Modeling of Quantization Effects in Digitally Controlled DC–DC Converters

In digitally controlled DC-DC converters with a single voltage feedback loop, the two quantizers, namely the A/D converter and the digital pulse-width modulator (DPWM), can cause undesirable limit-cycle oscillations. In this paper, static and dynamic models that include the quantization effects are derived and used to explain the origins of limit-cycle oscillations. In the static model, existence of DC solution, which is a necessary no-limit-cycle condition, is examined using a graphical method. A concept of amplitude and offset dependent gain is introduced to extend the describing function method and derive the dynamic system model. From the static and dynamic models, no-limit-cycle conditions associated with A/D, DPWM and compensator design criteria are derived. The conclusions are illustrated by simulation and experimental examples.

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional-scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional-scale storm tide flood impact assessments, but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reduced-complexity model of storm tide flooding that is computationally efficient and is applied at hyper-resolutions (<100 m cell size) over regional scales. We test the performance of this dynamic reduced-complexity model and a separate static model at three test sites where storm tide observational data are available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced-complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204 % and estimate more than twice the number of people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced-complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.

In this paper, we discuss the relation between liquidity and the business cycle.There are some reasons that we should link liquidity with the business cycle. The“flight to liquidity” or “flight to quality” hypothesis might be the most important one. It is more likely that an investor shifts his portfolio from those stocks with lower liquidity or higher risk when the economy is in a recession.Instead of business cycle measures, we use the NBER recession signal as the proxy of real economy. We apply dynamic and static probit model to monthly liquidity proxy, because the NBER recession is not always available in current period. Besides we use Amihud (2002) ILR as our liquidity proxy.The dynamic probit model with liquidity added does not result in a significance improvement on prediction. No matter in sample or out of sample, the pure dynamic probit model performs well with itself history only. However, as we mentioned earlier, NBER recession signal is usually announced well until the recession has happened for months, leading to an infeasibility to implement dynamic model. On the other hand, liquidity plays an important role in static probit model. We find a robust positive relation between liquidity and recession. Adding liquidity as a predictor in the static model would significantly improve the forecast accuracy in th short horizon. This observation remains robust with comparison to other financial variables ( e.g. term spread, credit spread, market volatility, and excess return). We also find the existence of “size effect” that small firm is more sensitive to future recession. Our finding above is consistent with our claims of “flight to liquidity; that is, it is more likely for market participants to shift his portfolio from those stock with lower liquidity or higher risk in advance when the economy would be in a recession. The primary contribution of this paper is that liquidity proxy, Amihud’s liquidity proxy here, does provide useful information when predicting the future recession. We find that the liquidity proxy performs well especially in a short prediction horizon. Besides, the liquidity of small firms is more informative in prediction.

As the main cause of large blackouts, understanding the complex interactions involved in cascading failure propagation is a critical area of ongoing research. The static model of cascading failures is a well-accepted method to investigate the cascading effects in power systems. However, as the power system evolves, dynamic behaviours become more variable, it is necessary to evaluate the validity of the assumptions in the static model. This paper compares and validates a conventional static model with a novel dynamic model capturing frequency dynamics. N-2 contingency analysis is used to trigger cascading events and the unserved demand and the number of branch outages are used as metrics. Analysis using the 39-bus, 200-bus and 2000-bus benchmark systems reveal similarities and differences between the static and dynamic models and reveal, for the first time, how these differences vary as network size grows. Sensitivity analysis on system dispatch capabilities provides some initial guidance to help reduce the differences between models and helps with understanding of the relative importance of cascading mechanisms.

Integration of dynamic and static modeling on guided inquiry learning can help students to understand chemical concepts at macroscopic, symbolic and microscopic levels. This research aimed to examine and to explain the differences of students’ multiple representation ability that was taught by guided inquiry with dynamic and static modeling in redox reaction concept. This research used descriptive and quasi-experimental design. Data were analyzed using descriptive and manova statistics analysis. The results showed that the ability of students’ macroscopic level that was taught by guided inquiry with dynamic and static modeling is the same. While the student’s symbolic and microscopic level ability that was taught by guided inquiry with dynamic modeling higher than student’s ability that was taught by guided inquiry with static modeling. The implication of this research is chemistry learning with dynamic modeling can help students to construct chemical concept more easily and gain the complete understanding.

