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A novel implicit hybrid machine learning model and its application for reinforcement learning
A novel methodology to develop implicit hybrid models is presented. PyTorch is used to integrate physics-based equations with machine learning models. Automatic differentiation of the hybrid model is leveraged to solve the implicit equations. Iterative solving enables gradient based updates to the machine learning model. The novel methodology is compared to an explicit hybrid approach on a continuously stirred tank reactor (CSTR). The novel method results in a lower modelling error. Both hybrid models effectively train with noisy data. To test the implicit hybrid model, it is employed as a reinforcement learning (RL) training model. The RL algorithm trained on the hybrid model outperforms real time optimization of the CSTR and performs nearly as well as RL trained directly on the CSTR and a traditional gradient based approach. Training RL directly on the CSTR requires over 60,000 system interactions compared to 6000 historical data points for hybrid model development.
Non-unimodal and non-concave relationships in the network Macroscopic Fundamental Diagram caused by hierarchical streets
Unimodal, concave relationships between average network productivity and accumulation or density aggregated across spatially compact regions of urban networks—so called network Macroscopic Fundamental Diagrams (MFDs)—have recently been shown to exist on homogeneous street networks. When present, MFD relationships facilitate the modeling of traffic congestion at a regional level and have led to the development of various regional traffic control strategies. However, real street networks are not homogeneous—they generally have a hierarchical structure where some streets (e.g., arterials) promote higher mobility than others (e.g., local roads). Here, this paper examines how the presence of hierarchical roadway structures may potentially cause non-unimodal patterns in a network's MFD. These are observed using three types of tools: analytical models of simple network structures, simulations of various idealized roadway networks, and empirical data. The impacts of street hierarchy depend on how vehicles use different roadway types to move within the network; i.e., their routing strategy. The findings suggest that the presence of roadway hierarchies may lead to MFDs that have non-unimodal or non-concave patterns on the free-flow branch when vehicles route themselves according to user equilibrium principles, which is closest to what would be observed in realistic situations. Such patterns are contrary to what is traditionally assumed in most MFD-based modeling frameworks. However, the unimodal and concave MFD should be expected under system optimal routing conditions that maximize network productivity for a given traffic state.
Asymptotic analysis of the fluid flow with a pressure-dependent viscosity in a system of thin pipes
We consider the incompressible fluid with a pressure-dependent viscosity flowing through a multiple pipe system. The viscosity–pressure relation is given by the Barus law commonly used in the engineering applications. Assuming that the ratio between pipes thickness and its length is small, we propose a rigorous asymptotic approach based on the concept of the transformed pressure. As a result, we obtain new macroscopic model describing the effective behavior of the fluid in the system. In particular, the generalized version of the Kirchhoff’s law is derived giving the explicit formula for the junction pressure. The error estimate for the asymptotic approximation is also provided. Mathematical analysis presented here can be applied to a general viscosity–pressure relation satisfied by other empiric laws.
Supervisory Control System for Multi-Modular Advanced Reactors
The proposed supervisory control system (SCS) may provide considerable benefits to advanced small modular reactors, including reduced plant staffing, optimized maintenance activities, greater plant availability, and higher operating efficiency. The SCS makes risk-informed decisions based on (1) a probabilistic assessment of the likelihood of success given the status of the plant/systems and component health, and (2) a deterministic assessment between plant operating parameters and reactor protection parameters to prevent unnecessary trips and challenges to plant safety system—one measure of SCS success. The probabilistic portion of the decision-making engine of the SCS is based on the control actions associated with an advanced liquid-metal reactor (ALMR) Power Reactor, Innovative, Small Module (PRISM). Within the SCS, the probabilistic assessment provides a ranking of viable control actions; however, certain instructions generated by the probabilistic model only include an abstract notion of action without specifications. For instance, one instruction may be to reduce power without specifying how much reduction is needed. The prognostic/diagnostic models incorporate the health of components into the decision-making process. Once the control options are identified and ranked based on the likelihood of success, the SCS transmits the options to the deterministic portion of the platform. The performance-based system models assess and rank each of the probabilistically identified control actions by taking into account the physical behavior (current and projected) of the system. The performance-based decision making module receives inputs from the probabilistic decision-making module and the ERM module to generate a single solution. Interfaces to these modules are defined later in the section. A utility theory algorithm factors into the decision making by estimating the distance from and approach to a trip setpoint for each control option. If the magnitude of a negative utility value increases rapidly as the system approaches the trip setpoint, that option is not likely to be the preferred option. This can lead to a re-ranking of the control options. The SCS then transmits a control signal(s) to a component or system and informs the operator of actions taken based on the action chosen. The SCS successfully coupled probabilistic and performance-based system models to arrive at optimal control decisions based on the actual status of the plant and components. The automatic, autonomous, and real-time performance requirements for a control system were met by the SCS. The value of coupling probabilistic and performance-based system models was demonstrated by the re-ranking of control options based on the use of a utility algorithm. The use of ERM monitors provides added value to the SCS as demonstrated by the re-ranking of control options based on a components degraded state.
