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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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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving Predictions of Spin-Crossover Complex Properties through DFT Calculations with a Local Hybrid Functional

We conducted a study on the performance of the local hybrid exchange-correlation functional PBE0r for a set of 95 experimentally-characterized iron spin crossover (SCO) complexes. The PBE0r functional is a variant of PBE0 where the exchange correction is restricted to on-site terms formulated within the basis of local orbitals. We determine the free parameters of the PBE0r functional against experimental data and other hybrid functionals. With a Hartree-Fock (HF) exchange factor of 4%, the PBE0r functional accurately reproduces the electronic and free energy trends predicted in prior DFT studies for these 95 complexes using the B3LYP functional. Larger values of HF exchange stabilize high-spin states. The PBE0r-predicted bond lengths tend to exceed the experimental bond lengths, and bond lengths are less sensitive to HF exchange. The predicted SCO transition temperatures T 1/2 from PBE0r correlate moderately with the experimental transition temperatures, showing a slight improvement compared to the previous modB3LYP-predicted T 1/2 . Furthermore, this study suggests the PBE0r functional as computationally cost-effective and offers the possibility of simulating larger complexes with accuracy comparable to other global hybrid functionals, provided the HF exchange parameter is carefully optimized.

25 ENERGY STORAGE↗
DOE OSTI2020

A robust and multifunctional calcium coordination polymer as a selective fluorescent sensor for acetone and iron (+3) and as a tunable proton conductor

A multifunctional calcium coordination polymer (Ca-CP) formulated as [(CH 3 ) 2 NH 2 ][Ca(Me 2 tcpbH)(H 2 O)] (1) has been synthesized and characterized. Compound 1 features a robust and novel three-dimensional (3D) network with cage-like cavities. The high thermal and moisture stability of the title compound makes it a good candidate for possible environment or energy related applications. Fluorescence (FL) studies demonstrate that 1 shows emission with a maximum at 350 nm under 270 nm excitation. The title compound acts as a selective and sensitive fluorescent sensor for the detection of acetone and Fe 3+ ions at low concentrations. The FL intensity quenching percentage and the concentration ratio of Fe 3+ /Fe 2+ showed a linear relation with a Fe 3+ concentration as low as 50 μM in Fe 2+ aqueous solutions, offering an easy way to probe the extent of Fe 2+ oxidation. Moreover, due to the relatively high hydration character of Ca 2+ , the terminal water molecules and undeprotonated carboxylate groups form a strong hydrogen-bonded network within the structure, facilitating its tunable proton conduction under various humidity and temperature conditions. The proton conducting mechanism has been investigated using PXRD, IR, and EA methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Distributionally Robust Variational Quantum Algorithms With Shifted Noise

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is the high sensitivity of quantum noise to environmental changes, and its propensity to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by real-time noise.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Extended Lagrangian Born–Oppenheimer molecular dynamics using a Krylov subspace approximation

It is shown how the electronic equations of motion in extended Lagrangian Born–Oppenheimer molecular dynamics simulations can be integrated using low-rank approximations of the inverse Jacobian kernel. This kernel determines the metric tensor in the harmonic oscillator extension of the Lagrangian that drives the evolution of the electronic degrees of freedom. The proposed kernel approximation is derived from a pseudoinverse of a low-rank estimate of the Jacobian, which is expressed in terms of a generalized set of directional derivatives with directions that are given from a Krylov subspace approximation. The approach allows a tunable and adaptive approximation that can take advantage of efficient preconditioning techniques. The proposed kernel approximation for the integration of the electronic equations of motion makes it possible to apply extended Lagrangian first-principles molecular dynamics simulations to a broader range of problems, including reactive chemical systems with numerically sensitive and unsteady charge solutions. This can be achieved without requiring exact full calculations of the inverse Jacobian kernel in each time step or relying on iterative non-linear self-consistent field optimization of the electronic ground state prior to the force evaluations as in regular direct Born–Oppenheimer molecular dynamics. We note the low-rank approximation of the Jacobian is directly related to Broyden’s class of quasi-Newton algorithms and Jacobian-free Newton–Krylov methods and provides a complementary formulation for the solution of nonlinear systems of equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities

Here, we present a framework for calibration of parameters in elastoplastic constitutive models that is based on the use of automatic differentiation (AD). The model calibration problem is posed as a partial differential equation-constrained optimization problem where a finite element (FE) model of the coupled equilibrium equation and constitutive model evolution equations serves as the constraint. The objective function quantifies the mismatch between the displacement predicted by the FE model and full-field digital image correlation data, and the optimization problem is solved using gradient-based optimization algorithms. Forward and adjoint sensitivities are used to compute the gradient at considerably less cost than its calculation from finite difference approximations. Through the use of AD, we need only to write the constraints in terms of AD objects, where all of the derivatives required for the forward and inverse problems are obtained by appropriately seeding and evaluating these quantities. We present three numerical examples that verify the correctness of the gradient, demonstrate the AD approach's parallel computation capabilities via application to a large-scale FE model, and highlight the formulation's ease of extensibility to other classes of constitutive models.

42 ENGINEERING↗
DOE OSTI2026

Bayesian Adaptive Polynomial Chaos Expansions

Polynomial chaos expansions (PCEs) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare—especially with implementations in R. Motivated by the success of adaptive Bayesian machine learning models such as BART, BASS and BPPR, we develop a new fully Bayesian adaptive PCE method with an efficient and accessible R implementation: khaos. Our approach includes a novel proposal distribution that enables data-driven interaction selection and supports a modified g-prior tailored to PCE structure. Through simulation studies and real-world UQ applications, we demonstrate that the Bayesian adaptive PCE provides competitive performance for surrogate modeling, global sensitivity analysis and ordinal regression tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Determining the profitability of energy storage over its life cycle using levelized cost of storage

Levelized cost of storage (LCOS) can be a simple, intuitive, and useful metric for determining whether a new energy storage plant would be profitable over its life cycle and to compare the cost of different energy storage technologies. However, researchers and industry decision makers still use conflicting definitions of LCOS. For example, some include charging cost, while others only include round trip efficiency (RTE) losses. Additionally, inputs to the existing formulations are not specific enough to generate repeatable results across studies, which reduces trust in the metric. To push for standardization in economic assessment of batteries and other energy storage devices, the authors review existing definitions of LCOS and identify the desired characteristics for a standard. They then propose a new definition and demonstrate that it fits these characteristics very well relative to other prominent options. Unit analysis is applied to this proposed definition to provide a deeper understanding of the equations and to demonstrate its effectiveness. Finally, the sensitivity of LCOS to different input parameters is investigated to help users understand how to compare analyses from literature to their own. The authors also provide a spreadsheet and a Python script to streamline adoption of the proposed definition.

25 ENERGY STORAGE↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $ \$ $6B annually (at $ \$ $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year), and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE) missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

42 ENGINEERING↗
DOE OSTI2023

Surface Roughness Effects from Additive Manufacturing in High Efficiency Gas Turbine Combustion Systems

