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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Unveiling the Mechanical and Electrochemical Evolution of Nanosilicon Composite Anodes in Sulfide-Based All-Solid-State Batteries

The utilization of silicon anodes in all-solid-state lithium batteries provides good prospects for facilitating high energy density. However, the compatibility of sulfide solid-state electrolytes (SEs) with Si and carbon is often questioned due to potential decomposition. Here, in this paper, operando X-ray absorption near-edge structure (XANES) spectroscopy, ex situ scanning electron microscopy (SEM), and ex situ X-ray nanotomography (XnT) are utilized to investigate the chemistry and structure evolution of nano-Si composite anodes. Results from XANES demonstrate a partial decomposition of SEs during the first lithiation stage, which is intensified by the presence of carbon. Nevertheless, the performances of first three cycles in Si–SE–C are stable, which proves that the generated media is ionically conductive. XnT and SEM results show that the addition of SEs and carbon improves the structural stability of the anode, with fewer pores and voids. A chemo-elasto-plastic model reveals that SEs and carbon buffer the volume expansion of Si, thus enhancing mechanical stability. The balance between the pros and cons of SEs and carbon in enhancing reaction kinetics and structural stability enables the Si composite anode to demonstrate the highest Si utilization with higher specific capacities and a better rate than pure Si and Si composite anodes with only SEs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Operability and control in process intensification and modular design: Challenges and opportunities

Abstract In this article, the importance of considering operability and control criteria in the analysis and design of intensified and modular processes is discussed. We first analyze the impact on operability of key factors including: (i) degrees of freedom, (ii) process constraints, (iii) numbering up vs. scaling up, and (iv) dynamic/periodic operation. Comparative examples are presented to showcase the pros and cons in intensified/modular systems vs. their conventional counterparts from operability and control aspects. Then we look into metrics and tools to address these challenges such as: (i) flexibility analysis, (ii) operability‐based design, and (iii) advanced model‐based control. Considering different conceptual design stages as synthesis intensification, steady‐state design, and dynamic operational optimization, we highlight the need to incorporate different levels of operability considerations. Future research opportunities and perspectives are also identified, particularly emphasizing the importance of a holistic strategy for integrated design, operability, and control of intensified and modular process systems.

Pistikopoulos, Efstratios N.↗
DOE OSTI2020

Tighter Confinement Increases Selectivity of d-Glucose Isomerization Toward l-Sorbose in Titanium Zeolites

Aqueous-phase isomerization of D-glucose to D-fructose and L-sorbose is catalyzed in parallel by Lewis acidic Ti sites in siliceous frameworks. Glucose isomerization rates (per Ti, 373 K) are undetectable when Ti sites are confined within mesoporous voids (Ti-MCM-41, TiO 2 -SiO 2 ) and increase to detectable values when Ti sites are confined within the smaller 12-membered ring (12-MR) micropores of Ti-Beta. Furthermore, isomerization rates decrease to lower values (by ≈20×) with further decreases in micropore size as Ti sites are confined within 10-MR pores (Ti-MFI, Ti-CON), likely because of intrapore reactant diffusion restrictions, and reach undetectable values within the 8-MR pores of Ti-CHA as size exclusion prevents glucose from accessing active sites. Remarkably, the selectivity toward l -sorbose over d -fructose increases systematically as spatial constraints around Ti sites become tighter, and is >10 on Ti-MFI. These findings demonstrate the marked influence of confinement around Ti active sites on the selectivity between parallel stereoselective sugar isomerization pathways.

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

Publishing Challenges in Energetic Materials Science

The editorial addressed the ethical dilemma that energetic materials scientists face in advancing their field for societal good and not providing information that could be used for nefarious purposes. The reaction to the editorial was heated, both pro and con. Here we concluded that PEP had done a good job in catalyzing a thoughtful debate in our community about what information should or should not be published.

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

Domain-Specific Type-Safe APIs for Hierarchical Scientific Data with Modern C++

General-purpose library application programming interfaces (APIs) for self-describing hierarchical scientific data storage, such as the HDF5 and NetCDF libraries, are traditionally of runtime nature. Runtime errors for entry existence and data types are typically caught later in the development process of higher-level application-specific APIs. In this paper, we propose exploiting modern C++ metaprogramming features to add compile-time type-safety to improve the interaction with a well-defined metadata-rich scientific schema in domain-specific hierarchical datasets. We tackle two aspects of common use: (i) direct data access, (ii) flexible “in-memory” index models for efficient search and data processing. The proposed APIs use C++17’s template type auto deduction features, C++11’s enum class for type-safety and C-style preprocessor macros for generative templated code. We showcase the pros and cons of our initial work on the standard NeXus schema used for annotating and storing experimental neutron scattering data at several facilities around the world on top of HDF5. Extendable compile-time type-safe APIs are a desirable feature that could be indexed by any modern integrated development environment (IDE). Hence, such APIs can help ease the learning curve for domain scientists using a less error-prone software interaction to enhance the findability of their data without resorting to a domain-specific language (DSL).