BackgroundThis paper compares cost-effectiveness results from two models of maternal immunization to prevent pertussis in infants in Brazil, one static, one dynamic, to explore when static models are adequate for public health decisions and when the extra effort required by dynamic models is worthwhile. MethodsWe defined two scenarios to explore key differences between static and dynamic models, herd immunity and time horizon. Scenario 1 evaluates the incremental cost/DALY of maternal acellular pertussis (aP) immunization as routine infant vaccination coverage ranges from low/moderate up to, and above, the threshold at which herd immunity begins to eliminate pertussis. Scenario 2 compares cost-effectiveness estimates over the models’ different time horizons. Maternal vaccine prices of $9.55/dose (base case) and $1/dose were evaluated. ResultsThe dynamic model shows that maternal immunization could be cost-saving as well as life-saving at low levels of infant vaccination coverage. When infant coverage reaches the threshold range (90–95%), it is expensive: the dynamic model estimates that maternal immunization costs $2 million/DALY at infant coverage > 95% and maternal vaccine price of $9.55/dose; at $1/dose, cost/DALY is $200,000. By contrast, the static model estimates costs/DALY only modestly higher at high than at low infant coverage. When the models’ estimates over their different time horizons are compared at infant coverage < 90–95%, their projections fall in the same range. ConclusionsStatic models may serve to explore an intervention’s cost-effectiveness against infectious disease: the direction and principal drivers of change were the same in both models. When, however, an intervention too small to have significant herd immunity effects itself, such as maternal aP immunization, takes place against a background of vaccination in the rest of the population, a dynamic model is crucial to accurate estimates of cost-effectiveness. This finding is particularly important in the context of widely varying routine infant vaccination rates globally. Clinical Trial registryClinical Trial registry name and registration number: Not applicable.

PurposeContinuum robots modeling, be it from a hard or soft class, is giving rise to several challenges compared with rigid robots. These challenges are mainly due to kinematic redundancy, dynamic nonlinearity and high flexibility. This paper aims initially at designing a hard class of continuum robots, namely, cable-driven continuum robot (CDCR) and equally at developing their kinematic and dynamic models.Design/methodology/approachFirst, the CDCR prototype is constructed, and its description is made. Second, kinematic models are established based on the constant curvature assumption and inextensible bending section. Third, by using the Lagrange method, the dynamic model is derived under some simplifications and based on the kinematic equations, in which the flexible backbone’s elasticity modulus was identified experimentally. Finally, the static model of the CDCR is also derived based on the dynamic model.FindingsNumerical examples are carried out using Matlab software to verify the static and dynamic models. Moreover, the static model is validated by comparing the simulation’s results to the real measurements that have been provided with satisfactory results.Originality/valueTo reduce the complexity of the dynamic model’s expressions and avoid the numerical singularity when the bending angle is close to zero, some simplifications have been taken, especially for the kinetic energy terms, by using the nonlinear functions approximation. Hence, the main advantage of this analytical-approximate solution is that it can be applied in the bending angle that ranges up to 2p with reasonable errors, unlike the previously proposed techniques. Furthermore, the resulting dynamic model has, to some extent, the proprieties of simplicity, accuracy and fast computation time. Ultimately, the obtained results from the simulations and real measurements demonstrate that the considered CDCR’s static and dynamic models are feasible.

Maximizing the Effectiveness of Integrated Reservoir Studies: Practical Approaches to Improving the Process and Results

Modeling travelers’ mode choice behavior is an important component of travel demand studies. In an effort to account for day-to-day dynamics of travelers’ mode choice behavior, the current study develops a dynamic random effects logit model to endogenously incorporate the mode chosen for a day into the utility function of the mode chosen for the following day. A static multinomial logit model is also estimated to examine the performance of the dynamic model. Per the results, the dynamic random effects model outperforms the static model in relation to predictive power. According to the accuracy indices, the dynamic random effects model offers the predictive power of 60.0% for members of car-deficient households, whereas the static model is limited to 43.1%. Also, comparison of F1-scores indicates that the predictive power of the dynamic random effects model with respect to active travels is 47.1% whereas that of the static model is as low as 15.0%. The results indicate a significant day-to-day dynamic behavior of transit users and active travelers. This pattern is found to be true in general, but not for members of car-deficient households, who are found more likely to choose the same mode for two successive days.

