CFEDR tritium fuel cycle model and tritium self-sufficiency analysis

BackgroundComputational models in biology are characterized by a large degree of uncertainty. This uncertainty can be analyzed with Bayesian statistics, however, the sampling algorithms that are frequently used for calculating Bayesian statistical estimates are computationally demanding, and each algorithm has unique advantages and disadvantages. It is typically unclear, before starting an analysis, which algorithm will perform well on a given computational model.ResultsWe present BCM, a toolkit for the Bayesian analysis of Computational Models using samplers. It provides efficient, multithreaded implementations of eleven algorithms for sampling from posterior probability distributions and for calculating marginal likelihoods. BCM includes tools to simplify the process of model specification and scripts for visualizing the results. The flexible architecture allows it to be used on diverse types of biological computational models. In an example inference task using a model of the cell cycle based on ordinary differential equations, BCM is significantly more efficient than existing software packages, allowing more challenging inference problems to be solved.ConclusionsBCM represents an efficient one-stop-shop for computational modelers wishing to use sampler-based Bayesian statistics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0339-3) contains supplementary material, which is available to authorized users.

An environment to support the modeling, analysis, simulation, and development of state transition models, SMOOCHES (State Machines for Object-Oriented Concurrent Hierarchical Engineering Specifications), is presented. SMOOCHES allows the hierarchical construction, analysis, and simulation of state transition models in an object-oriented distributed environment. Statecharts (see Harel 1987b), a powerful mechanism for state transition specification, are fundamental to the development of SMOOCHES. To assist in the specification of hierarchical state transition models for distributed and reactive systems, statecharts are extended by introducing the concept of exit-safe states. SMOOCHES allows the specification of objects in the system with hierarchical state transition models and the derivation of new classes of objects through inheritance. A graphical monitoring system has been developed to represent and simulate the object state life cycles and monitor event generations. The example presented illustrates the modeling and simulation of different state life cycles of an assembly robot.

A process-based model of nitrogen cycling in forest plantations: Part I. Structure, calibration and analysis of the decomposition model

BackgroundThe progress through the eukaryotic cell division cycle is driven by an underlying molecular regulatory network. Cell cycle progression can be considered as a series of irreversible transitions from one steady state to another in the correct order. Although this view has been put forward some time ago, it has not been quantitatively proven yet. Bifurcation analysis of a model for the budding yeast cell cycle has identified only two different steady states (one for G1 and one for mitosis) using cell mass as a bifurcation parameter. By analyzing the same model, using different methods of dynamical systems theory, we provide evidence for transitions among several different steady states during the budding yeast cell cycle.ResultsBy calculating the eigenvalues of the Jacobian of kinetic differential equations we have determined the stability of the cell cycle trajectories of the Chen model. Based on the sign of the real part of the eigenvalues, the cell cycle can be divided into excitation and relaxation periods. During an excitation period, the cell cycle control system leaves a formerly stable steady state and, accordingly, excitation periods can be associated with irreversible cell cycle transitions like START, entry into mitosis and exit from mitosis. During relaxation periods, the control system asymptotically approaches the new steady state. We also show that the dynamical dimension of the Chen's model fluctuates by increasing during excitation periods followed by decrease during relaxation periods. In each relaxation period the dynamical dimension of the model drops to one, indicating a period where kinetic processes are in steady state and all concentration changes are driven by the increase of cytoplasmic growth.ConclusionWe apply two numerical methods, which have not been used to analyze biological control systems. These methods are more sensitive than the bifurcation analysis used before because they identify those transitions between steady states that are not controlled by a bifurcation parameter (e.g. cell mass). Therefore by applying these tools for a cell cycle control model, we provide a deeper understanding of the dynamical transitions in the underlying molecular network.

