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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Understanding the Static Interfacial Polymer Layer by Exploring the Dispersion States of Nanocomposites

The dynamic and static properties of the interfacial region between polymer and nanoparticles have wide-ranging consequences on performances of nanomaterials. The thickness and density of the static layer are particularly difficult to assess experimentally due to superimposing nanoparticle interactions. Here, we tune the dispersion of silica nanoparticles in nanocomposites by preadsorption of polymer layers in the precursor solutions, and by varying the molecular weight of the matrix chains. Nanocomposite structures ranging from ideal dispersion to repulsive order or various degrees of aggregation are generated and observed by small-angle scattering. Preadsorbed chains are found to promote ideal dispersion, before desorption in the late stages of nanocomposite formation. The microstructure of the interfacial polymer layer is characterized by detailed modeling of X-ray and neutron scattering. Only in ideally well-dispersed systems a static interfacial layer of reduced polymer density over a thickness of ca. 2 nm is evidenced based on the analysis with a form-free density profile optimized using numerical simulations. This interfacial gradient layer is found to be independent of the thickness of the initially adsorbed polymer, but appears to be generated by out-of-equilibrium packing and folding of the preadsorbed layer. The impact of annealing is investigated to study the approach of equilibrium, showing that initially ideally well-dispersed systems adopt a repulsive hard-sphere structure, while the static interfacial layer disappears. Furthermore, this study thus promotes the fundamental understanding of the interplay between effects which are decisive for macroscopic material properties: polymer-mediated interparticle interactions, and particle interfacial effects on the surrounding polymer.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Tuning the electrocatalytic activity of Pt by structurally ordered PdFe/C for the hydrogen oxidation reaction in alkaline media

Tuning the hydrogen adsorption energy (E H ) by controlling the surface electronic structure of Pt is essential for enhancing the hydrogen oxidation reaction (HOR) performance in alkaline media. This could be achieved by forming a Pt skin on PdFe/C nanoparticles with structurally ordered intermetallic (O-PdFe@Pt/C) or disordered alloy (D-PdFe@Pt/C). The HOR activity on O-PdFe@Pt/C exhibits an exchange current density of 1.49 A mg Pt -1 , which is 3.87 and 7.56 times higher than that on D-PdFe@Pt/C (0.385 A mg Pt -1 ) and Pt/C (0.197 A mg Pt -1 ), respectively. The excellent electrocatalytic HOR performance on O-PdFe@Pt/C can be ascribed to the attenuation of E H on the Pt shell induced by the structurally ordered PdFe core, where the E H on O-PdFe@Pt surface is 0.18 eV smaller than that on Pt according to DFT calculations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Superfluid condensate fraction and pairing wave function of the unitary Fermi gas

The unitary Fermi gas is a many-body system of two-component fermions with zero-range interactions tuned to infinite scattering length. Despite much activity and interest in unitary Fermi gas and its universal properties, there have been great difficulties in performing accurate calculations of the superfluid condensate fraction and pairing wave function. In this paper, we present auxiliary-field lattice Monte Carlo simulations using a lattice interaction which accelerates the approach to the continuum limit, thereby allowing for robust calculations of these difficult observables. As a benchmark test, we compute the ground-state energy of 33 spin-up and 33 spin-down particles. As a fraction of the free Fermi gas energy $E_{\text{FG}}$, we find $E_0/E_{\text{FG}}$ = 0.369(2), 0.372(2), using two different definitions of the finite-system energy ratio, in agreement with the latest theoretical and experimental results. We then determine the condensate fraction by measuring off-diagonal long-range order in the two-body density matrix. We find that the fraction of condensed pairs is α = 0.43(2). Further, we also extract the pairing wave function and find the pair correlation length to be $ζ_pk_F$ = 1.8(3)ℏ, where $k_F$ is the Fermi momentum. Provided that the simulations can be performed without severe sign oscillations, the methods we present here can be applied to superfluid neutron matter as well as more exotic $\textit{P}$-wave and $\textit{D}$-wave superfluids.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

