**3. Conclusion**

Experimental parameters associated with cold plasma discharge at atmospheric air pressure for enhancing ethanol yield of *S. cerevisiae* has been successfully optimized in this research. The maximum theoretical ethanol yield of 0.49 g/g was predicted by the response model under three optimized parameters (1 min of exposure time, 26 V of power voltage and 9 mL of test sample volume), which was closely consistent with the experimental yield of 0.48 g/g. The model may be used as a reference for modulating the experimental parameters related with dielectric barrier discharge at air atmospheric pressure and a novel approach for improving ethanol yield in bio-manufacturing industry.

Furthermore, the potential mechanism that air cold plasma alters the cofactor metabolism of *S. cerevisiae* was explored by analyzing the changes in plasma membrane potential, cytoplasmic calcium concentration and the two cofactors of ATP and NADH. The sample of 1-min treatment presented a notable increment in plasma membrane potential, whereas the sample of 2-min treatment presented a distinct reduction in plasma membrane potential. In addition, the calcium concentrations for the samples treated by plasma for 1–5 min were remarkably improved prior to the beginning of the fermentation compared with that for the untreated sample. An increase of 7.0% in calcium concentration led to the remarkable reductions of 40% in ATP and 60% in NADH in the sample of 1-min treatment. At 9-h culture, the ATP concentration of treated sample for 1 min increased by 72%, whereas NADH concentration decreased by 88% relative to those of the control. Briefly, the mechanism that plasma promoted alterations in cofactor level in *S. cerevisiae* showed to be by improving the cell membrane potential, which then caused increases in cytosolic free Ca2+ concentrations within the cells, eventually enhancing microbial productivity. This may a potential and broad application in intensifying the biotransformation capability of microorganisms in the future.


*3.4. Plant–Soil Interaction*

Principal component analysis (PCA) did not reveal significant correlations between *B. prostrata* fresh and dry biomass and soil properties in 0–20 cm soil layers (Figure 4A). There were significant positive correlations between *B. prostrata* fresh and dry biomass

and the sum of salts and the sum of the contents of anions Ca2+, Mg2+, and SO4 2− in the 20–40 cm and, to a lesser degree, 40–60 cm soil layers (Figure 4B), as well as with K+ content in 40–60 cm layers (Figure 4C).

PCA revealed the negative dependencies of genetic polymorphism parameters (*P*, *H*e, and *H*o) on K+, Ca2+, and sulfate ions contents and, to a lesser degree, on the sum of salts and the sum of anions in the 0–20 cm soil layers (Figure 4D). In addition, a negative correlation was observed between *P, H*e, and *H*o from one side and Mg2+, K+, Ca2+, and SO4 2− contents in 20–40 cm soil layers from the other side (Figure 4E). There were no correlations between genetic polymorphism parameters and soil properties in the 40–60 cm layers (Figure 4F).

**Figure 3.** Genetic polymorphism in seven populations of *Bassia prostrata.* (**A**)—observed heterozygosity (*H*o) of polymorphic loci; (**B**)—expected heterozygosity (*H*e) of polymorphic loci; (**C**) polymorphic loci proportion of population (*P*), average (for all loci) observed heterozygosity (*H*o), and average (for all loci) heterozygosity (*H*e) of seven populations of *Bassia prostrata*.

**Figure 4.** Principle component analysis (PCA) of growth (**A**–**C**), genetic diversity (**D**–**F**) parameters of *Bassia prostrata*, and salinity of 0–20 cm (**A**,**D**), 20–40 cm (**B**,**E**), and 40–60 cm (**C**,**F**) soil layers. K+, Na+, Ca2+, Mg2+, Cl−, SO4 2−, HCO3 −—ions content in soil; Ss—sum of salts; Sa—sum of anions in soil; FW—fresh plant biomass; DW—dry plant biomass; W—water content in leaves; *P*—proportion of polymorphic loci; *H*o—observed heterozygosity; and *H*e—expected heterozygosity of *B. prostrata*.

#### **4. Discussion**

The habitats of *Bassia prostrata* in this study were characterized by significant diversity in the degree and chemistry of soil salinity; high salinity occurred at different soil depths (Table 1). *B. prostrata* has wide edaphic plasticity and can grow on various soil genesis, e.g., chestnut, light-chestnut alkaline soils, and solonetz, as well as on soil-forming rocks of different compositions, from light sandy to heavy loamy, stony, and gypsum [30,36].

Our results revealed differences in correlations between *B. prostrata* aboveground biomass accumulation and seed genetic polymorphism and the chemistry and degree of salinity of different soil layers. The genetic diversity level was affected by the salinity degree and the chemistry of the uppermost soil layers (0–20 cm, 20–40 cm), and biomass accumulation was mainly affected by the salinity of the 20–40 cm and 40–60 cm soil layers. Such differences may be associated with different seasons of aboveground biomass and seed pool formation. *B. prostrata* biomass accumulation (before flowering) occurs mainly in the summer, the hottest and driest season: 23–26 ◦C, 40–43% humidity, and 65.7 mm precipitation (Figure 1B). In the summer, the drying of the uppermost soil layers can be observed, and plants receive water and dissolved salt ions from lower soil layers, affecting biomass formation. Our study showed a positive dependence of *B. prostrata* productivity on the degree of salinity in 20–40 cm soil layers (Figure 4B). *B. prostrata*, as a halophyte, requires a certain amount of salt in the substrate for optimal growth [37] and has high productivity in soils with 20 dS/m (EC) salinity [31]. The content of the main plant nutrient K+ in seven soil habitats decreased from the upper to lower layers, whereas the Na+ concentration increased (means of K+/Na+ were 0.45 and 0.01 in the 0–20 cm and 40–60 cm soil layers, respectively; Table 1). Despite the fact that plants growing in saline habitats have acquired mechanisms that allow for selective uptake of K+ when Na+ dominates in the substrate [37], in *B. prostrata* plants, K+ content in tissues decreased when Na+ exceeded 100–200 mM NaCl [38]. Thus, the selective absorption of K+ from the 40–60 cm soil layer under conditions of increased competition with Na+ affects *B. prostrata* biomass accumulation in natural habitats (Figure 4C).

Ca2+ and Mg2+ ions are also essential mineral nutrients. Ca2+ is a universal signal in all eukaryotic cells and participates in many other cellular processes, for example, in the maintenance of cell membrane integrity, cation–anion balance, and osmoregulation [39,40]. Mg2+ is an activator of more than 300 enzymes, in particular, photosynthetic and respiratory ones, which are also needed for DNA and RNA synthesis [41,42]. It is well known that Ca2+ plays a protective role in a plant's response to salinity. Much less is known about the role of Mg2+ in the salt tolerance of plants [39]. However, it was shown that low concentrations of mixed salts with CaCl2, and MgSO4 are necessary for the successful seed germination of *B. prostrata* [43]. Our study showed that Ca2+ and Mg2+ contents contributed significantly to soil salinity in *B.prostrata* habitats (Table 1). Positive correlations between biomass accumulation and Ca2+ and Mg2+ contents in the 20–40 cm soil layer (Figure 4B) indicate their necessity for *B. prostrata* growth. The influence of magnesium in this soil layer can be associated with the optimal K+/Mg2+ ratio. The K+/Mg2+ ratio for soils and plant tissues is critical to maintaining optimal plant nutrition and, hence, plant productivity [42]. The K+/Mg2+ ratio (0.09 ± 0.03) in the 20–40 cm soil layer in *B. prostrata* habitats was less than that of the 0–20 cm soil layer (0.56 ± 0.17) but higher than that of the 40–60 cm soil layer (0.02 ± 0.01).

