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DOE OSTI2021

Linearized modeling and optimization of shared mooring systems

Shared mooring systems, where adjacent platforms are tethered directly to each other, can reduce anchor quantities and mooring line lengths in a floating array but also introduce new modeling and design challenges. This paper presents and demonstrates a first-order approach to modeling and designing shared mooring systems that simplifies these challenges. We formulate a general, linearized model for the force-displacement response of shared mooring systems, including inter-platform couplings. Using this linearization, we realize significant simplifications to the shared mooring system design problem and propose a corresponding design optimization approach. Finally, we demonstrate the complete approach on a variety of shared-mooring floating wind farm layouts with generic design assumptions and constraints. Some fundamental observations about shared mooring systems can be made from the results. Polygonal array layouts with perpendicular anchor positions appear to maximize shared mooring system efficiency. Also, arrays with greater levels of sharing tend to exhibit larger deviations in offsets between platforms. Overall, the results show how the linearized approach can be applied to efficiently evaluate and systematically optimize preliminary shared mooring system designs.

17 WIND ENERGY↗

DOE OSTI2021

Machine learning based rate optimization under geologic uncertainty

We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. The training data for the model is generated by running the forward simulator with randomly assigned well rates using multiple geologic realizations. A reduced order representation of the permeability heterogeneity in each of the realizations is derived using a grid connectivity transformation (GCT). This step involves finding basis functions corresponding to the different modal frequencies of the grid connectivity represented by the grid Laplacian. The projection of the heterogeneous property field along these basis functions gives the basis coefficients that form the reduced order representation. Subsequently, for each training datapoint, streamlines are traced and the minimum time of flight (TOF) representing the tracer breakthrough time at each producer is recorded. The basis coefficients and well rates are fed to a machine learning model as input and the minimum TOF at the producers forms the output of the model. This trained model can then be used along with an optimizer for computing the optimal injection rates to maximize the injection sweep efficiency. This corresponds to minimizing the variance in the minimum TOF within each well group. Different architectures of neural network are tested using 5-fold cross validation to decide the best ML model to compute the streamline time of flight. The trained model is used to perform well rate optimization over multiple realizations of geology by using a risk tolerance penalty. The optimal well rates thus obtained are compared with two cases: a) equal well rates assigned to all injectors and producers and b) well rates obtained by optimizing over a single realization without considering the uncertainty in geology. The optimal well rates are seen to offer better oil recovery and sweep efficiency than both cases.

02 PETROLEUM↗

DOE OSTI2024

Circumventing Kinetic Barriers to Metal Hydride Formation with Metal–Ligand Cooperativity

We report the two-electron, one-proton mechanism of cobalt hydride formation for the conversion of [Co III Cp(P Ph 2 N Bn 2 )(CH 3 CN)] 2+ to [HCo III Cp(P Ph 2 NBn 2 )] + . This complex catalytically converts CO 2 to formate under CO 2 reduction conditions, with hydride formation as a key elementary step. Through a combination of electrochemical measurements, digital simulations, theoretical calculations, and additional mechanistic and thermochemical studies, we outline the explicit role of the P Ph 2 N Bn 2 ligand in the proton-coupled electron transfer (PCET) reactivity that leads to hydride formation. We reveal three unique PCET mechanisms, and we show that the amine on the P Ph 2 N Bn 2 ligand serves as a kinetically accessible protonation site en route to the thermodynamically favored cobalt hydride. Cyclic voltammograms recorded with proton sources that span a wide range of pK a values show four distinct regimes where the mechanism changes as a function of acid strength, acid concentration, and timescale between electrochemical steps. Peak shift analysis was used to determine proton transfer rate constants where applicable. Furthermore, this work highlights the astute choices that must be made when designing catalytic systems, including the basicity and kinetic accessibility of protonation sites, acid strength, acid concentration, and timescale between electron transfer steps, to maximize catalyst stability and efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Transcript, protein and metabolite temporal dynamics in the CAM plant Agave