A reproducing kernel enhanced approach for peridynamic solutions
The most common discretization method for peridynamic models used in engineering problems is the node-based meshfree approach. This method discretizes peridynamic domains by a set of nodes, each associated with a nodal cell with a characteristic volume, leading to a particle-based description of continuum systems. The behavior of each particle is then considered representative of its cell. This limits the convergence rate to the first order. In this paper, we introduce a reproducing kernel (RK) approximation to the field variables in the peridynamic equations to increase the order of convergence of peridynamic numerical solutions. Furthermore, the numerical results demonstrate improved convergence rates in static peridynamic problems using the proposed method.
Traleika Glacier: A hardware-software co-designed approach to exascale computing
The move from current petascale machines to future exascale machines will need both hardware improvements and software changes. Hardware will need to evolve to focus primarily on features that lower energy consumption: near-threshold voltage operation, fine-grained power and clock management and heterogeneity. Software will also need to evolve and be able to express more parallelism, become more dynamic and adaptable in order to be able to operate on a much more variable hardware. Here, we present Traleika Glacier, an effort that seeks to evaluate radical design changes to meet the constraints, both in terms of power and cost, of exascale computing. The salient features of the hardware design presented in the work include a) a use of heterogeneous cores, b) a redesign of the memory system that centers around hierarchical scratchpads and a global address space, c) the hardware acceleration of certain memory and network operations through specialized engines and, d) very fine-grained control and monitoring capabilities. On the software side, we describe a task-based runtime system, the Open Community Runtime (OCR) which aims to express a wide range of higher-level programming models with a very limited set of core concepts: event-driven tasks for computation, events for synchronization and relocatable data-blocks for data management.
Addressing Human and Organizational Factors in Nuclear Industry Modernization: A Sociotechnically Based Strategic Framework
The modernization of nuclear power plants will require an advanced concept of operations, involving an integrated set of tightly coupled systems in which all stakeholders act in a coordinated manner. For this modernization effort to be enabled, we developed a human and organizational factors approach based on a broad sociotechnical framework. Starting from core human factors principles, we conducted a literature review of the methods and approaches relevant to the modernization problem. These included not only core disciplines such as cognitive systems engineering, systems theoretic accident modeling and processes, human systems integration, resilience engineering, and macroergonomics but also related topics of safety culture and organizational change. From this literature, we developed a conceptual framework centered around the work system with its four interacting components: people, technology, process, and governance. In an effective work system, these four components are jointly optimized according to three systems criteria: efficiency, effectiveness, and safety. System failure may result from excessive emphasis on any one criterion. The actual work of attaining joint optimization in a given work system can be accomplished by utilizing three high-level functions: knowledge elicitation, knowledge representation, and cross-functional integration. Finally, we illustrated the utility of this approach by applying it to practical problems and case studies.
Understanding the Reliability of Solder Joints Used in Advanced Structural and Electronics Applications: Part 2 - Reliability Performance.