AWB-GCN: A Graph Convolutional Network Accelerator with Runtime Workload Rebalancing

The recent development of deep learning has been mostly focusing on Euclidean data, such as images, videos, audios, etc. However, most real-world information and relation are often expressed as graphs. To efficiently learn from graph data, graph convolutional networks (GCNs) emerge as a promising approach, showing advantages in several practical applications such as social network analysis, knowledge discovery, 3D modeling, motion capturing, etc. Real-world graphs are usually extremely large and imbalanced, posting significant performance demand and design challenges on the hardware dedicated for GCN inference. In this paper, we propose an architecture design called UW-GCN to accelerate graph convolutional network inference. To tackle the major performance bottleneck from workload imbalance, we propose dynamic neighborhood stealing and remote chunk shuffling techniques, relying on hardware flexibility to achieve hardware auto-tuning under negligible area or delay overhead. Specifically, UW-GCN is able to smartly profile the sparse graph pattern while continuously adjusting the workload distribution via routing reconfiguration among parallel processing elements (PEs). The ideal configuration is then reused in the remaining iterations. To the best of our knowledge, this is the first accelerator design particularly for GCN and the first work relying on hardware auto-tuning, which is normally based on software, to achieve near-optimal workload balance in processing sparse structures.

Geng, Tong↗
DOE OSTI2018

Benefits of Higher-Temperature Operation in Boosted SI Engines Enabled by Advanced Materials

To meet the demand for greater fuel efficiency in passenger vehicles, various strategies are employed to increase the power density of light-duty SI engines, with attendant thermal or system efficiency increases. One approach is to incorporate higher-performance alloys for critical engine components. These alloys can have advantageous thermal or mechanical properties at higher temperatures, allowing for components constructed from these materials to meet more severe pressure and temperature demands, while maintaining durability. Advanced alloys could reduce the need for charge enrichment to protect certain gas-path components at high speed and load conditions, permit more selective cooling to reduce heat-transfer losses, and allow engine downsizing, while maintaining performance, by achieving higher cylinder temperatures and pressures. As a first step in investigating downsizing strategies made possible through high-performance alloys, a GT-Power model of a 4-cylinder 1.6L turbocharged direct-injection SI engine was developed. The model was tuned and validated against experimental dynamometer data collected from a corresponding engine. The model was then used to investigate various operating strategies for increasing power density. Results from these investigations will provide valuable insight into how new materials might be utilized to meet the needs of future light-duty engines and will serve as the basis for a more comprehensive investigation using more-detailed thermo-mechanical modeling.

