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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Real-Time Distributed Control of Smart Inverters for Network-level Optimization

The limitations of centralized optimization methods in managing electric power distribution systems operations have led to the distributed paradigm of computing and decision-making. Unfortunately, the existing distributed optimization algorithms are limited in their applicability to managing fast varying phenomena such as those resulting from highly variable Distributed Energy Resource (DER) generation patterns. They require a large number of communication rounds (in the order of 10 2 to 10 3 ) among the computing agents to solve one instance of the optimization problem. Related real-time distributed control methods are equally limited in their applications to power distribution systems with fast-changing DER generation; they require hundreds of rounds of communication and thus are slow in tracking the network-level optimal solutions. In this paper, we propose a novel distributed voltage controller that provides a fast-tracking of rapidly varying DER generation profiles while simultaneously converging to network-level optimal solutions within a few communication rounds. The proposed control algorithm leverages the radial topology of the system, which reduces the required communication rounds to reach the network-level optimum solution by order of magnitude. The novelty lies in carefully reducing the electrical network model from the perspective of each distributed controller and enabling appropriate data sharing among upstream and downstream nodes to achieve fast convergence. The simulation results demonstrate the effectiveness of the proposed approach in minimizing the feeder losses while maintaining the node voltage within the pre-specified limits.

voltage control, optimization, reactive power, inv↗
DOE OSTI2018

Simultaneous application of predictive model and least cost formulation can substantially benefit biorefineries outside Corn Belt in United States: A case study in Florida

Previously, a predictive model was developed to identify optimal blends of expensive high-quality and cheaper low-quality feedstocks for a given geographical location that can deliver high sugar yields. Here, the optimal process conditions were tested for application at commercially-relevant higher biomass loadings. We observed lower sugar yields but 100% conversion to ethanol from a blend that contained only 20% high-quality feedstock. The impact of applying this predictive model simultaneously with least cost formulation model for a biorefinery location outside of the US Corn Belt in Lee County, Florida was investigated. A blend ratio of 0.30 EC, 0.45 SG, and 0.25 CS in Lee County was necessary to produce sugars at high yields and ethanol at a capacity of 50 MMGY. This work demonstrates utility in applying predictive model and LCF to reduce feedstock costs and supply chain risks while optimizing for product yields.

09 BIOMASS FUELS↗
DOE OSTI2016

Optimizing AlF{sub 3} atomic layer deposition using trimethylaluminum and TaF{sub 5}: Application to high voltage Li-ion battery cathodes

Atomic layer deposition (ALD) of conformal AlF{sub 3} coatings onto both flat silicon substrates and high-voltage LiNi{sub 0.5}Mn{sub 0.3}Co{sub 0.2}O{sub 2} (NMC) Li-ion battery cathode powders was investigated using a Al(CH{sub 3}){sub 3}/TaF{sub 5} precursor combination. This optimized approach employs easily handled ALD precursors, while also obviating the use of highly toxic HF(g). In studies conducted on planar Si wafers, the film's growth mode was dictated by a competition between the desorption and decomposition of Ta reaction byproducts. At T ≥ 200 °C, a rapid decomposition of the Ta reaction byproducts to TaC led to continuous deposition and high concentrations of TaC in the films. A self-limited ALD growth mode was found to occur when the deposition temperature was reduced to 125 °C, and the TaF{sub 5} exposures were followed by an extended purge. The lower temperature process suppressed conversion of TaF{sub x}(CH{sub 3}){sub 5−x} to nonvolatile TaC, and the long purges enabled nearly complete TaF{sub x}(CH{sub 3}){sub 5−x} desorption, leaving behind the AlF{sub 3} thin films. NMC cathode powders were coated using these optimized conditions, and coin cells employing these coated cathode particles exhibited significant improvements in charge capacity fade at high discharge rates.

