Relevant Implication and Ordered Geometry

The leaf photosynthesis models developed by Farquhar et al. (1980, https://doi.org/10.1007/BF00386231) (FvCB) and Goudriaan et al. (1985, https://doi.org/10.1007/978‐1‐4899‐3665‐3_10) (G85) are both used in Earth system and weather models to quantify ecosystem carbon assimilation. Despite their common role, a systematic comparison between these two photosynthesis models is currently lacking in scientific literature. In this technical report we compared the two models in a systematic way. Hereto we performed a comparative analysis of the model structures as well as the modeled responses of photosynthesis to light and CO2 at leaf level. To facilitate future model comparison, we also constructed a lookup table that presents FvCB model parameters fitted to CO2‐response curves from G85 in a wide natural range in photosynthetic capacity at standardized temperature. The structure of the FvCB model differs fundamentally from the G85 model as the FvCB model considers rate‐limiting processes due to Rubisco capacity, electron transport and triose phosphate utilization in parallel, whereas the G85 model considers Rubisco activity and triose phosphate utilization limitation to act in series scaled with quantum use efficiency. The models also differ fundamentally in terms of the parametrization of dark respiration. Still, both models calculate near‐similar responses of photosynthesis to changes in light and CO2 across a wide range in photosynthetic capacity with only two free parameters each. Our work thereby highlights functional similarities between these model approaches despite fundamental differences in model structure. Hence, standardized parameter sets that yield similar photosynthesis responses to light and CO2 may facilitate intercomparison of Earth system and weather models.

Identification Aspects of SDP Based Polynomial Optimization Relaxations

A technique is presented to use a set of identified complex modes together with an analytical mathematical model of a structure under test to compute improved mass, stiffness and damping matrices. A set of identified normal modes, computed from the measured complex modes, is used in the mass orthogonality equation to compute an improved mass matrix. This eliminates possible errors that may result from using approximated complex modes as normal modes. The improved mass matrix, the measured complex modes and the higher analytical modes are then used to compute the improved stiffness and damping matrices. The number of degrees-of-freedom of the improved model is limited to equal the number of elements in the measured modal vectors. A simulated experiment shows considerable improvements, in the system's analytical dynamic model, over the frequency range of the given measured modal information.

Online adaptive radiation therapy (ART) offers a paradigm shift in radiotherapy by enabling adjustments to the planned dose based on daily anatomical variation. In the context of cone-beam computed tomography (CBCT) for online ART on a standard linac, thoracic and abdominal treatment sites in particular present unique challenges due to the typically large treatment volumes, mobile anatomy, scatter-induced image quality degradation, and hounsfield unit (HU) limitations. A recent hardware and software upgrade for a standard linac, Varian TrueBeam (TB) v4.1 HyperSight, seeks to overcome these challenges through implementation of a larger kV imager panel (i.e., 43×43 cm), increased gantry speed (i.e., from 6 to 9°/s), and improved HU accuracy. However, investigation of the new upgrade is essential to harness the full potential of these advancements. We report on physical characterization and a digital Monte Carlo (MC) model of the new imaging system hardware. The open-source GEANT4 Application for Tomographic Emission (GATE) MC toolkit, which allows scintillation systems, including CBCT, to be accurately modeled, was utilized. All physical components of the new TB upgrade were modeled from vendor-provided geometry and material specifications. The model was validated using physical measurements acquired on the upgraded system. Specifically, the modulation transfer function (MTF), noise power spectrum (NPS), profiles across the physically larger detector, scatter-to-primary ratio (SPR), and loss in spatial resolution as a function of the increased gantry speed and an object's distance from isocenter. The latter was quantified using the pixel distance between the 15% and 85% intensities of the over-sampled edge spread function (ESF) for source-to-edge-phantom distances (SEPDs) of 80, 100, and 120 cm. Focal spot motion was also characterized by the MTF at SEPD of 100 cm. The MTF50 was 0.901 and 0.889 mm-1 for the measurement and simulation, respectively, for a 125 kVp beam. The normalized root mean square error (nRMSE) was 0.013. While small, the model displayed degraded spatial resolution accuracy for other beam qualities. The general trend of the physically measured normalized noise power spectrum (nNPS) curves was reproduced with the model at all beam energies; however, a small systematic offset was observed. Excellent agreement was observed between central-image x- and y-profiles of measured and MC-generated projections, indicating correct modeling of the larger imaging detector. Simulated SPRs closely agreed with those of measurement. The measured ESF widths for the 6 and 9°/s acquisitions were both 2.5 pixels, indicating no reduction in spatial resolution in the projection space as a result of the increased acquisition speed. The effect of focal spot blurring due to increased gantry speed was accurately modeled, considering ESF width differences no larger than 0.5 pixels were observed for all SEPDs. Decreased spatial resolution in projection images was observed for SEPDs of 80 and 120 cm compared to 100 cm. The MC model of the novel TB 4.1 HyperSight upgrade accurately reproduced physical measurements acquired on the new system. The model will be used alongside physical testing on the new TB platform, working towards an online CBCT-based ART protocol for thoracic and abdominal treatments.