Mills, Zachary↗
DOE OSTI2016

SU-F-T-279: Impact of Beam Energy Drifts On IMRT Delivery Accuracy

Purpose: According to TG-40 percent-depth-dose (PDD) tolerance is ±2% but TG-142 is ±1%. Now the question is, which one is relevant in IMRT era? The primary objective of this study is to evaluate dosimetric impact of beam-energy-drifts on IMRT-delivery. Methods: Beam-energy drifts were simulated by adjusting Linac’s bending-magnet-current (BMC) followed by tuning the pulse-forming network and adjusting gun-current. PDD change of −0.6% and +1.2% were tested. Planar-dosimetry measurements were performed using an ionization-chamber-array in solid-water phantoms. Study includes 10-head-and-neck and 3-breast cancer patients. en-face beam-deliveries were also tested at 1.3cm and 5.3cm depths. Composite and single-field dose-distributions were compared against the plans to determine %Gamma pass-rates (%GPRs). For plan dose comparisons, changes in %Gamma pass-rates (cPGPRs) were computed/reported to exclude the differences between dose-computation and delivery. Dose distributions of the drifted-energies were compared against their baseline measurements to determine the% GPRs. A Gamma criteria of 3%/3mm was considered for plan-dose comparisons while 3%/1mm used for measured dose intercomparisons. Results: For composite-dose delivery, average cPGPRs were 0.41%±2.48% and −2.54%±3.65% for low-energy (LE) and high-energy (HE) drifts, respectively. For measured dose inter-comparisons, the average%GPRs were 98.4%±2.2% (LE-drift) and 95.8%±4.0 (HE-drift). The average %GPR of 92.6%±4.3% was noted for the worst-case scenario comparing LE-drift to HE-drift. All en-face beams at 5.3 cm depth have cPGPRs within ±4% of the baseline-energy measurements. However, greater variations were noted for 1.3cm depth. Average %GPRs for drifted energies were >99% at 5.3cm and >97% at 1.3cm depths. However, for the worst-case scenario (LE-drift to HE-drift) these numbers dropped to 95.2% at 5.3cm and 93.1% at 1.3cm depths. Conclusion: The dosimetric impact of beam-energy drifts was found to be within clinically acceptable tolerance. However, this study includes a single energy with limited range of PDD change. Further studies are on going and the results will be presented. Received funding from Varian Medical Systems, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluating the Performance of Integer Sum Reduction in SYCL on GPUs

SYCL is a promising programming model for heterogeneous computing—allowing a single-source code to target devices from multiple vendors. One significant task performed on these accelerators is a primitive operation for integer sum reduction. This paper presents several SYCL implementations of integer sum reduction—using atomic functions, shared local memory, vectorized memory accesses and parameterized workload sizes—to compare the performance and maturity of SYCL against open-source vendor-specific implementations of the same reduction. For a sufficiently large number of integers, tuning the parameters of our SYCL implementations achieves 1.4X speedup over the open-source implementations on an Intel UHD630 integrated GPU. The SYCL reduction is 3% faster than the templated reduction in Thrust, and 0.3% faster than the device reduction in CUB on an Nvidia P100 GPU. The SYCL reduction is 1.9% faster than the templated reduction in Thrust, and 0.4% faster than the device reduction in CUB on an Nvidia V100 GPU.

Jin, Zheming↗
DOE OSTI2018

Neutron Scattering Investigation of the Relationship between Molecular Structure, Morphology and Dynamics in Conjugated Polymers

Conjugated polymers (CP) have enabled significant technological advances such as inexpensive organic solar cells (OPVs), biomedical sensor devices and flexible displays incorporating organic light emitting diodes (OLEDs) among others. Some of the distinguishing properties of CPs include simple processing from solutions, synthesis from abundant raw materials (C, H, S, N), flexible device form-factors and a capacity to fine-tune optical and electronic properties. Moreover, conductive polymers can assemble into large organized structures, such as nanowires and networks, that can significantly boost their performance. However, it is observed that small changes in the structure of the molecules (e.g. addition of alkyl groups) results in drastic and unpredictable changes in organized structures and electronic properties. This project utilized a comprehensive suite of elastic (SANS and USANS) and quasi-elastic (NSE and backscattering spectroscopy) neutron scattering techniques along with simultaneous property evaluation to unravel the relationships between molecular structure, self-assembly, directed-assembly and the resulting properties. Completed project objectives were: 1) to evaluate the influence of chain architecture on solution conformation, 2) to determine of the influence of molecular structure on CP self and directed assembly and 3) to study the correlation between dynamic molecular relaxations and macroscopic properties of CPs in the solid state. The experimental components of the project were further complemented by advanced molecular simulation strategies (e.g. atomistic MD and DFT) to fully understand the relationship between chain structure, mesoscale morphology, dynamic relaxation processes and the macroscopic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Baseband Digital Network Analyzer Upgrade for LLRF Controllers

