-BN-cyclohexane (H) demonstrated the most favorable properties, particularly with respect to thermal stability: rather than decompose, a neat sample instead sublimed when heated above 150 °C. Nevertheless, two commercially available catalytic systems were identified to effect release of two H2 equivalents from H. Release of further equivalents were apparently impeded by the formation of either polymeric material or one of two dimeric cage compounds depending on the catalyst used. Notably, a method to regenerate H from these product mixtures remains to be developed. Thus, while H may prove useful for certain long-term energy storage needs, it is currently less suited applications involving frequent fuel consumption. Similar difficulties were also encountered in attempts to realize the complete fuel cycle of 1,3-BN-cyclohexane (E) and B,N-substituted derivatives thereof. It had been initially proposed that E would provide for readily reversible BN dehydrogenation through a measure of frustrated Lewis pair-type character. Indeed, computations predicted this reaction would be essentially thermoneutral in solution. In the course of attempts to fully hydrogenate the spent fuel, however, dimeric species formed and proved resistant to further BN reduction. While a number of monomeric cyclic compounds were also successfully synthesized as formal boron-nitrogen frustrated Lewis pairs, none demonstrated any capacity to split H2 across the BN unit. The challenge of developing a practical amine-borane-based material for readily reversible hydrogen storage thus remains unresolved at this time. As such, it deserves consideration as a major objective of any future work.

08 HYDROGEN↗

High‐Efficiency Nonfullerene Organic Solar Cells: Critical Factors that Affect Complex Multi‐Length Scale Morphology and Device Performance

Organic solar cells (OSCs) made of donor/acceptor bulk‐heterojunction active layers have been of widespread interest in converting sunlight to electricity. Characterizing of the complex morphology at multiple length scales of polymer:nonfullerene small molecular acceptor (SMA) systems remains largely unexplored. Through detailed characterizations (hard/soft X‐ray scattering) of the record‐efficiency polymer:SMA system with a close analog, quantitative morphological parameters are related to the device performance parameters and fundamental morphology–performance relationships that explain why additive use and thermal annealing are needed for optimized performance are established. A linear correlation between the average purity variations at small length scale (≈10 nm) and photovoltaic device characteristics across all processing protocols is observed in ≈12%‐efficiency polymer:SMA systems. In addition, molecular interactions as reflected by the estimated Flory–Huggins interaction parameters are used to provide context of the room temperature morphology results. Comparison with results from annealed devices suggests that the two SMA systems compared show upper and lower critical solution temperature behavior, respectively. The in‐depth understanding of the complex multilength scale nonfullerene OSC morphology may guide the device optimization and new materials development and indicates that thermodynamic properties of materials systems should be studied in more detail to aid in designing optimized protocols efficiently.

Ye, Long↗

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Shuttle payload S-band communications study

The work to identify, evaluate, and make recommendations concerning the functions and interfaces of those orbiter avionic subsystems which are dedicated to, or play some part in, handling communication signals (telemetry and command) to/from payloads (spacecraft) that will be carried into orbit by the shuttle is reported. Some principal directions of the research are: (1) analysis of the ability of the various avionic equipment to interface with and appropriately process payload signals; (2) development of criteria which will foster equipment compatibility with diverse types of payloads and signals; (3) study of operational procedures, especially those affecting signal acquisition; (4) trade-off analysis for end-to-end data link performance optimization; (5) identification of possible hardware design weakness which might degrade signal processing performance.

Springett, J. C.↗

On the rational design of compressible flow ejectors

A fluid mechanics review of chemical laser ejectors is presented. The characteristics of ejectors with single and multiple driver nozzles are discussed. Methods to compute an optimized performance map in which secondary Mach number and performance are computed versus mass ratio, to compute the flow distortion at each optimized condition, and to determine the thrust area for the design point to match diffuser impedence are examined.

Ortwerth, P. J.↗

Survey - Applications of structural optimization methods to fixed wing aircraft and spacecraft

Results of a technical survey of the practical applications of structural optimization methods in the U.S. aerospace industry through 1980s are summarized. One of the most important developments in the 80s is the more widespread acceptance of structural optimization as one of the design tools that support practical structural design. Another significant advance is the development of large software tools for production applications. Attention is also given to the tailoring of the computerized design process to the specific environment of each company. The two most important aspects of this tailoring are seamless and easy-to-use incorporation of structural optimization in the overall aerospace design/production process and multidisciplinary integration aimed at ultimate performance optimization of the final product. Some specific applications discussed include the X-29 forward swept wing demonstrator aircraft, composite wing and vertical tail program, fighter wing redesign evaluations, high speed aircraft design, and space structures.

