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A Scalable Meter Placement Method for Distribution System State Estimation
This paper studies the optimal meter placement problem for distribution system state estimation given limited measurement resources. We formulate the problem as a mixed integer semi-definite programming that minimizes the worst case estimation errors over a set of operating points. To solve the problem, we first relax the problem as a convex optimization problem. Motivated by the lack of scalability of existing solvers, we next leverage the special structure of the cost function and propose an algorithm based on barrier method that solves the problem with significantly better numerical performance. The proposed method has been validated on the IEEE 13-bus, IEEE 123-bus, and IEEE 8,500-bus feeders.
Constrained or unconstrained? Neural-network-based equation discovery from data
Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.
Protective barriers and related methods
Corner assemblies for protective barriers for protecting assets (e.g., electrical power transformers and other substation components). The corner assembly includes two guide structures oriented perpendicular to each other. The corner assembly includes at least two protective member assemblies disposed within the two guide structures. Each protective member assembly includes two side members within protective members extending horizontally between the two side members.
Thermal barrier systems and methods for access delay
Protective barrier includes a soft metal that inhibits or delays thermal or grinding attack tool penetration. The soft metal, which may be disposed between other layers and otherwise delays thermal attack by expanding or “puffing” during attack. The soft metal can inhibit mechanical attack by rapid ablation and wear of a cutting wheel or blade. The protective barrier may additionally include an oxide or carbide layer.
pnnl/neural_ODE_ICLR2020
We show how to model discrete ordinary differential equations (ODE) with algebraic nonlinearities as deep neural networks with varying degrees of prior knowledge. We derive the stability guarantees of the network layers based on the implicit constraints imposed on the weight's eigenvalues. Moreover, we show how to use barrier methods to generically handle additional inequality constraints. We demonstrate the prediction accuracy of learned neural ODEs evaluated on open-loop simulations compared to ground truth dynamics with bi-linear terms.
Geospatial analysis of preterm and small-for-gestational age births in Washington D.C.
Background: This study is based on the recognition that adverse pregnancy outcomes significantly affect maternal and infant health, leading to increased morbidity and mortality. These outcomes are shaped by a complex interplay of individual-level factors—like maternal age and education—and community-level influences, including socio-economic status and access to healthcare. Understanding these determinants is crucial for developing effective public health strategies, especially for marginalized populations, by identifying high-risk areas and informing targeted interventions that address both individual and structural barriers. Methods: We utilized geospatial analysis to explore the association between individual- and community-level factors and adverse pregnancy outcomes, specifically preterm birth (PTB) and small-for-gestational-age (SGA) birthweight in Washington, D.C. We used Empirical Bayes smoothing methods to calculate rates of adverse birth outcomes from 2010 to 2018 at the U.S. Census tract–level. Spatial scan statistics were used to investigate if adverse birth outcomes clustered in specific areas. ANOVA tests were conducted for individual- and community-level factors within identified clusters. Results: Spatial analysis identified significant high-risk clusters for PTB and SGA infants primarily in southeastern Washington, D.C., particularly in Wards 7 and 8. Individuals residing within these clusters experienced a 47% increased risk of PTB (RR = 1.467) and a 56% increased risk of SGA (RR = 1.560) compared to those outside clusters. Space–time analysis revealed temporal variation, with PTB clusters persisting from 2011 to 2014 and SGA clusters extending through 2017. Compared to low-risk clusters, high-risk clusters had younger birthing individuals (mean age ~26.5 vs. ~33 years), lower maternal college degree attainment (~20% vs. ~80%), higher rates of late or no prenatal care (~16% vs. 11%), and increased prevalence of smoking and hypertension (all P < 0.001). Community-level indicators showed lower median household incomes ($\$40,000$ vs. ~$\$105,000$), greater poverty (~16% vs. ~7% below $\$10,000$/year), higher public assistance use (~32% vs. ~5%), and reduced healthcare access (greater distances to emergency and specialty care) in high-risk areas (all P < 0.001). Neighborhood deprivation indices were significantly elevated, commutes were longer, and population density was lower in these clusters. These findings highlight that adverse birth outcomes cluster in neighborhoods with pronounced socioeconomic and health disparities. Conclusion: High-risk birth clusters highlight intertwined factors: individual, socio-economic, and geographic. Addressing these requires comprehensive interventions focusing on social and structural determinants of health.