Whether structural or electronic, all solder joints must provide the necessary level of reliability for the application. The Part 1 report examined the effects of filler metal properties and the soldering process on joint reliability. Filler metal solderability and mechanical properties, as well as the extents of base material dissolution and interface reaction that occur during the soldering process, were shown to affect reliability performance. The continuation of this discussion is presented in this Part 2 report, which highlights those factors that directly affect solder joint reliability. There is the growth of an intermetallic compound (IMC) reaction layer at the solder/base material interface by means of solid-state diffusion processes. In terms of mechanical response by the solder joint, fatigue remains as the foremost concern for long-term performance. Thermal mechanical fatigue (TMF), a form of low-cycle fatigue (LCF), occurs when temperature cycling is combined with mismatched values of the coefficient of thermal expansion (CTE) between materials comprising the solder joint “system.” Vibration environments give rise to high-cycle fatigue (HCF) degradation. Although accelerated aging studies provide valuable empirical data, too many variants of filler metals, base materials, joint geometries, and service environments are forcing design engineers to embrace computational modeling to predict the long-term reliability of solder joints.
Engineering Ralstonia eutropha for Production of Isobutanol (IBT) Motor Fuel from Carbon Dioxide, Hydrogen, and Oxygen Project Final Report
This research project is a collaboration between the Sinskey laboratory at MIT and the Worden laboratory at Michigan State University. The goal of the project is to produce Isobutanol (IBT), a branched-chain alcohol that can serve as a drop-in transportation fuel, through the engineered microbial biosynthesis of Carbon Dioxide, Hydrogen, and Oxygen using a novel bioreactor. This final technical report presents the findings of both the biological engineering work at MIT that extended the native branched-chain amino acid pathway of the wild type Ralstonia eutropha H16 to perform this biosynthesis, as well as the unique design, modeling, and construction of a bioreactor for incompatible gasses at Michigan State that enabled the operational testing of the complete system. This 105 page technical report summarizing the three years of research includes 72 figures and 11 tables of findings. Ralstonia eutropha (also known as Cupriavidus necator) is a Gram-negative, facultatively chemolithoautotrophic bacteria. It has been the principle organism used for the study of polyhydroxybutyrate (PHB) polymer biosynthesis. The wild-type Ralstonia eutropha H16 produces PHB as an intracellular carbon storage material while under nutrient stress in the presence of excess carbon. Under this stress, it can accumulate approximately 80 % of its cell dry weight (CDW) as this intracellular polymer. With the restoration of the required nutrients, the cells are then able to catabolize this polymer. If extracted from the cell, this PHB polymer can be processed into biodegradable and biocompatible plastics, however for this research, it is the efficient metabolic pathway channeling the captured carbon that is of interest. R. eutropha is further unique in that it contains two carbon-fixation Calvin–Benson–Bassham cycle operons, two oxygen-tolerant hydrogenases, and several formate dehydrogenases. It has also been much studied for its ability in the presence of oxygen, to fix carbon dioxide into complex cellular molecules using the energy from hydrogen. In this research project, engineered strains of R. eutropha redirected the excess carbon from PHB storage into the production of isobutanol and 3-methyl-1-butanol (branched-chain higher alcohols). These branched-chain higher alcohols can be used directly as substitutes for fossil-based fuels and are seen as alternative biofuels to ethanol and biodiesel. Importantly, these alcohols have approximately 98 % of the energy content of gasoline, 17 % higher than the current gasoline additive ethanol, without impacting corn market production for feed or food. Unlike ethanol, these branched-chain alcohols have low vapor pressure, hygroscopicity, and water solubility, which make them readily compatible with the existing pipelines, gasoline pumps, and engines in our transportation infrastructure. While the use of alternative energies from solar, wind, geothermal, and hydroelectric has spread for stationary power applications, these energy sources cannot be effectively or efficiently employed in current or future transportation systems. With the ongoing concerns of fossil fuel availability and price stability over the long term, alternative biofuels like branched-chain higher alcohols hold promise as a suitable transportation fuel in the future. We showed in our research that various mutant strains of R. eutropha with isobutyraldehyde dehydrogenase activity, in combination with the overexpression of plasmid-borne, native branched-chain amino acid biosynthesis pathway genes and the overexpression of heterologous ketoisovalerate decarboxylase gene, would produce isobutanol and 3-methyl-1-butanol when initiated during nitrogen or phosphorus limitation. Early on, we isolated one mutant R. eutropha strain which produced over 180 mg/L branched-chain alcohols in flask culture while being more tolerant of isobutanol toxicity. After