Miura, Hirokazu↗

A dual-task investigation of automaticity in visual word processing

An analysis of activation models of visual word processing suggests that frequency-sensitive forms of lexical processing should proceed normally while unattended. This hypothesis was tested by having participants perform a speeded pitch discrimination task followed by lexical decisions or word naming. As the stimulus onset asynchrony between the tasks was reduced, lexical-decision and naming latencies increased dramatically. Word-frequency effects were additive with the increase, indicating that frequency-sensitive processing was subject to postponement while attention was devoted to the other task. Either (a) the same neural hardware shares responsibility for lexical processing and central stages of choice reaction time task processing and cannot perform both computations simultaneously, or (b) lexical processing is blocked in order to optimize performance on the pitch discrimination task. Either way, word processing is not as automatic as activation models suggest.

Attention↗

A comprehensive line spread function error for the Off-plane Grating Rocket Experiment

The Off-plane Grating Rocket Experiment (OGRE) is a soft X-ray grating spectrometer to be flown on a suborbital rocket. The payload is designed to obtain the highest-resolution soft X-ray spectrum of Capella to date with a resolution goal of R(lambda/delta lambda) > 2000 at select wavelengths in its 10 - 55 A bandpass of interest. The optical design of the spectrometer realizes a maximum resolution of R 5000 with all components performing optimally, in perfect alignment, and not considering in-flight pointing errors; however, performance errors, misalignments, and in-flight pointing errors work to degrade this performance. A comprehensive line spread function (LSF) error budget has been constructed for the spectrometer to identify errors contributing to the LSF, to determine how each affects the LSF, and to inform performance requirements and alignment tolerances for the spectrometer. In this document, the comprehensive LSF error budget for the OGRE spectrometer is presented and its implications are discussed.

Benjamin D Donovan↗

Integration of Structural Analysis and Manufacturing Process Planning for Global Optimization with Automated Fiber Placement

Design of mass-efficient composite structures intended for Automated Fiber Placement (AFP) requires close interaction between structural analysis and manufacturing process planning. Tools exist for each of these disciplines, but software interplay has been insufficient for rapid and efficient design iteration. Within the NASA Advanced Composites Consortium (ACC), the Design for Manufacturing (DFM) task has made significant progress towards linking these disciplines and respective software – HyperX (design), CAPP (process planning), and VCP (tool path generation). The initial focus in previous work was on data exchange between disciplines. The ability to both export and consume composite design and manufacturing data to and from each tool. This paper focuses on the effort to automate and streamline the connection between the tools listed above, with the goal of being able to automatically generate a composite AFP design that is mass-efficient and manufacturable. The optimization method being pursued is a bi-level approach, where each tool performs optimization within its discipline. The optimization in HyperX is focused on mass and laminate strength, while CAPP is focused on maximizing manufacturability. VCP is used to generate fiber paths for each design iteration. These sub-processes are wrapped with a global level optimization, driven by HyperX, used to converge the design. This paper describes the current state of this effort, which is a completed HyperX-VCP iteration loop and initial work on the HyperX-CAPP iteration loop. Additionally, example results are shown for a wind blade structure with double curvature.

Automated Fiber Placement↗

A HUMAN AUTOMATION INTERACTION CONCEPT FOR A SMALL MODULAR REACTOR CONTROL ROOM

Many advanced nuclear power plant (NPP) designs incorporate higher degrees of automation than the existing fleet of NPPs. Automation is being introduced or proposed in NPPs through a wide variety of systems and technologies, such as advanced displays, computer-based procedures, advanced alarm systems, and computerized operator support systems. Additionally, many new reactor concepts, both full scale and small modular reactors, are proposing increased automation and reduced staffing as part of their concept of operations. However, research consistently finds that there is a fundamental tradeoff between system performance with increased automation and reduced human performance. There is a need to address the question of how to achieve high performance and efficiency of high levels of automation without degrading human performance. One example of a new NPP concept that will utilize greater degrees of automation is the SMR concept from NuScale Power. The NuScale Power design requires 12 modular units to be operated in one single control room, which leads to a need for higher degrees of automation in the control room. Idaho National Laboratory (INL) researchers and NuScale Power human factors and operations staff are working on a collaborative project to address the human performance challenges of increased automation and to determine the principles that lead to optimal performance in highly automated systems. This paper will describe this concept in detail and will describe an experimental test of the concept. The benefits and challenges of the approach will be discussed.