*B. prostrata* seeds are formed in autumn, during a cooler and rainier period (1–16 ◦C, 49–81% humidity, 77.1 mm precipitation; Figure 1B) when the upper soil layers are moist and plants receive water and dissolved salt ions from them. At the same time, the need for water decreases due to lower air temperatures and higher humidity. Therefore, the formation of seed genetic diversity in *B. prostrata*, upon which the future stability of populations in changing environments depends [12,44,45], is affected by the salinity level and ionic composition of the 0–40 cm soil layers. In heterogeneous environments, the processes of gene flow, mutation, and sexual reproduction generate local genetic variation, providing material for local adaptation [45]. The influence of soil factors such as soil type, pH, moisture, and soil layer depth on population genetic diversity has been demonstrated in different plant species [15,17–19]. A nine-year experiment on the influence of soil moisture and nitrogen, phosphorus, and potassium content in soil on allozyme frequency revealed an allele–habitat association in *Festuca ovina* [15]. It was found that in natural populations the *Pgi*-2-2 allele is

significantly associated with soil moisture and is affected by nutrient/water treatments [15]. Negative correlations between *B. prostrata* genetic diversity with inorganic ion content (except for Na+ and Cl−) and the sum of salts in the 0–40 cm soil layers (Figure 4D,E) indicate selection in favor of homozygotes. Since isozymes (allozymes) were also used in our study, a question arises regarding the functional significance of enzymes under selection. Loci *G6pd* and *Me* were the most polymorphic among the *B. prostrata* populations (Figure 3A). They encode the enzymes glucose-6-phosphate dehydrogenase (G6PD) and malik-enzyme (NADP-Me), respectively, which are associated with the regulatory nodes of dark respiration and photosynthesis. G6PD is a key enzyme in the alternative apotomous oxidative pentose phosphate pathway (OPPP), whose role is enhanced under stress [46]. Malik-enzyme is involved in photosynthesis and is especially active in C4 species, and it plays a vital role in the tolerance to salt stress [47]. The adaptive–compensatory reactions of plants under stress are always associated with additional energy costs, which leads to a change in the balance between photosynthesis and respiration [46]. Any shifts in this balance are reflected in the total plant productivity. Selection leads to local adaptation, and the strength of local adaptation depends on the strength of selection. Strong selection leads to strong local adaptation, which is significantly affected by landscape heterogeneity [48]. The negative influence of Ca2+, Mg2+, and SO4 2− contents in the 0–40 cm soil layer on heterozygosity indicates the formation of the local adaptation of *B. prostrata* to magnesium–calcium sulfate soil salinity. The detected level of sodium chloride salinity did not negatively impact seed genetic polymorphism (Figure 4D,E). This is probably due to the necessity of these ions in maintaining water balance in the aboveground organs of *B. prostrata* (Figure 4C).

#### **5. Conclusions**

Our study demonstrates that in natural habitats the productivity and seed genetic polymorphism of halophytes may be affected by the salinity of different soil layers. These differential plant responses to vertically heterogeneous soil salinity could be attributed to seasonal variables during biomass accumulation (summer) and seed formation (autumn). An excess of some ions in the uppermost soil layers can lead to increased local adaptation to a certain type of salinity and the appearance of genotype-environment associations. Genotype–environment association analyses may allow us to develop adaptive measures for natural resource management, pasture improvement, and the phytoremediation and restoration of lands with different salinity chemistries.

**Author Contributions:** Conceptualization, E.S., M.L., and K.T.; methodology, E.S. and M.L.; software, M.P.; validation, E.S., K.T. and M.L.; formal analysis, A.K.; investigation, E.S., A.K. and M.L.; resources, E.S. and K.T.; data curation, A.K.; writing—original draft preparation, E.S.; writing—review and editing, E.S., K.T. and M.L.; visualization, M.P.; supervision, K.T.; project administration, E.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Science and Technology Research Partnership for Sustainable Development (SATREPS), in collaboration with the Japan Science and Technology Agency (JST, JPMJSA2001) and the Japan International Cooperation Agency (JICA), and by the Ministry of Science and Higher Education of the Russian Federation (theme No.<PHONE_NUMBER>44-6) and FGUR-2022-001-<PHONE_NUMBER>664-8-4.1.4.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data are available from the authors upon request.

**Acknowledgments:** We are grateful to the administration of the Elton Regional Nature Park for the opportunity to conduct research in the park.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


**9.3. AVP and female human female depression and anxiety**

Much of the research on this topic is focused on the interaction between stress, AVP, and depression. Specific V1b receptor haplotypes are associated with protection against recurrent major depression in both males and females [193]. A more recent study has found the association between V1b gene variants, AVP single nucleotide polymorphisms (SNP's), and vulnerability to childhood onset depression in females [206, 207]. In a study of male and female depression patients, plasma AVP was highly correlated with depression in nontreated patients, but this correlation was not found in patients taking anti-depressants [191]. These studies suggest that the central AVP system is a valid target for treatments for depression and anxiety.


**3. Results and Discussion**

#### *3.1. Thermodynamic Analysis*

The amounts of the reactants and products under different roasting conditions were predicted using thermodynamic analysis. Two alkalis (NaOH and Na2CO3) were selected as additives to perform the thermodynamic analysis. The possible reactions and the changes in Gibbs free energy are shown in Equations (3)–(11) and Figure 4.

$$\text{Al}\_2\text{O}\_3 + 2\text{NaOH} = 2\text{NaAlO}\_2 + \text{H}\_2\text{O} \tag{3}$$

$$\text{MgAl}\_2\text{O}\_4 + 2\text{NaOH} = 2\text{NaAlO}\_2 + \text{MgO} + \text{H}\_2\text{O} \tag{4}$$

$$\text{Fe}\_2\text{O}\_3 + 2\text{NaOH} = 2\text{NaFeO}\_2 + \text{H}\_2\text{O} \tag{5}$$

$$2\text{AlN} + 2\text{NaOH} + 1.5\text{O}\_2 = 2\text{NaAlO}\_2 + \text{H}\_2\text{O} + \text{N}\_2\tag{6}$$

$$\text{Al}\_2\text{O}\_3 + \text{Na}\_2\text{CO}\_3 = 2\text{NaAlO}\_2 + \text{CO}\_2 \tag{7}$$

$$\rm MgAl\_2O\_4 + Na\_2CO\_3 = 2NaAlO\_2 + MgO + CO\_2 \tag{8}$$

$$\text{Fe}\_2\text{O}\_3 + \text{Na}\_2\text{CO}\_3 = 2\text{NaFeO}\_2 + \text{CO}\_2 \tag{9}$$

$$2\text{AlN} + \text{Na}\_2\text{CO}\_3 + 1.5\text{O}\_2 = 2\text{NaAlO}\_2 + \text{CO}\_2 + \text{N}\_2\tag{10}$$

$$\text{NaO}\_2 + \text{NaAlO}\_2 = \text{NaAlSiO}\_4 \tag{11}$$

**Figure 4.** Relationship of the changes in Gibbs free energy with the temperature for the reactions during the roasting process.

From the reaction curves (3)–(6), it can be seen that the Gibbs free energy of the reactions of Al2O3, MgAl2O4, Fe2O3 and AlN with NaOH were negative at room temperature, which indicates that the thermodynamic reaction conditions are sufficient. The Al2O3 of MgAl2O4 phase was combined with NaOH to obtain NaAlO2 and release MgO. Moreover, AlN was oxidized to generate non-toxic N2. The reaction curves (7)–(9) show that the Gibbs free energy values of the reactions were negative only at higher temperatures, which indicates that Na2CO3 was less active compared to NaOH. The reaction curve (10) shows that the thermodynamic conditions for the reaction of AlN and Na2CO3 to produce NaAlO2, CO2 and N2 were sufficient. From the reaction curve (11), it is clear that insoluble NaAlSiO4 was produced by the combination of SiO2 and NaAlO2, resulting in the loss of alumina in the SAD.

These results demonstrate that the reactions of the SAD with the two alkali additives (NaOH and Na2CO3) were feasible. All the Al-containing compounds (Al, Al2O3, AlN and MgAl2O4) in SAD can react with alkali additives to form readily soluble NaAlO2. In addition, the other impurities (Mg, Fe, and Si) generated insoluble species, thus achieving the separation and extraction of valuable aluminum elements from SAD.

#### *3.2. Effects of Different Factors on the Roasting System*

In order to ensure that the desired compounds are produced in the clinkers, the extent of the roasting of the mixed materials should be strictly controlled. Since both the alkali additives can thermodynamically react with SAD to produce the expected compounds, the effects of these alkali additives on the roasting system were examined.

The effects of different factors on the recoveries of Al and Na were studied by conducting a variety of experiments for each additive type, various roasting temperatures and ingredients depending on the n(N/A) (see Table 2 and Figure 5).

**Table 2.** List of roasting parameters studied in the experiments.

**Figure 5.** Recovery of Al and Na in roasting clinkers under different roasting conditions: roasting temperature with NaOH additive (**a**), n(N/A) with NaOH additive (**b**), roasting temperature with Na2CO3 additive (**c**), and n(N/A) with Na2CO3 additive (**d**).

3.2.1. Effects of Different Factors on the Recoveries of Al and Na

For the alkali additives, NaOH and Na2CO3, the recoveries of Al and Na in the clinkers under different temperatures are shown in Figures 5a and 5b, respectively. As is evident from Figure 5a,b, the roasting temperature had a significant effect on the recoveries of Al and Na. With the increase in the roasting temperature, the recoveries of both the Al and Na initially increased and then decreased. When NaOH was used as the additive, the recoveries of Al and Na reached the maximum values of 83.47% and 89.06%, respectively, at the roasting temperature of 950 ◦C. However, when Na2CO3 was used as the additive, the highest recoveries of Al and Na had values of 80.90% and 88.72%, respectively, at the roasting temperature of 1100 ◦C. Additionally, the recoveries of Al and Na were similar in the within ±50 ◦C interval of the optimum temperature, which indicates that the range of the optimum temperature was wide and simple to control.

