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DOE OSTI2022

Mapping structural heterogeneity at the nanoscale with scanning nano-structure electron microscopy (SNEM)

Here, in this work, we explore the use of scanning electron diffraction (also known as 4D-STEM) coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order (structure and chemistry) as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr 65 Cu 17.5 Ni 10 Al 7.5 bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don’t require energy filters on the instrument. We spatially map different quantities of interest (QoI’s), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI’s. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review

Sensors are the key information source for fault detection and diagnostics (FDD) in buildings. However, sensors are often not properly designed, installed, calibrated, located, and maintained, which negatively impacts FDD performance. Several sensor-related FDD topics have been widely studied, covering a wide range of fault types and applications. However, it is difficult to get a clear picture of the technical development of sensor-related topics in FDD. A systematic review of sensor topics is needed to summarize the existing research in a logical way, draw conclusions on the current development, and predict the future development of sensors in building FDD. To address this gap, we conducted a comprehensive literature review of more than 100 FDD-sensor-related papers. In this article, we subdivide the FDD tasks into building-level, system-level, and component-level FDD, and review sensor-related topics in each category. Our major conclusions are: (a) current data-driven FDD research focuses more on FDD algorithms than sensors, (b) sensor “hardware” research topics are less studied than sensor “software” topics, (c) very few papers focus on sensor engineering as an integral aspect of FDD development, and (d) some important sensor topics, such as sensor cost-effectiveness and sensor schema/layout/location, are not well studied. Finally, we discuss the need for a systematic framework of FDD sensors and models to integrate sensor design/selection, sensor data analysis/mining, feature selection, physics-based or data-driven algorithm development, sensor fault detection, sensor calibration, and sensor maintenance. Finally, expert interviews are conducted to validate the above findings and conclusions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗

DOE OSTI2019

Applications of the worldline Monte Carlo formalism in quantum mechanics

Highlights: Universal approach to the numerical computation of the Euclidean path integral. Inspired by recent work in relativistic quantum field theory. Here adapted to non-relativistic quantum mechanics. Worked out for the computation of propagators and ground-state energies. Special smoothing procedure for singular potentials. -- Abstract: In recent years efficient algorithms have been developed for the numerical computation of relativistic single-particle path integrals in quantum field theory. Here, we adapt this “worldline Monte Carlo” approach to the standard problem of the numerical approximation of the non-relativistic path integral, resulting in a formalism whose characteristic feature is the fast, non-recursive generation of an ensemble of trajectories that is independent of the potential, and is thus universally applicable. The numerical implementation discretises the trajectories with respect to their time parametrisation but maintains a continuous spatial domain. In the case of singular potentials, the discretised action gets adapted to the singularity through a “smoothing” procedure. We show for a variety of examples (the harmonic oscillator in various dimensions, the modified Pöschl–Teller potential, delta-function potentials, the Coulomb and Yukawa potentials) that the method allows one to obtain fast and reliable estimates for the Euclidean propagator and use them in a certain time window suitable for extracting the ground state energy. Usually Monte Carlo calculations in quantum mechanics are performed using algorithms that involve a more specific adaptation to the potential, and we briefly compare our method with the most widely used such algorithms, which are of Metropolis type. As an aside, we apply it for studying the classical limit where nearly classical trajectories are expected to dominate in the path integral. We expect the advances made here to be useful also in the relativistic case.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Power spectrum and form factor in random diagonal matrices and integrable billiards

Triggered by a controversy surrounding a universal behavior of the power spectrum in quantum systems exhibiting regular classical dynamics, we focus on a model of random diagonal matrices (RDM), often associated with the Poisson spectral universality class, and examine how the power spectrum and the form factor get affected by two-sided truncations of RDM spectra. Having developed a nonperturbative description of both statistics, we perform their detailed asymptotic analysis to demonstrate explicitly how a traditional assumption (lying at the heart of the controversy) – that the power spectrum is merely determined by the spectral form factor – breaks down for truncated spectra. This observation has important consequences as we further argue that bounded quantum systems with integrable classical dynamics are described by heavily truncated rather than complete RDM spectra. High-precision numerical simulations of semicircular and irrational rectangular billiards lend independent support to these conclusions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