This paper aimed to apply (dynamic and static) actuarial models to calculate the balanced contribution rates for the planned (at the minimum age) retirement benefit of the General Social Security System, based on the original and substitutive texts of the reform proposed by Michel Temer’s government. Even with the regular increases in life expectancy and the long-term nature of the analyses, national studies on social security are typically based on the static mortality hypothesis. The relevance of this study is evident due to the demographic changes, particularly the increase in life expectancy, experienced by the Brazilian population in recent decades and which put in question the sustainability of the national pension system. The use of dynamic actuarial models allows for more accurate discussions about the future of social security, besides contributing to the still scarce national literature. Static and dynamic actuarial models were applied to a representative individual, adjusting mortality tables from the United Nations covering 1950 to 2100. It was verified that the actuarially fair rates calculated by the dynamic actuarial model are typically higher than those obtained by the static model, especially for women. This difference is expected to increase as gains in life expectancy become more influenced by the reduction in mortality at more advanced ages. Moreover, if the social security reform is approved (in accordance with either the original or the substitutive text), there are indications from the dynamic model that the contributions rates currently charged would be excessive for men. In turn, these rates would be excessive for women considering the original text, and closer to the actuarially fair value considering the substitutive text. The development, disclosure, and regular updating of official dynamic tables (whether for mortality or other biometric assumptions) are also recommended.

This paper describes the proposed re-development of the Galapagos Field, comprising the abandoned NW Hutton field and the Darwin discovery (Block 211/27 UKCS) which forms a southerly extension. The paper covers the initial concept and analytical evaluation, the static uncertainty model build, the dynamic model history-match, the iterations between static and dynamic modelling, the development subsea and well locations, the optimisation workflow of the advanced Flow Control Valve (FCV) completions in both producers and injectors and the facilities constraints. The redevelopment plan involved several multi-disciplinary teams. 20 years of production data from 52 wells were analysed to identify the production behaviour and confirm the significant target that provided the basis for the development concept selection. The full Brent sequence compartmentalised stochastic static model was based on reprocessed seismic plus 14 exploration and appraisal wells. Streamlines, uncertainty sensitivities and mostly good detective work honed a history match to RFT, BHP, PLT and oil and water production. P50, P90/P10 models were selected and over 100 FCVs optimised to deliver the profiles against an identified FSPO facilities’ constraints. Over 1,000 static models were delivered consisting of sheet sands, incised valleys and channels in heterolithic facies overprinted by a depth trend with appropriate uncertainty ranges. The high well count gave a tight STOIIP probabilistic range of 790/883/937 million stb. The early RFTs illustrated extreme differential depletion between Brent zones and subzones of the Ness. To history-match these the dynamic model retained the static model definition in the Upper Ness to capture the thin but extensive shales. The early 18-month depletion and the late steady production-injection phases were simulated separately in prediction mode and matched the Production Analysis estimated ‘future’ production giving confidence to the history matched model. The initial concept development of 4 subsea-centres, to cover the large field area, with an injector in each compartment proved a robust selection. The horizontal wells increase PI where needed and mitigate internal faulting. The optimisation of the FCVs significantly increased oil production from all zones and drastically reduced water injection and production so that the identified FPSO modifications were relatively modest. The final First Stage Field Development Plan consists of 11 producers and 6 injectors across developed and undeveloped areas confirmed robust P50 reserves of 84 million boe. Robust concept selection allowed for early identification of production units so that constraints and modifications could be accounted for within the economic model. The Galapagos field re-development plan is an excellent example of how detailed static and fully history matched dynamic models can lay the foundations for new technology like the optimisation of the FCVs to access bypassed reserves using significantly smaller production units with reduced requirements for power, compression, gas lift, pumping pressure, injection and production. In short, they shrank the facilities.

FGIIP (Field Gas Initially in Place) is one of the most essential elements in building dependable static and Integrated Asset Model (IAM). A good estimation of FGIIP will improve history matching and generate reliable forecast. The mature gas field producing under depletion mode is an ideal example where P/Z technique can fit well to re-estimate the FGIIP. Even more, the estimation is also important to narrow down FGIIP uncertainties that initially existed in static model. Reliable FGIIP estimation is achieved by performing multiple P/Z calculations. The process involves dividing reservoir into key areas and each area is represented by individual P/Z prior to summing-up all P/Z to get the total FGIIP. Several scenarios are developed by defining key areas based on permeability variation, areal distribution and PVT behavior. The best FGIIP estimation is then fed back into the static model to generate numerous realizations considering the static uncertainties to produce the same FGIIP. Static models with realistic distribution of properties and good history match are used in the IAM model to generate forecast. The giant onshore gas field is highly heterogeneous having permeability, lateral composition variation and dynamic interaction between wells. To ensure that the heterogeneity observed in the field is honored, multiple key areas are defined by making areal sectorization and lateral PVT variation when estimating FGIIP with P/Z approach. Communication between areas was evidenced from the sectoral P/Z. The field history matching was improved after applying the new estimated FGIIP. It was observed that the sectoral history matching both for production and pressure matches from some selected realizations built in static model resulted in better matches. Succinctly the re-evaluation of static derived FGIIP with P/Z method for the mature gas field was able to reduce the uncertainty range that initially existed. Incorporating the correct estimation of FGIIP in IAM has helped to yield reliable forecast and has enabled the asset to plan proper work programs for further field development. Analytical material balance with P/Z is very applicable for mature gas reservoirs producing under depletion mode. The approach is worth doing to narrow down the uncertainty range that was previously calculated. Moreover, the integration of analytical P/Z with static and dynamic model (IAM) has achieved more reliable forecasting of the mature gas field to proceed with further development plan.