99 GENERAL AND MISCELLANEOUS↗

Approach to adaptive allocation of shared resources in computer systems

A request management subsystem is configured to establish service classes for clients that issue requests for a shared resource on a computer system. The subsystem also is configured to determine the state of the system with respect to bandwidth, current latency, frequency and voltage levels, among other characteristics. Further, the subsystem is configured to evaluate the requirements of each client with respect to latency sensitivity and required bandwidth, among other characteristics. Finally, the subsystem is configured to schedule access to shared resources, based on the priority class of each client, the demands of the application, and the state of the system. With this approach, the subsystem may enable all clients to perform optimally or, alternatively, may cause all clients to experience an equal reduction in performance.

Bolotin, Evgeny↗

A human automation interaction concept for a small modular reactor control room - 101

Many advanced nuclear power plant (NPP) designs incorporate higher degrees of automation than the existing fleet of NPPs. Automation is being introduced or proposed in NPPs through a wide variety of systems and technologies, such as advanced displays, computer-based procedures, advanced alarm systems, and computerized operator support systems. Research consistently finds that there is a fundamental tradeoff between system performance with increased automation and reduced human performance. Idaho National Laboratory (INL) researchers and NuScale Power human factors and operations staff are working on a collaborative project to address the human performance challenges of increased automation and to determine the principles that lead to optimal performance in highly automated systems. This paper will describe this concept in detail and will describe an experimental test of the concept. The benefits and challenges of the approach will be discussed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Achieving performance portability in Gaussian basis set density functional theory on accelerator based architectures in NWChemEx

The numerical integration of the exchange–correlation (XC) potential is one of the primary computational bottlenecks in Gaussian basis set Kohn–Sham density functional theory (KS-DFT). To achieve optimal performance and accuracy, care must be taken in this numerical integration to preserve local sparsity as to allow for near linear weak scaling with system size. This leads to an integration scheme with several performance critical kernels which must be hand optimized for each architecture of interest. As the set of available accelerator hardware goes more diverse, a key challenge for developers of KS-DFT software is to maintain performance portability across a wide range of computational architectures. In this article, we examine a modular software design pattern which decouples the implementation details of performance critical kernels from the expression of high-level algorithmic workflows in a device-agnostic language such as C++; thus allowing for developers to target existing and emerging accelerator hardware within a single code base. We consider the efficacy of such a design pattern in the numerical integration of the XC potential by demonstrating its ability to achieve performance portability across a set of accelerator architectures which are representative of those on current and future U.S. Department of Energy Leadership Computing Facilities.

97 MATHEMATICS AND COMPUTING↗

Biocybernetic Adaptation Strategies: Machine Awareness of Human Engagement for Improved Operational Performance

Human operators interacting with machines or computers continually adapt to the needs of the system ideally resulting in optimal performance. In some cases, however, deteriorated performance is an outcome. Adaptation to the situation is a strength expected of the human operator which is often accomplished by the human through self-regulation of mental state. Adaptation is at the core of the human operator's activity, and research has demonstrated that the implementation of a feedback loop can enhance this natural skill to improve training and human/machine interaction. Biocybernetic adaptation involves a “loop upon a loop,” which may be visualized as a superimposed loop which senses a physiological signal and influences the operator’s task at some point. Biocybernetic adaptation in, for example, physiologically adaptive automation employs the “steering” sense of “cybernetic,” and serves a transitory adaptive purpose – to better serve the human operator by more fully representing their responses to the sys- tem. The adaptation process usually makes use of an assessment of transient cog- nitive state to steer a functional aspect of a system that is external to the operator’s physiology from which the state assessment is derived. Therefore, the objective of this paper is to detail the structure of biocybernetic systems regarding the level of engagement of interest for adaptive systems, their processing pipeline, and the adaptation strategies employed for training purposes, in an effort to pave the way towards machine awareness of human state for self-regulation and improved operational performance.