Rethinking hysteresis in magnetic materials
Abstract Magnetic materials with zero hysteresis are posed to have a significant impact on sustainable energy conversion, electronics, and communication technologies. As the global market for soft magnetic materials continues to expand, driven by increasing demand in renewable energy, automotive, and power transmission sectors, it is important to design magnets that can be cycled under an external field reversibly and rapidly multiple times without a decay in magnetic response. However, designing magnets with small hysteresis has been a challenge because we do not fully understand its origins. In this article, I outline key research efforts investigating the fundamental mechanisms underpinning hysteresis in soft magnets and, highlight recent developments in the use of nonlinear analysis and nucleation barrier methods to predict coercivity in these materials. Graphical abstract
Role of momentum in the generator-coordinate method applied to barrier penetration
Nuclear fission at barrier-top energies is conventionally modeled by a one-dimensional Schrödinger equation applied to internal fission channels, but that treatment is hard to justify in the configuration-interaction approach to nuclear Hamiltonians. Here we show that inclusion of states of finite momentum by the generator coordinate method (GCM) considerably extends the range of energies at which GCM-based Hamiltonians could reproduce the Schrödinger treatment. Furthermore, the transmission probabilities for crossing the barrier are calculated by a discrete version of Kohn's variational method, which may also be useful for other systems of interacting fermions.
Systems and methods for thermal barrier coatings to modify engine component thermal characteristics
An engine component comprises one or more thermal barrier coatings applied to one or more areas of the engine component. The thermal barrier coatings reduce the temperature rate of change of the areas to which the thermal barrier coating is applied. Reducing the temperature rate of change can help reduce lattice structure damage caused by different temperatures in different areas of the engine component. The thermal barrier coating can be applied as a monolithic layer on a surface of the engine component or can be applied in different areas using patterns. The patterns allow for the tuning of the performance characteristics (temperature rate of change) of the areas of the engine component and can help reduce defect propagation, such as cracks, in the thermal barrier coating from one area of the thermal barrier coating to other areas of the thermal barrier coating.
Sorption agent, method of making a sorption agent and barrier system
An anionic sorption agent, method for forming the anionic sorption agent and a barrier system are disclosed. The anionic sorption agent including a modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites. The method for forming the anionic sorption agent includes providing a pseudo or glycol-boehmite base and contacting the pseudo or glycol-boehmite base a modifying composition comprising a metallic ion to form the modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites. The barrier system includes the anionic sorption agent comprising a first barrier component comprising a modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites and a second barrier component comprising a cationic sorption agent.
Synchronization between processes in a coordination namespace
A system and method of supporting point-to-point synchronization among processes/nodes implementing different hardware barriers in a tuple space/coordinated namespace (CNS) extended memory storage architecture. The system-wide CNS provides an efficient means for storing data, communications, and coordination within applications and workflows implementing barriers in a multi-tier, multi-nodal tree hierarchy. The system provides a hardware accelerated mechanism to support barriers between the participating processes. Also architected is a tree structure for a barrier processing method where processes are mapped to nodes of a tree, e.g., a tree of degree k, to provide an efficient way of scaling the number of processes in a tuple space/coordination namespace.
Coating compositions, elastic barrier coatings formed therefrom, and methods of applying such coatings
A coating composition includes an aqueous carrier medium, at least a first polymer, and polymeric core-shell particles dispersed in the aqueous carrier medium. The first polymer includes: (i) a barrier segment having aromatic groups and urethane linkages, urea linkages, or a combination thereof; and (ii) an elastomeric segment having a glass transition temperature of less than 0° C. The barrier segment can make up at least 30% of the first polymer, based on the total solids weight of the first polymer.
