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This project supports national goals for clean energy by providing a cost-effective pathway to eliminate up to 60 million tons of CO 2 annually in the U.S. steel sector. It enhances economic competitiveness by decreasing dependence on foreign steel imports and utilizing domestic natural gas to produce green products at commercial scale.
Replacing fossil fuels with renewable fuels derived from lignocellulosic biomass can contribute to the mitigation of global warming and the economic development of rural communities. This will require lignocellulosic biofuels to become price competitive with fossil fuels. Techno-economic analyses can provide insights into which parts of the biofuel production process need to be optimized to reduce cost or energy use. Here, we used data obtained from a pilot biorefinery to model a commercial-scale biorefinery that processes lignocellulosic biomass to ethanol, with a focus on the minimum ethanol selling price (MESP). The process utilizes a phosphoric acid-catalyzed pre-treatment of sweet sorghum bagasse followed by liquefaction and simultaneous saccharification and co-fermentation (L+SScF) of hexose and pentose sugars by an engineered Escherichia coli strain. After validating a techno-economic model developed with the SuperPro Designer software for the conversion of sugarcane bagasse to ethanol by comparing it to a published Aspen Plus model, six different scenarios were modeled for sweet sorghum bagasse Under the most optimistic scenario, the ethanol can be produced at a cost close to the energy-equivalent price of gasoline. Aside from an increase in the price of gasoline, the gap between ethanol and gasoline prices could also be bridged by either a decrease in the cost of cellulolytic enzymes or development of value-added products from lignin.
Competitive pressures in the oil and gas industry are requiring a much tighter integration of technical data into E and P business processes. The development of new systems to accommodate this business need must comprehend the significant numbers of large, complex data objects which the industry generates. The life cycle of the data objects is a four phase progression from data acquisition, to data processing, through data interpretation, and ending finally with data archival. In order to implement a cost effect system which provides an efficient conversion from data to information and allows effective use of this information, an organization must consider the technical data management requirements in all four phases. A set of technical issues which may differ in each phase must be addressed to insure an overall successful development strategy. The technical issues include standardized data formats and media for data acquisition, data management during processing, plus networks, applications software, and GUI's for interpretation of the processed data. Mass storage hardware and software is required to provide cost effective storage and retrieval during the latter three stages as well as long term archival. Mobil Oil Corporation's Exploration and Producing Technical Center (MEPTEC) has addressed the technical and cost issues of designing, building, and implementing an Advanced Computing Environment (ACE) to support the petroleum E and P function, which is critical to the corporation's continued success. Mobile views ACE as a cost effective solution which can give Mobile a competitive edge as well as a viable technical solution.
NASA systems need to be highly dependable to avoid catastrophic mission failures. This calls for rigorous engineering processes including meticulous validation and verification. However, NASA systems are often highly distributed and overwhelmingly complex, making the software portion of these systems challenging to understand, maintain, change, reuse, and test. NASA's systems are long-lived and the software maintenance process typically constitutes 60-80% of the total cost of the entire lifecycle. Thus, in addition to the technical challenges of ensuring high life-time quality of NASA's systems, the post-development phase also presents a significant financial burden. Some of NASA's software-related challenges could potentially be addressed by some of the many powerful technologies that are being developed in software research laboratories. Many of these research technologies seek to facilitate maintenance and evolution by for example architecting, designing and modeling for quality, flexibility, and reuse. Other technologies attempt to detect and remove defects and other quality issues by various forms of automated defect detection, architecture analysis, and various forms of sophisticated simulation and testing. However promising, most such research technologies nevertheless do not make the transition from the research lab to the software lab. One reason the transition from research to practice seldom occurs is that research infusion and technology transfer is difficult. For example, factors related to the technology are sometimes overshadowed by other types of factors such as reluctance to change and therefore prohibits the technology from sticking. Successful infusion might also take very long time. One famous study showed that the discrepancy between the conception of the idea and its practical use was 18 years plus or minus three. Nevertheless, infusing new technology is possible. We have found that it takes special circumstances for such research infusion to succeed: 1) there must be evidence that the technology works in the practitioner's particular domain, 2) there must be a potential for great improvements and enhanced competitive edge for the practitioner, 3) the practitioner has to have strong individual curiosity and continuous interest in trying out new technologies, 4) the practitioner has to have support on multiple levels (i.e. from the researchers, from management, from sponsors etc), and 5) to remain infused, the new technology has to be integrated into the practitioner's processes so that it becomes a natural part of the daily work. NASA IV&V's Research Infusion initiative sponsored by NASA's Office of Safety & Mission Assurance (OSMA) through the Software Assurance Research Program (SARP), strives to overcome some of the problems related to research infusion.