Godoy, William↗
DOE OSTI2021

Exact-WKB, complete resurgent structure, and mixed anomaly in quantum mechanics on S 1

We investigate the exact-WKB analysis for quantum mechanics in a periodic potential, with N minima on S 1 . We describe the Stokes graphs of a general potential problem as a network of Airy-type or degenerate Weber-type building blocks, and provide a dictionary between the two. The two formulations are equivalent, but with their own pros and cons. Exact-WKB produces the quantization condition consistent with the known conjectures and mixed anomaly. The quantization condition for the case of N-minima on the circle factorizes over the Hilbert sub-spaces labeled by discrete theta angle (or Bloch momenta), and is consistent with ’t Hooft anomaly for even N and global inconsistency for odd N. By using Delabaere-Dillinger-Pham formula, we prove that the resurgent structure is closed in these Hilbert subspaces, built on discrete theta vacua, and by a transformation, this implies that fixed topological sectors (columns of resurgence triangle) are also closed under resurgence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Recovering the QNEC from the ANEC

We study the relative entropy in QFT comparing the vacuum state to a special family of purifications determined by an input state and constructed using relative modular flow. We use this to prove a conjecture by Wall that relates the shape derivative of relative entropy to a variational expression over the averaged null energy (ANE) of possible purifications. Furthermore, this variational expression can be used to easily prove the quantum null energy condition (QNEC). We formulate Wall’s conjecture as a theorem pertaining to operator algebras satisfying the properties of a half-sided modular inclusion, with the additional assumption that the input state has finite averaged null energy. We also give a new derivation of the strong superadditivity property of relative entropy in this con- text. We speculate about possible connections to the recent methods used to strengthen monotonicity of relative entropy with recovery maps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

In this article, we report the growth of Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Neural Network-Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions

Here, in this paper, we propose and study neural network-based methods for solutions of high-dimensional quadratic porous medium equation (QPME). Three variational formulations of this nonlinear PDE are presented: a strong formulation and two weak formulations. For the strong formulation, the solution is directly parameterized with a neural network and optimized by minimizing the PDE residual. It can be proved that the convergence of the optimization problem guarantees the convergence of the approximate solution in the $L^1$ sense. The weak formulations are derived following (Brenier in Examples of hidden convexity in nonlinear PDEs, 2020) which characterizes the very weak solutions of QPME. Specifically speaking, the solutions are represented with intermediate functions who are parameterized with neural networks and are trained to optimize the weak formulations. Extensive numerical tests are further carried out to investigate the pros and cons of each formulation in low and high dimensions. This is an initial exploration made along the line of solving high-dimensional nonlinear PDEs with neural network-based methods, which we hope can provide some useful experience for future investigations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems. Among many AI techniques, deep reinforcement learning (DRL) has demonstrated great potential for solving sequential optimization problems, which are very common in the engineering domains. Its ability to handle nonlinearity and stochasticity in controlled systems has out-competed many traditional optimal control algorithms. Therefore in this chapter, we focus on the state-of-the-art of DRL concepts and related algorithms, compare their pros and cons with traditional optimal control approaches and discuss the typical workflow for leveraging RL in solving complex problems in modern energy systems.

artificial intelligence↗
DOE OSTI2021

Tool-workpiece stick-slip conditions and their effects on torque and heat generation rate in the friction stir welding

