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Using ethanol and isopropanol as biomass model compounds for understanding bond scission mechanisms over Cu/Mo 2 N catalysts
The conversion of biomass compounds into fuels and chemicals is an important step towards a more sustainable future. This work combines results from model surfaces and powder catalysts to demonstrate Cu-modified mo- lybdenum nitride (Cu/Mo 2 N) as a selective catalyst for dehydrogenation of the biomass model compounds, ethanol and isopropanol. Results from model surfaces showed that while Mo2N led to unselective decomposition via both dehydrogenation and dehydration, the addition of Cu increased the dehydrogenation activity and selectivity. DFT calculations showed how Cu influenced the structures of active sites, adsorbate interactions, and thus the product selectivity. Batch reactor studies on corresponding powder catalysts confirmed the trend that Cu modification increased dehydrogenation activity, and in situ X-ray absorption spectroscopy elucidated the Cu oxidation state under reaction conditions. Further, this work demonstrates a strategy for promoting dehydrogenation over Mo 2 N-based catalysts, as well as the feasibility of using model surfaces to guide the design of industrially relevant catalysts.
Intruder band mixing in an ab initio description of 12 Be
The spectrum of 12 Be exhibits exotic features, e.g., an intruder ground state and shape coexistence, normally associated with the breakdown of a shell closure. While previous phenomenological treatments indicated the ground state has substantial contributions from intruder configurations, it is only with advances in computational abilities and improved interactions that this intruder mixing is observed in ab initio no-core shell model (NCSM) predictions. In this work, we extract electromagnetic observables and symmetry decompositions from the NCSM wave functions to demonstrate that the low-lying positive parity spectrum can be explained in terms of mixing of rotational bands with very different intrinsic structure coexisting within the low-lying spectrum. These observed bands exhibit an approximate SU(3) symmetry and are qualitatively consistent with Elliott model predictions.
Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design
This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.
Neural network based decomposition in optimal structural synthesis
The present paper describes potential applications of neural networks in the multilevel decomposition based optimal design of structural systems. The generic structural optimization problem of interest, if handled as a single problem, results in a large dimensionality problem. Decomposition strategies allow for this problem to be represented by a set of smaller, decoupled problems, for which solutions may either be obtained with greater ease or may be obtained in parallel. Neural network models derived through supervised training, are used in two distinct modes in this work. The first uses neural networks to make available efficient analysis models for use in repetitive function evaluations as required by the optimization algorithm. In the second mode, neural networks are used to represent the coupling that exists between the decomposed subproblems. The approach is illustrated by application to the multilevel decomposition-based synthesis of representative truss and frame structures.
Unconventional low temperature decomposition of a saturated hydrocarbon over atomically-dispersed titanium-aluminum-boron catalyst
Sonochemically-synthesized atomically-dispersed titanium-aluminum-boron nanopowder (TiAlB NP) exhibits a remarkable low-temperature catalytic activation of aliphatic C-H bonds at 750 K followed by C-C bond activation thus emerging as a potent low-cost alternative to expensive platinum group metals. Here, the model saturated hydrocarbon, exo-tetrahydrodicyclopentadiene (C10H16), undergoes catalytic decomposition on TiAlB NPs in a chemical microreactor to produce 1,3-cyclopentadiene (c-C5H6), cyclopentene (c-C5H8), and molecular hydrogen (H2) as detected in situ via isomer-selective, single-photon ionization time-of-flight mass spectrometry. Extensive electronic structure theory calculations on model clusters of the catalyst decode a unique synergy among the atomic constituents of the catalyst and chemical bonding in this stepwise, retro Diels Alder reaction: Ti, although insensitive to C-H activation in its metallic state, initiates the catalysis via chemisorption of the hydrocarbon, adjacent B centers readily abstract hydrogen atoms and store them during the catalytic cycle, while Al stabilizes the catalyst structure yet providing space for critical docking sites for the departing hydrocarbons.
Kinetics of nitric oxide formation and decomposition
Experiments on shock wave structure were conducted in the 12 inch shock tube facility, and it is shown that this work was motivated by the need for improved kinetics for the modeling of NO production in space shuttle flow fields. The experiments on shock structure involved simultaneous observations of pressure and temperature on the wall of the shock tube during reflection of normal shock waves in inert gases.