The primary goal of the University of Oregon's DOE "ÃÂcompetitiveness" project was to create performance technology that embodies and supports knowledge of performance data, analysis, and diagnosis in parallel performance problem solving. The target of our development activities was the TAU Performance System and the technology accomplishments reported in this and prior reports have all been incorporated in the TAU open software distribution. In addition, the project has been committed to maintaining strong interactions with the DOE SciDAC Performance Engineering Research Institute (PERI) and Center for Technology for Advanced Scientific Component Software (TASCS). This collaboration has proved valuable for translation of our knowledge-based performance techniques to parallel application development and performance engineering practice. Our outreach has also extended to the DOE Advanced CompuTational Software (ACTS) collection and project. Throughout the project we have participated in the PERI and TASCS meetings, as well as the ACTS annual workshops.
Concentrating solar power (CSP), when coupled with a supercritical carbon dioxide (sCO 2 ) power cycle and sensible heat storage, presents a renewable and clean alternative for utility-scale power generation. However, in order to be competitive in the current and future markets, CSP facilities must limit their levelized cost of electricity (LCOE) by minimizing capital costs and reducing operating costs over the lifetime of the plant. Targeting this goal, this study investigates the LCOE impact of the power cycle pre-cooler. This study considers a compact dry cooler with micro-channel technology on the CO 2 side and formed fin geometry on the air side, using directly-coupled centrifugal fans and a transition duct to improve air distribution across the fins as well as protect the fins from contaminants which may cause blockage, soiling, fouling, and damage. In an effort to better understand the dry cooler impact on LCOE, a sensitivity study was conducted using various combinations of end-to-end approach temperatures, air-side pressure drop values, CO 2 -side pressure drop values, fan types, cooler turndown control schemes, cooler module sizes, and design-point ambient temperatures. Furthemore, off-design cycle performance data was calculated for each dry cooler design using NPSS simulation software; cycle performance data were then input to System Advisor Model (SAM) along with the associated capital costs for LCOE prediction of a 100 MW system over a 30 year plant lifetime. Results of this study show the LCOE is most sensitive to air-side performance, followed by heat transfer effectiveness and capital cost. It was found that a power cycle with a mid- to high-performance dry cooler will produce the most competitive power-production costs. Designing at the extreme ends for approach temperature (or effectiveness), design-point ambient temperature, and compactness (footprint) produce higher LCOE values; mid-range values for these parameters balance performance, operating costs, and associated capital cost to optimize LCOE.
Smart manufacturing technologies provide real-time data and insight to improve the productivity, efficiency, and competitiveness of U.S. manufacturing, creating the potential for new jobs in the manufacturing sector. These technologies can support U.S. manufacturers’ ability to increase throughput and energy efficiency, and decrease waste, defects, and costs. A wide range of manufacturing and industrial subsectors, particularly energy-intensive and energy-dependent industries, have the potential to benefit from smart manufacturing technologies. There are technical improvements still needed to reduce the costs and barriers (trained workforce and upskilling, software-hardware integration, cost, and technical barriers to deployment of advanced sensors, computing, and communication technologies for existing manufacturing assets) to the adoption of smart manufacturing technologies, especially to small and medium-sized manufacturers, and subsequently, increase the overall adoption rate of smart manufacturing technologies. This report outlines the Department of Energy’s (DOE) strategic plan to accelerate the development and implementation of smart manufacturing technologies in the United States and the actions DOE has taken to use these technologies in smart manufacturing for the United States. DOE’s plan to facilitate more rapid development, deployment and widespread adoption of smart manufacturing technologies derive from the 2018 Strategy for American Leadership in Advanced Manufacturing. The strategy outlines a vision for America’s leadership in advanced manufacturing including the development of intelligent manufacturing systems to optimize manufacturing facilities and support the manufacturing of transformative materials. DOE’s Clean Energy Smart Manufacturing Innovation Institute (CESMII), a Manufacturing USA Institute, focuses on accelerating the development and adoption of advanced sensors, controls, platforms, and models needed for smart manufacturing. The objective is to enhance U.S. manufacturing productivity and global competitiveness through the research and development of these technologies. Smart manufacturing has the potential to improve the overall performance, energy productivity, and efficiency of manufacturing, fostering the economic competitiveness of the manufacturing sector.