On the one hand, the mixed materials primarily relied on the solid−solid reaction during the roasting process. The generation of less liquid phase in the clinkers at low roasting temperatures led to slow reaction rates, large porosity and poor dissolution performance. On the other hand, the generation of a large amount of liquid phase in the clinkers at high roasting temperatures resulted in the rapid volatilization of the alkali, low porosity and poor dissolution performance [28]. Therefore, the appropriate liquid phase and porosity of clinkers are available only at a certain roasting temperature, which offers a better reaction rate and dissolution performance.

The n(N/A) is a critical parameter which affects the extraction of the valuable Al element from SAD. The n(N/A) represents the amount of alkali additives which have been added to the system. For the additives NaOH and Na2CO3, the recoveries of Al and Na in the clinkers under different n(N/A) are shown in Figures 5c and 5d, respectively. As shown in Figure 5c, when the n(N/A) was increased from 1.0 to 1.2, the recovery of Al increased from 83.47% to 91.83%, whereas that of Na increased from 89.36% to 93.64%. When Na2CO3 was used as the additive, the n(N/A) of 1.3 was optimal for extracting NaAlO2, while the Al and Na recoveries were 90.79% and 92.03%, respectively. This could be due to the fact that the low quantities of alkali were not sufficient enough to adequately support the reaction of the aluminum-containing compounds (Al, Al2O3, AlN, and MgAl2O4) in SAD with Na2O to produce soluble NaAlO2, thus resulting in a poor dissolution performance of the clinkers. With the increase in the n(N/A), the recoveries of Al and Na gradually declined. This can be attributed to the production of some insoluble substances due to excessive amounts of alkali, which prevented the further dissolution of NaAlO2.

It is worth mentioning that the NaOH additive resulted in a lower roasting temperature and slightly higher Al and Na recoveries compared to the Na2CO3 additive under the optimal roasting conditions. However, due to the NaOH deliquescence, the mixed materials were able to easily absorb the moisture in the air during the experiment, thus causing the hydrolysis of the AlN in SAD and releasing a large amount of toxic ammonia. Additionally, the produced cylindrical samples quickly lost their strengths, forming into a slurry. Therefore, Na2CO3 was selected as the appropriate additive for the subsequent experiments.


**5. Conclusion**

A fundamental consideration is that there are two types of learners when it comes to the field of architecture: the future experts, learning the methods, gaining the knowledge, skills and expertise in the field - *learners in architecture* and the non-experts, lay public, learning to understand cause and effect relationships in space, regarding built, cultural and natural environment, gaining an insight into the field, and into the expert logic - *learners about architecture*. Efforts of one group cannot compensate for the efforts of the other when dealing with built environment and spatial interventions as they more often than not, when not in sync, result in misunderstanding, misinterpretation, contradictions, which arise in response to physical manifestations discernable in spatial problems and unsustainable practices.

Both groups of learners can benefit from architectural e-learning, each group requiring a particular set of e-learning environments, tools, contents for suitable learning settings and positive learning outcomes. While some aspects are shared among the groups, such as blended learning, constructivist principles and project based learning, the use of visual messages, visuospatial multimedia learning principles, and the use of e-learning platforms, other aspects are particular for each group of learners as a whole and yet more particular within the same general group given the specific targeted population (as in the case mentioned: primary education pupils or a group of people who want to build their own dwellings in the near future, or public gathered around a specific spatial interest, etc).

teachers and the interface a tool that they could use for teaching (among many other tools on offer i.e. R.A.V.E. Space project suggestions: Demsar Mitrovic et al., 2007). They also suggested and insisted on more open system that would allow them to insert their own contents (graphics, photographs, texts) related to local environment and its specifics (which also proves that they understand the notion of acting locally and that understanding/explaining sustainability through the cause-and-effect relationships in our immediate surroundings is of the essence). The time constraint is beyond the topics of the chapter but it has to be emphasized, the range of topics to teach is so vast and the competition of themes so fervid that teachers feel an enormous time pressure, mentioned very often, much more than questioning of their competency to teach so specific topics

At the institutional level – the management of the particular schools involved has been a very understanding and cooperative body to work with on the topics of spatial sustainability, often asking for more contents and pedagogical contributions that we were able to provide. The Ministry of the environment and spatial planning on the yet higher institutional level has expressed an interest to implement the interface nation wide at primary schools but the discussions did so far not progress beyond the agreement that it would be a useful contribution provided it surpasses the prototypical phase. The talks however resulted in discussions about the necessity to look into the spatial sustainability issues, common curricula and education through a project in a Targeted research

From the technological perspective the potential improvements point in the direction of adaptability, networking and wider variety of topics covered with multiple tasks per one topic, offering more possibilities to show complex issues and asses the gained knowledge, more feedback to learners informing them whether they are on the right track, how good

A fundamental consideration is that there are two types of learners when it comes to the field of architecture: the future experts, learning the methods, gaining the knowledge, skills and expertise in the field - *learners in architecture* and the non-experts, lay public, learning to understand cause and effect relationships in space, regarding built, cultural and natural environment, gaining an insight into the field, and into the expert logic - *learners about architecture*. Efforts of one group cannot compensate for the efforts of the other when dealing with built environment and spatial interventions as they more often than not, when not in sync, result in misunderstanding, misinterpretation, contradictions, which arise in response to physical manifestations discernable in spatial problems and unsustainable practices. Both groups of learners can benefit from architectural e-learning, each group requiring a particular set of e-learning environments, tools, contents for suitable learning settings and positive learning outcomes. While some aspects are shared among the groups, such as blended learning, constructivist principles and project based learning, the use of visual messages, visuospatial multimedia learning principles, and the use of e-learning platforms, other aspects are particular for each group of learners as a whole and yet more particular within the same general group given the specific targeted population (as in the case mentioned: primary education pupils or a group of people who want to build their own dwellings in the near future, or public gathered around a specific spatial interest, etc).

have they done and challenging them with the complex game-related tasks.

(Demsar Mitrovic et al., 2007).

**5. Conclusion**

programme scheme (Zupancic et al., 2009).

Following the development of e-learning (and distributed learning) *in* and *about* architecture through the literature (i.e. CUMINCAD, a database of resources on the topic of Computer aided architecture and education) and recent publications, it is apparent that the practice has not yet established a foothold in architectural schools' curricula, nor found a place in general education (very few reports and even fewer scientific publications on the topic). However we can still discern two patterns of slow but gradual introduction of e-learning into architectural education: one approaching e-learning in architecture holistically – developing the whole system, including curricula, contents, tools (or concepts of them) and e-platforms (i.e. showcase example in the chapter VIPA: Kipcak, 2007; IMLAB: Gatermann & Czerner, 2003; etc), the other tackling specific and individual issues of e-learning, either developing prototypical tools (i.e. showcase example in the chapter Eco Spatial Interface: Juvancic & Zupancic, 2008; virtual design studios – VDS, etc) or integrating and combining the existing ones into systems. Neither of them is yet in the phase where we could claim that e-learning is institutionalized. It seems that as in other fields, e-learning in architecture is caught between the resistance to curricular change and the established ways of traditional teaching4. Mizban & Roberts, (2006) have analyzed e-learning in architectural education and established that a majority of cases in their analysis have been technologically driven – as a 'test bed': trying out the advances in ICT and applying them to architectural teaching or trying out new ways of supporting creativity or simply to develop students' ICT skills - and did not origin from pedagogical needs. On the other hand, some joint projects, due to their nature of distributed partners, teacher, students and teaching involved, introduce e-learning without intentionally emphasizing it. Archi21 is an example of ongoing European project (Hunter et al., 2011) that fosters Content & Language Integrated Learning (CLIL). The content in this case is architecture. The (envisioned and in some cases already executed) learning has all the characteristics of blended learning, with LMS, virtual environment (as a place to learn- and collaborate- in) and other e-communication tools at their core. It seems that such interdisciplinary endeavours are a short-term roundabout to gradual and persistent integration of e-learning in architecture.

In more general, lifelong education the hindrance is not so much avoidance of e-learning practices (the use of Moodle is quite wide spread already, with certain examples of educational interfaces already embraced), but the lack of architectural and spatial sustainability related topics in the learning programmes.