A review of machine learning in building load prediction

The surge of machine learning in recent years has been empowering engineer modeling in various fields. The decreasing hardware cost, increasing data accessibility, and advances of building automation system (BAS) allow the collection and storage of a significant amount of building operation data. The two facts provide great opportunities of applying machine learning to building energy systems modeling and analysis. There are a great number of research papers on this topic but there lacks a comprehensive and general review to summarize the current development, limitations, gaps and future trend. In this review paper series, machine learning techniques in building energy system modeling and analysis are reviewed under the organization and logic of the machine learning definition by Tom M. Mitchell: a computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E. This paper is the first part of the review paper series, which focuses on building load prediction. First, the applications of building load prediction model (task T) are reviewed. Then, the modeling algorithms improving machine learning performance and accuracy (performance P) are reviewed. At the same time, the literature on the data perspective for modeling (experience E), including data engineering from sensors level to data level, pre-processing, feature extraction and selection, is reviewed. Finally, what is well-studied and what is lacking but with great potential are concluded; the gaps between present and future utilization of machine learning techniques are identified; the future trend and development are also predicted. The target readers of this paper are not only researchers from the building side who can get exposed to cutting edge machine learning tools, but also those from machine learning side who can understand the potential and challenge to apply machine learning in buildings.

Liang, Zhang↗

DOE OSTI2020

Accelerating geostatistical modeling using geostatistics-informed machine Learning

Ordinary Kriging (OK) is a popular geostatistical algorithm for spatial interpolation and estimation. The computational complexity of OK changes quadratically and cubically for memory and speed, respectively, given the number of data. Therefore, it is computationally intensive and also challenging to process a large set of data, especially in three-dimensional (3D) cases. This paper develops a geostatistics-informed machine learning (GIML) model to improve the efficiency of OK by reducing the number of points required to be estimated using OK. Specifically, only a very few of the unknown points are estimated by OK to get the weights and estimations, which are used as the training dataset. Moreover, the governing equations of OK are used to guide our proposed machine learning to better reproduce the spatial distributions. Our results show that the proposed GIML can reduce the computational time of OK by at least one order of magnitude. The effectiveness of the GIML is evaluated and compared using a 2D case. Furthermore, we demonstrate its efficiency and robustness by considering a different number of training samples on various 3D simulation grids.

58 GEOSCIENCES↗

DOE OSTI2020

Hydrodynamics of countercurrent flows in a structured packed column: effects of initial wetting and dynamic contact angle

The structured packed column has gained considerable attention as a prospective technology to mitigate carbon dioxide emissions from thermal power plants. The computational flow investigation of such systems is a multiscale problem, and local hydrodynamics plays a key role in overall column efficiency. Accordingly, multiphase flow studies in the representative elementary unit (REU) of a packed column can offer insights into the local flow behavior such as flow pattern, interfacial area, etc. The interfacial area plays a key role in the overall interphase mass transfer and thereby in separation efficiency. The predicted effective area matches well with corresponding ones in experiments for Mellapak 250.Y packings. The contact angle (i.e., solid substrate behavior) is one of the critical factors that dictates wettings, and thereby the interfacial area. The dynamic contact angle (DCA) was considered in order to explore the effects of contact angle hysteresis on the interfacial area. DCA has more pronounced impact on the interfacial area for a solvent possessing a higher value of surface tension and equilibrium contact angle. The interfacial area shows temporal undulation and does not achieve a pseudo steady state due to the capillary force. In contrary, the interfacial area gets a net value for a solvent having low surface tension value. Further, specification of the static contact angle at the solid substrate shows the least value of the interfacial area. We also extensively studied the effect of the initial sheet condition (dry vs wet) on the interfacial area. The initially wetted sheets show slightly higher value of the interfacial area as compared to the initially dry sheets at a fixed flow rate for a solvent having high surface tension value. Wetting hysteresis decreases with increasing liquid loads. On the other hand, wetting hysteresis does not occur for a solvent having value of low surface tension and equilibrium contact angle.

structured packings, dynamic contact angle, interf↗

DOE OSTI2023

Perovskite evolution on La modified Mn 1.5 Co 1.5 O 4 spinel through thermal ageing with enhanced oxidation activity: Is sintering always an issue?