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

Variational Monte Carlo Calculations of A ≤ 4 Nuclei with an Artificial Neural-Network Correlator Ansatz

Here, the complexity of many-body quantum wave functions is a central aspect of several fields of physics and chemistry where nonperturbative interactions are prominent. Artificial neural networks (ANNs) have proven to be a flexible tool to approximate quantum many-body states in condensed matter and chemistry problems. In this work we introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei, and approximately solve the nuclear many-body Schrodinger equation. Using efficient stochastic sampling and optimization schemes, our approach extends pioneering applications of ANNs in the field, which present exponentially scaling algorithmic complexity. We compute the binding energies and point-nucleon densities of A ≤ 4 nuclei as emerging from a leading-order pionless effective field theory Hamiltonian. We successfully benchmark the ANN wave function against more conventional parametrizations based on two- and three-body Jastrow functions, and virtually exact Green's function Monte Carlo results.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Hyper-Differential Sensitivity Analysis of Uncertain Parameters in PDE-Constrained Optimization

Many problems in engineering and sciences require the solution of large scale optimization constrained by partial differential equations (PDEs). Though PDE-constrained optimization is itself challenging, most applications pose additional complexity, namely, uncertain parameters in the PDEs. Uncertainty quantification (UQ) is necessary to characterize, prioritize, and study the influence of these uncertain parameters. Sensitivity analysis, a classical tool in UQ, is frequently used to study the sensitivity of a model to uncertain parameters. In this article, we introduce "hyper-differential sensitivity analysis" which considers the sensitivity of the solution of a PDE-constrained optimization problem to uncertain parameters. Our approach is a goal-oriented analysis which may be viewed as a tool to complement other UQ methods in the service of decision making and robust design. We formally define hyper-differential sensitivity indices and highlight their relationship to the existing optimization and sensitivity analysis literatures. Assuming the presence of low rank structure in the parameter space, computational efficiency is achieved by leveraging a generalized singular value decomposition in conjunction with a randomized solver which converts the computational bottleneck of the algorithm into an embarrassingly parallel loop. Two multi-physics examples, consisting of nonlinear steady state control and transient linear inversion, demonstrate efficient identification of the uncertain parameters which have the greatest influence on the optimal solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Analysis of the Hessian for aeroelastic optimization

The symbol of the Hessian for an aeroelastic optimization model problem is analyzed. The flow is modeled by the small-disturbance full potential equation and the structure is modeled by an isotropic (von Karman) plate equation. The cost function consists of both aerodynamic and structural terms. In the new analysis the symbol of the cost function Hessian near the minimum is computed. The result indicates that under some conditions, which are likely fulfilled in most applications, the system is decoupled for the non-smooth components. The result also shows that the structure part in the Hessian is well-conditioned while the aerodynamic part is ill-conditioned. Applications of the result to optimization strategies are discussed.

Arian, Eyal↗
NASA NTRS1976

Evaluation of low wing-loading fuel conservative, short-haul transports

Fuel conservation that could be attained with two technology advancements, Q fan propulsion system and active control technology (ACT) was studied. Aircraft incorporating each technology were sized for a Federal Aviation Regulation (FAR) field length of 914 meters (3,000 feet), 148 passengers, and a 926 kilometer (500 nautical mile) mission. The cruise Mach number was .70 at 10100 meter (33,000 foot) altitude. The improvement resulting from application of the Q fan propulsion system was computed relative to an optimized fuel conservative transport design. The performance improvements resulting from application of ACT technology were relative to the optimized Q fan propulsion system configuration.

Pasley, L. H.↗
NASA NTRS1982

Process research on non-CZ silicon material

High risk, high payoff research areas associated with he process for producing photovoltaic modules using non-CZ sheet material are investigated. All investigations are being performed using dendritic web silicon, but all processes are directly applicable to other ribbon forms of sheet material. The technical feasibility of forming front and back junctions in non-CZ silicon using liquid dopant techniques was determined. Numerous commercially available liquid phosphorus and boron dopant solutions are investigated. Temperature-time profiles to achieve N(+) and P(+) sheet resistivities of 60 + or - 10 and 40 + or - s10 ohms per square centimeter respectively are established. A study of the optimal method of liquid dopant application is performed. The technical feasibility of forming a liquid applied diffusion mask to replace the more costly chemical vapor deposited SiO2 diffusion mask was also determined.