Object of the research and modeling are the main columns of catalytic reforming unit of Atyrau Oil Refinery (AOR) that are operated under conditions of deficiency and inaccuracy of initial information. System of mathematical models of the columns being researched is developed on the basis of statistical and indistinct information. Structures of mathematical models of columns K-1, K-2 and K-3 are identified in the form of the regression and fuzzy regression equations, i.e. combined models of main columns of Leningrad-Germany (LG) catalytic reforming unit are composed.

Modeling and Identification of a Nuclear Reactor

Trees store carbohydrates, in the form of sugars and starch, as reserves to be used to power both future growth as well as to support day-to-day metabolic functions. These reserves are particularly important in the context of how trees cope with disturbance and stress—for example, as related to pest outbreaks, wind or ice damage, and extreme climate events. In this project, we measured the size of carbon reserves in forest trees, and determined how quickly these reserves are used and replaced—i.e., their “turnover time”. Our work was conducted at Harvard Forest, a temperate deciduous forest in central Massachusetts. Through field sampling, laboratory-based chemical analyses, and allometric modeling, we scaled these measurements up to whole-tree NSC budgets. We used these data to test and improve computer simulation models of carbon flow through forest ecosystems. Our modeling focused on the mathematical representation of these stored carbon reserves, and we examined the sensitivity of model performance to different model structures. This project contributes to DOE’s goal to improve next-generation models of the earth system, and to understand the impacts of climate change on terrestrial ecosystems.

Model reduction is a technique commonly used to reduce the computation time of structural dynamic models. The results of the reduced model can then be expanded to full space using the transformation matrix developed in the reduction process. This work focuses on expanding the mode shapes of system models that are comprised of reduced component models. Typically, this expansion requires the full space system model to be computed in order to obtain the system model transformation matrix needed to perform the expansion. Computing the full space system model to determine the expansion matrix does not save computation time and therefore defeats the purpose of using model reduction. This paper proposes using the expansion matrices of the individual components to expand the assembled system model modes. In this work, System Equivalent Reduction Expansion Process (SEREP) is used for reduction and expansion. The accuracy of the expanded system model is shown to be dependent on the modes retained in the reduced component models. Recent work on Variability Improvement of Key Inaccurate Node Groups (VIKING) has shown that over specifying the number of modes used in the reduction/expansion process that span the space of the system model modes significantly improves the results. The VIKING technique is the basis for the expansion process developed in this work. Multiple analytical cases are presented to show how the selection of component modes affects the expansion results. The analytical cases demonstrate that accurate system model expansion results can be obtained when a sufficient set of component modes that span the space of the system model modes are used.

Complex engineered systems can carry risk of high failure consequences, and as a result, resilience—the ability to avoid or quickly recover from faults—is desirable. Ideally, resilience should be designed-in as early in the design process as possible so that designers can best leverage the ability to explore the design space. Toward this end, previous work has developed functional modeling languages which represent the functions which must be performed by a system and function-based fault modeling frameworks have been developed to predict the resulting fault propagation behavior of a given functional model. However, little has been done to formally optimize or compare designs based on these predictions, partially because the effects of these models have not been quantified into an objective function to optimize. The work described herein closes this gap by introducing the resilience-informed scenario cost sum (RISCS), a scoring function which integrates with a fault scenario-based simulation, to enable the optimization and evaluation of functional model resilience. The scoring function accomplishes this by quantifying the expected cost of a design's fault response using probability information, and combining this cost with design and operational costs such that it may be parameterized in terms of designer-specified resilient features. The usefulness and limitations of using this approach in a general optimization and concept selection framework are discussed in general, and demonstrated on a monopropellant system design problem. Using RISCS as an objective for optimization, the algorithm selects the set of resilient features which provides the optimal trade-off between design cost and risk. For concept selection, RISCS is used to judge whether resilient concept variants justify their design costs and make direct comparisons between different model structures.

