Monitoring the service life of HRSGs
by: P.S. Rop
For many years, it has been common practice to employ a robust, service-life monitoring system
on a combined-cycle/cogeneration plant’s most valuable turbomachinery: the gas turbines (GTs)
and steam turbines (STs). To date, such monitoring systems typically have not been installed on
the plant’s heat-recovery steam generators (HRSGs).
Yet HRSGs, just like turbines, are exposed to large and rapid temperature changes, are critical
to maintaining plant capacity, and can create enormous repair and business-interruption losses
when unscheduled maintenance is required.
A service-life monitoring system doesn’t just measure temperatures and pressures, and
display a snapshot of them on a computer screen. Rather, it determines the long-term effects of
large temperature changes, as well as the effects of pressure changes and sustained operation at
high temperature levels, on the system’s most vulnerable components.
Armed with this information, plant operators can fully understand the effects of different
modes of operation, and can take corrective action to improve availability and extend service
life. Such information also helps planners determine the best time for equipment inspections, and
understand the expected maintenance requirements during upcoming outages.
In other words, by monitoring what is happening inside GTs, STs, and HRSGs operating
procedures can be optimized, maintenance can be better planned and probably even reduced, and
smarter economic decisions can be made regarding the benefit of production versus the cost of
increased maintenance and reduced service life. Given today’s trends toward more and more
cyclic operation, and the industry’s better understanding of cycling’s impact on plant equipment,
manufacturers and users should now extend the use of service-life monitoring systems from their
GTs and STs to their HRSGs.
How it works
A service-life monitoring system comprises three systems: a sensor system, a stress evaluator,
and a service-life damage evaluator. The sensor system records the necessary operating
parameters—localized temperatures and pressures. From these inputs, the stresses are calculated
at critical locations in the HRSG. Finally, the stress data are combined with operating history to
determine the impact on remaining service life.
Three criteria are used in evaluating the remaining service-life: fatigue, creep, and magnetite
layer stress. The first two are based on the fraction of service life that is degraded. During every
stress cycle or time interval at high temperature and stress, the material is degraded until finally
it cannot withstand the stress—either fatigue or creep—anymore. At this point, the material will
start to crack and ultimately collapse (Fig 1).
The third criterion is based on absolute stress level, though indirectly also on cycles. When
the absolute stress becomes too high at any one location, the magnetite layer—the oxide layer
that protects an HRSG’s internal surfaces, especially in the HP drum—will crack. At each crack
location, new magnetite will form, but only at the expense of base material, and with reduced
material strength. Each time the stress exceeds a limit, the magnetite layer will crack at the same
location and the penetration into the base material will grow further until a serious crack is
formed that substantially lowers the strength of the drum. How fast this will happen is difficult to
predict, so it is preferable to avoid exceeding the stress limit at all locations.
Virtually all combined-cycle/cogen plants are unique designs. Even two side-by-side units
will not be perfectly identical, because of differences in site layout and geometry. This means
that behavior and responses in each HRSG will be different, thus each monitoring system will
have to be customized. For example, when geometries are not identical, the same temperature
differences in two HRSGs result in different thermal stresses. Therefore the calculation of stress
from the measured data must be customized.
For a cycling plant, fatigue is the primary criterion that will determine service life. So the
main focus should be on monitoring the largest stress ranges and their number of cycles. This
becomes even more important when you realize that fatigue is a logarithmic phenomenon-that is,
the allowable number of cycles, before a crack is initiated, is a logarithmic function of the stress
range. For this reason the allowable number of cycles is highly sensitive to the stress range. To
illustrate how sensitive, consider that when the peak stress range in a high-pressure (HP)
superheater outlet header is varied by 10%, the allowable number of cycles in that component
varies between -40% and +70%.
Bottom line: To get reliable results from a service-life monitoring system, both the input data
and the system evaluating that data must be as accurate as possible.
which component?
import measurements
+ geom. data
stress calculation
fatigue calculation
T>800°F
fatigue damage
no creep damage
creep calculation
creep damage
creep-fatigue
interaction
output fatigue
damage
output remaining
fatigue damage
output creep
damage
Fig. 1 Flow chart of a lifetime monitoring system for a HRSG. Magnetite criterion is not
incorporated.