In today's competitive environment, both industry and government agencies are under pressure to reduce the time and cost of multidisciplinary design projects. New tools have been introduced to assist in this process by facilitating the integration of and communication among diverse disciplinary codes. One such tool, a framework for multidisciplinary design, is defined as a hardware-software architecture that enables integration, execution, and communication among diverse disciplinary processes. An examination of current frameworks reveals weaknesses in various areas, such as sequencing, monitoring, controlling, and displaying the design process. The objective of this research is to explore how Web technology can improve these areas of weakness and lead toward a more flexible framework. This article describes a Web-based system that optimizes and controls the execution sequence of design processes in addition to monitoring the project status and displaying the design results.
The accuracy and precision of digital image correlation (DIC) is a function of three primary ingredients: image acquisition, image analysis, and the subject of the image. Development of the first two (i.e. image acquisition techniques and image correlation algorithms) has led to widespread use of DIC; however, fewer developments have been focused on the third ingredient. Typically, subjects of DIC images are mechanical specimens with either a natural surface pattern or a pattern applied to the surface. Research in the area of DIC patterns has primarily been aimed at identifying which surface patterns are best suited for DIC, by comparing patterns to each other. Because the easiest and most widespread methods of applying patterns have a high degree of randomness associated with them (e.g., airbrush, spray paint, particle decoration, etc.), less effort has been spent on exact construction of ideal patterns. With the development of patterning techniques such as microstamping and lithography, patterns can be applied to a specimen pixel by pixel from a patterned image. In these cases, especially because the patterns are reused many times, an optimal pattern is sought such that error introduced into DIC from the pattern is minimized. DIC consists of tracking the motion of an array of nodes from a reference image to a deformed image. Every pixel in the images has an associated intensity (grayscale) value, with discretization depending on the bit depth of the image. Because individual pixel matching by intensity value yields a non-unique scale-dependent problem, subsets around each node are used for identification. A correlation criteria is used to find the best match of a particular subset of a reference image within a deformed image. The reader is referred to references for enumerations of typical correlation criteria. As illustrated by Schreier and Sutton and Lu and Cary systematic errors can be introduced by representing the underlying deformation with under-matched shape functions. An important implication, as discussed by Sutton et al., is that in the presence of highly localized deformations (e.g., crack fronts), error can be reduced by minimizing the subset size. In other words, smaller subsets allow the more accurate resolution of localized deformations. Contrarily, the choice of optimal subset size has been widely studied and a general consensus is that larger subsets with more information content are less prone to random error. Thus, an optimal subset size balances the systematic error from under matched deformations with random error from measurement noise. The alternative approach pursued in the current work is to choose a small subset size and optimize the information content within (i.e., optimizing an applied DIC pattern), rather than finding an optimal subset size. In the literature, many pattern quality metrics have been proposed, e.g., sum of square intensity gradient (SSSIG), mean subset fluctuation, gray level co-occurrence, autocorrelation-based metrics, and speckle-based metrics. The majority of these metrics were developed to quantify the quality of common pseudo-random patterns after they have been applied, and were not created with the intent of pattern generation. As such, it is found that none of the metrics examined in this study are fit to be the objective function of a pattern generation optimization. In some cases, such as with speckle-based metrics, application to pixel by pixel patterns is ill-conditioned and requires somewhat arbitrary extensions. In other cases, such as with the SSSIG, it is shown that trivial solutions exist for the optimum of the metric which are ill-suited for DIC (such as a checkerboard pattern). In the current work, a multi-metric optimization method is proposed whereby quality is viewed as a combination of individual quality metrics. Specifically, SSSIG and two auto-correlation metrics are used which have generally competitive objectives. Thus, each metric could be viewed as a constraint imposed upon the others, thereby precluding the achievement of their trivial solutions. In this way, optimization produces a pattern which balances the benefits of multiple quality metrics. The resulting pattern, along with randomly generated patterns, is subjected to numerical deformations and analyzed with DIC software. The optimal pattern is shown to outperform randomly generated patterns.