A dynamic stiff string torque and drag (T&amp;D) model is presented that assumes steady state motion of the drillstring as its basis for calculations. Results are compared to previously published T&amp;D models that are based on static equilibrium. The novelty of the new dynamic model is the ability to solve T&amp;D operations of the entire drillstring from bit to top drive in reasonable time using standard engineering computers. The new approach is based on a 3D dynamic model of drillstring and BHA in an elastic borehole. It considers bending stiffness, torsional stiffness, contact forces, and friction with localization of contact points. A numerical method is described that has proven to have excellent convergence. Complete governing equations are provided and the method is described in detail to permit readers to replicate results. The dynamic model is compared to two static stiff string models. Comparisons are also provided for three conventional soft string models including the Lubinski-Paslay-Cernocky bending stress magnification factor. Three field case studies are presented for horizontal wells. One well is short radius with dogleg severity over 50 deg/100 ft and two wells are unconventional shale wells with doglegs up to 15 deg/100 ft. Predictions for surface torque and drag up and down for the new dynamic stiff string model are compared to the static stiff and soft string models. In many situations modeled the top-level results for surface torque and drag up/down are close enough for all six models to be within the uncertainty range associated with the commonly used, lumped-parameter friction factor. However, some major differences in hook load for sliding and slack off operations are observed, which are shown to be caused by differences in location and magnitude of contact force between the drillstring and wellbore. Further, significantly lower surface torque is predicted by the new dynamic stiff string compared to other models for one case history because of lower contact forces in the vertical section of the well. In fact, the key finding of this paper is that major differences are observed for contact forces for the new dynamic stiff string model compared to all five other models, including the two static stiff string models. These differences in contact forces are most significant when the drillstring has helically buckled or when doglegs in the wellbore are high. Contact forces have a large impact on local stress behavior, which is important for predictions of casing and drillpipe wear, drillstring fatigue, and failure points in the drillstring. Although several previous papers have published stiff string models there is no industry standard formulation. The main problem holding back the development of an industry standard stiff model is perhaps the complexity of the numerical algorithm and substantial running time. To address this problem, some previous stiff string models account for bending stiffness of the drillstring but not for radial clearance while others appear to model only portions of the drillstring as stiff. The new stiff string model accounts for bending stiffness and radial clearance for the entire drillstring while still giving reasonable computational times. For stiff string models the advantage of using a dynamic approach to solve the steady state position of the drillstring is mainly related to superior convergence of the numerical algorithm compared to static stiff string models because calculation of contact points is faster.

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

Staphylococcus aureus is an important pathogen and biofilm former. Biofilms cause problems in clinics and food production and are highly recalcitrant to antibiotics and sanitizers. Bacteriophage endolysins kill bacteria by degrading their cell wall and are therefore deemed promising antimicrobials and anti-biofilm agents. Depolymerases targeting polysaccharides in the extracellular matrix have been suggested as parts of a multi-enzyme approach to eradicate biofilms. The efficacy of endolysins and depolymerases against S. aureus biofilms in static models has been demonstrated. However, there is a lack of studies evaluating their activity against biofilms grown under more realistic conditions. Here, we investigated the efficacy of the endolysin LysK and the poly-N-acetylglucosamine depolymerase DA7 against staphylococcal biofilms in static and dynamic (flow cell-based) models. LysK showed activity against multiple S. aureus strains, and both LysK and DA7 removed static and dynamic biofilms from polystyrene and glass surfaces at low micromolar and nanomolar concentrations, respectively. When combined, the enzymes acted synergistically, as demonstrated by crystal violet staining of static biofilms, significantly reducing viable cell counts compared to individual enzyme treatment in the dynamic model, and confocal laser scanning microscopy. Overall, our results suggest that LysK and DA7 are potent anti-biofilm agents, alone and in combination.