Already a proven mechanism for drought resilience, crassulacean acid metabolism (CAM) is a specialized type of photosynthesis that maximizes water-use efficiency (WUE) via an inverse (compared to C 3 and C 4 photosynthesis-performing species) day/night pattern of stomatal closure/opening to shift CO 2 uptake to the night, when evapotranspiration rates are low. A systems-level understanding of temporal molecular and metabolic controls is needed to define the cellular behavior that underpins CAM. Here, we report high-resolution temporal behaviors of transcript, protein and metabolite abundances across a CAM diel cycle and, where applicable, compare those observations to the well-established C 3 model plant, Arabidopsis thaliana. A mechanistic finding that emerged is that CAM operates with a diel redox poise that is shifted relative to that in Arabidopsis thaliana. Moreover, we identified widespread rescheduled expression of genes associated with signal transduction mechanisms that regulate stomatal opening/closing. Controlled production and degradation of transcripts and proteins represents a timing mechanism by which to regulate cellular function, yet how this molecular timekeeping regulates CAM physiology remains unclear. In this paper, we provide new insights into complex post-transcriptional and -translational hierarchies that govern CAM in Agave. These data sets together provide a resource to inform efforts to engineer more water-use efficient CAM pathway traits into economically valuable C 3 crops.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Quantum chi-squared and goodness of fit testing

A quantum mechanical hypothesis test is presented for the hypothesis that a certain setup produces a given quantum state. Although the classical and the quantum problems are very much related to each other, the quantum problem is much richer due to the additional optimization over the measurement basis. A goodness of fit test for i.i.d quantum states is developed and a max-min characterization for the optimal measurement is introduced. We find the quantum measurement which leads both to the maximal Pitman and Bahadur efficiencies, and determine the associated divergence rates. We discuss the relationship of the quantum goodness of fit test to the problem of estimating multiple parameters from a density matrix. These problems are found to be closely related and we show that the largest error of an optimal strategy, determined by the smallest eigenvalue of the Fisher information matrix, is given by the divergence rate of the goodness of fit test.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Raman Vibrations, Domain Structures, and Photovoltaic Effects in A‐Site La‐Modified BiFeO 3 Multiferroic Ceramics

Micro‐Raman spectroscopy, X‐ray diffraction, high‐resolution transmission electron microscopy ( TEM ), oxygen vacancies, synchrotron X‐ray absorption spectroscopy, magnetizations, optical band gaps, and photovoltaic ( PV ) effects have been studied in (Bi 1− x La x )FeO 3 ( BFO 100 x L) ceramics for x = 0.0, 0.05, 0.10, and 0.15. XRD , Raman spectra, and TEM confirm a rhombohedral R 3 c symmetry with the tilted FeO 6 oxygen octahedra in all compounds. The low‐frequency Raman vibrations become broader and shift toward higher frequency as La 3+ increases. Fe K‐edge synchrotron X‐ray absorptions reveal that Fe 3+ valence and Fe–O–Fe bond angle are not modified by the La 3+ substitution. All compounds exhibit a linear antiferromagnetic feature. Optical transmission reveals band gaps in the range of 2.22–2.24 eV. The heterostructures of indium tin oxide ( ITO ) film/(Bi 1− x La x )FeO 3 ceramics/Au film show a p – n junction‐like I – V characteristic behavior. The maximal PV power conversion efficiency can reach 0.19% in ITO / BFO 15L/Au under illumination of λ = 405 nm. A junction‐like theoretical model can reasonably describe open‐circuit voltage and short‐circuit current as a function of illumination intensity.