In the past, clean combustion systems were characterized by high system development costs due to growing complexity and escalating manufacturing costs from conventional manufacturing processes. As a result, high efficiency concepts are difficult to design and more difficult to be cost-effectively manufactured. In recent years, with the introduction of additive manufacturing (AM) technologies, the rapid prototyping and mass production processes of clean combustion systems are promising to be significantly simplified with significant reduction in terms of engine manufacturing cost and engine energy cost. Compared with conventional manufacturing built parts, the AM process enabled a simpler design to be adopted for the nozzle, reducing the number of required braze and weld joints from twenty-five to just five. The resulting nozzle was 25% lighter and five times more durable and contributed to a 15% reduction in fuel burn in comparison with the previous model produced. Applying these improvements to a fleet of 5,000 turbofan engines at 150 kN thrust would result in fuel cost savings of $6B annually (at $5/gallon Jet-A fuel price, 700 gallon/hour fuel consumption rate, and 2,300 operational hours per year),and reduce CO2 emissions by more than 25 million tons per year. These advances support core Department of Energy (DOE)missions in energy efficiency, improving productivity, and environmental sustainability. Maximizing the benefit of these new capabilities will require high prediction capability of high speed turbulent flow with wall modeled Large Eddy Simulation (LES). GE Aviation maintains that advanced simulation technology and supercomputing is required in order to provide the appropriate boundary conditions to realize the maximum potential of AM and Ceramic Matrix Composite to reduce cooling flow, a first order penalty on the Brayton cycle. The algorithm developed using the ANSYS/Fluent software would provide the foundation for entirely new avenues of research and development with potential multi-billion dollar impact to the US economy, and significant reduction in carbon based emissions across the aerospace and power generation industries. The formulation of this iWLES (integral wall model for LES) is generic and allows to capture the changes in flow dynamics that have a significant impact on the wall bounded flow characteristics, such as swirler effective area, bulk swirl number, local pressure distribution, exit velocity profile, and turbulence kinetic energy profile. A periodic channel flow with rough flat plate is simulated using LES (Wall-Adapting Local Eddy-viscosity) model to verify the implementation of iWLES in the Fluent User Defined Function (UDF). As the flow fields are highly sensitive due to surface roughness of the wall bounded flows in the combustion systems, there is significant potential to conduct further LES studies focusing on the turbulence boundary layer interaction. Accurately capturing near wall physics will elucidate the impact of rough surfaces on combustor flow and aero-thermal interactions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Final-state interactions and spin structure in E 1 breakup of Li 11 in halo effective field theory

In this work, we calculate the E1 breakup of the 2n halo nucleus 11 Li in halo effective field theory (Halo EFT) at leading order. In Halo EFT, 11 Li is treated as a three-body system of a 9 Li core and two neutrons. We present a detailed investigation of final-state interactions (FSIs) in the neutron-neutron (nn) and neutron-core (nc) channels. We employ Møller operators to formulate an expansion scheme that satisfies the non-energy-weighted cluster sum rule and successively includes higher-order terms in the multiple-scattering series for the FSI. Computing the E1 strength up to third order in this scheme, we observe apparent convergence and good agreement with experiment. The neutron-neutron FSI is by far the most important contribution and largely determines the maximum value of the E1 distribution. However, inclusion of nc FSI does shift the peak position to slightly lower energies. Moreover, we investigate the sensitivity of the E1 response to the spin structure of the neutron- 9 Li interaction. We contrast results for an interaction that is the same in the spin-1 and spin-2 channels with one that is only operative in the spin-2 channel, and find that good agreement with experimental data is only obtained if the interaction is present in both spin channels. The latter case is shown to be equivalent to a calculation in which the spin of 9 Li is neglected.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Influence of gasoline fuel formulation on lean autoignition in a mixed-mode-combustion (deflagration/autoignition) engine