Source record↗
DOE OSTI2020

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β-glucosidase function in Cellvibrio japonicus

Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene-deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacterium Cellvibrio japonicus grown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics of C. japonicus using either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild-type strain as well as in β-glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non-diauxic growth and metabolite consumptions of the wild-type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially-useful strains. Importantly, the model is able to explain the role of the different β-glucosidases and their behavior under genetic perturbations. Here, this integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

$L^1$ penalization of volumetric dose objectives in optimal control of PDEs

This work is concerned with a class of PDE-constrained optimization problems that are motivated by an application in radiotherapy treatment planning. Here the primary design objective is to minimize the volume where a functional of the state violates a prescribed level, but prescribing these levels in the form of pointwise state constraints leads to infeasible problems. We therefore propose an alternative approach based on L 1 penalization of the violation that is also applicable when state constraints are infeasible. We establish well-posedness of the corresponding optimal control problem, derive first-order optimality conditions, discuss convergence of minimizers as the penalty parameter tends to infinity, and present a semismooth Newton method for their efficient numerical solution. Finally, the performance of this method for a model problem is illustrated and contrasted with an alternative approach based on (regularized) state constraints.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fast and Non-Destructive Determination of Water Content in Ionic Liquids at Varying Temperatures by Raman Spectroscopy and Multivariate Regression Analysis

Imidazolium acetate ionic liquids (ILs) have been utilized as promising solvents in many applications that involve varying water content and temperature. These experimental variables affect the anion-cation intermolecular interactions, which in turn influence the performance of the ILs in these applications. Here, this paper shows Raman spectroscopy can be used as an operando method to measure water content in IL solvents when simultaneous temperature changes may occur. The Raman spectra of 1-alkyl-3-methylimidazolium acetate ILs (alkyl chain length n = 2, 4, 6, 8) with varying water content (from 0.028 to 0.899 water mole fraction) and temperature (from 78.1 K to 423.1 K) were measured. Increasing the water content or decreasing the temperature of the tested ILs weakens the anion-cation intermolecular interactions. The water content of these ILs can be quantified even in conditions when the temperature is changing using Raman spectroscopy combined with multivariate regression analysis, including principal component regression (PCR), partial-least-squares regression (PLSR), and artificial neural networks (ANNs). The ANN model combined with partial-least-squares (PLS) achieves the highest prediction accuracy of water content in ILs at varying temperatures (RMSECV = 0.017, R 2 CV = 99.1%, RMSEP = 0.019, R 2 P = 98.8%, RPD = 8.93). Raman spectroscopy provides a potential fast non-destructive operando method to monitor the water content of ILs even in applications when the temperature may be simultaneously altered; this information can lead to the optimized use of these ILs in many applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Development of the dense plasma focus for short-pulse applications

The dense plasma focus (DPF) has long been considered a compact source for pulsed neutrons and has traditionally been optimized for the total neutron yield. Here, we describe the efforts to optimize the DPF for short-pulse applications by introducing a reentrant cathode at the end of the coaxial plasma gun. We reduced the resulting neutron pulse widths by an average of 21±921±9% from the traditional long-drift DPF design. Pulse widths and yields achieved from deuterium-tritium fusion at 2 MA are 61.8±30.761.8±30.7 ns FWHM and 1.84±0.49×10121.84±0.49×10 12 neutrons per shot. Simulations were conducted concurrently to elucidate the DPF operation and confirm the role of the reentrant cathode. Furthermore, a hybrid fluid-kinetic particle-in-cell modeling capability demonstrates correct sheath velocities, plasma instabilities, and fusion yield rates. Consistent with previous findings that the DPF is dominated by beam-target fusion from superthermal ions, we estimate that the thermonuclear contribution is at the 1% level.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2004

Hybrid Power Management Program Evaluated Fuel Cell/Ultracapacitor Combinations and Developed Other New Applications