A credible dynamic analysis
Once the decision is made to employ a service-life monitoring system for an HRSG, you first
must determine where the critical locations are. Critical means locations with the smallest
margins with respect to the three criteria explained earlier.
For the fatigue-damage criteria, the locations must be found with the most severe combination
of large stress variations and number of cycles. Because fatigue damage is for an important part
linked to thermal stresses, and because thermal stresses are caused by dynamic effects—
predominately transient temperature differences during startup and shutdown—a dynamic
analysis is the necessary starting point.
The term “dynamic analysis” has gained attention in recent years, and many HRSG designers
now are at least paying lip service to it. But to be useful, the dynamic analysis and the modeling
environment must be thorough, and must cover the entire range of startup and shutdown
conditions. Simplifications, quasi-static approaches, and the omission of thermodynamic
phenomena—such as local formation of condensate—have been attempted by some designers.
These attempts are not valid. For example, the maximum stress in an HP steam drum during a
cold startup occurs around 290 psia, which is only about 15% of the maximum pressure.
Dynamic linearization around the base-load condition, or a series of static cases around 15% of
the maximum pressure, clearly will not yield an accurate result.
A thorough and detailed dynamic model is important also because the peak stresses occur at
the sub-component level. For example, to simulate the temperature behavior of a tube-to-header
connection in an HP superheater module, the headers and the tube bundle have to be
distinguished separately in the dynamic model. Simulating only the superheater bundle is not
detailed enough, and therefore will not provide the proper temperature data for the stress model.
To keep to a reasonable number of details that must be modeled, you can perform an
assessment of the overall design to identify the possible critical locations. Modeling then can be
focused on these locations.
Calculating stresses
Once the critical locations are determined, their behavior must be known. Specifically, how do
the local temperature and pressure data translate into peak stress?
Finite-element analyses can help, particularly for the most critical spots. Such analyses assist
in determining how the peak stress develops as a function of different temperature histories (Fig
2).
Finite-element analyses also indicate how the critical parameters can best be measured. The
peak stress itself is mostly on the inside surface of a pressure part, and therefore is practically
impossible to measure. Actually, stress itself cannot be measured, only the related strain or
force—which is then used to calculate stress. So the measurement will always be of a derived
parameter on a location that is not the critical spot itself. Keeping the required accuracy in mind,
it therefore is important to select the right parameter to measure, to determine the right location
for it to be measured, and to properly relate that parameter to the peak stress.
Often the conditions in the undisturbed wall of the pressure part are used to derive a stress
situation in that part of the wall. Through a stress-concentration factor, the peak stress at the
critical location is then determined, based on the assumption that the peak stress and the
“undisturbed wall” stress are related. Essentially, the stress concentration factor describes this
relationship. Note, however, that this often is a complex relationship, not a simple multiplication
factor. This means designers must focus on truly understanding the relationship during all
possible operating conditions and transients.
Fig. 2 Maximum stress in a HP downcomer nozzle during a cold start. Clearly at a location that
is hard to monitor directly.
From studying the behavior of the peak stress and how it relates to the “undisturbed wall”
stress, it is possible to determine the best locations for parameter measurements. But it also is
important to determine the type of measurement and to specify the instrumentation. For example,
to determine the temperature profile through a drum wall, several temperature measurements
through the wall can be used. These include inside, mid-wall, and outside measurements.
But a reliable inside temperature measurement is difficult to achieve. To drill a hole from the
outside as close as possible to the inside wall, you must know with high accuracy the actual
localized wall thickness. Only then will we have a precisely defined thickness of the bottom of
the hole, hence will we achieve an accurate temperature measurement.
The temperature gradients are large in the first few millimeters extending away from the
inside wall, so every millimeter off the expected depth will yield considerable differences in
temperature measurements. To increase the accuracy, engineers can use additional measurements
that are derived from the process.
For example, on the steam side of an HP drum, the steam is condensing (as long as the steam is
warmer than the drum), therefore the inside temperature of the wall is very close to the saturation
temperature. Thus engineers can use the saturation temperature, which is easy to calculate from a
pressure measurement in the HP drum.
When cycling the hottest sections of the HRSG—the superheaters and reheaters—it is
important to monitor the effect of dynamic temperature differences between tube rows. When the
GT is ignited, a heat front will travel through the superheater and reheater towards the HP
evaporator (Fig 3). The tube rows warm up one-by-one, while not being cooled by steam (since
steam production has not started yet). This creates large expansion differences between tube
rows and bundles, as well as headers twisting or bending.
