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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Thermal Flux Maximization from a DD Neutron Generator

The University of Sharjah recently acquired a model DD-109.4M neutron generator from Adelphi Technology. The generator is moderated and has three neutron ports which are capable of providing fast and thermal neutrons. The neutron ports are plugged with polyethylene layers of different thickness in order to control the neutron flux at different regions in the port. Neutron Activation Analysis (NAA) is an important analysis technique to determine the amounts of isotopes in a sample at different concentrations. For many isotopes the ability to precisely determine their concentrations is enhanced by utilizing thermal neutrons in the activation process; where the capture cross section is greatest. It is thus important to subject the sample to the maximum thermal flux in order to minimize the time required to activate the sample and consequently extend the lifetime of the neutron generator. A detailed MCNP model of the neutron generator has been developed for the purpose of determining the moderator thickness required to maximize the thermal neutron flux in the neutron port. The maximization of the neutron flux must therefore take into account both the forward and backscattered neutrons reaching the sample. Also provided in this work is a mapping of the thermal flux at different moderator thicknesses in order to study the variation of the flux over the area of the sample. The developed model will be used as the basis for optimizing experiments and predicting their results. A study was performed to determine the moderator thickness needed to maximize the thermal neutron flux that can be obtained from a DD neutron generator. This has a significant role in the efficient utilization of the neutron generator. The maximum thermal neutron flux was found at approximately a 3.2 cm moderator thickness. The thermal neutron flux is resulting from both the directly moderated neutrons and the backscattered neutrons from the surrounding polyethylene. The results obtained in this work will be verified experimentally by irradiating samples at different moderator thickness and performing a neutron activation analysis for these samples. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2016

Dynamic model-based sensor network design algorithm for system efficiency maximization

Here, a dynamic model-based sensor network design (DMSND) algorithm has been developed for maximizing system efficiency for an estimator-based control system. The algorithm synthesizes the optimal sensor network in the face of disturbances or set point changes. Computational expense of the large-scale combinatorial optimization problem is significantly reduced by parallel computing and by using combination of three novel strategies: multi-rate sampling frequency, model order reduction, and use of an incumbent solution that enables early termination of evaluation of infeasible sensor sets. The developed algorithm is applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with carbon capture. Even though there are more than thousand process states and more than hundred candidate sensor locations, the optimal sensor network design problem for maximizing process efficiency could be solved within couple of hours for a given budget.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

GraMeR: Gra ph Me ta R einforcement learning for multi-objective influence maximization

Influence maximization (IM) is a combinatorial problem of identifying a subset of seed nodes in a network (graph), which when activated, provide a maximal spread of influence in the network for a given diffusion model and a budget for seed set size. IM has numerous applications such as viral marketing, epidemic control, sensor placement and other network-related tasks. However, its practical uses are limited due to the computational complexity of current algorithms. Recently, deep reinforcement learning has been leveraged to solve IM in order to ease the computational burden. However, there are serious limitations in current approaches, including narrow IM formulation that only consider influence via spread and ignore self-activation, low scalability to large graphs, and lack of generalizability across graph families leading to a large running time for every test network. In this work, we address these limitations through a unique approach that involves: (1) Formulating a generic IM problem as a Markov decision process that handles both intrinsic and influence activations; (2)incorporating generalizability via meta-learning across graph families. There are previous works that combine deep reinforcement learning with graph neural network, but this work solves a more realistic IM problem and incorporates generalizability across graphs via meta reinforcement learning. Extensive experiments are carried out in various standard networks to validate performance of the proposed Graph Meta Reinforcement learning (GraMeR) framework. Finally, the results indicate that GraMeR is multiple orders faster and generic than conventional approaches when applied on small to medium scale graphs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Relaxations of AC Maximal Load Delivery for Severe Contingency Analysis

This work considers the task of finding an AC-feasible operating point of a severely damaged transmission network while ensuring that a maximal amount of active power loads can be delivered. This AC Maximal Load Delivery (AC-MLD) task is a nonlinear, non-convex optimization problem, and is incredibly challenging to solve on large-scale transmission system datasets. This work demonstrates that convex relaxations of the AC-MLD problem provide a reliable and scalable method for finding high-quality bounds on the amount of active power that can be delivered in the AC-MLD problem. To demonstrate their effectiveness, the solution methods proposed in this work are rigorously evaluated on 1000 N – k scenarios on seven power networks ranging in size from 70 to 6000 buses. Furthermore, the most effective relaxation of the AC-MLD problem converges in less than 20 seconds on commodity computing hardware for all 7000 of the scenarios considered.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Effective description of sub-maximal chaos: stringy effects for SYK scrambling