A system dynamics model for tritium cycle of pulsed fusion reactor

Selection of a suitable Software Development Life Cycle (SDLC) model for project implementation is somewhat confusing as there are a lot of SDLC models with similar strengths and weaknesses. Also, the solutions proffered among the researchers so far have been the qualitative comparative analysis of SDLC models. Hence, this paper proposes a comparative analysis of SDLC models using quantitative approach in relation to strengths and weaknesses of SDLC models. The study adapted comparative analysis and Software Development Life Cycle (SDLC) models features’ classification using ten characteristics such as project complexity, project size, project duration, project with risk, implementation/initial cost, error discovery, associated cost, risk analysis, maintenance and cost estimation. A quantitative measure that employs online survey using experts in software design and engineering, project management and system analysis was carried out for the evaluation of SDLC models. Purposeful Stratified Random Sampling (SRS) technique was used to gather the data for analysis using XLSTAT after pre-processing, taking into consideration both benefit and cost criteria. The overall performance evaluation showed that Spiral-Model is the best followed by V-Model and lastly Waterfall Model with comparative values of 38.63%, 35.76% and 25.61% respectively. As regards cost estimation, Waterfall Model is the most efficient with value of 41%, then V-Model with 31% and lastly Spiral Model with 28%. V-Model has great error recovery capability with value of 45% which is closely followed by Spiral Model with 37% and lastly Waterfall Model with 18%. The study revealed that, a model with efficient risk assurance does not guarantee efficient cost management. In the future work, more characteristics regarding SDLC models shall be considered.

Stimulation of terrestrial productivity by rising CO~2~ concentration is projected to reduce the airborne fraction of anthropogenic CO~2~ emissions; coupled climate-carbon (C) cycle models, including those used in the IPCC Fourth Assessment Report (AR4), are sensitive to this negative feedback on atmospheric CO~2~^1^. The representation of the so-called CO~2~ fertilization effect in the 11 models used in AR4 and subsequent models^2,3^ was broadly consistent with experimental evidence from four free-air CO~2~ enrichment (FACE) experiments, which indicated that net primary productivity (NPP) of forests was increased by 23 +/- 2% in response to atmospheric CO~2~ enrichment to 550 ppm^4^. Substantial uncertainty remains, however, because of the expectation that feedbacks through the nitrogen (N) cycle will reduce the CO~2~ stimulation of NPP^5,6^; these feedbacks were not included in the AR4 models and heretofore have not been confirmed by experiments in forests^7^. Here, we provide new evidence from a FACE experiment in a deciduous Liquidambar styraciflua (sweetgum) forest stand in Tennessee, USA, that N limitation has significantly reduced the stimulation of NPP by elevated atmospheric CO~2~ concentration (eCO~2~). Isotopic evidence and N budget analysis support the premise that N availability in this forest ecosystem has been declining over time, and declining faster in eCO~2~. Model analyses and evidence from leaf- and stand-level observations provide mechanistic evidence that declining N availability constrained the tree response to eCO2. These results provide a strong rationale and process understanding for incorporating N limitation and N feedback effects in ecosystem and global models used in climate change assessments.

Using the “total energy cycle” methodology, we compare U.S. near term (to ~ 2015) alternative pathways for converting energy to light-duty vehicle kilometers of travel (VKT) in plug-in hybrids (PHEVs), hybrids (HEVs), and conventional vehicles (CVs). For PHEVs, we present total energy-per-unit-of-VKT information two ways (1) energy from the grid during charge depletion (CD); (2) energy from stored on-board fossil fuel when charge sustaining (CS). We examine “incremental” sources of supply of liquid fuel such as (a) oil sands from Canada, (b) Fischer-Tropsch diesel via natural gas imported by LNG tanker, and (c) ethanol from cellulosic biomass. We compare such fuel pathways to various possible power converters producing electricity, including (i) new coal boilers, (ii) new integrated, gasified coal combined cycle (IGCC), (iii) existing natural gas fueled combined cycle (NGCC), (iv) existing natural gas combustion turbines, (v) wood-to-electricity, and (vi) wind/solar. We simulate a fuel cell HEV and also consider the possibility of a plug-in hybrid fuel cell vehicle (FCV). For the simulated FCV our results address the merits of converting some fuels to hydrogen to power the fuel cell vs. conversion of those same fuels to electricity to charge the PHEV battery. The investigation is confined to a U.S. compact sized car (i.e. a world passenger car). Where most other studies have focused on emissions (greenhouse gases and conventional air pollutants), this study focuses on identification of the pathway providing the most vehicle kilometers from each of five feedstocks examined. The GREET 1.7 fuel cycle model and the new GREET 2.7 vehicle cycle model were used as the foundation for this study. Total energy, energy by fuel type, total greenhouse gases (GHGs), volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NOx), fine particulate (PM2.5) and sulfur oxides (SOx) values are presented. We also isolate the PHEV emissions contribution from varying kWh storage capability of battery packs in HEVs and PHEVs from ~ 16 to 64 km of charge depleting distance. Sensitivity analysis is conducted with respect to the effect of replacing the battery once during the vehicle’s life. The paper includes one appendix that examines several recent studies of interactions of PHEVs with patterns of electric generation and one that provides definitions, acronyms, and fuel consumption estimation steps.