Digital Network Analyzers (DNA) have been implemented in many Low-Level Radio Frequency (LLRF) systems, notably NSLS-II and CERN, to help tune feedback loops. DNA characterizes feedback loops by measuring the frequency-dependent magnitude and phase transfer functions. It enables the measurement of open loop gains, gain/phase margins, and loop delays to help fine-tune feedback loops. An FPGA-based DNA has been developed and integrated into the current Relativistic Heavy Ion Collider (RHIC) LLRF infrastructure. Its performance has been tested with an implementation of one-turn delay feedback (OTFB) on the bench to maximize gain and stability. The DNA has been used to characterize a RHIC 28 MHz cavity in a RHIC Accelerator Physics Experiment (APEX) to test transient beam loading compensation strategies.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Growth and Characterization of Arsenic-Doped CdTe 1-x Se x Single Crystals Grown by the Cd-Solvent Traveling Heater Method

The photovoltaic performance of CdTe solar cells is mainly limited by low doping and short minority carrier lifetime. Group-V element doping and Se-alloying have a significant impact on tuning these fundamental CdTe properties. In this paper, we report the growth of p-type As-doped, Cd-rich CdTe 1-x Se x single crystals using metallic Cd as the solvent in the traveling-heater method. The structural and electrical properties of CdTe1-xSex are examined for different Se concentrations. CdTe 1-x Se x single crystals (0 ≤ x ≤ 0.5) with zincblende structure indicate homogeneous composition. The 10 17 cm-3 As-doping activation efficiency can be maintained at close to 50% for x ≤ 0.2. Se alloying leads to bulk minority carrier lifetime exceeding 30 ns for samples doped near 1017 cm -3 . These results help us to overcome the current roadblocks in device performance.

14 SOLAR ENERGY↗
DOE OSTI2023

Enhancing isoprenol production by systematically tuning metabolic pathways using CRISPR interference in E. coli

Regulation of metabolic gene expression is crucial for maximizing bioproduction titers. Recent engineering tools including CRISPR/Cas9, CRISPR interference (CRISPRi), and CRISPR activation (CRISPRa) have enabled effective knock-out, knock-down, and overexpression of endogenous pathway genes, respectively, for advanced strain engineering. CRISPRi in particular has emerged as a powerful tool for gene repression through the use of a deactivated Cas9 (dCas9) protein and target guide RNA (gRNA). By constructing gRNA arrays, CRISPRi has the capacity for multiplexed gene downregulation across multiple orthogonal pathways for enhanced bioproduction titers. In this study, we harnessed CRISPRi to downregulate 32 essential and non-essential genes in E. coli strains heterologously expressing either the original mevalonate pathway or isopentenyl diphosphate (IPP) bypass pathway for isoprenol biosynthesis. Isoprenol remains a candidate bioproduct both as a drop-in blend additive and as a precursor for the high-performance sustainable aviation fuel, 1,4-dimethylcyclooctane (DMCO). Of the 32 gRNAs targeting genes associated with isoprenol biosynthesis, a subset was found to vastly improve product titers. Construction of a multiplexed gRNA library based on single guide RNA (sgRNA) performance enabled simultaneous gene repression, yielding a 3 to 4.5-fold increase in isoprenol titer (1.82 ± 0.19 g/L) on M9-MOPS minimal medium. We then scaled the best performing CRISPRi strain to 2-L fed-batch cultivation and demonstrated translatable titer improvements, ultimately obtaining 12.4 ± 1.3 g/L isoprenol. Our strategy further establishes CRISPRi as a powerful tool for tuning metabolic flux in production hosts and that titer improvements are readily scalable with potential for applications in industrial bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Vertically oriented arrays of ReS 2 nanosheets for electrochemical energy storage and electrocatalysis