Component-specific modeling
Accomplishments are described for a 3 year program to develop methodology for component-specific modeling of aircraft hot section components (turbine blades, turbine vanes, and burner liners). These accomplishments include: (1) engine thermodynamic and mission models, (2) geometry model generators, (3) remeshing, (4) specialty three-dimensional inelastic structural analysis, (5) computationally efficient solvers, (6) adaptive solution strategies, (7) engine performance parameters/component response variables decomposition and synthesis, (8) integrated software architecture and development, and (9) validation cases for software developed.
A Decomposition-Based Learn-To-Optimize Approach with Feasibility Layer Assistance for Sub-Hourly Unit Commitment
Sub-hourly unit commitment (UC) with 15-min intervals is gaining significant attention as a way to respond rapidly to the fluctuations in electricity supply and demand introduced by renewable resources. However, the increased temporal resolution and complex inter-temporal dependencies pose substantial computational challenges for traditional optimization methods. To this end, this paper explores a decomposition-based learn-to-optimize approach. Building on recent advances in machine learning, our method revisits the long- overlooked Lagrangian relaxation framework, which is a classical decomposition technique that enables tractable subproblem solving. These smaller subproblems are inherently well-suited for machine learning, as their reduced dimensionality and structural regularity allow predictive models to efficiently learn and generalize solution patterns. We thus propose a generic predictive model, which embeds Gated Recurrent Units (GRUs) and Attention in the encoder-decoder structure, and integrate a rule-based feasibility layer to capture temporal dependencies, reduce training effort, and improve feasibility w.r.t. unit-level constraints. Our method has been validated on the IEEE 118-bus system, demonstrating promising performance in solving sub-hourly UC problems efficiently and feasibly.
Lumley's PODT definition of large eddies and a trio of numerical procedures
Lumley's 1967 Moscow paper provided, for the first time, a completely rational definition of the physically-useful term 'large eddy', popular for a half-century. The numerical procedures based upon his results are: (1) PODT (Proper Orthogonal Decomposition Theorem), which extracts the Large Eddy structure of stochastic processes from physical or computer simulation two-point covariances, and 2) LEIM (Large-Eddy Interaction Model), a predictive scheme for the dynamical large eddies based upon higher order turbulence modeling. Earlier Lumley's work (1964) forms the basis for the final member of the triad of numerical procedures: this predicts the global neutral modes of turbulence which have surprising agreement with both structural eigenmodes and those obtained from the dynamical equations. The ultimate goal of improved engineering design tools for turbulence may be near at hand, partly due to the power and storage of 'supermicrocomputer' workstations finally becoming adequate for the demanding numerics of these procedures.
Advanced modeling and simulation of research reactors using dynamic mode decomposition
Full text of publication follows. Due to the ever-increasing safety requirements, the current trend of nuclear reactor analysis is shifting towards high-fidelity multi-physics models, which have a very high computational cost and modelling complexity. As the cost of even a single model run makes it impossible to analyse the behaviour and performance of these models on large-scale commercial plants, it has become even more significant to provide suitable benchmarks to validate and test them extensively. In this sense, research reactors offer a promising solution for the initial validation of high-fidelity models, as they are significantly smaller than commercial reactors and their characteristics are well known. In particular, the reactors of the TRIGA family have been used to assess and validate models and methods for Generation-IV designs, as they have some similar features (such as the dominance of natural convection as cooling mechanism and the difficulties in performing sub-channel analysis using standard codes). Still, the computational requirements of high-fidelity models make them unsuitable for real-time analysis, even following their assessment on research reactors. In this sense, Model Order Reduction (MOR) techniques give an additional strategy to reduce the computational cost of high-fidelity models (whilst preserving sufficient accuracy). In particular, this work focuses on Dynamic Mode Decomposition (DMD), a non-intrusive MOR technique that aims at representing models with explicit temporal dynamics by extracting the time-varying characteristics and the governing structures based only on a set of available data, thus without needing any underlying knowledge of the governing equations. In addition, DMD also computes a low-dimensional surrogate of the dynamic matrix of the system, making it suited for stability analysis and real-time evaluations. This work focuses on the application and validation of the DMD method on the Computational Fluid-Dynamics (CFD) model TRIGA Mark II reactor, also discussing in detail the potentiality of this algorithm as an advanced modelling tool for nuclear reactor analysis. (author)
Precipitation-site competition in duplex stainless steels: Cu clusters vs spinodal decomposition interfaces as nucleation sites during thermal aging
Competing microstructural evolution mechanisms can exist simultaneously when duplex stainless steels are operating for several decades in a high temperature service environment. Such competition between different microstructural evolution pathways can be difficult to ascertain using simple model alloy systems necessitating detailed structural and compositional analysis of phase transformation mechanisms in complex alloys Thus, in this study duplex stainless steels with complex but well understood chemistries were used to investigate the relative importance of different heterogeneous nucleation sites – specifically spinodal decomposition and Cu clustering – on precipitation of Ni-Si-Mn particles during long term thermal aging. Precipitation of Ni-Si-Mn particles in duplex stainless steels and ferritic steels during thermal aging and irradiation is known to have profound effect on mechanical properties. Using duplex stainless steels with custom modified compositions along with atom probe tomography characterization and first-passage kinetic Monte Carlo (FPKMC) simulations it is revealed that while the interface between Cr and Fe formed during spinodal decomposition can be a pathway for diffusion of Ni, Si, Mn, and Cu, it is not a site that encourages precipitation of Ni-Si-Mn rich particles. Instead, the presence of a higher concentration of Cu in these steels leads to formation of small Cu clusters with high energy interfaces that act as nucleation sites for Ni-Si-Mn particles because of its strong interaction particularly with Ni increasing the chemical driving force for precipitation. These results will help inform predictive models for the use of duplex stainless steels and other precipitation-hardened alloys for extended operation at high temperatures.