Throughout the chapter we have highlighted the benefits of e-learning *in* and *about* architecture, summing them up as: the ability to promote different types of collaboration, enhance students' set of skills, facilitate a flexible access to multimedia, educational resources anytime and anyplace (Mizban & Roberts, 2006), but also helping teachers introduce topics they are not expert in to non-expert public, enhance the experience of learning, introduce and present complex cause and effect spatial relationships that we are unable to grasp with the traditional teaching tools, simulate and test the processes prior to their physical manifestation, help in efforts for sustainable building practices and support informed decision making.

There are also barriers that hinder the widespread of e-learning *in* and *about* architecture. The main are the lack of pedagogical demand for them as observed by Mizban & Roberts

4 There are exceptions to this rule in fellow field of design, where the university and consequently also the design programme within is based on the distributed (e-learning) principles that stand at its core of teaching and is thus institutionalized. Such an example would be The Open University, UK

E-Learning in Architecture: Professional and Lifelong Learning Prospects 181

Greeno, J.G. (1998). The Situativity of Knowing, Learning, and Research. *American*

Hunter, M., Chase, S., Kligerman, B. & Zupancic, T. (2011). ARCHI21: Architectural and

Juvancic, M. & Zupancic-Strojan, T. (2008). Towards effective interfaces for general

*techniques,* ISBN 978-0-9541183-7-2, Antwerpen, Belgium, September 2008 Juvancic, M., Zupancic, T. & Hocevar, M. (2011). Towards effective interfaces for non-

Kipcak, O. (2007). The VIPA Project. What is VIPA? In: *VIPA virtual campus for virtual space*

Manovich, L. (2002). *The language of new media,* reprint ed., MIT Press, ISBN 978-0-262632-55-

Marjanovic, L. & Svetina, M. (2004). Spoznavni in govorni razvoj v srednjem in poznem

Mason, R. & Rennie, F. (2006). *E-Learning: the Key Concepts.* Routledge, ISBN 0-415-37307-7,

Mayer, E.R. (2005). Multimedia Learning: Guiding Visuospatial Thinking with Instructional

McDonough, W. (1998). Declaration of Interdependence, In: *Dimensions of sustainability:*

Meskanen, P. (2007). Shaping Spaces; Architecture and Children – Teaching the values of

Mizban, N. & Roberts, A. (2006). The Place of E-learning in Architectural Education - A

Mullins, M., Zupancic-Strojan, T. & Juvancic, M. (2002) Experts and Users Efficiency and

Neville, A. (1999). The problem-based learning tutor: Teacher? Facilitator? Evaluator?

an imprint of Routledge London, ISBN 0-419-23620-1, New York

Design based Education and Practice through Content & Language Integrated Learning using Immersive Virtual Environments for 21st Century Skills, *Proceedings of the 29th eCAADe Conference: Respecting Fragile Places,* ISBN<PHONE_NUMBER>013,

architectural learning: eco-spatial educational interface for pupils, *Proceedings of the 26th eCAADe Conference: Architecture "in computro : integrating methods and*

professional architectural learning: Architectural educational interface for pupils.

*design provided for european architects*, Marek, C., Edition mono/monochrom, pp. 6-9,

otroštvu, In: *Razvojna psihologija*, Marjanovic, L. & Zupancic, M., pp. 408-427, Znanstvenoraziskovalni inštitut Filozofske fakultete, ISBN<PHONE_NUMBER>, Ljubljana Marjanovic, L. & Zupancic, M. (2004). Teorije psihičnega razvoja, In: *Razvojna psihologija*,

Marjanovic, L. & Zupancic, M., pp. 28-63, Znanstvenoraziskovalni inštitut

Animation, In: *The Cambridge Handbook of Visuospatial Thinking*, P. Shah & A. Miyake, pp. 477-508, Cambridge University Press, ISBN 978-0-521-00173-0, New

*architecture, form, technology, environment, culture*, Scott A., pp. 45-55, E & FN Spon,

space to children and youth. *Presented at R.A.V.E. Space Project Final Conference,*

Critical Review, *Proceedings of the 24th eCAADe Conference: Communicating Space(s)*,

Accuracy in the Presentation of Design Ideas Across Networks, *Proceedings of the 20th eCAADe Conference: Connecting the Real and the Virtual - design e-ducation*, ISBN

*Psychologist,* Vol. 53, No. 1, pp. 5-26, ISSN-0003-066X

*Javnost - The Public*, accepted for publication in 2012

Filozofske fakultete, ISBN<PHONE_NUMBER>, Ljubljana

Ljubljana, Slovenia, December 2007.

0-9541183-0-8, Warsaw, September 2002

ISBN 0-9541183-5-9, Volos, Greece, September 2006

*Medical Teacher*, Vol. 21, No. 4, pp. 393-401, ISSN 0142-159X

Ljubljana, September 2011

ISBN 3-9500731-8-3, Graz

3, Cambridge, Mass.

New York

York

Haralambos, M. (1989). *Sociology*, Unwin Hyman, ISBN<PHONE_NUMBER>872, London

(2006), the rigidity of curricula and inertia – "we have been doing it for years, why change it?" The students themselves often lack the motivation to acquire additional skills required to efficiently work in e-learning environment (students perceive additional learning as extra and non-essential workload). The rest usually consists of variety of technological issues. The systems are staying on the prototypical level, riddled with bugs and technological constraints hindering the usual design process or lacking the functionalities of common tools in use, making expressing oneself more difficult than in traditional learning, lacking the integration, having compatibility issues, problems with communication, bandwidth and simply put: things not working consistently and reliably - in effect, transferring the emphasis from the learning architecture to solving technological problems. All these reduce the motivation of learners and teachers even further. Overcoming the prototypical levels mean also overcoming institutional, investment, ICT barriers and requires a critical mass of teachers and learners seeing the benefits of-, feeling the pedagogical need for- and eventually using the e-learning in architecture.

On the path to maturity, however, there are many opportunities for widening the scope and usefulness of e-learning tools *in* and *about* architecture: introduction of networked learning, adaptive learning systems, extensive usage of graphic capabilities, integration of tools, and more, that the fast technological advance will offer us in the future.

## **6. References**

(2006), the rigidity of curricula and inertia – "we have been doing it for years, why change it?" The students themselves often lack the motivation to acquire additional skills required to efficiently work in e-learning environment (students perceive additional learning as extra and non-essential workload). The rest usually consists of variety of technological issues. The systems are staying on the prototypical level, riddled with bugs and technological constraints hindering the usual design process or lacking the functionalities of common tools in use, making expressing oneself more difficult than in traditional learning, lacking the integration, having compatibility issues, problems with communication, bandwidth and simply put: things not working consistently and reliably - in effect, transferring the emphasis from the learning architecture to solving technological problems. All these reduce the motivation of learners and teachers even further. Overcoming the prototypical levels mean also overcoming institutional, investment, ICT barriers and requires a critical mass of teachers and learners seeing the benefits of-, feeling the pedagogical need for- and

On the path to maturity, however, there are many opportunities for widening the scope and usefulness of e-learning tools *in* and *about* architecture: introduction of networked learning, adaptive learning systems, extensive usage of graphic capabilities, integration of tools, and

Abel, C. (1997). *Architecture and identity: towards a global eco-culture,* Architectural Press, ISBN

Bates-Brkljac, N. (2007). Investigating perceptual responses and shared understanding of

Chou, J. R. & Hsiao, S. W. (2007). A usability study on human-computer interface for

Demsar Mitrovic, P., Miklavcic, T., Resnik Planinc, T., Urbanc, M., Fridl, J., Simoneti, M.,

the Environment and Spatial Planning,<PHONE_NUMBER>-56-3, Ljubljana Dondi, C., Behnn, E. & Moretti, M. (2004). Why Choose a Game for Improving Learning and

Dondi, C., pp. 20-76, PABST, ISBN<PHONE_NUMBER>, Lengerich, Berlin, etc Fischman, J. (2011). The Rise of Teaching Machines, In: *The Chronicle of Higher Education, The*

Springer Verlag, ISBN 978-3-540-46246-0, Berlin Heidelberg

Birkhäuser, ISBN 3-7643-6659-1, Basel, Berlin, Boston

architectural design ideas when communicated through different forms of visual representations, *Proceedings of Information visualisation 2007 conference*, ISBN- 0-

middle-aged learnes, *Computers in Human Behavior*, Vol. 23, No. 7, pp. 2040-2063,

Sorn, M. et al. (2007). *R.A.V.E Space: project final report. Raising awareness of values of space through the education process,* Demsar Mitrovic, P. in Rihar, J. (ed), Ministry of

Teaching Processes? In: *Guidelines for Game-Based Learning*, Pivec, M., Koubek, A. &

*Digital Campus*, 8.5.2011. Available from http://chronicle.com/article/The-Rise-of-

4200 *Intelligent Computing in Engineering and Architecture*, I.F.C. Smith, pp. 241-257,

*Proceedings of the 21th eCAADe Conference: Digital Design*, ISBN 0-9541183-1-6, Graz,

Fruchter, R. (2006). The FishbowlTM: Degrees of Engagement in Global Teamwork, In: LNAI

Gatermann, H. & Czerner, J. (2003). Modular E-Learning-Environment for Architecture,

Gauzin-Müller, D. (2002). *Sustainable architecture and urbanism: concepts, technologies, examples,*

eventually using the e-learning in architecture.

