Cost-optimal wave-powered persistent oceanographic observation
Not Available
Dillon, Trent (ORCID:0000000201036449)↗
SEARCH · Engineering Papers
Results for “optimality”
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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An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models
Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.
CPM↗
Optimal arrangements of 1,3-diphenylisobenzofuran molecule pairs for fast singlet fission
A simplified version of the frontier orbital model has been applied to pairs of C 2 , C 2v , C s , and C 1 symmetry 1,3-diphenylisobenzofuran rotamers to determine their best packing for fast singlet fission (SF). For each rotamer the square of the electronic matrix element for SF was calculated at 2.2 × 10 9 pair geometries and a few thousand most significant physically accessible local maxima were identified in the six-dimensional space of mutual arrangements. At these pair geometries, SF energy balance was evaluated, relative SF rate constants were approximated using Marcus theory, and the SF rate constant k SF was maximized by further optimization of the geometry of the molecular pair. The process resulted in 142, 67, 214, and 291 unique geometries for the C 2 , C 2v , C s , and C 1 symmetry molecular pairs, respectively, predicted to be superior to the C 2 symmetrized known crystal pair structure. Furthermore, these optimized pair geometries and their triplet biexciton binding energies are reported as targets for crystal engineering and/or covalent dimer synthesis, and as possible starting points for high-level pair geometry optimizations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Generalizing the self-healing diffusion Monte Carlo approach to finite temperature: A path for the optimization of low-energy many-body bases
A statistical method is derived for the calculation of thermodynamic properties of many-body systems at low temperatures. This method is based on the self-healing diffusion Monte Carlo method for complex functions [F. A. Reboredo, J. Chem. Phys. 136, 204101 (2012)] and some ideas of the correlation function Monte Carlo approach [D. M. Ceperley and B. Bernu, J. Chem. Phys. 89, 6316 (1988)]. In order to allow the evolution in imaginary time to describe the density matrix, we remove the fixed-node restriction using complex antisymmetric guiding wave functions. In the process we obtain a parallel algorithm that optimizes a small subspace of the many-body Hilbert space to provide maximum overlap with the subspace spanned by the lowest-energy eigenstates of a many-body Hamiltonian. We show in a model system that the partition function is progressively maximized within this subspace. We show that the subspace spanned by the small basis systematically converges towards the subspace spanned by the lowest energy eigenstates. Possible applications of this method for calculating the thermodynamic properties of many-body systems near the ground state are discussed. The resulting basis can also be used to accelerate the calculation of the ground or excited states with quantum Monte Carlo.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Reversals and collisions optimize protein exchange in bacterial swarms
Not Available
Amiri, Aboutaleb↗
Optimal Zeno Dragging for Quantum Control: A Shortcut to Zeno with Action-Based Scheduling Optimization
The quantum Zeno effect asserts that quantum measurements inhibit simultaneous unitary dynamics when the “collapse” events are sufficiently strong and frequent. This applies in the limit of strong continuous measurement or dissipation. It is possible to implement a dissipative control that is known as “Zeno dragging” by dynamically varying the monitored observable, and hence also the eigenstates, which are attractors under Zeno dynamics. This is similar to adiabatic processes, in that the Zeno-dragging fidelity is highest when the rate of eigenstate change is slow compared to the measurement rate. We demonstrate here two theoretical methods for using such dynamics to achieve control of quantum systems. The first, which we shall refer to as “shortcut to Zeno,” is analogous to the shortcuts to adiabaticity (counterdiabatic driving) that are frequently used to accelerate unitary adiabatic evolution. In the second approach, we apply the Chantasri-Dressel-Jordan stochastic action [PRA 88, 042110 (2013)], and demonstrate that the extremal-probability readout paths derived from this are well suited to setting up a Pontryagin-style optimization of the Zeno-dragging schedule. A fundamental contribution of the latter approach is to show that an action suitable for measurement-driven control optimization can be derived quite generally from statistical arguments. Implementing these methods on the Zeno dragging of a qubit, we find that both approaches yield the same solution, namely, that the optimal control is a unitary that matches the motion of the Zeno-monitored eigenstate. We then show that such a solution can be more robust than a unitary-only operation and we comment on solvable generalizations of our qubit example embedded in larger systems. These methods open up new pathways toward systematically developing dynamic control of Zeno subspaces to realize dissipatively stabilized quantum operations. Published by the American Physical Society 2024
Physics↗
Distributed Direction-Optimizing Label Propagation for Community Detection
Designing a scalable algorithm for community detec-tion is challenging due to the tradeoff between the performance and the quality of the result. We propose a new distributed algorithm for community detection —Direction-Optimizing Label Propagation Algorithm, based on a novel Label Propagation Algorithm in community detection. The algorithm is inspired by the direction optimization technique for graph traversal algorithms and relies on the use of frontiers and alternates between label push and label pull operations. This organization creates ?exibility and affords opportunities for balancing the performance and the quality of the solution. We implement our algorithm in distributed memory with an active-message based asynchronous many-task runtime, AM++. We experiment with two seeding strategies for initial seeding stage, namely, random seeding and degree seeding. With the Graph Challenge datasets, our distributed implementation, in conjunction with the runtime support, achieves very good performance and reasonable quality of solution.