It has been proposed that the exponential decay and subsequent power law saturation of out-of-time-order correlation functions can be universally described by collective ‘scramblon’ modes. We develop this idea from a path integral perspective in several examples, thereby establishing a general formalism. After reformulating previous work on the Schwarzian theory and identity conformal blocks in two-dimensional CFTs relevant for systems in the infinite coupling limit with maximal quantum Lyapunov exponent, we focus on theories with sub-maximal chaos: we study the large-q limit of the SYK quantum dot and chain, both of which are amenable to analytical treatment at finite coupling. In both cases we identify the relevant scramblon modes, derive their effective action, and find bilocal vertex functions, thus constructing an effective description of chaos. The final results can be matched in detail to stringy corrections to the gravitational eikonal S-matrix in holographic CFTs, including a stringy Regge trajectory, bulk to boundary propagators, and multi-string effects that are unexplored holographically.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2014

Study of the zero modes of the Faddeev–Popov operator in the maximal Abelian gauge

A study of the zero modes of the Faddeev–Popov operator in the maximal Abelian gauge is presented in the case of the gauge group SU(2) and for different Euclidean space–time dimensions. Explicit examples of classes of normalizable zero modes and corresponding gauge field configurations are constructed by taking into account two boundary conditions, namely: (i) the finite Euclidean Yang–Mills action, (ii) the finite Hilbert norm. -- Highlights: •We study the zero modes of the Faddeev–Popov operator in the maximal Abelian gauge. •For d=2 we obtain solutions with finite action but not finite Hilbert norm. •For d=3,4 we obtain solutions with finite action and finite Hilbert norm. •These results can be compared with those previously obtained in the Landau gauge.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Optimal scheduling for profit maximization of energy storage merchants considering market impact based on dynamic programming

This paper analyzes how electricity merchants' market impact affects merchants' profit. Energy storage has long been studied for its role in maximizing profit, and merchant decisions are assumed to have no impact on market prices. However, the trading decisions of large-scale energy storage merchants (e.g., pumped storage hydro) will affect the market prices. This paper employs dynamic programming theory to investigate merchants' optimal economic dispatch considering the market impact and physical characteristics of storage systems. Our findings show that the State-of-Charge (SOC) based analytical solution significantly facilitates energy storage merchants' decision-making. The SOC range is segmented into three regions by two optimal SOC reference points, which depend on the available energy in storage, given prices, and market impact. By comparing the current storage SOC with the reference points, the merchant can get the corresponding optimal actions. We analytically show that if the merchant neglects the market impact on the power market, she will exaggerate her expected profit when the price-taker and price-maker merchants have the same generating and pumping upper limits offered to Independent System Operators (ISOs). Furthermore, the profit-maximizing merchant must, therefore, assay to balance the trade-off correctly between the intensity of market impact and the dispatched power. Our findings are verified by numerical simulation, and results demonstrate the ramifications for electricity merchants in energy arbitrage decisions.

25 ENERGY STORAGE↗
DOE OSTI2022

Natural gas maximal load delivery for multi-contingency analysis

An increasing dependence on natural gas has amplified existing vulnerabilities to the power grid, including disruptions to gas transmission networks from natural and man-made disasters. To address the operational challenges arising from these disruptions, we, in this study, consider the problem of estimating the steady-state operating capacity of a damaged gas pipeline network while ensuring the maximal delivery of load. Specifically, we formulate the mixed-integer nonconvex maximal load delivery (MLD) problem, which proves difficult to solve on large-scale networks. To address this challenge, we present a relaxation of the MLD problem and use it to determine bounds on the transport capacity of a gas pipeline system. A rigorous computational evaluation over network models ranging in size from 11 to 4,197 junctions shows that the relaxation-based method is suitable for analyzing the impacts of multi-contingency network disruptions, often converging to the optimal solution of the relaxation in less than ten seconds.