Non-precious metal oxides have great potential in catalytic deep oxidation of emitted hydrocarbons to help address various environmental pollution concerns. However, sintering issue is a stumbling block in practical application upon long-term high-temperature operation or thermal shock experience. Herein, La was applied to modify Mn 1.5 Co 1.5 O 4 spinel to get a highly active oxidation catalyst with exceptional stability against high-temperature thermal ageing treatment. With thermal ageing at 750 °C for 100 h, the La modified Mn–Co composite reached 90 % conversion in toluene oxidation at 265 °C under the high WHSV of 120,000 mL g -1 h -1 , while this value increased to 312 °C over blank Mn–Co spinel. The addition of La not only inhibited the growth of spinel nanocrystals, but also brought about perovskite formation through sintering, generating perovskite-spinel interfaces, thus enhancing both activity and stability. Meanwhile, the La modified Mn–Co spinel exhibited superior performance in presence of water or sulfur dioxide after thermal ageing treatment. In conclusion, this work offers a versatile strategy to design anti-sintering and active oxidation nanocatalysts based on non-precious metal oxides for industrial applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Development of ceria-supported metal-oxide (MO x /CeO 2 ) catalysts via a one-pot chemical vapor deposition (OP-CVD) technique: Structure and reverse water gas shift reaction study

Current synthesis techniques for metal oxide (MO x )-supported catalysts have certain limitations of undesired target loading, ineffective dispersion of active species over the surface, uncontrolled particle size of active species, and complicated synthesis steps. Here, we developed a one-pot chemical vapor deposition (OP-CVD) methodology; by using which a solid metal precursor forms a vapor in a controlled condition and gets supported over the surrounding matrix. The theoretical stability followed by experimental validation using TGA is crucial for selecting the metal precursors. Three simple steps viz. premixing, dispersion, and rapid fixation by calcination are involved in the catalyst development via the OP-CVD approach. This study solely focused on the synthesis of 3d transition MO x over ceria support. The physicochemical characterizations of the prepared catalysts were performed by XRD, ICP-OES, SEM-EDX, CO pulse chemisorption, XANES, and EXAFS analyses to understand the crystal structure of involved species, target metal loading, dispersion, and particle size and prove the feasibility and viability of OP-CVD. The prepared catalysts were further tested for reverse water gas shift (RWGS) reaction to link their structural information with activity. The RWGS reaction data showed that the CO activity and CO selectivity were metal - and metal precursor-dependent. Higher CO activity of > 0.1 mol/h g-cat was observed for Cu and Co-based catalysts, with CO selectivity of ~100 %. This study provides an opportunity to produce efficient supported catalysts in a convenient way, providing effective catalytic activity.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Understanding the setting and hardening process of wollastonite-based brushite cement. Part 2: Influence of the boron and aluminum concentrations in the mixing solution

The mixing solution of wollastonite-based brushite cement is a phosphoric acid solution containing metallic cations and borax. This work complements a previous study devoted to the influence of the H{sub 3}PO{sub 4} concentration, Ca/P and liquid-to-solid (l/s) ratios on the setting and hardening process of the binder by providing new insight into the role of aluminum and boron. Boron retards the setting and decreases the heat released during the process. It also contributes to reduce the macroporosity of the hardened material but yields to poor compressive strength. With aluminum in the mixing solution, the mechanical properties are greatly improved thanks to the precipitation of an amorphous aluminophosphate which increases the density of the cement matrix. But aluminum alone leads to fast setting. A joint addition of boron and aluminum to the mixing solution makes it possible to get a material with optimized properties both in fresh and hardened states.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Fracturing process of micro-concrete under uniaxial and triaxial compression: Insights from in-situ X-ray mechanical tests