Liu, Xu↗
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms appropriate for training. Considering that these networks often have very different performance characteristics than traditional neural networks, it is often unclear how to set either the network topology or the hyperparameters to achieve optimal performance. In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware. We show that by optimizing the hyperparameters on this algorithm for each dataset, we can achieve improvements in accuracy over the previous state-of-the-art for this algorithm on each dataset (by up to 15 percent). This jump in performance continues to emphasize the potential when converting traditional neural networks to binary communication applicable to neuromorphic hardware.
Parsa, Maryam↗
Evaluating tantalum oxide stoichiometry and oxidation states for optimal memristor performance
Tantalum oxide has shown promising electrical switching characteristics for memristor devices. Consequently, a number of reports have investigated the electrical behavior of TaO{sub x} thin films. Some effort has been made to characterize the composition of the TaO{sub x} films and it is known that there must be an optimal stoichiometry of TaO{sub x} where forming and switching behavior are optimized. However, many previous reports lack details on the methodology used for identifying the chemistry of the films. X-ray photoelectron spectroscopy has been the most commonly used technique; however, peak fitting routines vary widely among reports and a native surface oxide of Ta{sub 2}O{sub 5} often confounds the analysis. In this report a series of large area TaO{sub x} films were deposited via sputtering with controlled O{sub 2} partial pressures in the sputtering gas, resulting in tunable oxide compositions. Spectra from numerous samples from each wafer spanning a range of oxide stoichiometries were used to develop a highly constrained peak fitting routine. This procedure allowed for the composition of the TaO{sub x} films to be identified with greater detail than elemental ratios alone. Additionally, the peak fitting routine was used to evaluate uniformity of deposition across individual wafers. The appearance of a greater contribution of Ta{sup 4+} oxidation states in the oxygen starved films are believed to relate to films with optimal forming characteristics.
36 MATERIALS SCIENCE↗
Isopod (inverse Optimization And Design): Moose-based Application For Performing Inverse Optimization On Multiphysics Modeling And Simulation
ISOPOD (InverSe OPtimizatiOn and Design) will be a MOOSE (Multiphysics Object Oriented Simulation Environment)-based framework which will iteratively estimate the parameters by minimizing the difference between simulated and experimental observables. While inverse optimization has long been used for parameter estimation, the unique feature of ISOPOD is rapid solution of new inverse problems, leading to accelerated Research, Development and Demonstration (RD&D) for complex parameter estimation problems. This will be achieved by extending the software structure of MOOSE to inverse optimization, as well as building on the unique automatic differentiation feature of MOOSE.
Munday, LynnB↗
Optimal Membrane Cascade Design for Critical Mineral Recovery Through Logic-based Superstructure Optimization
Critical minerals and rare earth elements play an important role in our climate change initiatives, particularly in applications related with energy storage. Here, we use discrete optimization approaches to design a process for the recovery of Lithium and Cobalt from battery recycling, through membrane separation. Our contribution involves proposing a Generalized Disjunctive Programming (GDP) model for the optimal design of a multistage diafiltration cascade for Li-Co separation. By solving the resulting nonconvex mixed-integer nonlinear program model to global optimality, we investigated scalability and solution quality variations with changes in the number of stages and elements per stage. Results demonstrate the computational tractability of the nonlinear GDP formulation for design of membrane separation processes while opening the door for decom-position strategies for multicomponent separation cascades. Future work aims to extend the GDP formulation to account for stage installation and explore various decomposition techniques to enhance solution efficiency.
Ovalle, Daniel↗
A Real-Time Optimization with Warm-Start of Multiperiod AC Optimal Power Flows
We present a real-time optimization strategy based on warm-start for solving a moving horizon of multi period AC optimal power flow (ACOPF) problems. In each horizon, ACOPFs are temporally interlinked via generator ramp constraints, and we assume that each horizon needs to be solved every few seconds or minutes. We introduce two approximate tracking schemes that closely follow a solution path consisting of strongly regular points. We present theoretical results bounding the tracking error by the square of the parameter changes between time periods. Experimental results for networks of sizes up to 9K buses show a fast computation time while maintaining a good solution quality, thus making our approach well suited for real-time circumstances.