Insights into the thermal decomposition of plutonium(IV) oxalate – a DFT study of the intermediate structures
The thermal decomposition of plutonium oxalate to oxide is one of the most studied reactions in actinide chemistry but the intermediates have been the subject of debate for decades. Recent experimental data suggest that the decomposition of Pu(IV) oxalate in air undergoes dehydration first, then reduction to Pu(III) oxalate. The precise structural modifications that take place are unknown as experiments have not been able to fully characterize the intermediates at the microscopic level. To rectify this, we employed solid state density functional theory calculations at the PBE-D3 level with a Hubbard U correction to model the structures and energetics of potential dehydrated Pu(IV) and Pu(III) oxalate intermediate compounds. Based on the theoretical study presented here, the anhydrous analogues of the known hydrated Pu(IV) and Pu(III) oxalates are the preferred crystal structures formed through an overall exothermic reaction process. However, decomposition could proceed through the formation of a higher energy, more complicated 3D lattice structure with frustrated oxalate binding. It is expected that the intermediates presented here could be identified using spectroscopic techniques to enable further insight into the reaction mechanism.
Towards COP27: Decarbonization patterns of residential building in China and India
As the two largest emerging emitters with the highest growth in operational carbon emissions from residential buildings, the historical emission patterns and decarbonization efforts of China and India warrant further exploration. This study aims to be the first to present a carbon intensity model considering end-use performances, assessing the operational decarbonization progress of residential building in India and China over the past two decades using the newest decomposing structural decomposition approach. Results indicate (1) the annual operational carbon intensity increased by 1.4% and 2.5% in China and India, respectively, between 2000 and 2020. Household expenditure-related energy intensity and emission factors were crucial in decarbonizing residential buildings. (2) Building electrification played a significant role in decarbonizing space cooling (–87.7 in China and – 130.2 kg of carbon dioxide (kgCO 2 ) per household in India) and appliances (~ –169.7 in China and ~ –43.4 kgCO 2 per household in India). (3) China and India collectively decarbonized 1498.3 and 399.7 M-tons of CO 2 in residential building operations, respectively. In terms of decarbonization intensity, India (164.8 kgCO 2 per household) nearly caught up with China (182.5 kgCO 2 per household) in 2020 and is expected to surpass China in the upcoming years, given the country's robust annual growth rate of 7.3%. Overall, this study provides an effective data-driven tool for investigating the building decarbonization potential in China and India, and offers valuable insights for other emerging economies seeking to decarbonize residential buildings in the forthcoming COP28 1 age.
Lawrence Graduate Research Scholar, Allegra Mayer, 2021 (Final Report)
Carbon storage in tropical forest soil depends on the balance between large C inputs from high rates of productivity, and high rates of soil C loss due to rapid decomposition rates. Disturbances to tropical forests, including severe hurricanes, could result in globally significant fluxes of C either to or from forest soils. Severe hurricane events are increasing in frequency in the tropics, yet the vulnerability of soil carbon to a frequent severe hurricane regime is unknown. By constraining a model with samples from before and after three major hurricanes within thirty years, this study examines the cycling of C between soil pools and with depth, under conditions marked by sudden organic matter pulses, rapid shifts in forest structure, and fast decomposition rates.