Tu, Chi‐Shun↗

DOE OSTI2015

Sustained Energy Savings Achieved through Successful Industrial Customer Interaction with Ratepayer Programs: Case Studies

Many states have implemented ratepayer-funded programs to acquire energy efficiency as a predictable and reliable resource for meeting existing and future energy demand. These programs have become a fixture in many U.S. electricity and natural gas markets as they help postpone or eliminate the need for expensive generation and transmission investments. Industrial energy efficiency (IEE) is an energy efficiency resource that is not only a low cost option for many of these efficiency programs, but offers productivity and competitive benefits to manufacturers as it reduces their energy costs. However, some industrial customers are less enthusiastic about participating in these programs. IEE ratepayer programs suffer low participation by industries across many states today despite a continual increase in energy efficiency program spending across all types of customers, and significant energy efficiency funds can often go unused for industrial customers. This paper provides four detailed case studies of companies that benefited from participation in their utility’s energy efficiency program offerings and highlights the business value brought to them by participation in these programs. The paper is designed both for rate-payer efficiency program administrators interested in improving the attractiveness and effectiveness of industrial efficiency programs for their industrial customers and for industrial customers interested in maximizing the value of participating in efficiency programs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Advanced Non-Tread Materials for Fuel-Efficient Tires

PPG Industries, Inc. proposes to develop a new silica filler that can increase fuel efficiency by 2% while maximizing key performance properties in non-tread tire components compared to current carbon black/silica filler blends. In this project PPG focused on a sidewall compound. The developed compounds/components will reduce energy losses by approximately 25%, with no more than a 5% loss of resistance to degradative forces (targeting better performance). PPG plans to achieve this goal by characterizing the impact the silica filler can have on rubber properties and then tuning the silica morphology and surface chemistry for this end use. PPG will also provide guidance in optimizing the compound formulation to make the best use of this new filler. A key aspect is to understand and maintain, if not improve, the aging performance as evaluated by crack growth resistance under ozone environment. Two key strategies were used to meet the rolling resistance goal while maintaining other performance properties. The first factor was understanding the surface treatments and chemical makeup of the silica and its effects on compound performance. The second factor was using the best performing silicas and determining the optimum compound modifications to achieve the final sidewall compound performance. Using statistical analysis throughout the optimization process, it was determined that changing the silica morphology played a role in improving the reduction of tear strength while reducing rolling resistance and the improvements observed varied depending on the CB type.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Using Reinforcement Learning to Optimize Quantum Circuits in the Presence of Noise

As we move towards devices which utilize more qubits, it becomes increasing more important to map quantum circuits in a way that uses resources efficiently as well as maximizes the reliability of the results of that circuit. To this end, we will need to rely on heuristic algorithms, specifically reinforcement learning (RL) as a method of building quantum circuits based on observations of the noise characteristics in its environment.

Guy, Khalil↗

DOE OSTI2020

Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise

Many quantum computing frameworks currently use noise aware algorithms for implementing quantum circuits which do not scale efficiently as the size of the hardware architecture increases. As we move towards devices which utilize more qubits, it becomes increasing more important to map quantum circuits in a way that uses resources efficiently as well as maximizes the reliability of the results of that circuit. However, as the hardware increases to the point where Quantum supremacy is attainable, it will infeasible for a brute-force algorithm to find the most optimal circuit layout for circuits of medium to large depth sizes. To this end, we will to rely on reinforcement learning (RL) as a method of building quantum circuits based on observations of the noise characteristics in its environment. In this work, we create a working reinforcement learning environment in which an agent is able to make action which will build the class of circuits which creates the GHZ state. In addition to this, we also get preliminary results of the performance of a Deep Q Neural Network, which initially does not perform as well as we believe it can. In the future, we want to improve the performance of the agent and potentially generalize this environment to more classes of circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Position Papers for the ASCR Workshop on Reimagining Codesign