The objective of the present research is to develop a formal modeling method, aimed at realising concurrent development and integration of various manufacturing system models. An object-oriented model repository of the manufacturing system is discussed to establish a systematic and logical criteria for classifying the manufacturing system models from various viewpoints. Contents and structures of manufacturing system models are discussed from the viewpoint of the concurrent and hierarchical production planning problems, and model components and their relations to be stored in the model repository are proposed. A prototype of a model repository is developed and applied to the production planning problems.KeywordsModelingmanufacturing systemmodel repositoryobject-oriented approachproduction planning

The emerging clean-energy smart grid environment in the electric power sector has necessitated that related educational programs evolve to meet the needs of students, faculty, and employers alike. In order to prepare the next generation of power engineering professionals to meet the challenges ahead in the electric power sector, a new curriculum must be developed that includes core power engineering principals coupled with emerging aspects of smart grid technologies and clean energy integration. Such curriculum also needs to consider not only the end-use side of the power system within the smart grid definition, such as smart metering, communications and demand response aspects, but also other key enabling technologies throughout the whole transmission and distribution system and the entire energy supply chain. These include areas such as energy storage technologies, advanced power electronics at the transmission and distribution levels, networked control systems, automation, renewable and alternative energy systems integration, system optimization, real-time control, and other related topics. In addition, the evolution of power programs and curriculum in this emerging area must take into account significant input from industry constituents engaged in the manufacturing, implementation, operation, and maintenance of the new smart grid technologies and systems. By working collaboratively with industry to meet future employer needs, programs with newly developed course offerings will be able to better prepare students and existing professionals alike for the rapidly growing clean-energy, smart grid environment. This paper will provide an overview of a potential model for program structures and course developments in this critical area, including examples of initiatives already being developed and deployed.

Irregular ANCF model and experimental investigations of the irregular membrane structures with variable cross-section

Finite element based N-Port model for preliminary design of multibody systems

The activity in respiratory nerves and muscles in response to electrical stimulation of vocal substrates in the brain and to CO2 stimulation of the respiratory centers was studied in 28 adult chickens. It was found that the same nerves and muscles were active during both vocalization and respiration. Stimulation of vocal substrates resulted in short latency bursting in the expiratory nerves and muscles. As stimulation intensity increased, progressively longer duration bursts composed of numerous subbursts were produced. By relating muscle activity with sound production , such bursting was shown to underlie evoked vocalizations. Background activity in inspiratory nerves and muscles continued uninterruptedly past stimulus onset only stopping when expiratory activity began. Thereafter inspiratory bursting reciprocated with expiratory bursting and was shown to underlie the intervals between vocalizations. The pattern of activity which was evoked by stimulating vocal substrates was found to strongly interact with the pattern of activity evoked by CO2 stimulation of the respiratory system. Simultaneous records of respiratory and tracheal muscles demonstrated that the same information was sent to both groups of muscles during evoked vocalization. Activity in the respiratory muscles was recorded during spontaneous vocalization of a free-moving bird and was found to resemble that recorded from anesthetized birds. Finally the activity of single units in the obex region of the medulla was recorded during electrical stimulation of vocal substrates and during CO2 stimulation of the respiratory system. Rhythmically active units were found only in the medulla. Unit activity paralleled that found in the nerves and muscles. On the basis on the data accumulated, two models of the chicken vocal system are presented. The first is a model of the sound-producing structures of the chicken. The second is a model of the neural machinery which controls the sound-producing structures. The two models are used as a basis for an explanation of the production of voclizations by the chick of the same species.

To develop a physiologic model of contrast medium enhancement by incorporating available physiologic data and contrast medium pharmacokinetics and to predict organ-specific contrast medium enhancement at computed tomography (CT) with various contrast medium injection protocols in patients of variable height and weight. A computer-based, compartmental model of the cardiovascular system was generated by using human physiologic parameters and more than 100 differential equations to describe the transport of contrast medium. Blood volume, extracellular fluid volume, and regional blood flow were estimated from available data. Local structures were modeled mathematically to describe the distribution and dispersion of intravascularly administered iodinated contrast medium. A global model was formed by integrating regional circulation parameters with the models of local structures. Aortic and hepatic CT contrast-enhancement curves were simulated for three protocols and were compared with mean enhancement curves in three groups of 25-28 patients (80 patients total; 28 in one group, 25 in one group, and 27 in one group) receiving the same protocols. The percent difference in maximum enhancement between the simulated and empiric curves and the enhancement difference index (sum of the area difference between the simulated and empiric curves divided by the total area under the empiric curve) were computed. The simulated and empiric enhancement curves closely agreed in maximum enhancement (the mean percent difference in the aorta was 7.4%; liver, 4.8%) and in variation over time (mean enhancement difference index in the aorta was 11.6%; liver, 12.7%). A computer-based, physiologic model that may help predict organ-specific CT contrast medium enhancement for different injection protocols was developed. Such a physiologic model may have many clinical applications.