the targeted elimination of genes encoding several potential carbon sinks (ilvE, bkdAB, and aceE), the production titer of the improved to 270 mg/L isobutanol and 40 mg/L 3-methyl-1-butanol. Semicontinuous flask cultivation supplied the cells with sufficient nutrients while minimizing the toxicity caused by isobutanol. Under this cultivation, the R. eutropha mutant grew and produced more than 14 g/L branched-chain alcohols over the duration of 50 days. These results demonstrate that R. eutropha carbon flux can be redirected from PHB to branched-chain alcohols and that engineered R. eutropha can be cultivated over prolonged periods of time for product biosynthesis. While this bioengineering work was being done at the Sinskey laboratory at MIT, the researchers at the Worden laboratory at Michigan State were working on the design and construction of the required specialty bioreactor for incompatible gasses (BIG) that would allow the safe feeding of microbes on Carbon Dioxide, Hydrogen, and Oxygen without explosive results. The early design and assembly work in year 1 incorporated a novel microbubble generator to maximize the bioavailability of gasses within the system comprised of small scale hollow fiber reactors. The early success of the microbubble generator eliminated the need to investigate potentially toxic surfactants within the system. For operational control, the system design incorporated a Opto22-based control network. The researchers also selected the specific hollow fiber material suitable for the bioreactor application. A variety of commercially available hollow fiber membranes were compared with regard to their pore sizes, cell affinity, and potential interference to cell viability assays. The selected membrane with its spongy layer was then tested for diffusivity of O2 and CO2. The instrumented system was then fully assembled for experimentally measuring the heterotrophic growth rate of immobilized R. eutropha cells. The requisite procedures for inoculation, measurement, and cleaning were established enabling the system performance to be validated under controlled laboratory conditions. In year 2, the researchers completed the Opto22 based cross-platform control network, and the system’s communications across the Sartorius fermentation system and Bruker gas chromatograph was established via open platform communications (OPC) protocol. Using the revised system, measurements were taken of the R.eutropha cell growth rate and substrate mass transfer rate in the hollow fiber membrane. Several IBT recovery strategies were explored and resin adsorption was determined to be optimal solution for lab scale operations. The adsorption capacity of the resin column was then measured and IBT desorption using methanol has been demonstrated. With the growing body of experimental data in hand, mathematical models were constructed to demonstrate and map the cellular kinetics, mass transfer of heterotrophic and autotrophic substrates in the hollow fiber, and the adsorption process in the resin column. A structured kinetic model was constructed to describe the competition between cell mass generation and IBT production. The reactor was then scaled up from single fiber to a membrane area of 180 cm2 and then further to 1 ft2. In Year 3 of the research, the IBT mass transfer across the membrane was characterized within the system with experiments to empirically measure the IBT diffusion coefficient in the BIG spongy layer. Using the refined mathematical models, the researchers are now able to explain the experimental observations and predict bioreactor performance under a wide range of experimental conditions. The Big system is able to demonstrate continuous controlled operations with the integrated IBT recovery system. Both heterotrophic and autotrophic production have been shown during continuous operation with heterotrophic and autotrophic stages. Performance of BIG system has been measured during continuous run with alternating heterotrophic growth on fructose and autotrophic product formation on H2, CO2, and O2. Volumetric productivities of IBT at 325 mg/(L day) and of 3M1B at 50 mg/(L day) were achieved, which were comparable to that achieved under heterotrophic conditions. Using the mathematical model, researchers are able to predict system performance for scaled-up BIG system. The apparent diffusion coefficient of IBT in the spongy layer of XM-50 hollow fiber membranes has been measured at various lumen liquid flow rates. The experiment is simulated in COMSOL to validate the results. The constructed COMSOL model is able to simulate BIG system performance in both batch and continuous mode. Mathematical simulations of the system performance have been run to identify the most crucial operational conditions, identifying the rate-limiting factors in autotrophic production of IBT, and quantitating the rate of IBT catabolism. Investigations of the productivity of the production system have suggested and the modeling of the system has revealed a particular sensitivity to the catabolism of the produced IBT by the engineered R. eutropha. Experiments have been designed and executed to quantify the IBT catabolism of R. eutropha, which open up possibilities for further system improvements through future, targeted bioengineering of the strain. Finally, the researchers at Michigan State performed an economic analysis of the system, based on the collective results, and their findings are presented in full within the report.