This report documents a demonstration that compared the energy consumption and efficiency of a limited sample size of server-type IT equipment from different manufacturers by measuring power at the server power supply power cords. The results are specific to the equipment and methods used. However, it is hoped that those responsible for IT equipment selection can used the methods described to choose models that optimize energy use efficiency. The demonstration was conducted in a data center at Lawrence Berkeley National Laboratory in Berkeley, California. It was performed with five servers of similar mechanical and electronic specifications; three from Intel and one each from Dell and Supermicro. Server IT equipment is constructed using commodity components, server manufacturer-designed assemblies, and control systems. Server compute efficiency is constrained by the commodity component specifications and integration requirements. The design freedom, outside of the commodity component constraints, provides room for the manufacturer to offer a product with competitive efficiency that meets market needs at a compelling price. A goal of the demonstration was to compare and quantify the server efficiency for three different brands. The efficiency is defined as the average compute rate (computations per unit of time) divided by the average energy consumption rate. The research team used an industry standard benchmark software package to provide a repeatable software load to obtain the compute rate and provide a variety of power consumption levels. Energy use when the servers were in an idle state (not providing computing work) were also measured. At high server compute loads, all brands, using the same key components (processors and memory), had similar results; therefore, from these results, it could not be concluded that one brand is more efficient than the other brands. The test results show that the power consumption variability caused by the key components as a group is similar to all other components as a group. However, some differences were observed. The Supermicro server used 27 percent more power at idle compared to the other brands. The Intel server had a power supply control feature called cold redundancy, and the data suggest that cold redundancy can provide energy savings at low power levels. Test and evaluation methods that might be used by others having limited resources for IT equipment evaluation are explained in the report.
In today's competitive environment, both industry and government agencies are under pressure to reduce the time and cost of multidisciplinary design projects. New tools have been introduced to assist in this process by facilitating the integration of and communication among diverse disciplinary codes. One such tool, a framework for multidisciplinary computational environments, is defined as a hardware and software architecture that enables integration, execution, and communication among diverse disciplinary processes. An examination of current frameworks reveals weaknesses in various areas, such as sequencing, displaying, monitoring, and controlling the design process. The objective of this research is to explore how Web technology, integrated with an existing framework, can improve these areas of weakness. This paper describes a Web-based system that optimizes and controls the execution sequence of design processes; and monitors the project status and results. The three-stage evolution of the system with increasingly complex problems demonstrates the feasibility of this approach.
A conceptual design study of a large civil compound helicopter is presented. The objective is to determine how a compound helicopter performs when compared to both a conventional helicopter and a tiltrotor using a design mission that is shorter than optimal for a tiltrotor and longer than optimal for a helicopter. The designs are generated and analyzed using conceptual design software and are further evaluated with a comprehensive rotorcraft analysis code. Multiple metrics are used to determine the suitability of each design for the given mission. Plots of various trade studies and parameter sweeps as well as comprehensive analysis results are presented. The results suggest that the compound helicopter examined for this study would not be competitive with a tiltrotor or conventional helicopter, but multiple possibilities are identified for improving the performance of the compound helicopter in future research.
At Lyncean, a private company spun-off from technology developed at the SLAC National Lab, we have been using EPICS for over a decade. EPICS is ubiquitous on our flagship product – the Compact Light Source. EPICS is not only used to control our laser and accelerator systems, but also to control our x-ray beamlines. The goal of this SBIR is for Lyncean Technologies to spearhead a worldwide collaborative effort for the development of control system tools for EPICS using the Qt framework, a C++-based coding environment that could serve as a competitive alternative to the Java-based Control System Studio (CSS). This grant's Phase I, not unlike a feasibility study, is designed for planning and scoping the preparatory work needed for Phase II or other funding opportunities. The three main objectives of this Phase I are (1) to become better acquainted with the existing EPICS Qt software and Qt framework in order to evaluate the best options for ongoing development, (2) to demonstrate that our engineers can lead the EPICS community and jump-start the Qt collaboration, and (3) to identify a scope for our future work with solicited feedback from the EPICS community. This Phase I report includes key technical findings. It clarifies the differences between the two apparently-competing EPICS Qt implementations, caQtDM and the QE Framework; it explains how to create python-bindings, and compares Qt graphical libraries. But this report is also a personal story that narrates the birth of a collaboration. Starting a collaboration is not the work of a single individual, but the work of many. Therefore this report is also an attempt to publicly give credit to many who supported the effort. The main take-away from this grant is the successful birth of an EPICS Qt collaboration, seeded with existing software from the PSI and the Australian Synchrotron. But a lot more needs to be done for the collaboration founders' vision to be realized, and for the collaboration to reach its full potential. To help define the scope of future work, a useful approach we have identified is user experience design (UXD) and is discussed herein.
The U.S. Department of Energy's Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario Model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.
