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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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Methods are considered for analysis of the kinematic and dynamic errors in reproducing programmed tool trajectories in mechatronic manipulators with control loops.
Optical gas imaging through multispectral cameras is a promising technique for mitigation of methane emissions through localization and quantification of emissions sources. While more advanced cameras developed in recent years have led to lower uncertainties in measuring gas concentrations, a systematic analysis of the uncertainties associated with leak rate estimation have been overlooked. We present a systematic categorization of the involved uncertainties with a focus on a theoretical analysis of projection uncertainties that are inherent to this technique. The projection uncertainties are then quantified using Large Eddy Simulation experiments of a point source release into the atmosphere. Our results show that while projection uncertainties are typically about 5% of the emission rate, low acquisition times and observation of the gas plume at small distances from the emission source (<10 m) can amount to errors of about 20%. Further, we found that acquisition times on the order of tens of seconds are sufficient to significantly reduce (>50%) the projection uncertainties. These findings suggest robust procedures on how to reduce projection uncertainties, however, a balance between other sources of uncertainty due to operational conditions and the employed instrumentation are required to outline more practical guidelines.
This dataset contains the data and scripts required to generate all the figures in the paper "Error Field Measurements and Correction on MUSE Permanent Magnet Stellarator”. The data includes electron mapping images and related metadata.
Among the most eagerly anticipated opportunities made possible by Advanced LIGO/Virgo are multimessenger observations of compact mergers. Optical counterparts may be short-lived so rapid characterization of gravitational wave (GW) events is paramount for discovering electromagnetic signatures. One way to meet the demand for rapid GW parameter estimation is to trade off accuracy for speed, using waveform models with simplified treatment of the compact objects’ spin. We report on the systematic errors in GW parameter estimation suffered when using different spin approximations to recover generic signals. Component mass measurements can be biased by >5σ using simple-precession waveforms and in excess of 20σ when non-spinning templates are employed. This suggests that electromagnetic observing campaigns should not take a strict approach to selecting which LIGO/Virgo candidates warrant follow-up observations based on low-latency mass estimates. For sky localization, we find that searched areas are up to a factor of ∼2 larger for non-spinning analyses, and are systematically larger for any of the simplified waveforms considered in our analysis. Distance biases for the non-precessing waveforms can be in excess of 100% and are largest when the spin angular momenta are in the orbital plane of the binary. We confirm that spin-aligned waveforms should be used for low-latency parameter estimation at the minimum. Including simple precession, though more computationally costly, mitigates biases except for signals with extreme precession effects. Our results shine a spotlight on the critical need for development of computationally inexpensive precessing waveforms and/or massively parallel algorithms for parameter estimation.
The response matrix method has been a useful solution method for radiation transport problems for decades. The method is desirable for many reasons. It can be extended to both fixed source and eigenvalue problems. In addition, a key aspect of the method is a shift of computational expense to a precomputation phase. Methods and codes employing response matrix or response matrix-like methods have enjoyed formidable computational efficiency while solving large problems. However, current applications of the method rely on large node sizes and few nodes to maximize computational efficiency. As part of a desire to make these methods more amenable to applications such as parallel computing in the future, the number of nodes in problems may increase, and node size will decrease. The effect of this change has not been well studied except for a few cases. In the past, it was determined that a poor approximation of angular dependence of flux on node interfaces could lead to large error propagation for fixed source problems, but a newer study indicates that changing node sizes as well as number of nodes poses little challenge for response methods in the eigenvalue case. This paper presents the unintuitive result that for response matrix methods increasing the spatial resolution of the problem may dramatically decrease accuracy - this effect is demonstrated on a simple analytic 1-D benchmark problem. (authors)
There has been a calorimetric misbalance between the primary and secondary loops of the National Bureau of Standards Reactor (NBSR) since the installation of a set of new heat exchangers in the early 90's. The NBSR's primary and secondary process instrumentation were investigated to resolve the underlying causes. The main issue was found to be the immersion length of the thermowells, resulting in non-exemplary process measurements. Furthermore, thermal insulation on various temperature sensors was found to be degraded. Hence, the following were found to be inconsistent: the heat exchanger output temperatures, previously-used differential temperature sensors, reactor inlet sensor, and the reactor outlet temperature indications. Therefore, thermodynamic analyses using these sensor measurements were inconclusive. Secondly, the discrepancy between primary and secondary loops and the gradual inconsistency between primary side sensors went largely unnoticed. We implemented sustainable, state-of-the-art upgrades to resolve systematic errors in the NBSR reactor thermal process instrumentation. Several digital upgrades were completed, along with detailed 50.59 reviews. Redundancy, defense-in-depth, reliability, diversity and accuracy of the thermal monitoring system was established by implementing an inclusive engineering approach by analyzing the sensors as a whole system instead of individual assessments. The upgrades produced two important outcomes. First, the long-term existing calorimetric discrepancy between the primary and secondary loops was resolved. Secondly, excellent agreement was achieved within the primary process measurement instrumentation, resulting in reliable and stable thermal power assessment. We will present lessons learned performing instrumentation upgrades and our future enhancement plans in process monitoring for the NBSR reactor. (authors)
Filter saliency based channel pruning is a state-of-the-art method for deep convolutional neural network compression and acceleration. This channel pruning method ranks the importance of individual filter by estimating its impact of each filter’s removal on the training loss, and then remove the least important filters and fine-tune the remnant network. In this work, we propose a systematic channel pruning method that significantly reduces the estimation error of filter saliency. Different from existing approaches, our method largely reduces the magnitude of parameters in a network by introducing alternating direction method of multipliers (ADMM) into the pre-training procedure. Therefore, the estimation of filter saliency based on Taylor expansion is significantly improved. Extensive experiments with various benchmark network architectures and datasets demonstrate that the proposed method has a much improved unimportant filter selection capability and outperform state-of-the-art channel pruning method.