Friction stir welding (FSW) has found increased applications in automotive and aerospace industries due to its advantages of solid-state bonding, no fusion and melting, and versatility in various working conditions and material combinations. However, the relationship among processing parameters, material properties, and bonding extent and fidelity remains largely empirical, primarily because of the lack of the mechanistic understanding of the tool-workpiece frictional behavior that affects our subsequent understanding of microstructural evolution and interface bonding formation. While the tool-workpiece stick-slip condition is believed to dictate the resulting torque and heat generation rate during the welding process, it remains rare and elusive to conduct a quantitative experimental measurement of such interfacial field. On the other hand, numerical simulations based on Computational Fluid Dynamics (CFD) rely on ad hoc assumptions of interfacial pressure and shear-stress conditions, but predictions can only be validated via the medium- and far-range temperature field which is known to be insensitive to the interfacial frictional behavior. This work first presents a comparison among two CFD-based simulation methodologies and the Coupled Eulerian Lagrangian (CEL) model in finite element method, the last of which uses the Coulomb friction so that the stick-slip is naturally developed. Based on the Hill-Bower similarity relationship in the contact analysis, an analytical model is developed here to prove why a constant stick-slip fraction will be developed in the steady state, to correlate the stick-slip fraction to processing parameters such as the tool spin rate, and further to derive dimensionless functions for torque and heat-generation-rate predictions. Pros and cons of various numerical approaches in predicting stick-slip are discussed, and our analytical model has been found to agree well with our numerical simulation and literature experimental results. These analyses provide the critical strain-rate and temperature fields that are needed for the bonding analysis in our future work.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effects of SO 2 poisoning and regeneration on spinel containing CH 4 oxidation catalysts

Methane oxidation under periodic conditions and the oxygen storage capacity of a bilayer Pt/Pd/Al 2 O 3 over a Mn 0.5 Fe 2.5 O 4 spinel catalyst were studied before and after SO 2 exposure, and after simulated regeneration conditions. Prior to sulfur exposure, improvement in CH 4 oxidation conversion under periodic conditions compared to steady-state conditions was observed. After sulfur exposure at 100 °C, there was a loss in CH 4 oxidation performance and a loss of oxygen storage capacity of the spinel material. Here, the extent of regeneration from sulfur poisoning depends on the ability to induce the decomposition of sulfate species, and while all regeneration methods tested in this study did improve CH 4 conversion, regeneration methods under periodic conditions induced greater sulfur species desorption from the catalyst surface leading to improved CH 4 con version. Key regeneration parameters – temperature, feed composition, modulation amplitude and frequency – were optimized to induce S species decomposition and correlated to CH 4 oxidation activity recovery.

03 NATURAL GAS↗
DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗
DOE OSTI2025

Investigation of heat pump technologies for high-temperature applications above 250 °C

This paper examines heat pump (HP) technologies to achieve heat supply temperatures above 250 °C, referred to as ultra-high-temperature heat pump (UHTHP). UHTHP, as a low-carbon heat delivery and heat augmentation technology, can offer an alternative to traditional combustion heating for decarbonizing high-temperature industrial processes. However, the heat supply temperature of over 250 °C surpasses the temperature range typically covered in the high-temperature HP literature and existing capabilities of commercial HP systems. This paper reviews HP technologies with the potential to evolve into UHTHP. Additionally, UHTHP studies in the literature are analyzed to compare the pros, cons, and technical potentials of various HPs using different thermodynamic cycles and fluids. Furthermore, we then pinpoint the technical gaps and challenges that have hindered widespread adoption of UHTHP in industry, explore potential improvements and solutions, and discuss the feasibility. Current efforts aim to lay the ground for future research, development, and commercial deployment for UHTHP.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2021

Assessment of asymmetrical rheological behavior of cementitious material for 3D printing application

Among the multiplicity of construction methods based on digital concrete technologies, the most common one remains the extrusion-based process. Two opposed strategies have been developed in order to print a concrete structure using extrusion: the first one emphasizes the ease-of-pumping of material which is massively accelerated to ensure the stability of the structure, when leaving the extruder; the second one uses a firm mortar able to sustain the own weight of the structure during the printing, without requiring any accelerator. Both methods have their pros and cons and don't imply the same rheological requirements. Most of the recent literature assumes that both types of material (fluid and firm) follow the same trend of behavior and obey to the Von Mises plasticity criterion, while it has been demonstrated that firm cement-based materials can exhibit a pressure dependent behavior. Moreover, rheological study is mainly based on shear rheometry, while the extrusion-based printing process requires a description of the rheological behavior in compression to predict the global stability of the structure, and in tension to prevent cracks formation. This paper investigates the fresh behavior of cement paste and mortar under different solicitations (shear, compression, tension) for different water to cement ratios. The analysis of these results allows to describe the transition between a ductile fluid material (symmetrical in tension and compression) that obeys to a Von Mises plasticity criterion, and a brittle and firm material (asymmetrical in tension and compression) that requires a pressure dependent plasticity criterion in order to predict its strength under a given solicitation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Zirconium-diboride silicon-carbide composites: A review