A Research Roadmap for Computation-Based Human Reliability Analysis
The United States (U.S.) Department of Energy (DOE) is sponsoring research through the Light Water Reactor Sustainability (LWRS) program to extend the life of the currently operating fleet of commercial nuclear power plants. The Risk Informed Safety Margin Characterization (RISMC) research pathway within LWRS looks at ways to maintain and improve the safety margins of these plants. The RISMC pathway includes significant developments in the area of thermalhydraulics code modeling and the development of tools to facilitate dynamic probabilistic risk assessment (PRA). PRA is primarily concerned with the risk of hardware systems at the plant; yet, hardware reliability is often secondary in overall risk significance to human errors that can trigger or compound undesirable events at the plant. This report highlights ongoing efforts to develop a computation-based approach to human reliability analysis (HRA). This computation-based approach differs from existing static and dynamic HRA approaches in that it: (i) interfaces with a dynamic computation engine that includes a full scope plant model, and (ii) interfaces with a PRA software toolset. The computation-based HRA approach presented in this report is called the Human Unimodels for Nuclear Technology to Enhance Reliability (HUNTER) and incorporates in a hybrid fashion elements of existing HRA methods to interface with new computational tools developed under the RISMC pathway. The goal of this research effort is to model human performance more accurately than existing approaches, thereby minimizing modeling uncertainty found in current plant risk models.
Design, Modeling, and Experiment of a Piezoelectric Pressure Sensor Based on a Thickness-Shear-Mode Crystal Resonator
Not provided.
Preliminary Development of Heat Transfer Model-Based Control Algorithms of Liquid Sodium Purification System: Advanced Sensors and Instrumentation Advanced Controls
Monitoring the operation of sodium purification system is essential for efficient operation of sodium fast reactors. In this work, a heat transfer model has been developed for monitoring the plugging meter and cold trap systems at the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The model of the purification system was developed by treating the respective aspects of the cold trap purification loop and plugging meter diagnostic loop as two separate control volumes using information from the METL piping and instrumentation diagram (P&ID). A model predictive controller was designed using first order differential equations with the specified boundary conditions. The system behavior was studied with a tuned optimized procedure using the internal cold trap temperature and plugging meter outlet temperature as control variables, and the air blower temperature as an independent variable respectively. Results of computer simulations obtained in this study compared favorably with experimental data showing very good reference tracking response with negligible overshoot as both plugging meter and cold trap physical models approach the setpoint.
Traffic Signal Optimization by Integrating Reinforcement Learning and Digital Twins
Machine learning (ML) methods, especially reinforcement learning (RL), have been widely considered for traffic signal optimization in intelligent transportation systems. Most of these ML methods are centralized, lacking in scalability and adaptability in large traffic networks. Further, it is challenging to train such ML models due to the lack of training platforms and/or the cost of deploying and training in a real traffic networks. This paper presents an approach for the integration of decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin (DT) to optimize traffic signals for the reduction of traffic congestion and network-wide fuel consumption related to stopping. Specifically, the DGMARL agents learn traffic state patterns and make decisions regarding traffic signal control with assistance from a Digital Twin module, which simulates and replicates the traffic behaviors of a real traffic network. The proposed approach was evaluated using PTV-Vissim [1], a microscopic traffic simulation platform. PTV-Vissim is also the simulation engine of the DT, enabling emulation and optimization of the traffic signals on the MLK Smart Corridor in Chattanooga, Tennessee. Compared to an actuated signal control baseline approach, experiment results show that Eco_PI, a developed performance measure capturing the impact of stops on fuel consumption, was reduced by 44.27% in a 24-hour and an average of 29.88% in a PM-peak-hour scenario.