In fiscal year 2003, the continuation of the Hybrid Power Management (HPM) Program through NASA Glenn Research Center's Commercial Technology Office resulted in several new successful applications of this pioneering technology. HPM is the innovative integration of diverse, state-of-the-art power devices in an optimal configuration for space and terrestrial applications. The appropriate application and control of the various power devices significantly improves overall system performance and efficiency. The advanced power devices include ultracapacitors, fuel cells, and photovoltaics. HPM has extremely wide potential, with applications from nanowatts to megawatts--including power generation, transportation systems, biotechnology systems, and space power systems. HPM has the potential to significantly alleviate global energy concerns, improve the environment, and stimulate the economy. Fuel cells provide excellent efficiency and energy density, but do not have good power density. In contrast, ultracapacitors have excellent power density and virtually unlimited cycle life. To improve the power density of the fuel cell, the combination of fuel cells and ultracapacitors was evaluated.

Eichenberg, Dennis J.↗
DOE OSTI2017

A CPS Based Optimal Operational Control System for Fused Magnesium Furnace

Fused magnesia smelting for fused magnesium furnace (FMF) is an energy intensive process with high temperature and comprehensive complexities. Its operational index namely energy consumption per ton (ECPT) is defined as the consumed electrical energy per ton of acceptable quality and is difficult to measure online. Moreover, the dynamics of ECPT cannot be precisely modelled mathematically. The model parameters of the three-phase currents of the electrodes such as the molten pool level, its variation rate and resistance are uncertain and nonlinear functions of the changes in both the smelting process and the raw materials composition. In this paper, an integrated optimal operational control algorithm proposed is composed of a current set-point control, a current switching control and a self-optimized tuning mechanism. The tight conjoining of and coordination between the computational resources including the integrated optimal operational control, embedded software, industrial cloud, wireless communication and the physical resources of FMF constitutes a cyber-physical system (CPS) based embedded optimal operational control system. Successful application of this system has been made for a production line with ten fused magnesium furnaces in a factory in China, leading to a significant reduced ECPT.

Cyber-Physical Systems↗
NASA NTRS2015

Method and Apparatus for Generating Flight-Optimizing Trajectories

An apparatus for generating flight-optimizing trajectories for a first aircraft includes a receiver capable of receiving second trajectory information associated with at least one second aircraft. The apparatus also includes a traffic aware planner (TAP) module operably connected to the receiver to receive the second trajectory information. The apparatus also includes at least one internal input device on board the first aircraft to receive first trajectory information associated with the first aircraft and a TAP application capable of calculating an optimal trajectory for the first aircraft based at least on the first trajectory information and the second trajectory information. The optimal trajectory at least avoids conflicts between the first trajectory information and the second trajectory information.

Ballin, Mark G.↗
NASA NTRS2007

Reusable, Extensible High-Level Data-Distribution Concept

A framework for high-level specification of data distributions in data-parallel application programs has been conceived. [As used here, distributions signifies means to express locality (more specifically, locations of specified pieces of data) in a computing system composed of many processor and memory components connected by a network.] Inasmuch as distributions exert a great effect on the performances of application programs, it is important that a distribution strategy be flexible, so that distributions can be adapted to the requirements of those programs. At the same time, for the sake of productivity in programming and execution, it is desirable that users be shielded from such error-prone, tedious details as those of communication and synchronization. As desired, the present framework enables a user to refine a distribution type and adjust it to optimize the performance of an application program and conceals, from the user, the low-level details of communication and synchronization. The framework provides for a reusable, extensible, data-distribution design, denoted the design pattern, that is independent of a concrete implementation. The design pattern abstracts over coding patterns that have been found to be commonly encountered in both manually and automatically generated distributed parallel programs. The following description of the present framework is necessarily oversimplified to fit within the space available for this article. Distributions are among the elements of a conceptual data-distribution machinery, some of the other elements being denoted domains, index sets, and data collections (see figure). Associated with each domain is one index set and one distribution. A distribution class interface (where "class" is used in the object-oriented-programming sense) includes operations that enable specification of the mapping of an index to a unit of locality. Thus, "Map(Index)" specifies a unit, while "LocalLayout(Index)" specifies the local address within that unit. The distribution class can be extended to enable specification of commonly used distributions or novel user-defined distributions. A data collection can be defined over a domain. The term "data collection" in this context signifies, more specifically, an abstraction of mappings from index sets to variables. Since the index set is distributed, the addresses of the variables are also distributed.