Special conformal field theories can have symmetry groups which are interesting sporadic finite simple groups. Famous examples include the Monster symmetry group of a c = 24 twodimensional conformal field theory (CFT) constructed by Frenkel, Lepowsky and Meurman, and the Conway symmetry group of a c = 12 CFT explored in detail by Duncan and Mack-Crane. The Mathieu moonshine connection between the K3 elliptic genus and the Mathieu group M24 has led to the study of K3 sigma models with large symmetry groups. A particular K3 CFT with a maximal symmetry group preserving (4, 4) superconformal symmetry was studied in beautiful work by Gaberdiel, Taormina, Volpato, and Wendland [41]. The present paper shows that in both the GTVW and c = 12 theories the construction of superconformal generators can be understood via the theory of quantum error correcting codes. The automorphism groups of these codes lift to symmetry groups in the CFT preserving the superconformal generators. In the case of the N = 1 supercurrent of the GTVW model our result, combined with a result of T. Johnson-Freyd implies the symmetry group is the maximal subgroup of M24 known as the sextet group. (The sextet group is also known as the holomorph of the hexacode.) Building on [41] the Ramond-Ramond sector of the GTVW model is related to the Miracle Octad Generator which in turn leads to a role for the Golay code as a group of symmetries of RR states. Moreover, (4, 1) superconformal symmetry suffices to define and decompose the elliptic genus of a K3 sigma model into characters of the N = 4 superconformal algebra. The symmetry group preserving (4, 1) is larger than that preserving (4, 4).
In this letter, we present the NNLL-NNLO transverse momentum Higgs distribution arising from gluon fusion. In the regime p ⊥ << m h we include the resummation of the large logs at next to next-to leading order and then match on to the α 2 s fixed order result near p ⊥ ~m h . By utilizing the rapidity renormalization group (RRG) we are able to smoothly match between the resummed, small p ⊥ regime and the fixed order regime. We give a detailed discussion of the scale dependence of the result including an analysis of the rapidity scale dependence. Our central value differs from previous results, in the transition region as well as the tail, by an amount which is outside the error band. Lastly, this difference is due to the fact that the RRG profile allows us to smoothly turn off the resummation.
This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system of controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.
Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.
Grid supportive modes integrated within inverter-based resources can improve the frequency response of renewable-rich microgrids. The synthesis of grid supportive modes to guarantee frequency trajectory constraints under a predefined disturbance set is challenging but essential. To tackle this challenge, a numerical optimal control (NOC)-based control synthesis methodology is proposed. Without loss of generality, a wind-diesel fed microgrid is studied, where we aim to design grid supportive functions in the wind turbine. In the control design, linearized models are used, and the linearization-induced errors are quantitatively analyzed by reachability and interval arithmetics and represented in the form of interval uncertainties. Then, the NOC problem can be formulated into a robust mixed-integer linear program. The control structure is strategically configured into two levels to realize online deployment. The proposed control is verified on the modified 33-node microgrid with a full-order three-phase nonlinear model in Simulink. In conclusion, the simulation results show the effectiveness of the proposed control paradigm and the necessity of considering linearization-induced uncertainty.
Simulating order–disorder phase transitions in magnetic materials requires the accurate treatment of both the atomic and magnetic interactions, which span a vast configuration space. Taking FeCo as a prototype system, we demonstrate that this can be accurately and efficiently addressed by the combined Cluster-Expansion method and Wang–Landau algorithm. All involved key parameters in this method are carefully tested. Based on these tests, error controlling approaches are also developed. Furthermore our method is readily applicable to the study of similar second-order phase transitions in other binary and multi-component systems.