03 NATURAL GAS↗
DOE OSTI2023

Ce nanoparticles and sol-gel hybrid organic-inorganic coatings maximize corrosion protection in the anodized AA2024-T3

Herein we demonstrate that the corrosion resistance of the AA2024-T3 alloy can be maximized with the application of top protective sol-gel hybrid organic-inorganic coatings. Samples combining Ce nanoparticles within the pores of the anodic layer with the top sol-gel coatings were observed to outperform samples featuring only the coating. This research unravels the self-healing capability against corrosion of the sol-gel coating when Ce nanoparticles are present, as well as a good coverage of defective regions within the oxide by the hybrid layer. This research opens a paradigm aiming at maximizing the corrosion resistance for non-ferrous anodized alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Maximizing oil production from water alternating gas (CO 2 ) injection into residual oil zones: The impact of oil saturation and heterogeneity

Residual oil zones (ROZs) are widespread reservoirs, characterized by oil at residual saturation, either underlying oil fields (brownfield) or lateral (greenfield) to such fields. These reservoirs have the potential to produce volumes of oil sufficiently significant to make appreciable impacts on the US’s oil reserves and associated incidental CO 2 sequestration. The objective of this study is to improve our understanding the impact of heterogeneous and low oil saturations, in brownfield ROZs, on the effectiveness of water alternating gas (WAG) injection strategies. ROZs occur in the Permian Basin and elsewhere, and operators are using CO 2 injection for enhanced oil recovery (EOR) in these zones. The consensus model for the formation of ROZs is that they were formed by the effect of faster regional aquifer flow, acting over millions of years. Both the magnitude of oil saturation and the spatial distribution of oil differ from water-flooded main pay zones (MPZs). To explore the most effective injection strategies, we conducted simulations of CO 2 injection into synthetic geologic reservoirs. These simulations focused on injection into reservoirs subject to either man-made waterflooding or long-term natural waterflooding. By exploring the impact of varying: oil saturation; well patterns; reservoir heterogeneity; and permeability anisotropy, we attempt to quantify the factors that most influence the effectiveness of WAG injection. WAG ratios (the ratio of injected water and CO 2 , in reservoir volumes) of interest are those that either minimize the net CO 2 utilization ratios or maximize oil production rates. In general, the most effective WAG ratios for ROZs, are consistently less than those observed undergoing CO 2 injection in the same geologic reservoir models after traditional (man-made) waterflooding. This work demonstrates that most favorable WAG ratios for oil production in ROZs are different from those in traditional MPZs because of oil saturation differences. Therefore, CO 2 injection into both zones or directly copying WAG injection designs from MPZs to ROZs might not maximize oil production.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2017

Multi-mode evaluation of power-maximizing cross-flow turbine controllers

A general method for predicting and evaluating the performance of three candidate cross-flow turbine power-maximizing controllers is presented in this paper using low-order dynamic simulation, scaled laboratory experiments, and full-scale field testing. For each testing mode and candidate controller, performance metrics quantifying energy capture (ability of a controller to maximize power), variation in torque and rotation rate (related to drive train fatigue), and variation in thrust loads (related to structural fatigue) are quantified for two purposes. First, for metrics that could be evaluated across all testing modes, we considered the accuracy with which simulation or laboratory experiments could predict performance at full scale. Second, we explored the utility of these metrics to contrast candidate controller performance. For these turbines and set of candidate controllers, energy capture was found to only differentiate controller performance in simulation, while the other explored metrics were able to predict performance of the full-scale turbine in the field with various degrees of success. Finally, effects of scale between laboratory and full-scale testing are considered, along with recommendations for future improvements to dynamic simulations and controller evaluation.

13 HYDRO ENERGY↗
DOE OSTI2012

Long-Term Outcomes After Maximal Surgical Resection and Intraoperative Electron Radiotherapy for Locoregionally Recurrent or Locoregionally Advanced Primary Renal Cell Carcinoma

Purpose: To report outcomes of a multimodality therapy combining maximal surgical resection and intraoperative electron radiotherapy (IOERT) for patients with locoregionally (LR) recurrent renal cell carcinoma (RCC) after radical nephrectomy or LR advanced primary RCC. Methods and Materials: From 1989 through 2005, a total of 22 patients with LR recurrent (n = 19) or LR advanced primary (n = 3) RCC were treated with this multimodality approach. The median patient age was 63 years (range 46-78). Twenty-one patients (95%) received perioperative external beam radiotherapy (EBRT) with a median dose of 4,500 cGy (range, 4,140-5,500). Surgical resection was R0 (negative margins) in 5 patients (23%) and R1 (residual microscopic disease) in 17 patients (77%). The median IOERT dose delivered was 1,250 cGy (range, 1,000-2,000). Overall survival (OS) and disease-free survival (DFS) and relapse patterns were estimated using the Kaplan-Meier method. Results: The median follow-up for surviving patients was 9.9 years (range, 3.6-20 years). The OS and DFS at 1, 5, and 10 years were 91%, 40%, and 35% and 64%, 31%, and 31%, respectively. Central recurrence (within the IOERT field), LR relapse (tumor bed or regional lymph nodes), and distant metastases at 5 years were 9%, 27%, and 64%, respectively. Mortality within 30 days of surgery and IOERT was 0%. Five patients (23%) experienced acute or late National Cancer Institute Common Toxicity Criteria (NCI-CTCAE) Version 4 Grade 3 to 5 toxicities. Conclusions: In patients with LR recurrent or LR advanced primary RCC, a multimodality approach of perioperative EBRT, maximal surgical resection, and IOERT yielded encouraging results. This regimen warrants further investigation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Synthesis of MOF having hydroxyl functional side groups and optimization of activation process for the maximization of its BET surface area