Here, transition-metal dichalcogenide (TMD) nanolayers show potential as high-performance catalysts in energy conversion and storage devices. Synthetic TMDs produced by chemical-vapor deposition (CVD) methods tend to grow parallel to the growth substrate. Here, we show that with the right precursors and appropriate tuning of the CVD growth conditions, ReS 2 nanosheets can be made to orient perpendicular to the growth substrate. This accomplishes two important objectives; first, it drastically increases the wetted or exposed surface area of the ReS 2 sheets, and second, it exposes the sharp edges and corners of the ReS 2 sheets. We show that these structural features of the vertically grown ReS 2 sheets can be exploited to significantly improve their performance as polysulfide immobilizers and electrochemical catalysts in lithium–sulfur (Li–S) batteries and in hydrogen evolution reactions (HER). After 300 cycles, the specific capacity of the Li–S battery with vertical ReS 2 catalyst is retained above 750 mA h g –1 , with only ~0.063% capacity decay per cycle, much better than the baseline battery (without ReS 2 ), which shows ~0.184% capacity decay per cycle under the same test conditions. As a HER catalyst, the vertical ReS 2 provides very small onset overpotential (<100 mV) and an exceptional exchange-current density (~67.6 μA/cm 2 ), which is vastly superior to the baseline electrode without ReS 2 .

2D materials↗
DOE OSTI2019

Atomically Defined Edge-Doping of Graphene Nanoribbons for Mesoscale Electronics

The outstanding transformative potential of graphene, an infinite two-dimensional sheet of carbon atoms tightly packed into a honeycomb lattice, has been recognized mostly due to its exceptionally high electric conductivity, thermal conductivity, and tensile strength. These undeniably very desirable properties, however, represent only a very small facet of the true potential of all-sp2 carbon materials and its promise to revolutionize the field of molecular electronics. Graphene's most unusual characteristics emerge when the infinite macroscopic sheet is scaled down to nanometer dimensions. The exploration, realization, and implementation of these truly exotic magnetic and electronic properties rely on the development of innovative synthetic strategies that provide atomically precise control over the self-assembly of mesoscale objects. While traditional device architectures based on inorganic semiconductors have been fabricated using a top-down approach, we pursued a diametrically opposite strategy. Our design of a new generation of high-performance electronic materials relies on a modular bottom-up strategy. This highly multidisciplinary research program has been rooted in the synthesis of functional organic materials with precisely defined properties, their controlled assembly into hierarchical structures, and the evaluation of their performance at the molecular and the macroscopic scale. A part of this research program we have developed the chemical tools required to synthesize and to fine-tune the physical properties of graphene nanoribbons (GNR) with atomic precision. We further demonstrated that the innovative techniques and the expanded knowledge can be used to rationally tailor desired physical properties and function into nanometer-scale carbon-based electronic devices; e.g. transistors as logic gates in computing, data storage media based on electrically gated spin valves, or molecular amplifiers, all functionally integrated in atomically defined GNRs. We demonstrated the design and experimental realization of a rationally engineered graphene nanoribbon superlattice that hosts a 1D array of symmetry-protected topological states. We developed a hierarchical bottom-up fabrication strategy for graphene nanoribbons that preferentially exhibit a single heterojunction interface rather than a random statistical sequence of junctions along the ribbon. We reported the bottom-up synthesis of a series of atomically precise graphene nanoribbons featuring trigonal planar heteroatom dopants (nitrogen, oxygen, and sulfur) along the edges that induce a narrowing of the intrinsic band gap by ~0.2–0.3 eV per dopant atom. We reported the bottom-up synthesis of a graphene nanoribbon heterostructure featuring a highly tunable porphyrin quantum dot embedded at the center of a solution processable graphene nanoribbon. We reported a new technique for fabricating bottom-up graphene nanoribbon heterojunctions through a late-stage chemical transformation that results in atomically sharp heterojunction structures from a single precursor. We demonstrated the intrinsic performance enhancement of a composite material comprised of gold nanoparticles (AuNPs) embedded in a bottom-up synthesized graphene nanoribbon matrix for the electrocatalytic reduction of CO 2 . We developed a scalable, solution-based approach toward the synthesis of an exotic class of polycyclic aromatic hydrocarbons that exhibit unusual electronic and magnetic properties in the ground state. We reported the bottom-up synthesis and characterization of atomically-defined graphene nanoribbons featuring a regioregular pattern of group III dopant atoms (boron) along the backbone of the ribbon.