978-0750607902, Oxford

Teaching-Machines/127389/

Austria, September 2003

ISSN 0747-5632

**6. References**

more, that the fast technological advance will offer us in the future.

76795-2900-3, Zurich, Switzerland, July 2007

Greeno, J.G. (1998). The Situativity of Knowing, Learning, and Research. *American Psychologist,* Vol. 53, No. 1, pp. 5-26, ISSN-0003-066X

Haralambos, M. (1989). *Sociology*, Unwin Hyman, ISBN<PHONE_NUMBER>872, London

Prensky, M. (2001). *Digital Games-Based Learning,* McGraw Hill, ISBN<PHONE_NUMBER>634, New

Quintana, C., Carra, A., Krajcik, J. & Soloway, E. (2002). Learner-Centered Design:

Rhee, C., Moon, J. & Choe, Y. (2006). Web Interface Consistency in E-learning*. Online*

Severin, W.J. & Thankard, J.W.Jr. (1992). *Communication theories: Origins, Methods, and Uses in*

Sherman, W. R. & Craig, A. B. (2003). *Understanding virtual reality: interface, application and*

Shneiderman, B. (1998). *Designing the user interface: strategies for effective human-computer interaction,* 3. Ed, Addison-Wesley, ISBN 978-0-201694-97-0, Reading Stager, G. (2005). Towards a Pedagogy of Online Constructionist Learning, *Proceedings of 2005 World Conference on Computers in Education*, Stellenbosch, South Africa, July 2005 Stibbe, A. & Luna, H. (2009). Introduction, In: *The Handbook of Sustainability Literacy: Skills for*

Strojan, T. Z. & Mullins, M. (2002). The Identity of Place in Virtual Design Studios. *Journal of*

Svetina, M., Istenic Starcic, A., Juvancic, M. , Novljan, T., Subic Kovac, M., Verovsek, S. &

Tos, N. et al. (2004). *Vrednote v prehodu III. Slovensko javno mnenje 1999-2004,* Tos, N. (ed), Fakulteta za družbene vede, IDV – CJMMK, ISBN 978-*3*-901941-23-8, Ljubljana Ucelli, G., Conti, G. & Klercker, A. J. (1999). Visualisation: The Customer's Perception,

Verovsek, S. & Juvancic, M. (2009). Identifying spatial values in the opinions of teenagers.

Verovsek, S., Juvancic, M. & Zupancic, T. (2011). Interpretation Model of Urban Space

von Glaserfield, E. (1991). An exposition of Constructivism: Why some like it radical, In:

Wenger, E. (1998). *Communities of Practice: Learning, Meaning and Identity*, Cambridge

Zupancic, T., Novljan, T., Juvancic, M., Verovsek, S., Subic Kovac, M., Istenic Starcic, A. &

Zupancic, T., Verovsek, S., Juvancic, M., Svetina, M., Novljan, T., Subic Kovac, M. & Istenic

in Slovenia, *AR - Architecture, Research*, No. 1, pp. 70-77, ISSN 1580-5573

*a changing world*, A. Stibbe, pp. 9-16, Green Books, ISBN 978-1-900322-60-7, Foxhole,

Zupancic, T. (2011). How Children Come to Understand Sustainable Development : a Contribution to Educational Agenda, *Sustainable development*, Vol. 20, No. 20, pp.

*Proceedings of the 17th eCAADe Conference: Computing from Turing to 2000*, ISBN 0-

Coherence, *Proceedings of the 29th eCAADe Conference: Respecting Fragile Places,* ISBN

*Constructivist Views of the Teaching and Learning of Mathematics,* R. B. Davis, C.A. Maher & N. Noddings, pp. 19-29, VA: National Council of Teachers of

Svetina, M. (2009). The concretization of the term sustainable spatial development for the assessment of child and juvenile awareness. *Urbani izziv*, Vol. 20, No. 1, pp.

Starcic, A. (2010). Education for Sustainable Development in the Built Environment

*Information Review*, Vol. 30, No. 1, pp. 53-69, ISSN 1468-4527

*design,* Morgan Kaufmann, ISBN<PHONE_NUMBER>530, San Francisco

*Architectural Education,* Vol. 56, No. 1, pp. 15-21, ISSN 1046-4883

*Mass Media*. Longman, ISBN<PHONE_NUMBER>637, New York

Dartington, Totnes, Devon, UK

9523687-5-7, Liverpool, September 1999

978-9491207013, Ljubljana, September 2011

*Urbani izziv*, Vol. 20, No. 1, pp. 164-174, ISSN 0353-6483

University Press, ISBN 0-521-66363-6, Cambridge New York

1-10, ISSN 0968-0802

Mathematics, Reston

153-163, ISSN 0353-6483

Reflections and New Directions*,* In: *Human-Computer interaction in the new millennium,* Carroll, J. M., pp. 605-626, ACM Press, ISBN<PHONE_NUMBER>471, New York

York


**4. Results**

This section, which presents the main results obtained in this work, is divided into two subsections in order to differentiate between type 3 and type 4 validation test cases. For each field test shown in Table 2, results will be addressed in two different ways: (i) three figures with the time series of the three measured (in black) and simulated (in blue) key parameters (*u*, *p* and *q*), as well as the error time series (in red); (ii) one table summarizing the three validation errors (*xME*, *xMAE* and *xMXE*) at each validation window.

#### *4.1. Type 3 WT Validation Test Cases*

This subsection discusses the validation results for the DFIG field tests: test ID 1, test ID 2 and test ID 3.

#### 4.1.1. Test ID 1

Figure 4 shows the results of test ID 1, which was performed on a DFIG WT with a dc-link chopper as active protection device, i.e., a type 3A WT. The measured voltage profile shown in Figure 4a was obtained through the connection of a series impedance at the FRT mobile test unit before the measurement starts, which is disconnected at *t* = 4.12 s.

Regarding the active power response, Figure 4b, a considerable constant deviation is observed between field and simulation when the fault was cleared (*tclear* = 2.05 s) and the active power recovery ramp has finished. This deviation is due to the far greater complexity found in the pitch model and torque controller in the actual WT compared to the simplified generic IEC WT model. Therefore, a significant validation error was found for the average value during the post-fault period, as observed in Table 3, *pME* = *pMAE* = 0.09 pu. This active power oscillation also occurs because the drive-train model of the real WT is more complex than the two-mass model considered for the generic WT model. Nevertheless, it can be observed that the oscillation frequency fits properly.

Regarding the reactive power response, Figure 4c, the IEC generic WT model generally emulates the behavior of the actual WT with great accuracy. However, a negative reactive power peak appears in the field at the fault clearance due to the transformer inrush current, which is a non-linear effect that cannot be properly represented by transformer RMS models. Therefore, as observed in Table 3, mean reactive power errors are considerably low (≤0.01 pu), while the maximum error is large (0.15 pu) due to the disconnection of the series impedance of the FRT test unit.

**Figure 4.** *Cont*.

**Table 3.** Validation errors for test ID 1, in pu.

#### 4.1.2. Test ID 2

Test ID 2 presents the second field case performed by the vendor Siemens–Gamesa. Figure 5 shows the results of this test, while Table 4 provides the calculation of the validation errors. It is worth noting that the voltage dip characteristics of test ID 2 were quite similar to those of test ID 1, with the main difference being the loading condition of the WT and the WT topology.

**Figure 5.** Test ID 2 results.

**Table 4.** Validation errors for test ID 2, in pu.

Firstly, regarding the voltage profile, Figure 5a, the identical response between simulation and field, as also found in Figure 4a, should be highlighted. This is due to the implementation of the play-back validation approach.

The active power response, Figure 5b, shows a slight deviation between field and simulation due to the difficulties of representing exactly the same active power delivery during the voltage dip. This is because the actual WT integrates a particular active power limitation algorithm that cannot be represented with the generic WT model. In addition, the power dynamic in the actual WT during the voltage dip is slower than in continuous operation and this different dynamic behavior cannot be represented by the IEC model. This also has an impact on the ramp-up once the fault is cleared. Furthermore, the real WT absorbs active power at the fault clearance (*tclear* = 2.22 s), which cannot be properly emulated by the generic WT model due to the simplification in terms of transients. Therefore, the active power validation errors have a significant value during both fault and post-fault periods, as shown in Table 4.