Stephens, Chad↗

A Framework for Preliminary Design of Aircraft Structures Based on Process Information

This report discusses the general framework and development of a computational tool for preliminary design of aircraft structures based on process information. The described methodology is suitable for multidisciplinary design optimization (MDO) activities associated with integrated product and process development (IPPD). The framework consists of three parts: (1) product and process definitions; (2) engineering synthesis, and (3) optimization. The product and process definitions are part of input information provided by the design team. The backbone of the system is its ability to analyze a given structural design for performance as well as manufacturability and cost assessment. The system uses a database on material systems and manufacturing processes. Based on the identified set of design variables and an objective function, the system is capable of performing optimization subject to manufacturability, cost, and performance constraints. The accuracy of the manufacturability measures and cost models discussed here depend largely on the available data on specific methods of manufacture and assembly and associated labor requirements. As such, our focus in this research has been on the methodology itself and not so much on its accurate implementation in an industrial setting. A three-tier approach is presented for an IPPD-MDO based design of aircraft structures. The variable-complexity cost estimation methodology and an approach for integrating manufacturing cost assessment into design process are also discussed. This report is presented in two parts. In the first part, the design methodology is presented, and the computational design tool is described. In the second part, a prototype model of the preliminary design Tool for Aircraft Structures based on Process Information (TASPI) is described. Part two also contains an example problem that applies the methodology described here for evaluation of six different design concepts for a wing spar.

Rais-Rohani, Masoud↗

Reinforcement Learning Control for Buildings Co-Optimizing Energy, Comfort, and Indoor Air Quality: An Annual Assessment

Efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial for optimizing energy use and maintaining indoor comfort in buildings. Traditional control methods, such as PID control, cannot handle energy use trade-offs among multiple components in the building energy system at a supervisory level. Reinforcement learning (RL) presents a promising solution, offering adaptive and data-driven control strategies that optimize performance over time. However, RL also faces several challenges, including the conflicts encountered in co-optimizing energy savings, occupant comfort, and indoor air quality, and the requirement for extensive interactions with the environment in training. We proposed a flexible simulation platform that integrates a hybrid model for RL training and designed an RL agent to control the entire central HVAC system, focusing on co-optimizing energy consumption, thermal comfort, and indoor air quality ($\text{CO}_{2}$ and PM2.5 concentrations). Finally, we evaluated the RL agent's performance over an annual cycle. Our findings indicate that the RL agent can effectively manage the HVAC system with 14.7 % energy savings annually and balance multiple objectives, which demonstrates significant potential for improving HVAC system control and sustainability in buildings.

Guo, Fangzhou↗

Shifting Between Compute and Memory Bounds: A Compression-Enabled Roofline Model

In the evolving landscape of high-performance computing, especially to fight the end of Moore’s Law and Dennard’s Scaling, the ability to shift between compute-bound and memory-bound states is critical for enhancing adaptability and flexibility to diverse system and domain-specific architectures. Such capability is vital for optimizing performance across distinguished hardware configurations, such as accelerators, memory hierarchies, and cache systems. Despite that ad hoc optimization techniques, such as compressed/approximate computation, have been enabled for compute-/data-intensive computing for improved performance in distinct hardware settings, there lacks an understanding of 1) the rational behind performance improvement; 2) capability of different optimizations; 3) what optimization to respond to specific computational and memory demands. This work proposes a compression-enabled roofline model to facilitate this adaptability with data compression techniques to balance and transform between computational and memory demands. This model enables applications to adjust in response to the specific strengths and limitations of the underlying hardware and system to optimize resource utilization. The effectiveness of this approach is demonstrated with matrix multiplication kernels on different input sizes, with turning on/off various compression techniques, including 1) low-precision floating point; 2) sparse matrix formulation; and 3) compressed arrays with ZFP. By reducing memory transfer volumes and cache misses and increasing data locality and computational intensity through compression, the specific roofline model can transform between compute and memory bounds to align more efficiently with system capabilities. This advancement not only improves overall performance but also maximizes adaptability in diverse computing environments.

Naraparaju, Ramasoumya [University of Washington]↗

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