On behalf of the Advanced Scientific Computing Research (ASCR) program in the US Department of Energy (DOE) Office of Science, we are organizing a Workshop on Reimagining Codesign (ReCoDe). Codesign is the process of jointly designing interoperating components of a computing system—in particular: applications, algorithms, system software, programming models, and the hardware on which they run. The goal is to maximize the overall performance, efficiency, and other desirable qualities of the system as a whole. Codesign is a standard methodology in the embedded-systems community, where space, power, and cost constraints are commonly pitted against execution speed for a tightly constrained feature set. Over the last decade, the DOE has invested in codesign efforts to foster the development of exascale computing systems for broad classes of scientific and engineering applications. The ReCoDe workshop hopes to explore how scientific applications of interest to the DOE can be accelerated through close interactions with hardware designers and software-stack developers, in which all components adapt to each other’s requirements and constraints. We want to answer the question of what are the key tools and methodologies for accomplishing codesign in today’s computing landscape, and what will be the highest impact targets for meeting DOE’s emerging mission requirements. This workshop aims to bring together DOE, industry, and academia to identify opportunities to build on past codesign successes and identify new areas that are either emerging or that may need reimagining for the future. We want to continue to find opportunities that can be pursued as a joint effort and continue to break down the traditional customer/vendor dichotomy with true partnerships. From this work, DOE will benefit from increased application performance relative to what stock hardware or existing general-purpose roadmaps can provide, and vendors will benefit from expanding their hardware’s capabilities to address needs they might have not otherwise anticipated and thereby create more widespread interest in their products. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees—from DOE, industry, and academia—will produce a report for ASCR that summarizes the findings made during the workshop.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Geranium Photodiodes for Hard Xray Detection

This report summarizes the investigations engaged at UC Davis under subcontract B632083, as related to the LLNL-LDRD Project “High-Speed X-Ray Imager Arrays.” The first section entails work done within the Electrical and Computer Engineering (ECE) Department. The purpose of the work done so far is to understand photodiode design and which parameters are conducive to the design goals, which are: I: Maximize the detector quantum efficiency. The device must have a quantum efficiency of roughly 5% at 80 keV. II: Minimize temporal response. Collection time is limited to 1ns. All charge must be collected in that time. III: Minimize dark current. In this document, various photodiode parameters and designs are explored, and their impact on these goals are tabulated. In addition to these, various instruments that have been constructed for either testing these parameters or helping teammates with their experiments. The second section entails work done with the Materials Science Engineering (MSE) Department. In accordance with the proposed development of the germanium ROIC backside imagers, a critical step in the micromachining of these devices was the removal of the substrate on which the PIN junctions were epitaxially grown. A common technique for the micromachining of silicon wafers is the use of chemical etching alongside an “etch-stop” layer. In this technique, a chemical etchant will remove material until it reaches the etch-stop layer, at which the etch rate will drop significantly. This high selectivity (ratio of etch rates) allows any unevenness in the initial etching to be effectively smoothed away in the event that part of the wafer surface reaches the etch-stop layer sooner than another. An initial literature review found a paper by Divan et al. in which the authors used ion implantation to dope germanium wafers and achieve selectivity on the order of 100, sufficient for a reliable etch-stop.

42 ENGINEERING↗

DOE OSTI2023

Simulating and Optimizing Quantum State Transport

The goal of this project was to produce an accurate simulator of the “pitch and catch” process of sending quantum states between SRF cavities, and to find a method for optimizing the control pumps. We developed a program in C++ to integrate the equations of motion with high speed and precision, and used this as a basis for finding an optimal control function to maximize the state transfer efficiency. To demonstrate this we have shown an example of an optimal drive function and the resulting system dynamics, as well as a heat map of the cost function for a range of drive amplitudes.

Weston, Jacob↗

DOE OSTI2015

Evaluating lignocellulosic biomass, its derivatives, and downstream products with Raman spectroscopy

The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gage the recalcitrance of the plants and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real-time, while also providing integral chemical information. Finally, this review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