36 MATERIALS SCIENCE↗
DOE OSTI2021

New insight into strain and composition of BaZrO 3 nanorods in REBCO superconductor

We report on strain and composition effects associated with growth of self-assembled BaZrO 3 (BZO) nanorods in REBa 2 Cu 3 O 7–δ (REBCO) superconductors (RE = rare earth = Y and Gd), which have a profound effect on flux pinning and in-field critical current performance. The a-b plane mismatch between BZO and REBCO is never fully coherently accommodated. Instead, the nanorods always assume a size at least one unit cell smaller than the corresponding 'hole' in the REBCO matrix, thus providing deep minima of in-plane mismatch strain. Next, we show that the nominal BZO nanorods are in fact solid solution Ba 2+ (Zr 4+ 1–z RE 3+ z )O 3–δ perovskite, thus strongly affecting the stoichiometry and relative amounts of REBCO and BZO. We demonstrate that by varying only the Ba content in the nominal composition of 15 mol.% BZO + REBCO, the unit cell density of BZO can be tuned from 5% to 23%, and the linear density of RE 2 O 3 (REO) precipitates from 18 to 1 μm –1 . Furthermore, the results explain the wide range of pinning performances observed for the same nominal amount of Zr addition and provide insight into the mechanisms behind the complex phenomenon of growth of nanorods by self-assembly in REBCO superconductors.

2G-HTS↗
DOE OSTI2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

The extrapolation of wind speeds measured at a meteorological mast to wind turbine rotor heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power-law and logarithmic profiles. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power-law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the rotor-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power-law and log profiles, which, by contrast, are not highly tuned to rotor-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where winds at the heights relevant for wind energy production are known; rather, its performance at nearby locations (i.e., across a wind farm site) without rotor-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random-forest machine-learning model is trained and evaluated at different sites and then compared against the power-law and logarithmic profiles. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50 and 100 km apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power-law and logarithmic profiles, respectively. Using the round-robin approach proposed here, this improvement drops to 20 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based wind speed extrapolation methods.

17 WIND ENERGY↗
DOE OSTI2018

Introducing Fe 2+ into Nickel–Iron Layered Double Hydroxide: Local Structure Modulated Water Oxidation Activity

Abstract Exploring materials with regulated local structures and understanding how the atomic motifs govern the reactivity and durability of catalysts are a critical challenge for designing advanced catalysts. Herein we report the tuning of the local atomic structure of nickel–iron layered double hydroxides (NiFe‐LDHs) by partially substituting Ni 2+ with Fe 2+ to introduce Fe‐O‐Fe moieties. These Fe 2+ ‐containing NiFe‐LDHs exhibit enhanced oxygen evolution reaction (OER) activity with an ultralow overpotential of 195 mV at the current density of 10 mA cm −2 , which is among the best OER catalytic performance to date. In‐situ X‐ray absorption, Raman, and electrochemical analysis jointly reveal that the Fe‐O‐Fe motifs could stabilize high‐valent metal sites at low overpotentials, thereby enhancing the OER activity. These results reveal the importance of tuning the local atomic structure for designing high efficiency electrocatalysts.