To accomplish the postsynthetic modification of MOF with organic-metal precursors (OMPs) described in our previous researches more efficiently, synthesis of MOF (HCC-2) possessing relatively larger pore size as well as higher number of hydroxyl functional side groups per its base unit than those of HCC-1 has been successfully conducted via adopting 1,4-di-(4-carboxy-2,6-dihydroxyphenyl)benzene as an organic ligand and Zn(NO{sub 3}){sub 2}{center_dot}6H{sub 2}O as a metal source, respectively. Also, optimization about the Activation process of HCC-2 was performed to maximize its BET (Brunauer-Emmett-Teller) surface area which was proved to be proportional to the number of exposed active sites on which its postsynthetic modification occurred. However, Activation process having been validated to be so effective with the acquirement of highly-purified HCC-1 (CO{sub 2} supercritical drying step followed by vacuum drying step) was less satisfactory with the case of HCC-2. This might be attributed to relatively higher hydrophilicity and bulkier molecular structure of organic ligand of HCC-2. However, it was readily settled by simple modification of above Activation process. Moreover, indispensable residues composed of both DMF and its thermally degraded derivatives which were chemically attached via coordination bond with hydroxyl functionalities even after Activation process III might enable their H{sub 2} adsorption properties to be seriously debased compared to that of IRMOF-16 having no hydroxyl functionalities. - Graphical abstract: Synthesis of new-structured MOF (HCC-2) simultaneously possessing relatively larger pore size as well as higher number of hydroxyl functional side groups per its base unit at the same time than those of HCC-1 has been performed via adopting 1,4-di-(4-carboxy-2,6-dihydroxyphenyl)benzene as an organic ligand and Zn(NO{sub 3}){sub 2}{center_dot}6H{sub 2}O as a metal source, respectively. Also, the optimization of activation process for HCC-2 was conducted to maximize its BET surface area while the suitability of this activation process was proved via SEM, TGA, EA, XRF, and PSD. Being compared with the crystal structures of IRMOF-16 and HCC-1 via XRD and FT-IR analysis, the crystal structure of HCC-2 having an identical chemical structure except the introduction of four hydroxyl functional side groups on the backbone of its organic ligand showed no noticeable change. Specifically, HCC-2 was established as a cubic structure with each axis of about 21.5 A. Moreover, H{sub 2} adsorption isotherms for these HCCs were attained to ultimately examine that hydroxyl functionalities inside their pores have any influence on their H{sub 2} adsorption properties. Highlights: Black-Right-Pointing-Pointer HCC-2 having higher number of hydroxyl groups than that of HCC-1 was prepared. Black-Right-Pointing-Pointer The optimization of activation process for HCC-2 was studied. Black-Right-Pointing-Pointer The crystal structure of HCC-2 was a cubic-shaped structure with each axis of 21.5 A. Black-Right-Pointing-Pointer -OH functionalities on HCCs had negative influence on their H{sub 2} adsorption abilities. Black-Right-Pointing-Pointer This might be due to impurities rigidly attached to their functional side groups.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Maximizing Diversity from a Kinase Screen: Identification of Novel and Selective pan-Trk Inhibitors for Chronic Pain

We have identified several series of small molecule inhibitors of TrkA with unique binding modes. The starting leads were chosen to maximize the structural and binding mode diversity derived from a high throughput screen of our internal compound collection. These leads were optimized for potency and selectivity employing a structure based drug design approach adhering to the principles of ligand efficiency to maximize binding affinity without overly relying on lipophilic interactions. This endeavor resulted in the identification of several small molecule pan-Trk inhibitor series that exhibit high selectivity for TrkA/B/C versus a diverse panel of kinases. We have also demonstrated efficacy in both inflammatory and neuropathic pain models upon oral dosing. Herein we describe the identification process, hit-to-lead progression, and binding profiles of these selective pan-Trk kinase inhibitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Epidermal growth factor receptor cascade prioritizes the maximization of signal transduction