Life cycle assessment provides a comprehensive framework to evaluate the total greenhouse gas (GHG) emissions from electrified vehicles (EVs) and their potential for GHG reduction as they gain market share. The magnitude of EVs¿ contribution will depend on the specific combinations of fueling strategies and the other vehicle technologies adopted. For instance, the GHG emissions from plug-in electric vehicles (PHEVs) could increase life cycle emissions if the vehicle is driven in a region with a high carbon grid. Also, vehicle lightweighting with lighter, high strength materials decreases use phase emissions but can increase emissions throughout the material production process. This research develops a method to evaluate life cycle emissions from a lightweight PHEV for use in diverse electric fueling regions. A life cycle model is constructed using: 1) Autonomie, a vehicle simulation software, 2) GREET, a vehicle and fuel cycle model, and 3) eGrid, a database with regional information about the US electric power sector. The life cycle analysis demonstrates the importance of considering vehicle production emissions when using energy intensive materials to reduce mass from a vehicle, since life cycle GHGs for the 10% lightweight carbon fiber vehicle are higher than the baseline steel vehicle. However, as a higher percentage of steel is replaced with carbon fiber, total life cycle GHGs decrease. Regional impacts of the electric grid are shown to be significant, with the potential to decrease life cycle emissions by more than four times the reductions possible with the best-case lightweight scenario.

This document is a plan for an intermodel comparison of atmospheric CO{sub 2} projections that includes uncertainty analysis of the global carbon cycle models used to make those projections. The plan includes a procedure for the documentation, support, and archiving of global carbon cycle models within the Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL). The best'' global carbon cycle model is not one of the objectives. Rather, the principal goals are to develop a picture of where global carbon cycle modeling stands, at present, in the projection of future atmospheric CO{sub 2} concentrations and to acquire information that can be used to determine research needs for CO{sub 2} modeling. The plan involves three phases: model implementation --- the acquisition and computer implementation of the global carbon cycle models; model analysis --- sensitivity and uncertainty analysis of each model; and synthesis --- an intermodel comparison of the projections of future atmospheric CO{sub 2} concentrations and characterization of across-model patterns in the atmospheric projections and associated uncertainties. 40 refs., 7 figs., 1 tab.

Hepatitis C virus (HCV) is a blood-borne, enveloped, single-stranded, (+)-oriented RNA virus that mainly infects hepatocytes. Most infections progress into chronicity and eventually lead to severe liver disease. Although effective treatments have been developed, access to diagnosis and treatment is low, particularly in non-developed countries. Thus, eradication of the disease is unlikely without a prophylactic vaccine. Research, therefore, has to continue despite the high cure rates of today’s HCV regimens. We use mathematical modeling to study HCV replication and its intricate connection with the infected host cell. A model that is able to simulate intracellular HCV RNA replication suggested a host factor species (HF), representing a protein (complex) or a host process, to be critically involved in HCV replication. Gene expression profiling revealed several candidates potentially representing this HF. We validated those candidates in two variants of the human hepatoma cell line Huh7 and could confirm that five of them indeed played a role for HCV replication, namely CRAMP1, LBHD1, CRYM, THAP7, and NR0B2. The latter three are nuclear receptors or transcriptional (co )repressors, suggesting they could influence HCV replication indirectly, e.g. through glucose, lipid, or cholesterol metabolism. Follow-up studies will help to understand the implication of those factors in HCV replication and reveal important insights into the metabolic pathways regulating HCV replication. Model analyses also revealed the most sensitive steps in HCV RNA replication that could potentially be targeted by specific intervention. The standard of care for chronic HCV infection has been interferon alpha (IFN α) therapy that elicited a very broad but rather unspecific antiviral response of the host cell and came along with severe side effects. IFN-α activates signaling cascades that lead to the expression of hundreds of interferon stimulated genes that exert antiviral action. Despite its decades-long use, the exact mechanism of the suppression of HCV replication by IFN α treatment remains elusive. We thus combined experimental data with an intracellular model for HCV replication and revealed the steps in the viral replication cycle that are most probably affected by IFN α treatment. The obtained findings were well in line with in vitro data and confirmed the validity of our intracellular model to make such analyses. Recently, direct-acting antivirals (DAAs) have replaced IFN-α-containing regimens as the standard of care for chronic HCV infection. Those DAAs possess much less side effects, can be taken orally, and give extraordinarily high cure rates. Mainly three classes exist: inhibitors of the viral protease, the viral polymerase, and a viral multifunctional phosphoprotein. The latter class constitutes highly potent inhibitors of the HCV NS5A protein, exerting effects in the low picomolar range. However, due to the many roles of NS5A in the HCV life cycle, the exact mechanism of action of those DAAs remains unclear. For the other two classes, the mode of action is distinct and well defined. We, thus, used one representative member of each of these classes to validate the capacity of our model to implement drug effects and predict HCV replication correctly. Model predictions upon a priori fixing of the affected parameters in the model qualitatively resembled HCV replication dynamics under the respective drug treatment. This allowed us to apply our model to HCV replication data under treatment with an NS5A inhibitor in order to gain insights into its mode of action. The model revealed that the translation rate of HCV RNA as well as RNA synthesis steps in the HCV replication compartment are most probably affected by the drug. These findings were reasonable and supported by known roles of NS5A in the HCV life cycle. However, our model was limited to intracellular HCV replication and did not account for steps like particle assembly or infection of target cells. Therefore, we extended our intracellular model to cover the full viral life cycle. Our new full life cycle model could simulate viral (+)- and (-)-strand RNA, viral titers as well as spread of the infection, and was able to correctly predict HCV replication under drug treatment. Our new model will be helpful in further elucidating the mode of action of NS5A inhibitors and IFN α and in deciphering the role of host factors that determine permissiveness for HCV. Hence, this study provides a novel, extended mathematical model of the full HCV life cycle with the proven capacity of simulating and analyzing HCV replication even under pharmacological intervention. It can serve as an invaluable tool to study further molecular details of HCV replication and to devise and test novel therapeutic approaches.