Microstructure geometry for thermal barrier coatings to mitigate CMAS attack
Thermal barrier coatings and methods to make such coatings present improved resistance to CMAS infiltration. The method for forming a thermal barrier coating includes applying a layer of the thermal barrier coating to a component having a surface, forming a plurality of first channels in the thermal barrier coating, and forming a plurality of second channels in the thermal barrier coating. The first channels extend through a thickness of the thermal barrier coating from an interface with the surface of the component to a free surface opposite the interface. The second channels are disposed between the free surface and the interface and extending lengthwise generally parallel to the free surface of the thermal barrier coating, wherein the thermal barrier coating comprises a material comprising yttrium aluminum garnet (YAG) or yttria stabilized zirconia (YSZ).
A Robust Methodology to Elucidate Kinetics of Room Temperature Electrochemical Propane Adsorption on Platinum
Electrocatalytic activation of alkanes can further decarbonize chemical manufacturing by leveraging affordable renewable electricity and readily available shale gas reserves in the United States. Earlier works have identified the unique role of Pt in adsorbing and activating alkanes, like propane, at room temperature in acidic, aqueous electrolytes, revealing spontaneous formation of deeply dehydrogenated propane-derived surface species with an intact C 3 - backbone. Although an adsorption mechanism was hypothesized, it has not been explicitly investigated to date, preventing the quantification of kinetic rate parameters. A robust methodology to investigate and benchmark propane adsorption kinetics on Pt is critical for the rational design of electrocatalysts that exhibit higher selectivity toward desired partially oxidized products. Herein, we analyze an oxidative current transience that appears during the adsorption of propane on Pt in aqueous electrochemical conditions and develop a methodology that elucidates the adsorption mechanism and enables quantification of rate parameters such as order dependences and apparent activation barriers. This method yields an expected first-order dependence with respect to propane concentration at low coverage and reveals a second-order dependence with respect to the concentration of surface active sites. Additionally, the apparent activation barrier for propane adsorption was calculated using an Arrhenius analysis of the current transience under temperature control. The experimentally measured activation barrier of 35 kJ mol –1 is in excellent agreement with the theoretical barrier calculated by density functional theory (DFT). The kinetic analysis was extended, via the use of transition state theory, to extract entropy and enthalpy of activation, yielding consistent results with the proposed two-step adsorption mechanism and DFT calculations. These results demonstrate reliable quantification of kinetic parameters for electrocatalytic activation of C–H bonds in alkanes that can be employed for rational catalyst development for a versatile range of electrocatalytic conditions.
Heavy-ion fusion cross section formula and barrier height distribution
Methods for obtaining fusion cross section formulas are discussed, especially for those that take on an empirical form. A new expression starting with σ( E) = $\frac{1}{E}$ $\int ^{E}_{E_0}$ $(\int^{E'}_{E_0} B (E^\shortparallel) dE^\shortparallel)dE'$ has been explored. Here, B(E) is a reasonable, assumed function of the barrier height distribution, $\frac{d^2 (\sigma E)}{dE^2}$. Further, the resulting analytic cross section formula reproduces very well the excitation functions for many light and heavy fusion systems across wide energy ranges, when B(E) is assumed to be a multi-Gaussian function. This study offers an improved determination of the fusion barrier height distribution over other numerical techniques.
Hardware-in-the-Loop Efficiency Analysis of Tidal Power Systems
Marine renewable energy presents an opportunity for sustainable power in areas located near and in the marine environment. In particular, tidal energy offers a consistent and reliable resource. However, tidal energy harvesting systems seeking to take advantage of this resource face challenges associated with power electronics. Power conversion electronics struggle with low efficiencies, lack of off-the-shelf options for small-scale energy harvesting systems, and limited opportunities for testing with deployed turbines. This work demonstrates a hardware-in-the-loop experimental design and methodology for characterizing efficiency of tidal turbine power electronics and control methods. The suggested method removes barriers to testing these power electronics by eliminating the need for a physical tidal turbine and generator to produce accurate harvested tidal power profiles.