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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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Dynamics-based deep learning methods to modernize current scientific computing methods. Neuromancer is currently capable of solving inverse problems for a system of ordinary differential equations. The functionality includes system identification and constrained optimal control of unknown or partially known ODEs.
We develop physics-constrained and control-oriented predictive deep learning models for the thermal dynamics of a real-world commercial office building. The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture. Specifically, our model mimics the structure of the building thermal dynamics model and leverages penalty methods to model inequality constraints. Additionally, we use constrained matrix parameterization based on the Perron-Frobenius theorem to bound the eigenvalues of the learned network weights. We interpret the stable eigenvalues as dissipativeness of the learned building thermal model. We demonstrate the effectiveness of the proposed approach on a dataset obtained from an office building with $20$ thermal zones.
Here, this letter investigates the importance of integrating the coherency knowledge for designing controllers to dampen sustained oscillations in wide-area power networks with significant penetration of inverter-interfaced resources. Coherency is a fundamental property of power systems, where time-scale separation in frequency dynamics leads to clustered behavior among generators of different groups. Large-scale penetration of inverter-driven low inertia resources replacing conventional synchronous generators (SGs) can lead to perturbation in the coherent partitioning; hence, integrating such information is of utmost importance for oscillation control designs. We present the coherency-aware design of a distributed output feedback-based reinforcement learning method that additionally incorporates risk constraints to capture the uncertainties related to net-load fluctuations. The use of domain-aware coherency information has produced improved training and oscillation performance than the coherency-agnostic control design, hence proving to be effective in controller design. Finally, we validated the proposed method with numerical experiments on the benchmark IEEE 68-bus test system.
Total variation integer optimal control problems admit solutions and necessary optimality conditions via geometric variational analysis. In spite of the existence of said solutions, algorithms which solve the discretized objective suffer from high numerical cost associated with the combinatorial nature of integer programming. Hence, such methods are often limited to small and medium-sized problems. We propose a globally convergent, coordinate descent–inspired algorithm that allows tractable subproblem solutions restricted to a partition of the domain. Our decomposition method solves relatively small trust-region subproblems that modify the control variable on a subdomain only. Given nontrivial subdomain overlap, we prove that a global first-order necessary optimality condition is equivalent to a first-order necessary optimality condition per subdomain. We additionally show that a sufficient decrease is achieved on a single subdomain by way of a trust-region subproblem solver using geometric measure–theoretic arguments, which we integrate with a greedy patch selection to prove convergence of our algorithm. In conclusion, we demonstrate the practicality of our algorithm on a benchmark large-scale, PDE-constrained integer optimal control problem and find that our method is faster than the state of the art.
Aggregation of electric vehicles (EVs) is a promising technique for providing secondary frequency regulation (SFR) in highly renewable energy-penetrated power systems. Equipped with energy storage devices, EV aggregation can provide reliable SFR. However, the main challenge is to guarantee reliable intra-interval SFR capacities and inter-interval delivery following the automatic generation control (AGC) signal. Furthermore, aggregated EV SFR provision will be further complicated by the EV charging time anxiety because SFR provision might extend EV's charging time. This paper proposes a deliverable EV SFR provision with a charging-time-constrained control strategy. First, a charging-time-constrained EV aggregation is proposed to address the uncertainty of EV capacity based on the state-space model considering the charging-time restriction of EV owners. Second, a real-time economic dispatch and time domain simulation (RTED-TDS) cosimulation framework is proposed to verify financial results and the dynamic performance of the EV SFR provision. Last, the proposed charging time-constrained EV aggregation is validated on the IEEE 39-bus system. In conclusion, the results demonstrate that with charging time-constrained EV aggregation, the dynamic performance of the system can be improved with a marginal increase in total cost. More importantly, the charging time constraint can be respected in the proposed SFR provision of the EV aggregation.
In this study, isotopic information from 81 snowpits was collected over a 5-year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land-surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d-excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post-depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt-freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d-excess decrease) in snowmelt was 0.02‰ per day per 100-m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.
This Letter reports the observation of τ-lepton pair production in ultraperipheral lead-lead collisions, Pb+Pb→Pb(γγ→ττ)Pb, and constraints on the τ-lepton anomalous magnetic moment, a τ . The dataset corresponds to an integrated luminosity of 1.44 nb -1 of LHC Pb+Pb collisions at $\sqrt{s_{NN}}$ =5.02 TeV recorded by the ATLAS experiment in 2018. Selected events contain one muon from a τ-lepton decay, an electron or charged-particle track(s) from the other τ-lepton decay, little additional central-detector activity, and no forward neutrons. The γγ→ττ process is observed in Pb+Pb collisions with a significance exceeding 5 standard deviations, and a signal strength of μ ττ =1.03$^{+0.06}_{-0.05}$ assuming the Standard Model value for a τ . To measure a τ , a template fit to the muon transverse-momentum distribution from τ-lepton candidates is performed, using a dimuon (γγ→μμ) control sample to constrain systematic uncertainties. The observed 95% confidence-level interval for aτ is -0.057 < a τ < 0.024.
Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.
We report a measurement of the total cross section for muon antineutrino charged-current quasi-elastic scattering on hydrogen, $\bar ν_μ{\rm H} \to μ^+ n$, in the NOvA near detector using a $1.2\times10^{21}$ proton-on-target exposure in the NuMI beam. A selection based on topological and kinematic constraints yields 35,509 signal events in the hydrogen-rich ($10.8\%$) detector, providing the highest statistics of (anti)neutrino--hydrogen interactions measured to date. Backgrounds from (anti)neutrino interactions on heavier nuclei are constrained using dedicated data control samples, significantly reducing the related systematic uncertainties. We obtain a value $σ(\bar ν_μ{\rm H} \to μ^+ n) = 0.538 \pm 0.009 ({\rm stat}) \pm 0.010 ({\rm syst}) \pm0.055 ({\rm flux}) \times 10^{-38}$ cm$^2$ for the total cross section at an average energy of 1.9 GeV, the most precise total cross-section measurement of this process to date. The combined statistical and non-flux systematic uncertainty is more than four times smaller than the flux uncertainty, allowing a future use of this measurement to constrain the absolute $\bar ν_μ$ flux.