Physics and technology considerations for the deuterium–tritium fuel cycle and conditions for tritium fuel self sufficiency

The dynamic analysis of fusion power plant (FPP) fuel cycles highlights the challenge of achieving tritium self-sufficiency in future FPPs. While state-of-the-art fuel cycle models offer valuable insights into the necessary design parameters for attaining tritium self-sufficiency, none of these models currently consider the impact of tritium trapping within fuel cycle components. However, detailed analysis of individual components reveals that substantial amounts of tritium can be trapped within the first wall, divertors, and breeding blanket systems, suggesting that tritium trapping may significantly influence the FPP ability to achieve self-sufficiency. The compounded effects of additional tritium traps generated by irradiation effects and component replacements further exacerbate this challenge. The novelty of this work is the integration of an explicit, physics-based model for tritium trapping, evolution of damage-induced traps, and component replacements into a dynamic, system-level model of a fuel cycle. The results show an increase of a factor 10 3 − 10 4 of tritium inventory in the first wall and vacuum vessel of an ARC-class FPP when accounting for the aforementioned phenomena. This, coupled with the replacement of components subject to significant tritium trapping, slows down fuel cycle dynamics, resulting in an extended tritium doubling time (50% increase), higher start-up inventory (30% increase), and higher required tritium breeding ratio (2%–5%) compared to a scenario without tritium trapping.

The issues of increasing the efficiency and improving the environmental friendliness of heat engines are urgent problems of mankind. The article deals with the use of biofuel as a fuel for reciprocating internal combustion engines. A brief overview of current research on this topic is presented in the article. This paper presents the results of tuning the working cycle of a turbocharged diesel engine (cylinder diameter 123 mm, piston stroke 156 mm) for operation on bio-diesel fuel (a mixture of diesel fuel and soy methyl ester in a ratio of 80/20). This scientific study is based on mathematical modelling of the working cycle of piston machines. Mathematical modelling of the working cycle was carried out in the Diesel-RK software. A brief description of the physicochemical properties of biofuels is given in the article. A brief analysis of the mathematical model for the engine under study is also presented in the article. The mathematical model was tuned according to more than 30 parameters typical for diesel engines. Based on the results of numerical simulation, a new design of the nozzle atomizer is proposed, the optimal fuel injection advance angle and the fuel supply law for the main modes of diesel engine operation are selected. The proposed measures have led to an increase in effective power (up to 5%), a decrease in specific fuel consumption (up to 6%) and an improvement in environmental friendliness.

In this paper, we investigate a Kaldor-Kalecki model of business cycle with delay in both the gross product and the capital stock. Stability analysis for the equilibrium point is carried out. We show that Hopf bifurcation occurs and periodic solutions emerge as the delay crosses some critical values. By deriving the normal forms for the system, the direction of the Hopf bifurcation and the stability of the bifurcating periodic solutions are established. Examples are presented to confirm our results.