We report stoichiometric spark-ignition engines suffer efficiency penalties due to throttling losses at low loads, a low specific-heat ratio of the stoichiometric working fluid, and limits on compression ratio due to end-gas autoignition leading to undesirable knocking. Mixed-Mode Combustion (MMC) mitigates these shortcomings by using a lean working fluid where a spark-initiated pilot-stabilized deflagrative flame front is followed by controlled end-gas autoignition. This MMC study investigates the effects of initial conditions (intake air temperature, intake pressure, equivalence ratio, and intake oxygen fraction) on autoignition tendency of four gasoline-range fuels with varying properties and composition. The use of fuels with varying octane sensitivity (S) allowed exploring the importance of low-temperature heat release in triggering autoignition. Fuels with high S were less reactive for conditions that promote low-temperature chemistry (operation at high intake air pressure or without N2 dilution). Conversely, an Alkylate fuel with low S showed a greater autoignition resistance at operating conditions that were unfavorable for low-temperature chemistry. Next, the effect of residual gas composition on autoignition tendency of fuels was examined with a chemical-kinetics model. Among the various molecules in the residual gas, nitric oxide (NO) enhanced the low-temperature chemistry and increased the autoignition tendency most significantly. The fuels’ autoignition response to increasing NO amount corroborates the experimental observations. Next, the sequential autoignition of the end-gas was assessed to be less impacted by thermal stratification because of lean mixtures showing relatively less low-temperature chemistry, when compared to stoichiometric mixtures. Next, the effect of changing equivalence ratio on the autoignition was found to be similar for all fuels, regardless of their S. With changing intake air temperature, the response of fuels’ autoignition tendency depended on the dilution level used. At high dilution (i.e. low intake [O2]), fuels’ reactivity increased with increasing intake air temperature. In contrast, for operation without dilution, the autoignition tendency of the low-S Alkylate fuel decreased with increasing intake air temperature, while that of high-S High Cycloalkane fuel still increased with increasing intake air temperature. In conclusion, conventional octane metrics (RON and MON) have utility in assessing the autoignition tendency under lean MMC operation. Moreover, the fuel requirements for MMC align with that of stoichiometric operation: i.e., high RON and high S fuels are desirable for stable non-knocking operation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗
DOE OSTI2022

Impact of the plastic deformation microstructure in metals on the kinetics of recrystallization: A phase-field study

The sensitivity of recrystallization kinetics in metals to the heterogeneity of microstructure and deformation history is a widely accepted experimental fact. However, most of the available recrystallization models employ either a mean field approach or use grain-averaged parameters, and thus neglecting the mesoscopic heterogeneity induced by prior deformation. Here in the present study, we investigate the impact of deformation-induced dislocation (subgrain) structure on the kinetics of recrystallization in metals using the phase-field approach. The primary focus here is upon the role of dislocation cell boundaries. The free energy formulation of the phase-field model accounts for the heterogeneity of the microstructure by assigning localized energy to the resulting dislocation microstructure realizations generated from experimental data. These microstructure realizations are created using the universal scaling laws for the spacing and the misorientation angles of both the geometrically necessary and incidental dislocation boundaries. The resulting free energy is used into an Allen-Cahn based model of recrystallization kinetics, which are solved using the finite element method. The solutions thus obtained shed light on the critical role of the spatial heterogeneity of deformation in the non-smooth growth of recrystallization nuclei and on the final grain structure. The results showed that, in agreement with experiment, the morphology of recrystallization front exhibits protrusions and retrusions. By resolving the subgrain structure, the presented algorithm paves the way for developing predictive kinetic models that fully account for the deformed state of recrystallizing metals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Replicating HCCI-like autoignition behavior: What gasoline surrogate fidelity is needed?

