Various reactor core models have been developed to address the specific requirements of different classes of submarines. The nuclear power plants for U. In each case, the design was developed in secret, but it is generally believed that they are all rather similar; the demands of the application usually lead to similar solutions.
Russia also has a small fleet of nuclear-powered icebreakers , whose reactors are thought to be essentially the same as those of the earliest Soviet submarines. As with naval vessels, the ability to operate without refueling is an enormous advantage for Arctic icebreakers. Prototypes of nuclear-powered commercial cargo ships were built and operated by a handful of countries in the latter half of the 20th century, but they were soon decommissioned.
These vessels did not operate very economically, and opposition to their docking in a number of major ports was also a factor in their decommissioning. The prototypes were powered by reactors of the pressurized-water type. The very first nuclear reactors were built for the express purpose of manufacturing plutonium for nuclear weapon s, and the euphemism of calling them production reactors has persisted to this day.
At present, most of the material produced by such systems is tritium 3 H, or T , the fuel for hydrogen bombs. Plutonium has a long half-life of approximately 24, years specific to plutonium , so countries with arsenals of nuclear weapons using plutonium as fissile material generally have more than they expect to need. In contrast, tritium has a half-life of approximately 12 years; thus, stocks of this radioactive hydrogen isotope have to be continuously produced to maintain the required stockpiles. The United States, for example, operates several reactors moderated and cooled by heavy water that produce tritium at the Savannah River facility in South Carolina.
The plutonium isotope that is most desirable for sophisticated nuclear weapons is plutonium If plutonium is left in a reactor for a long time after production, plutonium builds up as an undesirable contaminant. Accordingly, a significant feature of a production reactor is its capability for quick throughput of fuel at a low energy-production level. Any reactor that can be operated this way is a potential production reactor. It is believed that the early Soviet production reactors were the same sort, and the French and British versions differed only in that they were cooled with gas.
The first significant power reactor, the Calder Hall reactor in Cumbria, northwestern England, was actually a dual-purpose production reactor. Nuclear reactors have been developed to provide electric power and steam heat in far-removed isolated areas. Russia , for instance, operates smaller power reactors specially designed to supply both electricity and steam for heating to accommodate the needs of a number of remote Arctic communities , and in China as noted above , the megawatt HTR reactor supplies both heat and electricity for the research institution at which it is located.
Independent developmental work on small automatically operated reactors with similar capabilities has been undertaken by Sweden and Canada. Between and , the U. Army used small pressurized-water reactors to provide power for remote bases in Greenland and Antarctica. They were replaced with oil-fired power plants, but it is still technically feasible to employ nuclear power for such applications, as nuclear reactors require less fuel maintenance than a traditional fossil fuel plant and, in general, run at a consistently high capacity.
Small modular reactors being designed in the United States might offer unique capabilities such as remote operations, as noted above see Global status of LWR reactors. Reactors have been developed to supply power and propulsion in space. Between and , the Soviet Union deployed small intermediate reactors in Earth-orbiting satellites mostly in the Cosmos series for powering equipment and telemetry, but this policy became a target for criticism.
The United States launched only one reactor-powered satellite, in , but developmental activity continues for such possible deep-space missions as manned exploration of other planets or the establishment of a permanent lunar base. Reactors for these applications would necessarily be high-temperature systems based on either the HTGR or the LMR design but would use enriched fuel to last the entire life of a prolonged space mission.
A power cycle in space must be run at a very high temperature to minimize the size of the radiator from which heat is to be rejected. In addition, a reactor for space applications has to be compact so that it can be shielded with a minimum amount of material and reduce the weight during launch and space flight.
Nuclear reactor. Article Media. Info Print Print. Table Of Contents. Submit Feedback. Thank you for your feedback. Large volumes and complexity of the interactions of different flow and thermal structures make analysis a daunting task. Current major system analysis codes either have no models or only 0-D models for thermal mixing and stratification in large enclosures. The lack of general thermal mixing and stratification models in those codes severely limits their application and accuracy for safety analysis, especially for passively safe advanced light-water reactors ALWRs , where the primary system and containments are more strongly coupled Zhao and Peterson, The SASSYS code developed by argonne national laboratory ANL , only provides lumped-volume-based 0-D models that can only give very approximate results and can only handle simple cases with one mixing source Dunn et al.