We report a measurement of the total cross section for muon antineutrino charged-current quasi-elastic scattering on hydrogen, $\bar ν_μ{\rm H} \to μ^+ n$, in the NOvA near detector using a $1.2\times10^{21}$ proton-on-target exposure in the NuMI beam. A selection based on topological and kinematic constraints yields 35,509 signal events in the hydrogen-rich ($10.8\%$) detector, providing the highest statistics of (anti)neutrino--hydrogen interactions measured to date. Backgrounds from (anti)neutrino interactions on heavier nuclei are constrained using dedicated data control samples, significantly reducing the related systematic uncertainties. We obtain a value $σ(\bar ν_μ{\rm H} \to μ^+ n) = 0.538 \pm 0.009 ({\rm stat}) \pm 0.010 ({\rm syst}) \pm0.055 ({\rm flux}) \times 10^{-38}$ cm$^2$ for the total cross section at an average energy of 1.9 GeV, the most precise total cross-section measurement of this process to date. The combined statistical and non-flux systematic uncertainty is more than four times smaller than the flux uncertainty, allowing a future use of this measurement to constrain the absolute $\bar ν_μ$ flux.
Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.
Functional oxides have extensively been investigated as a promising class of materials in a broad range of innovative applications. Harnessing the novel properties of functional oxides in micro- to nano-scale applications hinges on establishing advanced fabrication and manufacturing techniques able to synthesize these materials in an accurate and reliable manner. Oxidative scanning probe lithography (o-SPL), an atomic force microscopy (AFM) technique based on anodic oxidation at the water meniscus formed at the tip/substrate contact, not only combines the advantages of both “top-down” and “bottom-up” fabrication approaches, but also offers the possibility of fabricating oxide nanomaterials with high patterning accuracy. While the use of self-assembled monolayers (SAMs) broadened the application of o-SPL, significant challenges have emerged owing to the relatively limited number of SAM/solid surface combinations that can be employed for o-SPL, which constrains the ability to control the chemistry and structure of oxides formed by o-SPL. Here, in this work, a new o-SPL technique that utilizes room-temperature ionic liquids (RTILs) as the functionalizing material to mediate the electrochemistry at AFM tip/substrate contacts is reported. The results show that the new IL-mediated o-SPL (IL-o-SPL) approach allows sub-100 nm oxide features to be patterned on a model solid surface, namely steel, with an initiation voltage as low as -2 V. Moreover, this approach enables high tunability of both the chemical state and morphology of the patterned iron oxide structures. Owing to the high chemical compatibility of ILs, which derives from the possibility of synthesizing ILs able to adsorb on a wide variety of solid surfaces, IL-o-SPL can be extended to other material surfaces and provide the opportunity to accurately tailor the chemistry, morphology, and electronic properties within nanoscale domains, thus opening new pathways to the development of novel micro- and nano-architectures for advanced integrated devices.
Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.
Double- and single-differential cross sections for inclusive charged-current 𝜈 𝜇 -nucleus scattering are reported for the kinematic domain 0 to 2 GeV/𝑐 in three-momentum transfer and 0 to 2 GeV in available energy, at a mean 𝜈 𝜇 energy of 1.86 GeV. The measurements are based on an estimated 995,760 𝜈 𝜇 charged-current (CC) interactions in the scintillator medium of the NOvA Near Detector. The subdomain populated by 2-particle-2-hole (2p2h) reactions is identified by the cross section excess relative to predictions for 𝜈 𝜇 -nucleus scattering that are constrained by a data control sample. Models for 2-particle-2-hole processes are rated by 𝜒 2 comparisons of the predicted-versus-measured 𝜈 𝜇 CC inclusive cross section over the full phase space and in the restricted subdomain. Shortfalls are observed in neutrino generator predictions obtained using the theory-based València and SuSAv2 2p2h models.
Attaining molecular-level control over solidification processes is a crucial aspect of materials science. To control ice formation, organisms have evolved bewildering arrays of ice-binding proteins (IBPs), but these have poorly understood structure–activity relationships. We propose that reverse engineering using de novo computational protein design can shed light on structure–activity relationships of IBPs. We hypothesized that the model alpha-helical winter flounder antifreeze protein uses an unusual undertwisting of its alpha-helix to align its putative ice-binding threonine residues in exactly the same direction. We test this hypothesis by designing a series of straight three-helix bundles with an ice-binding helix projecting threonines and two supporting helices constraining the twist of the ice-binding helix. Our findings show that ice-recrystallization inhibition by the designed proteins increases with the degree of designed undertwisting, thus validating our hypothesis, and opening up avenues for the computational design of IBPs.
This poster describes a typing discipline for high-assurance industrial systems based on three novel type systems. The first type system, information flow control (IFC), controls the flow of data through the system. The second system, dependent session types, restricts messages exchanged during the execution of a communication protocol to avoid dangerous states. The third system uses dimensional analysis to avoid subtle bugs that adversaries can exploit to cause the system to enter a dangerous state. This poster describes how a combination of these approaches can prevent sophisticated cyber attacks, such as the infamous Stuxnet incident, from occurring. In addition, we provide experimental evidence to support the claim that these approaches can be applied in control systems that are resource-constrained.