Many studies have been performed to quantify cell signaling. Cell signaling molecules are phosphorylated in response to extracellular stimuli, with the phosphorylation sequence forming a signal cascade. The information gain during a signal event is given by the logarithm of the phosphorylation molecule ratio. The average information gain can be regarded as the signal transduction quantity (ST), which is identical to the Kullback–Leibler divergence (KLD), a relative entropy. We previously reported that if the total ST value in a given signal cascade is maximized, the ST rate (STR) of each signaling molecule per signal duration (min) approaches a constant value. To experimentally verify this theoretical conclusion, we measured the STR of the epidermal growth factor (EGF)-related cascade in A431 skin cancer cells following stimulation with EGF using antibody microarrays against phosphorylated signal molecules. The results were consistent with those from the theoretical analysis. Thus, signaling transduction systems may adopt a strategy that prioritizes the maximization of ST. Furthermore, signal molecules with similar STRs may form a signal cascade. In conclusion, ST and STR are promising properties for quantitative analysis of signal transduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Metal oxide supported Ni-impregnated bifunctional catalysts for controlling char formation and maximizing energy recovery during catalytic hydrothermal liquefaction of food waste

Nickel (Ni)-impregnated metal oxide catalysts, Ni/CeZrO x , Ni/ZrO 2 , and Ni/CeO 2 , were investigated to maximize energy recovery and reduce char yield during catalytic hydrothermal liquefaction (CHTL) of food waste. Yields of char, biocrude, water soluble products, and gas were measured at 300 °C and 1 hour for both the parent oxides (CeZrO x , ZrO 2 , and CeO 2 ) and the Ni-impregnated versions. Using Ni-based catalysts reduced the carbon-weighted char yield from 16–24% to <10% and decreased the energy recovery of char from 39–47% to <21%, as compared with control tests. In particular, using Ni/ZrO 2 resulted in the greatest biocrude yield, greatest reduction of char yield, and greatest energy recovered as biocrude (39.2%). After factoring in all forms of usable energy produced from food waste, the total energy recovery obtained for the catalysts studied here was >60%. Ni/ZrO 2 and Ni/CeO 2 show the greatest potential for controlling char growth and maximizing energy recovered from food waste. The crystalline structures of all three oxides were hydrothermally stable. Catalyst reuse tests indicate that the biocrude and char yields remained the same for the first and second use (within uncertainty) and that the catalyst retains its initial crystallinity and 93% of its initial Ni content. Molecular composition of biocrudes analyzed by the state-of-the-art analytical platforms (including GC-MS, GC × GC, FT-ICR MS, and 1H NMR) revealed minor differences in the chemical constituents of biocrudes obtained using different catalysts and provided some insight regarding reaction mechanism.

09 BIOMASS FUELS↗
DOE OSTI2022

Variational approach to quantum state tomography based on maximal entropy formalism

Quantum state tomography is an integral part of quantum computation and offers the starting point for the validation of various quantum devices. One of the central tasks in the field of state tomography is to reconstruct, with high fidelity, the quantum states of a quantum system. From an experiment on a real quantum device, one can obtain the mean measurement values of different operators. With such data as input, in this report we employ the maximal entropy formalism to construct the least biased mixed quantum state that is consistent with the given set of expectation values. Even though, in principle, the reported formalism is quite general and should work for an arbitrary set of observables, in practice we shall demonstrate the efficacy of the algorithm on an informationally complete (IC) set of Hermitian operators. Such a set possesses the advantage of uniquely specifying a single quantum state from which the experimental measurements have been sampled and hence renders the rare opportunity not only to construct a least-biased quantum state but even replicate the exact state prepared experimentally within a preset tolerance. Here, the primary workhorse of the algorithm is reconstructing an energy function which we designate as the effective Hamiltonian of the system, and parameterizing it with Lagrange multipliers, according to the formalism of maximal entropy. These parameters are thereafter optimized variationally so that the reconstructed quantum state of the system converges to the true quantum state within an error threshold. To this end, we employ a parameterized quantum circuit and a hybrid quantum-classical variational algorithm to obtain such a target state, making our recipe easily implementable on a near-term quantum device.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