moving horizon↗
Hierarchical optimization for neutron scattering problems
We present a scalable optimization method for neutron scattering problems that determines confidence regions of simulation parameters in lattice dynamics models used to fit neutron scattering data for crystalline solids. The method uses physics-based hierarchical dimension reduction in both the computational simulation domain and the parameter space. We demonstrate for silicon that after a few iterations the method converges to parameters values (interatomic force-constants) computed with density functional theory simulations.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Three-Phase AC Optimal Power Flow Based Distribution Locational Marginal Price
Designing market mechanisms for electricity distribution systems has been a hot topic due to the increased presence of smart loads and distributed energy resources (DERs) in distribution systems. The distribution locational marginal pricing (DLMP) methodology is one of the real-time pricing methods to enable such market mechanisms and provide economic incentives to active market participants. Determining the DLMP is challenging due to high power losses, the voltage volatility, and the phase imbalance in distribution systems. Existing DC Optimal Power Flow (OPF) approaches are unable to model power losses and the reactive power, while single-phase AC OPF methods cannot capture the phase imbalance. To address these challenges, in this paper, a three-phase AC OPF based approach is developed to define and calculate DLMP accurately. The DLMP is modeled as the marginal cost to serve an incremental unit of demand at a specific phase at a certain bus, and is calculated using the Lagrange multipliers in the three-phase AC OPF formulation. Extensive case studies have been conducted to understand the impact of system losses and the phase imbalance on DLMPs as well as the potential benefits of flexible resources.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control
Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modeling approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Noise Optimization for MKIDs with Different Design Geometries and Material Selections
The separation and optimization of noise components is critical to microwave-kinetic inductance detector (MKID) development. We analyze the effect of several changes to the lumped-element inductor and interdigitated capacitor geometry on the noise performance of a series of MKIDs intended for millimeter-wavelength experiments. We extract the contributions from two-level system noise in the dielectric layer, the generation-recombination noise intrinsic to the superconducting thin-film, and system white noise from each detector noise power spectrum and characterize how these noise components depend on detector geometry, material, and measurement conditions such as driving power and temperature. We observe a reduction in the amplitude of two-level system noise with both an elevated sample temperature and an increased gap between the fingers within the interdigitated capacitors for both aluminum and niobium detectors. We also verify the expected reduction of the generation-recombination noise and associated quasiparticle lifetime with reduced inductor volume. This study also iterates over different materials, including aluminum, niobium, and aluminum manganese, and compares the results with an underlying physical model.
generation-recombination noise↗
Medical imaging using ionizing radiation: Optimization of dose and image quality in fluoroscopy
The 2012 Summer School of the American Association of Physicists in Medicine (AAPM) focused on optimization of the use of ionizing radiation in medical imaging. Day 2 of the Summer School was devoted to fluoroscopy and interventional radiology and featured seven lectures. These lectures have been distilled into a single review paper covering equipment specification and siting, equipment acceptance testing and quality control, fluoroscope configuration, radiation effects, dose estimation and measurement, and principles of flat panel computed tomography. This review focuses on modern fluoroscopic equipment and is comprised in large part of information not found in textbooks on the subject. While this review does discuss technical aspects of modern fluoroscopic equipment, it focuses mainly on the clinical use and support of such equipment, from initial installation through estimation of patient dose and management of radiation effects. This review will be of interest to those learning about fluoroscopy, to those wishing to update their knowledge of modern fluoroscopic equipment, to those wishing to deepen their knowledge of particular topics, such as flat panel computed tomography, and to those who support fluoroscopic equipment in the clinic.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Toward optimal organ at risk sparing in complex volumetric modulated arc therapy: An exponential trade-off with target volume dose homogeneity
Purpose: Conventional radiotherapy typically aims for homogenous dose in the planning target volume (PTV) while sparing organs at risk (OAR). The authors quantified and characterized the trade-off between PTV dose inhomogeneity (IH) and OAR sparing in complex head and neck volumetric modulated arc therapy plans. Methods: Thirteen simultaneous integrated boost plans were created per patient, for ten patients. PTV boost{sub (B)}/elective{sub (E)} optimization priorities were systematically increased. IH{sub B} and IH{sub E}, defined as (100% − V95%) + V107%, were evaluated against the average of the mean dose to the combined composite swallowing and combined salivary organs (D-OAR{sub comp}). To investigate the influence of OAR size and position with respect to PTV{sub B/E}, OAR dose was evaluated against a modified Euclidean distance (DM{sub B}/DM{sub E}) between OAR and PTV. Results: Although the achievable D-OAR{sub comp} for a given level of PTV IH differed between patients, excellent logarithmic fits described the D-OAR{sub comp}/IH{sub B} and IH{sub E} relationship in all patients (mean R{sup 2} of 0.98 and 0.97, respectively). Allowing an increase in average IH{sub B} and IH{sub E} over a clinically acceptable range, e.g., from 0.4% ± 0.5% to 2.0% ± 2.0% and 6.9% ± 2.8% to 14.8% ± 2.7%, respectively, corresponded to a decrease in average dose to the composite salivary and swallowing structures from 30.3 ± 6.5 to 23.6 ± 4.7 Gy and 32.5 ± 8.3 to 26.8 ± 9.3 Gy. The increase in PTV{sub E} IH was mainly accounted for by an increase in V107, by on average 5.9%, rather than a reduction in V95, which was on average only 2%. A linear correlation was found between the OAR dose to composite swallowing structures and contralateral parotid and submandibular gland, with DM{sub E} (R{sup 2} = 0.83, 0.88, 0.95). Only mean ipsilateral parotid dose correlated with DM{sub B} (R{sup 2} = 0.87). Conclusions: OAR sparing is highly dependent on the permitted PTV{sub B/E} IH. PTV{sub E} IH substantially influences OAR doses. These results are relevant for clinical practice and for future automated treatment-planning strategies.
62 RADIOLOGY AND NUCLEAR MEDICINE↗