However, the restrictiveness and shortcomings of such applications have been recognized and further research needed to extend the applications to large complex pool mixing systems as highlighted in the review report by ANL Kasza et al. Considering the limitations of the inadequate 0-D methods and the inefficient 3-D CFD methods, new accurate and efficient thermal mixing and stratification methods are needed to improve accuracy and reduce modeling uncertainties, especially for system safety analysis.
The uncertainty in these predictions could be only reduced by verifying the codes for different passive systems and relying more on experimental data. Treatment of the residual uncertainties e.
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The methods implemented so far for reliability assessment of passive systems do not consider dynamic failure of components or process. These pdfs are assumed to be invariant in time. In actual, the parameter variations from their nominal values could be time dependent. APSRA relies in calculating failure probabilities of components for treatment of variation of process parameters through classical fault tree and event tree. These methods only consider binary states of any component failure, i. Examples of such components are control valves and relief valves.
Some components do not fail directly; they fail after some considerable amount of time, while degradation of function is taking place during accident progression or otherwise. It may so happen that while one component is failing, it accelerates or induces some other component failure, which in turn may lead to system failure much before it is predicted.
To justify the effect of dynamism of failure of valves, let us take an example of a benchmark problem Aldemir, ; Deoss, ; Cojazzi, details of which can be found in the Supplementary Material. This system consists of a fluid containing tank, which has three separate level control units.
Figure 3 shows the diagram of the system. Each control unit control valve is independent of the others and has a separate level sensor associated with it. The level sensors measure the fluid level in the tank, which is a continuous process variable.
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Based on the information from the level sensors, the operational state of the control units is determined. Each flow control unit can be thought of as containing controller, which turns the unit on or off based on the signal from the level sensors, as shown in Figure 3. Failure of the system occurs when the tank either runs dry or overflows. Two cases of particular interest are as follows. Probability of overflow and dryout in these two cases is evaluated by methodology presented by Chandrakar et al. The results shown in Figure 4 clearly explains that probabilities of failure are very different when dynamism of valves are considered as compared to classical stuck open and stuck close fault considerations.
In addition, classical PSA tools consider component failure rates as constant. Failure rates based on these databases generally represent the failure of components, which are operating within the designed limits and are independent of process parameter effects. During extreme events such as that happened in Fukushima, components of passive systems may be subjected to extreme stress and can have the failure rates, which are much higher than the ones that are adopted from above databases. In such cases, the entire bath-tub curve may be shifted upward as shown in the Figure 6.
Also, the process parameter values at the time of operation will have a dominant effect on these failure rates and also on the sequence of the failure of components. In view of the above, there is a need of dynamic reliability analysis, which considers the evolution of process variable and their effects on component failure rates in reliability analysis. Dynamic reliability methods provide a framework for explicitly capturing the influence of time and process dynamics on scenarios and control actions simultaneously.
Dynamic reliability attempts to take into account the ordering and timing of events in the accident propagation, the dependence of transition rates and failure criteria on the process variable values and human operator actions. This can be achieved by integrating the methods for dynamic reliability, also known as dynamic PSA methods with currently developed methodologies for passive system reliability analysis.
Dynamic reliability analysis methods can be broadly categorized as a state transitions or Markov models; b continuous dynamic event trees DETs ; c direct simulation. A review of the dynamic reliability methodology development reveals that the first comprehensive continuous-time method is the continuous-event tree CET approach Devooght and Smidts, a , b ; Smidts, To account for the shortcomings of the CET method, continuous cell-to-cell mapping CCMT Tombuyes and Aldemir, and stimulus-driven theory of probabilistic dynamics Labeau and Izquierdo, were developed and implemented.
Since continuous time based methods are very computationally intensive, they could not be applied successfully to highly complex problems of real world. To overcome these computational issues, a discrete version of this method was developed Aldemir, However, these tools suffer the problem of handling and processing the huge amount of data generated during the analysis and they are computationally intensive. Integration of dynamic reliability analysis with T-H models is needed for the realistic evaluation of passive systems reliability.
In order to enhance capability of the present methodologies to capture the interaction between process parameters and dynamical evolution of system state, it is thus required to use the dynamic reliability methodologies like discrete DETs and advanced Monte Carlo simulations. Reliability methods for passive safety functions follow classical event tree approach for integrating the passive system failure probability into PSA.