This work seeks to characterize the fidelity needed in a gasoline surrogate with the intent to replicate the complex autoignition behavior exhibited within advanced combustion engines, and specifically Homogeneous Charge Compression Ignition (HCCI). A low-temperature gasoline combustion (LGTC) engine operating in HCCI mode and a rapid compression machine (RCM) are utilized to experimentally quantify fuel reactivity, through autoignition and preliminary heat release characteristics. Fuels considered include a research grade E10 U.S. gasoline (RD5-87), three multi-component surrogates (PACE-1, PACE-8, PACE-20), and a binary surrogate (PRF88.4). Each fuel was studied at lean/HCCI-like conditions covering a wide range of temperatures and pressures that are representative of naturally aspirated to high boost engine operation. Detailed chemical kinetic modeling is also undertaken using a recently updated gasoline surrogate kinetic model to simulate the RCM experiments and to provide chemical insight into surrogate-to-surrogate differences. The LGTC engine experiments demonstrate nearly identical reactivity between PACE-20 and RD5-87 across studied conditions, while faster phasing is seen for both PACE-1 and PACE-8 due to their stronger intermediate- and low-temperature heat release (ITHR/LTHR) at naturally aspirated and boosted conditions, respectively. The RCM experiments reveal typical low-temperature, negative temperature coefficient (NTC) and intermediate-temperature autoignition behaviors at all pressure conditions for RD5-87, which are qualitatively reproduced by all surrogates. Quantitative discrepancies in both autoignition and preliminary heat release are observed for all surrogates, while their ability to replicate RD5-87 autoignition behavior follows the order of PACE-20 > PACE-1 > PACE-8 > PRF88.4. Excellent mapping is obtained between the LGTC engine and the RCM, where the engine pressure-time trajectories can be characterized by the regimes represented by the RCM autoignition isopleths. The kinetic model performs commendably when simulating both autoignition and preliminary heat release of PACE-20, while typically overpredicting ignition delay times for PACE-1, PACE-8 and PRF88.4 at high-pressure and low-temperature/NTC conditions. Sensitivity and rate of production (ROP) analyses highlight surrogate-to-surrogate differences in the governing chemical kinetics where n-pentane initiates rapid OH branching at a faster rate and an earlier timing for PACE-20 than iso-pentane does for PACE-1 and PACE-8, making it computationally more reactive than the other surrogates. The current study highlights the need to include non-standardized properties, such as the lean/HCCI-like autoignition characteristics, in addition to ASTM properties (e.g., RON, MON) as metrics of fuel reactivity and targets to be matched when formulating high-fidelity surrogates that fully capture gasoline advanced combustion behavior such as HCCI-like autoignition.

42 ENGINEERING↗
DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2021

Cyber Physical Grid-Interactive Distributed Energy Resources Control for VPP Dispatch and Regulation

This paper presents a cyber-physical algorithm for grid-interactive Distributed Energy Resource (DER) control to enable two features of Virtual Power Plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. We first formulate the DER dispatch problem as a real-time, iterative, and grid-interactive DER control problem. Thereafter, we consider a probabilistic traffic model to characterize packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control center that coordinately execute this dispatch algorithm. Finally, a strategy combining delay threshold and modified message update rules is proposed to immune the asynchrony resulting from the communications network traffic and it avoids possible numerical instabilities and sensitivities of the tracking and regulation capabilities of this DER control algorithm. By implementing the proposed cyber-physical algorithm on the modified IEEE 37-node system, our preliminary results exhibit that the uncertainties of the underlying communications infrastructure must be considered for the VPP tracking and regulation capabilities of any DER in a generic Cyber-Physical System (CPS), because the delayed voltage measurements in the uplink/bi-link cases result in the off-track in VPP dispatch and jittery in voltage regulation.

cyber-physical algorithm↗
DOE OSTI2022

Solid state NMR Investigation of Structural Differences in Pelletized DEB Samples

Upon hydrogenation of DEB pellets, which are composed of 75% virgin DEB and 25% of nanometer scale Pd catalysts on activated carbon, the uptake rate is strong, and the hydrogenation process is fast. However, the last batch of DEB pellets from KCNSC uptakes 10 times slower than traditional DEB pellets after ~ 50% consumption. In addition, the last batch of DEB getter pellets also became partially molten/wet during the hydrogenation process. This molten/wet phase during hydrogenation has only been observed with DPB and never with DEB formulation. DRIFT (diffuse reflection infrared Fourier transform) and XRD (x-ray diffraction) confirm that the questionable batch of DEB pellets from KCNSC are truly 100% DEB and not a mixture of DEB and DPB. So, there must be something different in the composition (like concentration and distribution of Pd) or interaction between the DEB molecules with the surrounding environment (activated carbon spreading medium). The possibility of some chemical contamination in the processing of this batch of questionable DEB getter pellets also comes to mind. Due to the ability of NMR to detect intermolecular interaction and sensitivity to small chemical concentration, NMR investigation was requested for this questionable batch of DEB getter pellets from KCNSC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