Small-Signal Angle Stability-Oriented False Data Injection Cyber-Attacks on Power Systems
The small-signal angle stability (SSAS) of a power system is determined by the property of operation points. The widely applied false data injection (FDI) cyber-attack, however, is able to stealthily mislead the optimal power flow (OPF) and thus compromise operation points, leading to damages to the SSAS margin. Here, to provide insights for cyber defenders, this paper proposes and investigates a stealthy SSAS-oriented FDI cyber-attack focusing on two attacking purposes, i.e., the SSAS margin and operation cost, with higher priority on the former one. First, this paper establishes a novel bi-level model with an implicit SSAS constraint based on a structure preserving model to compromise operation points. Then, for the SSAS interarea mode in a typical two-area system, this paper formulates closed-form expressions of how the SSAS margin and operation cost behave with respect to stealthy injections. By comparison, for the SSAS local mode in general power systems, this paper proposes a moving target cyber-attack-based hierarchical solution algorithm. Simulation results on a two-area system, a Kundur 11 bus system, and a modified IEEE 14 bus system demonstrate the significant damaging effects of the proposed SSAS-oriented FDI cyber-attack and the conflict between the two attacking purposes.
Hicks Pries, Sulman, et al 2018. In situ incubation of 13C-labeled litter in three soil pits at Blodgett Forest, CA from 2013 to 2016
Even though over half of the world's soil organic carbon (SOC) is stored in subsoils (>20 cm deep), and the old ages of subsoil OC indicate its cycling differs from surface SOC, there are few studies examining in situ decomposition processes in deep soils. The purpose of this dataset is to elucidate these processes. In this study we added 13C-labeled fine roots to 15, 55, and 95 cm depths of a well-characterized coniferous forest Alfisol and monitored the amount of root-derived C remaining over 6, 12, and 30 months. We recovered the root-derived C in microbial phospholipid fatty acids (PLFAs) after 6 months and in coarse (>2 mm) particulate, fine (<2 mm) particulate, and dense, mineral-associated pools after 6, 12, and 30 months. Overall, root decomposition in the first 6 months was similar among all depths but significantly diverged at 30 months with faster decomposition at 15 cm than at 95 cm. There were more fungal and Gram negative-associated PLFAs at 15 cm than at 95 cm, and 13C analysis revealed those microbial groups preferred the added root carbon to native SOC. Mineral-associations were not the cause of slower decomposition at depth because similar amounts of applied root C was recovered in the dense fraction at all depths. The largest difference among depths was in the amount of root C recovered in the coarse particulate fraction, which was greater at 95 cm (50%) than at 15 cm (15%). Slower decomposition of the particulate pool at depth likely contributed to the increase in C:N ratios and depletion of δ13C values below 60 cm depth in our soil profiles. Simulations of these soils using the CORPSE model, which incorporates microbial priming effects and mineral stabilization of SOC, reproduced patterns of particulate and mineral-associated SOC over both time and depth and suggested that a lack of priming by root exudates at depth could account for the slower decomposition rate of particulate root material. Decomposition of deep particulate SOC may increase if root exudation or dissolved OC transport to depth increases.This dataset includes (1) characterization of the soil pits prior to litter addition, (2) characterization of how the litter changed after 6, 12, and 30 months, and (3) the CORPSE model structure, parameterization, and output.
Data-driven recovery of hidden physics in reduced order modeling of fluid flows
In this article, we introduce a modular hybrid analysis and modeling (HAM) approach to account for hidden physics in reduced order modeling (ROM) of parameterized systems relevant to fluid dynamics. The hybrid ROM framework is based on using first principles to model the known physics in conjunction with utilizing the data-driven machine learning tools to model the remaining residual that is hidden in data. This framework employs proper orthogonal decomposition as a compression tool to construct orthonormal bases and Galerkin projection (GP) as a model to build the dynamical core of the system. Our proposed methodology hence compensates structural or epistemic uncertainties in models and utilizes the observed data snapshots to compute true modal coefficients spanned by these bases. The GP model is then corrected at every time step with a data-driven rectification using a long short-term memory (LSTM) neural network architecture to incorporate hidden physics. A Grassmannian manifold approach is also adopted for interpolating basis functions to unseen parametric conditions. The control parameter governing the system's behavior is thus implicitly considered through true modal coefficients as input features to the LSTM network. The effectiveness of the HAM approach is then discussed through illustrative examples that are generated synthetically to take hidden physics into account. Furthermore, our approach thus provides insights addressing a fundamental limitation of the physics-based models when the governing equations are incomplete to represent underlying physical processes.