This paper presents an experimental study of concrete at meso-scale (aggregates, macro-pores and mortar matrix) in order to get a better understanding of the local failure mechanisms known to drive the macroscopic mechanical behaviour of the material. The main originality comes from conducting in-situ X-ray mechanical tests on micro-concrete samples of realistic composition (including cement, sand, aggregates and water), under uniaxial compression and, for the first time, under triaxial compression at 5 MPa, 10 MPa and 15 MPa confining pressures. A timeseries analysis of the set of 3D images coming from each test allows for the measurement of the 3D kinematic fields (displacement and strain fields) throughout the experiments. The different failure patterns observed for each loading path are discussed, along with a quantification of the 3D fracturing processes at the scale of the largest heterogeneities (aggregates and macro-pores). With an increasing level of confinement, the transition from brittle to ductile response is observed, as well as an increase of the strength of the material. The pronounced impact of the meso-scale heterogeneities of concrete on their local failure mechanisms is highlighted. It is shown that strain localisation mainly originates between aggregates and mortar matrix, with the shape and location of the largest aggregates and macro-pores essentially driving the propagation of the cracking network.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Optimal scheduling for profit maximization of energy storage merchants considering market impact based on dynamic programming

This paper analyzes how electricity merchants' market impact affects merchants' profit. Energy storage has long been studied for its role in maximizing profit, and merchant decisions are assumed to have no impact on market prices. However, the trading decisions of large-scale energy storage merchants (e.g., pumped storage hydro) will affect the market prices. This paper employs dynamic programming theory to investigate merchants' optimal economic dispatch considering the market impact and physical characteristics of storage systems. Our findings show that the State-of-Charge (SOC) based analytical solution significantly facilitates energy storage merchants' decision-making. The SOC range is segmented into three regions by two optimal SOC reference points, which depend on the available energy in storage, given prices, and market impact. By comparing the current storage SOC with the reference points, the merchant can get the corresponding optimal actions. We analytically show that if the merchant neglects the market impact on the power market, she will exaggerate her expected profit when the price-taker and price-maker merchants have the same generating and pumping upper limits offered to Independent System Operators (ISOs). Furthermore, the profit-maximizing merchant must, therefore, assay to balance the trade-off correctly between the intensity of market impact and the dispatched power. Our findings are verified by numerical simulation, and results demonstrate the ramifications for electricity merchants in energy arbitrage decisions.

25 ENERGY STORAGE↗

DOE OSTI2022

Optimal economic dispatch policy for prosumer with energy storage considering self-consumption demand

This paper analyzed the effects of self-consumption demand on the joint economic dispatch of prosumers (energy consumers who are also producers), particularly for prosumers with both energy storage and distributed energy sources (DERs). Studies in the existing literature on the economic dispatch scheduling policy of energy storage, mostly from the perspective of electricity merchants, do not address the impacts of self-consumption demand. However, due to the intermittent and high levels of uncertainty regarding DERs generation and the dynamic demand of the prosumer, production and consumption are not always simultaneous; there are two possible scenarios in each period depending on whether DERs generation can meet prosumers' self-consumption or not. Incorporating the self-consumption demand will pose modeling challenges since these two scenarios cannot occur simultaneously in each period, and different scenarios require different decisions for prosumers. Further, this paper analyzed the two scenarios separately to find the optimal storage scheduling strategy, and the results were combined to get the optimal global solution. We focused on prosumers' economic decision-making while considering self-consumption demand and the physical constraints of a battery based on dynamic programming. Our study showed that the feasible state of charge (SOC) range of storage can be segmented into several sub-ranges by SOC reference points under the above two scenarios. As a result, a prosumer's optimal scheduling can be uniquely and conveniently selected based on the sub-ranges within which the current SOC falls. The results, therefore, provided multistage decision-making guidance for prosumers with energy storage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Correcting distortions of thin-walled machined parts by machine hammer peening