Furthermore, reactive power validation errors also present a larger value in comparison to test ID 1, which is directly related to the crowbar dynamics, as observed in Figure 5c at both fault inception and fault clearance.

#### 4.1.3. Test ID 3

Figure 6 shows the results of the last field test performed on a DFIG WT. Specifically, this WT is a Senvion MM series WT that implements the same IEC model type as that used for test ID 1, i.e., Type 3A. In addition, both the WT loading condition and the residual voltage were almost identical. Table 5 summarizes the validation of test ID 3.

In this field test, the grid was modeled by the full system simulation approach of IEC 61400-27-1, as commented in Section 3. Figure 6a compares both the measured and the simulated voltage at the wind turbine terminals, where the three-phase voltage dip occurs at *t* = 1.05 s. Due to the reactive current infeed of the WT during the dip, the voltage level rises. In contrast to tests ID 1 and ID 2, a small hysteresis was observed in the measured voltage at voltage dip clearance (*tclear* = 2.02 s), which cannot be represented by the simulated voltage due to the lack of hysteresis in the transformer model.

The eigenfrequency between active power measurement and simulation shown in Figure 6b is quite similar during both the fault and post-fault period. Specifically, the simulated response is almost identical to the measured one during the fault period, which causes a notably reduced validation error: *pME* = *pMAE* = 0.01 pu. However, a delay was identified in the measurement at fault clearance, which is caused by the power converter operation (further details are provided in [16]). This power converter effect was not considered in the generic IEC WT model.

**Table 5.** Validation errors for test ID 3, in pu.

**Figure 6.** Test ID 3 results.

#### *4.2. Type 4 WT Validation Test Cases*

This subsection discusses the validation results for the full-scale converter WT topology: tests ID 4, ID 5 and ID 6.

#### 4.2.1. Test ID 4

The WT used for the test ID 4 was model *3.4M* (*Pn* = 3.4 MW), belonging to the manufacturer *Senvion*. It is represented by IEC 61400-27-1 as a Type 4A WT model. The grid was the same as that used for test ID 3, which was modeled by the full system simulation approach.

**Figure 7.** Test ID 4 results.

Figure 7 shows the results of validation test ID 4. A three-phase voltage dip with a residual voltage of 0.5 pu occurred at *tf ault* = 1.05 s and ends at *tclear* = 1.54 s, Figure 7a. As previously mentioned, this residual voltage was increased by the reactive current infeed during the voltage dip. In addition, a small hysteresis effect of the transformer is observed on the measured voltage (in black) at fault clearance. However, the hysteresis was not implemented in the transformer model and, hence, this effect cannot be reproduced by the simulation (in blue).

The measured active power, Figure 7b, fluctuates according to the wind speed variations. However, the simulated active power is set to rated power because the wind speed was assumed to be constant, according to IEC 61400-27-1, as stated in Section 1. The fit of the active power during the fault period was reasonably accurate: *pME* = *pMXE* = 0.01 pu. However, a small deviation in the active power ramp is observed once the fault is cleared. This was caused by the simplifications included in the generic WT model, which should be neglected for power system stability studies.

Furthermore, the reactive power, Figure 7c, which was controlled according to the voltage level, also presents quite an accurate adjustment between field and simulation. There was a small deviation in the reactive power at fault clearance due to the already mentioned hysteresis effect in the measurement, which was not represented in the generic WT model. This effect was not considered in the IEC validation methodology because it was a question of transients, which were outside the scope of system stability studies, as commented in Section 2.

Finally, Table 6 summarizes the validation errors estimated for test ID 4, where it can be observed that the representation of the generic IEC type 4A model is reasonably accurate before, during and after the voltage dip.

**Table 6.** Validation errors for test ID 4, in pu.

#### 4.2.2. Test ID 5

Figure 8 shows the validation results for test ID 5, which are provided by an ENERCON E-82 WT model with 2 MW rated power. For the simulation case, the generic IEC type 4A model was used, implementing a full system validation approach.

Figure 8A shows a three-phase voltage dip down to 25% of the nominal voltage. The WT was operating at rated active power and zero reactive power. As can be observed, the series impedance of the FRT container was switched on at *t* = 1 s. The short-circuit impedance was switched on at *tf ault* = 3 s and the fault duration was 1.5 s (*tclear* = 4.5 s).

Once the FRT series impedance is connected, a small deviation is found in the reactive power response, Figure 8c. This is caused by an additional voltage regulation of the actual WT, which is not represented by IEC 61400-27-1 type 4A model. When the fault occurs, the WT starts injecting reactive power according to the adjusted factor *K* = 2, as detailed in Equation (5), where *Iq* represents the reactive current, *In* the nominal current, *U*+ the positive sequence voltage, *Un* the nominal voltage and *U*0 the reference voltage.

$$
\Delta I\_{\overline{q}} = K \cdot I\_{\overline{n}} \cdot \frac{-\Delta l I\_{+}}{\mathcal{U}\_{n}} \text{; with, } \Delta l I\_{+} = \mathcal{U}\_{+} - \mathcal{U}\_{0} \tag{5}
$$

As observed in Figures 8b,c, quite an accurate fit between the simulation and the measurements was observed during the fault period. This implies that the current limitation model was well represented by the generic type 4A model. However, a transient transformer effect is shown when the fault is cleared, which cannot be accurately emulated. In summary, considerably accurate validation results were obtained for both active and reactive power, as summarized in Table 7.

**Figure 8.** Test ID 5 results.

**Table 7.** Validation errors for test ID 5, in pu.

#### 4.2.3. Test ID 6

Test ID 6 provides the validation results of an ENERCON E-126 WT with 6 MW rated power, as shown in Figure 9. As for test ID 5, the generic IEC type 4A model was used in a full system validation approach.

Figure 9a shows a three-phase voltage dip down to 75% of the rated voltage. The WT was operating at partial active power and zero reactive power. As observed in Figure 9a, the series impedance of the FRT container was switched on at *t* = 1 s, the short-circuit impedance is switched on at *tf ault* = 2 s and the fault duration is 3 s. In fact, ID 6 was the test with the longest dip duration.

Once the short-circuit occurred, quite an accurate response of both active and reactive power was observed, Figure 9b,c, respectively. As in test ID 5, the WT turbine starts injecting reactive power when the fault occurs according to the adjusted factor *K* = 2, as detailed in Equation (6), where *UUVRT or OVRT* defines an additional dead band for the reactive current calculation and the other parameters are the same as those defined for Equation (5).

$$
\Delta I\_{\rm q} = K \cdot I\_{\rm n} \cdot \frac{-\Delta l I\_r}{\cdot \mathcal{U}\_{\rm n}}; \text{with, } \Delta l I\_l = \Delta l I \pm \left( \mathcal{U}\_{\rm n} - \mathcal{U}\_{\rm lIVRT \, or \, \mathcal{UVRT}} \right). \tag{6}
$$

A small constant deviation between the simulation and the reactive power measurements is found during the fault period, Figure 9c. In contrast, higher deviations are observed for active power, Figure 9b. These deviations were due to the current injection method of the actual WT, which was based on a regulation algorithm that includes the dc-link voltage. In fact, the dc-link part and the regulation algorithm were not represented by the generic IEC type 4A model. Once the fault is cleared, some saturation effects are observed in the measurements, which were not represented by the generic transformer model.

Finally, Table 8 summarizes the key validation errors for test ID 6. As observed, very low validation errors were found for active and reactive power during both fault and post-fault periods.

**Figure 9.** *Cont*.

 **Figure9.**Test ID6results.

**Table 8.** Validation errors for test ID 6, in pu.


**3.2 Investigation of aquatic ecosystem**

*Water Chemistry*

**2. Experimental**

sum the of NH4

+ , NO2

**3. Results and discussion**

**3.1 Backgrounds of the present study**

**2.1 Reagent and algae cell culture**

blue-green algae, iron salt, aluminum salt, permanganate potassium composite, or calcium phosphate precipitation was employed as a coagulation reagent [31–34]. In the calcium phosphate precipitation method, phosphate ion was intensively precipitated with calcium ion by addition of sodium hydroxide [34]. Blue-green algae are coagulated by calcium phosphate precipitation due to that the algae cell surface is negatively charged [35, 36]. The coagulate substance is suitable for algae biomass

As described above, most of chemical remediation methods have problems as the environmentally friendly method. In the present study, we propose a water chemical remediation (WCR) system for simultaneous removal of phosphate ion and blue-green algae from the surface water of the anthropogenically eutrophied pond [37]. The system employs calcium chloride dihydrate for both precipitation with phosphate ion and coagulation with blue-green algae (**Figure 4**). The calcium chloride dihydrate has no ecotoxicity, low toxicity reagent (LD50 2045 mg/kg, rat male/oral), and widely used as food additive and snow-melting agent in Japan. Further, calcium chloride has high water solubility (74.5 g/100 mL at 20°C); the feature is able to reduce the amount of the reagent. For the study, we had been investigating the water quality of the anthropogenically eutrophied pond from

Calcium chloride dihydrate was purchased from Wako Chemicals, and the other chemicals were used for analytical grade. The growth of *M. aeruginosa* NIES-87 as a model of blue-green algae: *M. aeruginosa* NIES-87 was purchased from the National Institute for Environmental Studies and grown by two-step incubations. As preincubation, *M. aeruginosa* was grown in a test tube with 10 mL of MA medium until reaching stationary phase (120 rpm, 27°C, light-dark cycle of 12 h) [39]. As present incubation, the growth medium of the preincubation was added to a glass incubation bottle containing 700 mL of MA medium and grown under 27°C for 2 weeks.