A lot of tube-to-header failures can be traced to stresses induced by these large differentialexpansion forces and bending moments that occur during every startup. It follows that accurate
measurement of the tube row temperatures is essential to a service-life monitoring system. Such
accuracy, however, comes at the cost of progressively more data that has to be handled and
interpreted.
Response of HPSH and RH tubes; cold start
600
500
Temperature [°C]
400
300
200
100
0
0
5
10
15
20
time [min]
25
30
35
40
Fig. 3 Temperature response of HPSH and RH tube rows during a cold start. Note the
maximum temperature difference between hottest and coldest row can be more than
280°C/505°F.
Interpreting the results
Finally, once the stresses and their cycles are determined, the impact on the material must be
established. More important, the impact must be presented in such a way that it can be used in
economic decision-making.
Often the damage resulting from fatigue and creep—plus their very important interaction—is
presented as a “damage fraction.” This assumes that every stress cycle or interval at high
temperature inflicts a certain amount of damage. After a 100% damage level is reached, the
material is predicted to collapse. The damage fraction tells you how far, linearly speaking, the
material is from failure.
In reality, the material’s behavior is not linear. It is more complicated than that. In fact, both
fatigue and creep are probabilistic phenomena. This means that a probability of failure develops
according to a certain function of the number of stress cycles and time at high temperature. That
particular failure represents an amount of money that will be lost when it actually occurs.
Therefore the probability of failure represents a “risk”—defined as the probability multiplied by
the consequence—which is a parameter that also can be expressed as an amount of money. That
amount of money can be compared directly with the benefits that are expected if the plant takes
that risk.
So instead of presenting a damage fraction, a robust service-life monitoring system can
present the chance of failure at a certain critical location. Combined with the cost of that failure,
it provides operators with an actual monetary risk, which can be taken into account when
deciding on a certain mode of operation. This approach requires good understanding of the
probability of failure and its behavior, and the cost of failure of the critical locations.
The latter might also be a function of the way the probability function develops. For example,
repairing a crack in a steam nozzle obviously is different than replacing that nozzle because of its
complete failure.
Nevertheless, presenting the data merely as a damage fraction can be quite useful because it
makes it easy to compare different modes of operation. This is particularly true at the beginning
of service life, when chances of complete failure are still small.
Ideally, a service-life monitoring system would be employed on new HRSGs, before they have
accumulated any run time. The difficulty in retrofitting such a monitoring system on existing
HRSGs is that you somehow must estimate the damage that has already been inflicted on each
unit since its initial startup.
One option might be to connect the service-life monitoring system to a dynamic model that
runs the operational data accumulated by the plant, from initial startup to the moment the
service-life monitoring system was installed. Depending on the chosen method of presenting the
data, an initial damage fraction, a probability of failure, or a risk could then be determined for
each of the critical locations.
Summarizing the points made
• A service-life monitoring system for an HRSG is essential to optimizing operating procedures
and to improving maintenance strategies.
• Since no two combined-cycle/cogen plants are perfectly identical, the service-life monitoring
system must be customized for each HRSG and balance-of-plant configuration.
• For cycling units, fatigue is the predominate damage mechanism that reduces service life.
Therefore, a thorough dynamic analysis of the HRSG and balance-of-plant must be performed to
identify the critical locations that must be monitored.
• For a service-life monitoring system to yield credible results, the sensitivity of fatigue damage
for stress requires the best attainable accuracy. This begins with proper measurements, which
allow calculation of the actual peak stresses.
• Peak stress not only depends on local effects at the component level, but also of interactions at
the sub-component level. Therefore, these interactions also must be monitored, requiring a
significant increase in the number of measurements that must be collected.
• Proper measurement of essential parameters requires extra attention. This is particularly true
for the many critical locations that are hard to reach, and therefore must rely on indirect
measurements.
• To gain a better insight into the damage mechanisms that are affecting your HRSG, you can use
several different methods for presenting the data. Both fatigue and creep are probabilistic
phenomenon that can be used to determine how the probability of crack initiation develops.
Translating this probability into a “risk” factor provides operators with an expected financial cost
of failure that can be weighed against the expected financial benefits of continuing to operate.
Published in Combined Cycle Journal 20007 Outage Handbook