Image guided radiation therapy (IGRT) is widely used, but data directly relating set-up errors to patient outcome are scarce. This study investigates the relationship between residual IGRT shifts and overall patient survival and uses the observed relations to identify structures sensitive to radiation dose.
Battery storage is increasingly identified as being among the least-cost mix of technologies in the evolving U.S. electricity mix. This study explores the marginal capacity credit of batteries using a probabilistic, reliability-based, effective firm capacity method, which we apply for multiple battery power ratings, durations, coupling types, deployment locations, and dispatch profiles within a test system that is based on the Texas Interconnection in the year 2024. We find that the capacity credits for all battery durations depend on their ability to predict the timing of reliability events. Even 1-2 h forecast errors - resulting in early or delayed battery discharging relative to the onset of a reliability event - lead pronounced capacity credit reductions, especially for 4-h duration batteries. Coupling batteries with solar mitigates the uncertainty associated with a shorter-duration battery's availability during reliability events, primarily due to the relatively high solar capacity credit in our test system. Coupled (or hybrid) system designs with oversized solar arrays, the ability to charge the coupled battery with grid energy, and larger batteries lead to the greatest capacity credit benefits of hybridization. We do not see evidence that the hybrid capacity credit exceeds the sum of the separate battery and solar capacity credits.
The random ray method is a recently developed stochastic method for solving neutral particle transport problems based on the method of characteristics. Perhaps surprisingly for a characteristics-based method using flat sources, we note that the random ray method can produce negative fluxes which may be numerically troublesome in several situations. These occur most severely in fixed source problems where the source is in a region with a small cross section. Additionally, we briefly discuss another source of bias which can occur in similar situations, namely a ray missing a mesh with a strong source and small cross section, resulting in the entirety of the source being unphysically deposited locally. This paper describes the mechanism by which negative fluxes may occur and several different methods to mitigate their effects. These fixes are tested on an eigenvalue problem, a ‘fusion-like’ shielding problem, and a shielding problem featuring an adjoint calculation. Even when extremely coarse random ray quadratures are used such that 20%–30% of cells are missed during a given iteration, use of the preferred fix technique ensures local flux tally errors remain trivial (below 1%). The preferred fix is now the default option in SCONE and OpenMC.
We examine the role of self-interaction errors (SIE) removal on the evaluation of magnetic exchange coupling constants. In particular we analyze the effect of scaling down the self-interaction-correction (SIC) for three non-empirical density functional approximations (DFAs) namely, the local spin density approximation, the Perdew-Burke-Ernzerhof generalized gradient approximation, and recent SCAN family of meta-GGA functionals. To this end, we employ three one-electron SIC methods: Perdew-Zunger [Perdew, J. P.; Zunger, A. Phys. Rev. B, 1981, 23, 5048] SIC, the orbitalwise scaled SIC method [Vydrov, O. A. et al., J. Chem. Phys. 2006, 124, 094108], and the recent local scaling method [Zope, R. R. et al., J. Chem. Phys. 2019, 151, 214108]. We compute the magnetic exchange coupling constants using the spin projection and non projection approaches for sets of molecules composed of dinuclear and polynuclear H-He models, organic radical molecules, and chlorocuprate, and compare these results against accurate theories and experiment. Our results show that for the systems that mainly consist of single electron regions, PZSIC performs well but for more complex organic systems and the chlorcuprates, an overcorrecting tendency of PZSIC combined with the DFAs utilized in this work is more pronounced, and in such cases LSIC with kinetic energy density ratio performs better than PZSIC. Furthermore, analysis of the results in terms of SIC corrections to the density and to the total energy shows that both density and energy correction are required to obtain an improved prediction of magnetic exchange couplings.
Energy barriers, which control the rates of chemical reactions, are seriously underestimated by computationally efficient semilocal approximations for the exchange-correlation energy. The accuracy of a semilocal density functional approximation is strongly boosted for reaction barrier heights by evaluating that approximation non-self-consistently on Hartree–Fock electron densities, which has been known for ~30 years. Here, the conventional explanation is that the Hartree–Fock theory yields the more accurate density. This work presents a benchmark Kohn–Sham inversion of accurate coupled-cluster densities for the reaction H 2 + F → HHF → H + HF and finds a strong, understandable cancellation between positive (excessively overcorrected) density-driven and large negative functional-driven errors (expected from stretched radical bonds in the transition state) within this Hartree–Fock density functional theory. This confirms earlier conclusions (Kaplan, A. D., et al. J. Chem. Theory Comput. 2023, 19, 532–543) based on 76 barrier heights and three less reliable, but less expensive, fully nonlocal density functional proxies for the exact density.