Cai, Zhao↗
DOE OSTI2021

Progress on Optimizing Wind Farms and Rotor Designs Using Adjoints

Modern wind plants are increasingly tasked with multiple performance objectives. In addition to designing plants that maximize power output and minimize the levelized cost of energy (LCOE), the design and operation of wind plants is increasingly influenced by challenges regarding grid integration of variable generation renewables. This places a growing emphasis on making wind plants more controllable and predictable. WindSE is a Reynolds-averaged Navier-Stokes (RANS) model designed around analytical gradient and adjoint methods, with the ability to capture terrain-induced effects, as shown in Figure 1. The recent addition of an unsteady solver with an actuator line method (ALM) and ongoing work to enable massively parallel optimizations gives it a unique niche to explore coupled plant-level controls and design problems. This code is an open source python package built on the FEniCS framework that utilizes fast, parallel PETSc solvers to model fluid flow throughout wind-farm scale domains. Two recent studies performed using WindSE demonstrate the capability to optimize under a wide variety of flow conditions and objective functions. In the first, we present an optimization focused on modifying the layout of a wind farm with a fixed number of turbines for maximum total power output [1]. This study highlights the ability to quickly perform simulations using the steady Navier-Stokes solver combined with rotors represented as actuator disks while also stressing the importance of capturing terrain-induced effects. Gradient-based optimization using the RANS equations is viable due to the inclusion of efficiently computed adjoint derivatives. We interpret the physical results of the optimal layout and also discuss the computational cost of scaling to larger problems. In the second study, we present the capabilities of the unsteady Navier-Stokes solver, where rotor-blade profiles represented by actuator lines are optimized to enhance wake steering effects and overall power production [2]. We quantify the wind plant performance gains obtained from this type of simultaneous control co-design optimization as compared to optimizing the blade design and yaw independently. Figure 2 shows the differences between a baseline two-turbine system and an optimized system where we fine-tune the blade chord profile. Results and challenges from each study are quickly summarized and used to motivate the current development efforts within WindSE. Current and future work is focused on enabling higher-resolution studies with more degrees of freedom through parallelization of both the simulation and optimization algorithms. We present benchmarking results to show that WindSE performs well in both weak- and strong-scaling tests and further demonstrate that the optimizer obtains the same convergence rates in both shared- and distributed-memory environments. Using larger wind farms, we can study deep-array effects within an optimization context, allowing the use of objective functions that have been previously unstudied. As an example, we present ongoing work on a blockage metric which characterizes the loss of available kinetic energy due to wake effects from multiple upstream turbines.

adjoint optimization↗
DOE OSTI2019

Reference Natural Gas Flames at Nominally Autoignitive Engine-Relevant Conditions

Laminar natural gas flames are investigated at engine-relevant thermochemical conditions where the ignition delay time r is short due to very high ambient temperatures and pressures. At these conditions, it is not possible to measure or calculate well-defined values for the laminar flame speed S-l, laminar flame thickness delta(l), and laminar flame time scale tau(l) = delta(l)/S-l due to the explosive thermochemical state. Here, the corresponding reference values, S-R, delta(R), and tau(R) = S-R/S-R, that account for the effects of autoignition, are numerically estimated to investigate the enhancement of flame propagation, and the competition with autoignition that arises under nominally autoignitive conditions (characterised here by the number tau/tau(R)). Large values of tau/tau(R) indicate that autoignition is unimportant, values near or below unity indicate that flame propagation is not possible, and intermediate values indicate that a combination of both flame propagation and autoignition may be important, depending upon factors such as device geometry, turbulence, stratification, et cetera. The reference quantities are presented for a wide range of temperatures, equivalence ratios, pressures, and hydrogen concentrations, which includes conditions relevant to stationary gas turbine reheat burners and boosted spark ignition engines. It is demonstrated that the transition from flame propagation to autoignition is only dependent on residence time, when the results are non-dimensionalised by the reference values. The temporal evolution of the reference values are also reported for a modelled boosted SI engine. It is shown that the nominally autoignitive conditions enhance flame propagation, which may be an ameliorating factor for the onset of engine knock. The calculations are performed using a recently-developed, detailed 177 species mechanism for C0-C3 chemistry that is derived from theoretical chemistry and is suitable for a wide range of thermochemical conditions as it is not tuned or optimised for a particular operating condition. (C) 2018 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

autoignition↗