The U.S. Department of Energy's (DOE's) Bioenergy Technologies Office and the National Renewable Energy Laboratory (NREL) developed the BSM (Bioenergy Scenario model) to explore the development of a U.S. biofuels industry. The BSM is a system dynamics model built on the STELLA software platform. The model represents the dynamic interactions of the major sectors of the biofuels industry—feedstock production, feedstock logistics, biomass to biofuels conversion, and biofuels end use, including fuels inventory, dispensing, distribution, fuel use, and the vehicle fleet. The BSM represents contextual aspects of the developing biofuels industry, including investment in new biomass to biofuel conversion technologies, competition from petroleum fuels, vehicle demand for biofuels, and various government policies, using all of these to simulate the development of the industry. The purpose of the BSM is to generate and explore plausible scenarios for the evolution of a biofuels industry in the United States, and as a high-level system model it is not designed for precise, quantitative forecasting. Instead, it is best used to (1) analyze and evaluate alternate policies; (2) generate scenarios; (3) identify high-impact levers and bottlenecks to system evolution; and (4) seed focused discussion among policymakers, analysts, and stakeholders.
The ignition, oxidation, and pyrolysis chemistry of methyl propyl ether (MPE) was probed experimentally at several different conditions, and a comprehensive chemical kinetic model was constructed to help understand the observations, with many of the key parameters computed using quantum chemistry and transition state theory. Experiments were carried out in a shock tube measuring time variation of CO concentrations, in a flow tube measuring product concentrations, and in a rapid compression machine (RCM) measuring ignition delay times. The detailed reaction mechanism was constructed using the Reaction Mechanism Generator software. Sensitivity and flux analyses were used to identify key rate and thermochemical parameters, which were then computed using quantum chemistry to improve the mechanism. Validation of the final model against the 1-20 bar 600-1500 K experimental data is presented with a discussion of the kinetics. The model is in excellent agreement with most of the shock tube and RCM data. Strong non-monotonic variation in conversion and product distribution is observed in the flow-tube experiments as the temperature is increased, and unusually strong pressure dependence and significant heat release during the compression stroke is observed in the RCM experiments. These observations are largely explained by a close competition between radical decomposition and addition to O2 at different sites in MPE; this causes small shifts in conditions to lead to big shifts in the dominant reaction pathways. The validated mechanism was used to study the chemistry occurring during ignition in a diesel engine, simulated using Ignition Quality Test (IQT) conditions. At the IQT conditions, where the MPE concentration is higher, bimolecular reactions of peroxy radicals are much more important than in the RCM.
In 2017, the Chinese government announced an ambitious, broad strategy to become the global leader in artificial intelligence (AI) theories, technologies, and applications by 2030 and indicated that China’s ability to indigenously produce cutting-edge AI chips would be integral to its success. China’s goal to become a dominant producer of AI hardware within a decade is a bold undertaking because China faces domestic chip production challenges and fierce competition from US chip producers. Our review of Chinese AI chip product lines shows various levels where Chinese companies compete with global firms in the production chain. China lacks a robust indigenous AI chip production infrastructure, and the United States and its allies are tightening controls on the supply of advanced semiconductor manufacturing equipment (SME) and electronic design automation (EDA) software to China. In contrast to their Chinese counterparts, US companies operate in all areas of AI chip production and have consistently driven the development of new chip designs. AI chip production is a costly endeavor, and China’s chip producers currently lack the customer base to lead global AI chip development by the 2030 goal. An expanded customer base may help China attract the necessary expertise to bolster its AI chip production chain. This would also help Chinese producers make their investments in the requisite technology economically sustainable. Chinese researchers continue to pursue promising next-generation AI chip designs, such as neuromorphic computing-based architectures, which in the next five to ten years could position Chinese companies to make or contribute to key innovations in the field.
On November 30, 2020, IBM announced an open science price competition aimed at improving the fidelity of a SWAP gate on their 7 qubit Quantum system named ”Casablanca”. The details of the SWAP gate problem was outlined in a Jupyter notebook, which utilizes the IBM developed open source package Qiskit. The participants were allowed to work in teams with up to five people and restricted to only use open source software in solving the problem. Their results were due on April 16, 2021. Our approach to solving the SWAP gate challenge is based on the quantum optimal control techniques implemented in our open source packages Juqbox.jl and Quandary. The results from the optimal control approach is only as good as the accuracy in the description of the quantum system dynamics, characterized by a Hamiltonian model that was provided by IBM. Based on the calibrated control pulses IBM provide for the standard gate set, we developed a reverse engineering approach to calibrate our computational model. Techniques were developed to translate between Qiskit’s pulse representation and the B-spline formulation used in Juqbox and Quandary. The fidelity of the optimized pulse sequences were estimated using Qiskit’s randomized bench marking techniques.