Concentration of phosphate ion (PO4-P) was determined by the molybdenum blue method (JIS K-0102). Inorganic nitrogen (inorganic-N) value was calculated from

a pH electrode (model, IOL-50, DKK, Japan). Dissolved oxygen (DO) was measured using a DO meter (model, MM-60R, TOA-DKK, Japan). Chemical oxygen demand (COD) was measured by a potassium permanganate method. Total organic carbon (TOC) was measured using a TOC meter (model, TOC-5000A, Simadzu, Japan).

When the author was a doctoral student, the author studied on enzyme biosensors for phosphate ion in the reserved water for drinking [40, 41]. Then after, the author had interests to the phenomenon of anthropogenic eutrophication [38] and subsequent organic pollution [1] and also had interests to environmental protection

− concentrations. The pH value was measured using

production due to contain calcium phosphate precipitation as nutrient salt.

2006 to 2009 [38]. The results are also shown in the present study.

**2.2 Instruments and methods for water quality monitoring**

−, and NO3

**26**

The pond investigated in this time is on the site of the university and is an adjustment reservoir with an area of 20,000 m2 , an average depth of ca. 1 m, a maximum depth of ca. 3 m, and a storage capacity of about 20,000 kL. Most of the pond water is the inflow of rainwater that has fallen into the university. In addition, the surplus of the treated sewage that is not used for the regeneration of toilet flushing water is discharged to the pond as drainage. In addition, the pond bottom is covered with a rubber sheet to prevent the penetration of the pond water [38].

In this pond, water blooms were often observed by the rise of the water temperature (**Figure 2d**). In 2006, the thick layer of the water bloom on the surface water had been seen at the corner of the pond (**Figure 5**). Using a microscope, two types of blue-green algae in the form of spherical *Microcystis* sp. and filamentous *Planktothrix* sp. were observed from the surface water. Subsequently, the ecology of aquatic animals was investigated. In the case of hypertrophic water body, DO is consumed by aerobic respiration of blue-green algae at night, and then the water body changes to reductive environment [14]. In such reductive condition, survival of small fishes becomes more difficult than that of large fishes. In fact, only large carps and large turtles were observed by visual observation of the water surface. Then, we tried to capture small aquatic organisms such as small fishes using a cell bottle, a four-way net, and casting net. As a result, small fishes such as *Cyprinus carpio* and *Carassius* were observed. Therefore, it was found that the pond maintained the water quality necessary for the small aquatic organisms to survive.

#### **3.3 Periodic monitoring of water quality**

Periodic monitoring was conducted twice a month to examine several items related to weather conditions, basic properties of water quality, eutrophication, and organic pollution. Water sampling was conducted by collecting reservoir water with a depth of about 10 cm at 10:30 am.

**Figure 6.**

*Periodic monitoring for indications of basic water quality (2008) [38]. \* The Ministry of the Environment (Japan), water-pollution standard, Appendix 2.*

**Figure 7.**

*Periodic monitoring for indications of eutrophication (fiscal years between 2006 and 2009) [38]. a) Phosphate ion and b) inorganic nitrogen. \* The Ministry of the Environment (Japan), water-pollution standard, Appendix 2.*

In the indications of the basic water quality, the values of both pH and DO are influenced by the intensive photosynthesis of blue-green algae (**Figure 6**). The annual average values of both pH and DO were pH 9.2 ± 0.32 and 14.2 ± 4.4 mg O2/L, respectively. The pH value was above the environmental standard, and the DO value indicated that it was supersaturated. It was speculated that these water quality conditions were caused by intensive photosynthesis of the blue-green algae [13].

In the indications of eutrophication, the concentrations of phosphate ion (PO4-P) and inorganic nitrogen (inorganic-N) exceeded each environmental standard value in most cases (**Figure 7**). In general, intensive growth of blue-green algae consumes these nutrient salts [44]. Nevertheless, such reduction of nutrient salt concentrations was not observed in this pond. The results supported that the treated sewage flowing into this pond always supplies these nutrients.

**29**

**Figure 9.**

*Water Chemical Remediation for Simultaneous Removal of Phosphate Ion and Blue-Green Algae…*

In the indications of organic pollution, the values of COD and TOC did not exceed each environmental standard value in most cases (**Figure 8**). The results showed that the organic substances did not suspend so much in the surface water, although measurement sample was filtrated using 0.45 μm filter as pretreatment. However, it was found that sludge had been accumulated on the bottom of the pond

*The Ministry of the Environment*

A coagulation test was performed using growth medium of *M. aeruginosa* NIES-87. CaCl2 solution (0.1 M) of 250 mL and was added to 600 mL of the growth medium, and distilled water was finally added to become 1 L in a measuring

*Constitution of a sand filter for pre-examination. (a) Growth medium of M. aeruginosa NIES-87, (b) coagulation of M. aeruginosa using CaCl2, (c) enlarged view of the coagulation, (d) micrograph of*

*M. aeruginosa coagulated (×20), (e) M. aeruginosa filtered, and (f) filtrate.*

**3.4 Coagulation test and simultaneous removal tests**

*Periodic monitoring for indications of organic pollution (2008) [38]. \**

*(Japan); water-pollution standard, Appendix 2.*

*DOI: http://dx.doi.org/10.5772/intechopen.88490*

(data not shown).

**Figure 8.**

*Water Chemical Remediation for Simultaneous Removal of Phosphate Ion and Blue-Green Algae… DOI: http://dx.doi.org/10.5772/intechopen.88490*

**Figure 8.**

*Water Chemistry*

**Figure 6.**

*Periodic monitoring for indications of basic water quality (2008) [38]. \**

*(Japan), water-pollution standard, Appendix 2.*

**28**

**Figure 7.**

*Appendix 2.*

blue-green algae [13].

*ion and b) inorganic nitrogen. \**

In the indications of the basic water quality, the values of both pH and DO are influenced by the intensive photosynthesis of blue-green algae (**Figure 6**).

*Periodic monitoring for indications of eutrophication (fiscal years between 2006 and 2009) [38]. a) Phosphate*

*The Ministry of the Environment (Japan), water-pollution standard,*

*The Ministry of the Environment*

In the indications of eutrophication, the concentrations of phosphate ion (PO4-P) and inorganic nitrogen (inorganic-N) exceeded each environmental standard value in most cases (**Figure 7**). In general, intensive growth of blue-green algae consumes these nutrient salts [44]. Nevertheless, such reduction of nutrient salt concentrations was not observed in this pond. The results supported that the

The annual average values of both pH and DO were pH 9.2 ± 0.32 and 14.2 ± 4.4 mg O2/L, respectively. The pH value was above the environmental standard, and the DO value indicated that it was supersaturated. It was speculated that these water quality conditions were caused by intensive photosynthesis of the

treated sewage flowing into this pond always supplies these nutrients.

*Periodic monitoring for indications of organic pollution (2008) [38]. \* The Ministry of the Environment (Japan); water-pollution standard, Appendix 2.*

In the indications of organic pollution, the values of COD and TOC did not exceed each environmental standard value in most cases (**Figure 8**). The results showed that the organic substances did not suspend so much in the surface water, although measurement sample was filtrated using 0.45 μm filter as pretreatment. However, it was found that sludge had been accumulated on the bottom of the pond (data not shown).