09 BIOMASS FUELS↗

DOE OSTI2015

Strategies for optimizing algal biology for enhanced biomass production

One of the most environmentally sustainable ways to produce high-energy density (oils) feed stocks for the production of liquid transportation fuels is from biomass. Photosynthetic carbon capture combined with biomass combustion (point source) and subsequent carbon capture and sequestration has also been proposed in the intergovernmental panel on climate change report as one of the most effective and economical strategies to remediate atmospheric greenhouse gases. To maximize photosynthetic carbon capture efficiency and energy-return-on-investment, we must develop biomass production systems that achieve the greatest yields with the lowest inputs. Numerous studies have demonstrated that microalgae have among the greatest potentials for biomass production. This is in part due to the fact that all alga cells are photoautotrophic, they have active carbon concentrating mechanisms to increase photosynthetic productivity, and all the biomass is harvestable unlike plants. All photosynthetic organisms, however, convert only a fraction of the solar energy they capture into chemical energy (reduced carbon or biomass). To increase aerial carbon capture rates and biomass productivity, it will be necessary to identify the most robust algal strains and increase their biomass production efficiency often by genetic manipulation. We review recent large-scale efforts to identify the best biomass producing strains and metabolic engineering strategies to improve aerial productivity. In addition, these strategies include optimization of photosynthetic light-harvesting antenna size to increase energy capture and conversion efficiency and the potential development of advanced molecular breeding techniques. To date, these strategies have resulted in up to twofold increases in biomass productivity.

09 BIOMASS FUELS↗

DOE OSTI2018

Development of a Cold Start Fuel Penalty Metric for Evaluating the Impact of Fuel Composition Changes on SI Engine Emissions Control

The U.S. Department of Energy’s Co-Optimization of Fuels and Engines initiative (Co-Optima) aims to simultaneously transform both transportation fuels and engines to maximize performance and energy efficiency. Researchers from across the DOE national laboratories are working within Co-Optima to develop merit functions for evaluating the impact of fuel formulations on the performance of advanced engines. The merit functions relate overall engine efficiency to specific measurable fuel properties and will serve as key tools in the fuel/engine co-optimization process. This work focused on developing a term for the Co-Optima light-duty boosted spark ignition (SI) engine merit function that captures the effects of fuel composition on emissions control system performance. For stoichiometric light-duty SI engines, the majority of NOx, NMOG, and CO emissions occur during cold start, before the three-way catalyst (TWC) has reached its “light-off” temperature. This light-off temperature depends on the exhaust composition, which changes with fuel formulation. Thus, the time to achieve light-off, and therefore the cold start emissions, depends on fuel composition. Since the time to reach light-off must be minimized to meet emissions regulations, modern vehicles employ cold start strategies that intentionally release fuel energy into the engine exhaust to heat the TWC above the light-off temperature as quickly as possible. The fuel penalty associated with catalyst heating during cold start provides a link between fuel properties, emissions performance, and engine efficiency. This paper describes the derivation of the Co-Optima SI engine merit function term for emissions control, which is based on the cold start fuel penalty for catalyst heating and the catalytic light-off temperature for a particular fuel composition. It also discusses the process used for extracting cold start fuel penalties from chassis dynamometer data sets previously collected at ORNL. The form of the merit function term is simple, but it is only intended as a starting point for comparing the potential impacts of changes in fuel formulation on emissions control system performance.

Pihl, Josh↗

NASA NTRS1972

Basic Economic Principles

An economic approach to design efficient transportation systems involves maximizing an objective function that reflects both goals and costs. A demand curve can be derived by finding the quantities of a good that solve the maximization problem as one varies the price of that commodity, holding income and the prices of all other goods constant. A supply curve is derived by applying the idea of profit maximization of firms. The production function determines the relationship between input and output.

Tideman, T. N.↗

NASA NTRS1976

Heat trap - An optimized far infrared field optics system

The article deals with the design and performance of a heat trap IR system designed to maximize the concentration and efficient reception of far IR and submillimeter wavelength radiation. The test object is assumed to be extended and/or viewed at wavelengths much longer than the detector, and the entrance aperture is limited to the size of the telescope Airy diffraction disk. The design of lenses, cavity, bolometers, light collectors, and mirrors for the system is discussed. Advantages and feasibility of arrays of heat traps are considered. Beam patterns, flux concentration, and performance variation with wavelength are dealt with. The heat trap is recommended for sensing all types of far IR sources and particularly for extended far IR sources.-

Harper, D. A.↗