Development of F and B Operation Strategy with LPSI and H-SIT
In the current nuclear power plants (NPPs), most of the critical safety functions are provided by many active safety systems. However, in order to meet the futuristic goals of safety, relying on these active safety systems alone does not seem to be viable. As passive system has a strength on the mitigation of station black out accident and it also contributes to increase the diversity of mitigation way, application of passive system becomes a paramount in the nuclear industry to enhance the nuclear plant safety. Recently, many researches for developing passive systems and operation strategies have been carried out in the nuclear field. General passive systems and strategies, however, have a critical limitation, especially for passive injection systems. As these systems use gravity as their driving force, it is difficult to inject coolant in a passive way in a high-pressure condition thus depressurization has to be preferentially performed to do passive injection. The problem is that depressurization inevitably causes the loss of core inventory thus it is not preferred in order to secure plant safety. Hybrid safety injection tank (H-SIT) system was newly suggested a few years ago to passively inject the coolant into the reactor coolant system (RCS) without depressurization in a high-pressure condition. The H-SIT system is planned to be integrated into advanced power reactor plus (APR+). Generally, the H-SIT system can inject water using the pressure from nitrogen gas as a conventional safety-injection tank in low-pressure accidents, such as large and medium-break loss of coolant accidents (LOCA). Additionally, the H-SIT system can also inject water using gravitational force in high-pressure accidents. If a high pressure accident occurs, the pressure of each H-SIT is equalized with pressure of pressurizer through equalizing pipes, thus allowing the H-SITs to inject water by gravitational force. In the target plant, four H-SITs are planned to be installed. Each has a 2.5 in diameter equalizing pipe and it can be operated manually. Long-term cooling of core is an ultimate goal of all mitigation actions for plant safety and feed and bleed (F and B) operation strategy is one of the long-term cooling strategies in conventional pressurized water reactor (PWR). The important point of F and B operation is that injection for feed operation is performed by only high pressure safety injection (HPSI) pump because operation of low pressure safety injection (LPSI) pump needs the depressurization and it causes core dry out. As H-SIT is passively operated in an accident situation, it was originally designed for mitigation of station black out accident. Besides, operator can use the H-SIT system same as high pressure injection pump in any accident situations. Thus, function of high pressure injection by H-SIT can be used for feed operation when HPSI pumps fail. If it is possible, H-SIT can make up the core during depressurization then LPSI pumps can be used for F and B operation. In this study, F and B operation strategy with LPSI and HSIT is developed. This is a new concept for the long-term cooling operation. If this strategy is applied, low pressure injection pump can be successfully used for F and B operation thus operator has the additional mitigation way. As this strategy make plant safe even though HPSI and PAFS are both failed, it can effectively enhance the plant safety. For this strategy two RCGVSs and two POSRVs are needed as a depressurization system for bleed operation and only one LPSI is enough for feed operation. H-SIT operation is also needed to make up core inventory during bleed operation. For this operation, four H-SITs have to be used to make up core safely. Based on the risk analysis using PSA method, if this strategy is applied, core damage frequency is 1.868 e-6 which declined 7 percent from original model. (authors)
Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization
Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.
Knowledge-matching based computational framework for genome-scale metabolic model refinement
Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.
Energize: An Interactive Evaluation Tool for Engaging the General Public with Energy Decision Making - Final Report
The public has an ever-increasing interest in the economic, environmental, and social impacts of global energy production. To support informed decision making, the scientific community has a responsibility to communicate reliable and straight-forward information to the general public, in an engaging way, regarding energy systems and how choices made at different stages of an energy technology life cycle can impact the cost, amount of materials used, and waste produced. Our objective is to enhance public engagement via an interactive electrical energy game (Energ!ze) through which players interact with one another in their quest to develop an electrical energy portfolio that optimizes economic (e.g., company profit), environmental (e.g., reduced CO 2 emissions), and social (e.g., public opinion) impacts. The design and development of Energ!ze was an iterative process involving an interdisciplinary team of engineers, discipline-based scientists, computer scientists, and education experts. The development objectives were two-fold: (1) Accurate and quantifiable modeling of electrical energy systems and (2) Engaging and interactive user interface within the construct of the game narrative. The major game mechanics in the current playable Energ!ze version focus on economic metrics, and the database is scaffolded to add environmental and social metrics for future versions. This report details the outcome of the funding period (Section 2 and 3) and describes the development process, along with recommendations for future development (Section 4).