As said earlier, passive systems performance and reliability heavily depends on the initial conditions, which in RMPS methodology, is considered only for a particular event sequence. However, in real accidental and transient situations, the boundary conditions, and process parameter variations may not necessarily follow the predicted event sequence considered in a classical event tree. The event sequence in actual can be very dynamic.
This dynamism of accident and transients can be attributed to several factors like varying operating conditions of reactor, subjective decisions of operator, hardware failure or their degradation with respect to time, and sever conditions generated by some unpredicted natural events like tsunami, earthquake, etc. It is to be noted here, even though passive systems do not need any human intervention for their operation, still the effect of subjective decisions of operator for other active systems working in combination with passive systems can have an adverse effect on passive system performance and reliability.
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In order to capture this dynamism in RMPS methodology, event trees must be replaced with discrete DETs, which can capture the combinations of interaction between the different scenarios with varying conditions of hardware functionalities and effects of human intervention. In addition, considering a time invariant pdf for all the process parameters for example, atmospheric temperature must be corrected accordingly. In addition, the hardware failure states considered in this methodology does not capture their dynamic failure behavior. In order to capture the overall dynamic behavior of passive system, APSRA methodology, must incorporate the dynamic reliability methodology in propagating the effect of component and hardware failures with respect to time.
In addition to the incorporation of dynamic reliability methodology for the hardware failure or degradation, fault tree representation must be modified to discrete dynamic event tree to capture the dynamic accident scenario and human errors. Broadly, the parameters affecting passive system performance can be classified into two types: a dependent parameters and b independent parameters.
Dependent parameters are the ones whose deviations depend upon the output or state of certain hardware or control units, example of such dependent parameters are pressure, sub-cooling, non-condensable gas. Many of dependent parameters are not independent to have their own deviations; rather they are correlated or interdependent Burgazzi, Independent parameters are the ones whose deviations do not depend upon certain components rather they have their own patterns and deviations, which cannot be predicted easily; example of such parameter is atmospheric temperature.
The dependence of system performance on these types of parameter is quite significant in many passive systems for example passive decay heat removal system. Performance of these systems is very sensitive to the water inlet temperature sink temperature or atmospheric temperature or the environment temperature. These parameters vary in time due to their evolution along the mission period of system operation.
Treatment of dynamic variation of such kind of parameters is another unresolved problem in reliability analysis of passive systems. As an example, let us look at the inlet water temperature variation Figure 7 for one of the natural circulation experimental facility in BARC Jain et al. One can easily infer from the data that this water temperature has seasonal and temporal variations.
Figure 7. Inlet water temperature variation for experimental natural circulation loop at BARC. To resolve the uncertainties in the reliability calculations because of assumptions around the parameters like atmospheric temperature, one has to build the models of such parameters from the data that has been continuously monitored around the applications of passive systems.
These parameters could be given as real-time data into the simulations once the models are built. Many of the advanced reactor concepts propose to adopt passive safety systems in order to enhance the defense-in-depth and make nuclear power plants inherently safe even during extreme events like earthquake, tsunami, and floods.
Passive safety systems are believed to be more reliable than the active safety systems because of elimination of the need for human intervention, avoidance of external electrical supply, etc. However, incorporation of these systems in the nuclear reactors needs to be tested adequately due to several technical issues; for example:. Evaluation of passive system reliability is a challenging task. It involves a clear understanding of the physics of the phenomena and failure mechanism of the system, which the designer must do before prediction of its reliability.
That introduces large uncertainties and errors when such codes are applied to evaluate passive system performance. It is observed that while these methodologies have certain features in common; but they differ significantly particularly in the treatment of deviations of process parameters from their nominal values and model uncertainty in best estimate codes which are paramount for evaluation of reliability of such systems.
Passive system performance is greatly affected by deviations of process parameters from their nominal values. In addition, components of passive systems can fail at any intermediate positions of operations instead of classical assumption of binary state failure. Current methodologies lack treatment of these dynamic failure characteristics of components of passive systems. It is also required to pay attention to the treatment of dynamic variations of independent process parameters such as atmospheric temperature in passive system reliability analysis in future.
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Forgot Password? Suggest a Research Topic. Introduction Ever since the inception of nuclear fission, nuclear energy is considered as one of the potential sources of energy for electricity production, which can eliminate or reduce the dependency of human beings on the conventional sources of energy. Category A In this category, passive systems do not have moving mechanical components or parts or any moving working fluids. Category B Unlike category A, these systems have moving working fluids.
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