James, Mark↗
DOE OSTI2016

Uncertainty and Sensitivity Analysis of Reactivity Insertion Accidents using BISON Fuel Performance Code

The development of Accident Tolerant Fuels (ATF) has expanded into a considerable research program in the wake of the events at the Japanese Fukushima Daiichi nuclear power plant in 2011. The ATF goal is to insert a test assembly into a commercial reactor by 2022. To support this, testing of the ATF design under off-normal conditions is needed to assess the 'enhanced accident tolerance' capability of the fuel rod design over standard UO{sub 2}-Zircaloy concepts. Transient testing capabilities are necessary for off-normal testing of events such as loss of coolant accidents (LOCA) and reactivity insertion accidents (RIA). A transient testing program has been developed to support the testing of ATF designs. The Transient Reactor Test (TREAT) facility has been chosen as the site to perform the transient testing of the ATF designs. TREAT is a versatile test facility capable of subjecting fuel specimens to various off-normal conditions. Historic TREAT experiments were performed in a variety of experimental test vehicles including static environment capsules, once-through flowing systems, and also recirculating flowing systems. The planned TREAT experiments will be operated under PWR conditions. Therefore, a static capsule vehicle is under development to handle required temperatures and pressures. The static capsule, termed the Static Environment Rodlet Transient Test Apparatus (SERTTA), will be used for irradiation tests in TREAT to subject ATF fuel rodlets to a range of power pulse conditions and environments. To aid in the design and analysis of future tests in TREAT, the fuel performance code BISON is being used. Fuel performance codes capable of accident modeling have been developed for some time and they have shown an ability to reproduce experimental results with some degree of adequacy, but they still require extensive verification and validation. Future experiments in TREAT will add to a limited database of past transient tests performed in transient reactors to support LWR systems. These tests can be used for future verification and validation of fuel performance codes. Understanding the impact of the uncertainty of influential parameters is critical for not only analyzing the results of the experiment but also for modeling and validation of codes. For example, uncertainties associated with the initial manufactured state of the sample, instrument measurements, material property models, and planned experiment conditions can provide important insight into model performance, experimental data, and play an important role in experimental design. The objective of this work is to perform an uncertainty and sensitivity analysis on a simplified RIA case of a standard UO{sub 2}-Zircaloy sample using expected nominal conditions in the SERTTA capsule in TREAT. The BISON fuel performance code is used in conjunction with DAKOTA (Design Analysis Kit for Optimization and Tera-scale Applications) to perform the uncertainty and sensitivity analysis. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Controlled Synthesis of SnO 2 Nanocrystals with Tunable Band Gaps

Tin(IV) oxide nanocrystals (SnO 2 NCs) have significant potential in various applications, with their performance closely related to their band gap. The band gap is influenced by the size and shape of the NCs, which can be precisely controlled by adjusting reaction conditions. In this study, we present deliberately designed synthesis protocols to produce high-quality SnO 2 NCs with tunable band gaps using different methods. Key factors affecting the synthesis include control of the oxidizing agent, reaction temperature, solvent selection, and reaction time optimization. The resulting NCs were characterized by using TEM, XRD, XPS, and optical spectroscopy. Notably, SnO 2 NCs synthesized by controlling the oxidizing agent (air injection) in a hot organic solution were smaller in size and exhibited abundant oxygen vacancies. In contrast, extending the reaction time or using ethanol as a solvent in hydrothermal systems facilitated larger spherical or rod-like SnO 2 NCs with fewer oxygen vacancies. Further analysis of the band gap and valence band maximum energy revealed that the abundant vacancies in SnO 2 NCs synthesized with the air-controlled hot organic solution method resulted in a narrower band gap and an upshifted valence band. These synthetic strategies illustrate the potential for deliberately designing SnO 2 NCs with optimized electronic structures for various applications.

band gap↗

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