Zirconium diboride (ZrB 2 ) and silicon carbide (SiC) composites have long been of interest since it was observed that ZrB 2 improved the thermal shock resistance of SiC. However, processing of these materials can be difficult due to high and different sintering temperatures and differences in the thermodynamic stability of each material. ZrB 2 –SiC composites have been processed in a variety of ways including hot-pressing, spark-plasma sintering, reactive melt infiltration, pack cementation, chemical vapor deposition, chemical vapor infiltration, stereolithography, direct ink writing, selective laser sintering, electron beam melting, and binder jet additive manufacturing. Each manufacturing method has its own pros and cons. This review serves to summarize more than 60 years of research and provide a coherent resource for the variety of methods and advancements in development of ZrB 2 –SiC composites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effects of non-thermal termolecular reactions on detonation development in hydrogen (H 2 )/methane (CH 4 ) - air mixtures

The binary fuel blend of H 2 /CH 4 is one of the most promising hydrogen-enriched hydrocarbon fuels in spark-ignition (SI) engines. Yet, the undesirable phenomenon of super-knock, which can severely and in-stantaneously damage an SI engine, limits its widespread adoption. Moreover, there is still a lack of con-sensus on the precise mechanism by which this phenomenon occurs i.e. via flame acceleration or spon-taneous ignition, despite numerous previous investigations. At the same time, recent studies [M. P. Burke, S. J. Klippenstein, Nat. Chem. 9 (2017) 1078 -1082, Y. Tao, A. W. Jasper, Y. Georgievskii, S. J. Klippenstein, R. Sivaramakrishnan, Proc. Combust. Inst. 38 (2021) 515-522] have demonstrated a high probability of occurrence of non-thermal reactions in premixed flames of such H 2 /CH 4 fuel blends with air due to the presence of non-trivial amounts of highly reactive radicals including H, O and OH apart from O 2 . The present study focuses on the evolution of an initial deflagration front to a detonation wave in H 2 /CH 4 - air mixtures under SI engine relevant conditions through fully resolved, constant volume 1D simulations with and without non-thermal reactivity. Non-thermal reactions were included in the macroscopic kinetics model as chemically termolecular reactions facilitated by the H + CH 3 and H + OH radical-radical recombination and the H + O 2 radical-molecule association reactions. Further, the nonthermal reactions result in a corresponding decrease in the reaction fluxes of the incipient recombination/association reactions. Therefore, an additional set of simulations were performed by applying corrections to the respective incipient recombination/association rate constants using the methodology demonstrated by Tao et al. [Y. Tao, A. W. Jasper, Y. Georgievskii, S. J. Klippenstein, R. Sivaramakrishnan, Proc. Combust. Inst. 38 (2021) 515-522]. Compared to the baseline case, the onset of spontaneous ignition in the end-gas region was observed to be delayed in the presence of non-thermal termolecular reactions. Concurrently, the developing detonation was observed to be significantly stronger. In contrast, applying corrections to the recombi-nation/association rate constants resulted in a completely different behavior. Specifically, detonation was observed to occur due to self acceleration of the primary flame in the absence of spontaneous ignition in the end-gas region. Sensitivity analysis was performed to quantify the effects of non-thermal reactions on the duration of heat release rate and thereby the mechanism of detonation formation. In addition, chemical explosive mode analysis (CEMA) was performed to identify the dominant species/reactions re-sponsible for the observed results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Spatially-resolved soot evolution and statistics in high-pressure diesel spray flames using two-color pyrometry

Previous studies of diesel spray flames have focused on details of the sooting behavior mainly during the quasi-steady period, but few have considered the spatially-resolved transient evolution in combination with injection-to-injection variations. In this study, a 500-injection data set is utilized to investigate the temporal evolution of a spray flame during auto-ignition, the premixed burn phase, and the quasi-steady period. Spatially-resolved OH* chemiluminescence data provide ignition delay times and reaction zone locations. Two-color pyrom-etry with a vastly improved optical system is used to quantify spatially-resolved soot evolution and its statistical variations. The ambient thermodynamic con-ditions are slightly below those in modern diesel engines, resulting in longer lift-off lengths and lower overall soot production. Spatially, soot formation in the lift-off region is small, but appreciable soot forms in the jet core and jet head regions, while it oxidizes gradually on the jet periphery. Total soot mass profiles indicate that regions with larger local soot mass take longer to form, but are the first to oxidize. Probability distributions of soot mass in localized regions indicate that a few injections with high soot mass bias the average soot mass data towards higher values. Altogether, results show good agreement with previous studies employing different diagnostic techniques while providing statistical details of transient and localized soot behavior in high pressure diesel spray flames in support of the related modeling efforts.

09 BIOMASS FUELS↗