Thin-walled aerostructural components frequently get distorted after the machining process. Reworking to correct distortions or eventually rejecting parts significantly increases the cost. This paper proposes a new approach to correct distortions in thin-walled components by strategically applying hammer peening on target surfaces of a machined component. Aluminium alloy 7475-T7351 was chosen for this research. The study was divided in two stages. First, the residual stresses (RS) induced by four different pneumatic hammer peening conditions (modifying the stepover distance and initial offset) were characterised in a test coupon, and one of the conditions was selected for the next stage. In the second stage, a FEM model was used to predict distortions caused by machining in a representative workpiece. Then, the RS induced by hammer peening were included in an FEM model to define two hammer peening strategies (varying the coverage area) to analyse the capability to reduce distortions. Two workpieces were machined and then treated with the simulated hammer peening strategies for experimental validation. Results in the test coupon showed that pneumatic hammer peening can generate high compressive RS (-50 to –350 MPa) up to 800 μm depth, with their magnitude increasing with a reduced stepover distance. Application of hammer peening over 4% of the surface of the representative workpiece reduced the machining-induced distortions by 37%, and a coverage area of 100% led to and overcorrection by a factor of five. This confirms that hammer peening can be strategically applied (in target areas and changing the percentage of coverage) to correct low or severe distortions.

42 ENGINEERING↗

DOE OSTI2023

From electronic structure to model application of key reactions for gasoline/alcohol combustion: Hydrogen-atom abstractions by $CH_3\dot{O}$ radical

H-atom abstraction by methoxy radical ($CH_3\dot{O}$) plays an important role in capturing the kinetics of reactions between gasoline components and alcohols. This study focuses on determining the reaction rates and thermodynamic properties of methoxy radical reactions with five gasoline fuel components: n-heptane, iso-octane, 1-hexene, cyclopentane and toluene. Electronic structure calculations were per-formed for all the stationary points with M06-2X/6 -311 ++ g(d,p) method. G3 composite method with atomization method is used for determining Δ f H 0 of all the closed shell and radical species, using which the necessary thermodynamic data of all the species was determined. Coupled cluster theory QCISD(T)/cc-pVXZ (where X = D and T) and Møller-Plesset perturbation theory MP2/cc-pVXZ (where X = D, T and Q) were used to calculate single point energies. Subsequently, rate constants for all hydrogen atom ab-straction channels have been performed using conventional transition state theory with unsymmetric tunneling corrections. A systematic comparison of rates for abstraction from different sites within the same species and same site from different species is done in order to get insights into this reaction class. Here, the computed thermodynamic properties and rate constants were incorporated into a recent gasoline mechanism to investigate the impact of the calculations performed in this work. A shift in predicted NTC (negative temperature coefficient) behavior and significant reduction in model reactivity is observed upon incorporating the rates calculated herein.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Tight-binding study of beryllium

In this work, we have applied the Naval Research Laboratory Tight-binding (NRL-TB) Method to the hcp alkaline-earth metal Beryllium. This approach consists of fitting to energy band and total energy data generated by the Linearized Augmented Plane Wave (LAPW) for the fcc, bcc, sc, and hcp structures as a function of volume. First, we found that using the Generalized Gradient Approximation (GGA) for the input data secures a better agreement to experimental volumes than the LDA. Second, we found that including small volumes to the fit is needed to get TB parameters that are transferable enough to successfully predict the energies of structures that were not fitted, and to perform molecular dynamics (MD) simulations. The non-orthogonal Hamiltonian obtained, is successful in correctly predicting the hcp lattice as the ground state of Be and finding eight other structures, that were not fitted to LAPW, positioned at higher energies. In addition, the NRL-TB produces accurate energy bands, densities of states, elastic constants, the Bain path and several quantities derived from MD simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