## **3.4 Coagulation test and simultaneous removal tests**

A coagulation test was performed using growth medium of *M. aeruginosa* NIES-87. CaCl2 solution (0.1 M) of 250 mL and was added to 600 mL of the growth medium, and distilled water was finally added to become 1 L in a measuring

#### **Figure 9.**

*Constitution of a sand filter for pre-examination. (a) Growth medium of M. aeruginosa NIES-87, (b) coagulation of M. aeruginosa using CaCl2, (c) enlarged view of the coagulation, (d) micrograph of M. aeruginosa coagulated (×20), (e) M. aeruginosa filtered, and (f) filtrate.*

cylinder (P:Ca = 1:100 in molar ratio). Then, *M. aeruginosa* was coagulated immediately (**Figure 9**). After leaving for half a day, the medium was used for subsequent simultaneous removal tests.

A medium of 100 mL flowed into a sand filtration column which was prepared for three columns (**Figure 10**). The column was designed for reverse cleaning to recover both phosphate ion and *M. aeruginosa* from the sample. The results showed the possibility as a simultaneous removal of phosphate ion and *M. aeruginosa* (**Table 1**). Next, we examined using the pond water in the same way. As a result, phosphate ion in the pond water (adjusted to pH 10.5) could be removed 65.8% from 0.719 to 0.246 ± 0.0023 mg/L (*n* = 3). Based on these results, we next constructed a simultaneous removal system.


**8. References**

[1] Atlas User's Manual (2011) Device Simulation Software, Silvaco Inc.

[2] Sentaurus Device User Guide (2009) Synopsys Inc., v. C-2009.06.

[3] Colinge J-P (2008) FinFETs and Other MOSFET Multi-Gate Transistors, Springer.

	- [4] Gimenez S P, Bellodi M (2010) Diamond SOI MOSFET. PI0802745-5 A2, Publication Date: 03/23/2010, (RPI 2046).

**Chapter 14**

© 2012 Lăncrănjan et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

**Numerical Simulation of Passively**

**Q-Switched Solid State Lasers**

I. Lăncrănjan, R. Savastru, D. Savastru and S. Micloş

Short laser light pulses have a large number of applications in many civilian and military applications [1-10]. To a large percentage these short laser pulses are generated by solid state lasers using various active media types (crystals, glasses or ceramic) operated in Qswitching and/or mode-locking techniques [1-10]. Among the short light pulses laser generators, those operated in Q-switching regime and emitting pulses of nanosecond FWHM duration occupy a large part of civilian (material processing - for example: nanomaterials formation by using ablation technique [3,7]) and military (projectile guidance over long distances and range finding) applications. Basically, Q-switching operation relies on a fast switching of laser resonator quality factor Q from a low value (corresponding to large optical losses) to a high one (representing low radiation losses). Depending on the proposed application, two main Q-switching techniques are used: active, based on electrical (in some cases operation with high voltages up to about 1 kV being necessary) or mechanical (spinning speeds up to about 1 kHz being used) actions on an optical component at least, coming from the outside of the laser resonator, and passive relying entirely on internal to

the laser resonator induced variation of one optical component transmittance.

Figure 1 displays a general schematic of electronic energy levels of laser active centers embedded in an active medium and of saturable absorption centers embedded into solid state passive optical Q-switch cell laser oscillator operated in passive optical Q-switching regime [11-18,26]. As laser active centers, rare earth (RE) triple ions included by substitution into crystalline or poly micro-crystalline structure of the solid state active medium are used to a large extent. The four electronic energy levels of a laser active center can be observed together with the transitions between them, transitions which constitute the laser emission

and reproduction in any medium, provided the original work is properly cited.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/47812

**1. Introduction**

**2. Theory**


**3. Results**

This section presents the results of the model from the SHYFEM-TSS setup between 2016 and 2019, including detailed analyses of the surface circulation, vertical water mass structure, and validation against the observations. All the time mean results used here are averaged over the whole period of the model simulation unless stated otherwise. Time series are presented of the volume transports at the two gateways of the Marmara Sea— northern and southern parts of the Bosphorus Strait, and northern and southern parts of the Dardanelles Strait.

#### *3.1. Mean Surface Circulation and Water Mass Structure*

Figure 2 shows the mean of the surface speed averaged over the simulation period. The Bosphorus Jet reaches the southern coast of the Marmara Sea (Bozburun Peninsula) and turns west towards the middle of the basin. The mean speed of the jet is around 0.6 m/s at the exit of the Bosphorus into the Marmara Sea. The jet separates into two branches due to the Imrali island at the west of the Bozburun Peninsula. The upper branch, which is also the main branch, also splits into two different branches. One part of the upper branch forms an anticyclonic gyre in the northern part of the Marmara Sea, whereas the second part crosses the Marmara Sea after turning south and exits the Marmara Sea through the Dardanelles Strait. The lower branch splits at the Imrali Island, circulates in between the Marmara islands and merges with the upper branch to exit the Marmara Sea.

Previous studies have shown similar circulation patterns [5,17,21]. One of the numerical studies which was lacking wind forcing demonstrated a cyclonic gyre in the central basin due to potential vorticity injection from the Bosphorus Jet [19]. This finding is similar to another previous study where a cyclonic circulation has been found in the Arctic Ocean using an idealized barotropic model [30]. However, the mean state in our model, with a more realistic forcing, is different from those idealized simulations. The mean circulation

pattern in the central Marmara Sea was persistent with different strengths throughout the simulation.

**Figure 2.** Averaged surface speed between 2016 and 2019. Stream lines are shown in black lines.

The mean kinetic energy (MKE) and eddy kinetic energy (EKE) fields are shown in Figure 3. We decompose the model velocity into low-pass mean (*U*) and fluctuation values (*u*) with respect to this low-pass mean (i.e., *u* = *U* + *u*), and compute MKE as 0.5 × (*U*2 + *V*2) and EKE as 0.5 × (*u*2 + *v*2). We used a 60 day low pass filter, which is approximately the mean residence time at the surface layer [5], to compute *U*. The mean kinetic energy field presents intensities on the rim current in the Black Sea, the Bosphorus Jet, the current out of Dardanelles, and the cyclonic gyre in the Marmara Sea. These regions are also evident in the mean speed field in the previous figure. The EKE, which represents the mesoscale eddy kinetic energy field, differs from the MKE, especially in the Marmara Sea and the northwest of the Aegean Sea. The magnitude of the EKE field is comparable to that of the MKE field. In the Marmara Sea, the EKE field is high around the Bosphorus Jet, Izmit Bay, and southern shelf. MKE decreases and EKE increases downstream of the Bosphorus Jet where it starts to turn to the west in the Marmara Sea. This indication of energy for the EKE field is provided by the release of energy of the mean flow through baroclinic instability processes. There is another high EKE region in the northwestern part of the Aegean Sea exit of the Black Sea water from the Dardanelles. Once again, there is an energy conversion from the MKE field into the EKE field.

**Figure 3.** (**a**) Time-averaged mean kinetic energy (MKE); (**b**) time-averaged eddy kinetic energy (EKE) computed from four years of model velocities.

The sea surface temperature (SST) averaged over four years ranges from 15 ◦C at the northern part of the Black Sea to 21 ◦C at the eastern part of the Aegean Sea (Figure 4). The cold jet flow coming from the Bosphorus can be easily traced to the southern coast of the Marmara Sea. The jet is carrying the Black Sea water, approximately 16 ◦C, into the Marmara at the surface. This is consistent with the mean circulation mentioned above. One of the interesting features in the simulation is the eastern part of the Aegean Sea, outside of the Dardanelles Strait and Saroz Bay. The cold water upwelling process in the summer months dominates the averaged SST field in these regions.

**Figure 4.** The mean sea surface temperature for 2016–2019.

Our model shows that the mean sea surface salinity (SSS) changes within 17 psu in the Black Sea and 38 psu in the Aegean Sea (Figure 5). SSS in the Marmara Sea (MS) ranges between 20 psu (Bosphorus exit) to 27 psu (west part of the MS). There is also a meridional gradient where SSS is around 22 psu in the northern MS and 25 psu in the south. Previous observations suggested that surface salinity is in a range of 23 ± 2 psu in the Marmara Sea, using the long-term measurements from 1986 to 1992 [5]. However, both our model simulation and recent observations show higher salinity ranges in the Marmara Sea (see the Water Mass Validation section).

**Figure 5.** The mean sea surface salinity for 2016–2019. Note that the colorbar is not linear.

Figure 6 shows the vertical section of time averaged salinity and temperature fields across the section shown in Figure 1 (red line). This cross section follows the deepest part of the Bosphorus and Dardanelles Straits and also passes through three deep basins of the Marmara Sea. The interface depth between the fresh-warm surface layer and salty-cold lower layer is at around 25 m in the central basin of the Marmara Sea. The salinity below 50 m becomes uniform in the Marmara Sea due to the influence of the Mediterranean Sea. However, during the simulation period, deeper layers (i.e., below 200 m) were getting modified with the relatively cold Aegean Sea water coming from the Dardanelles (temperature field at 150 km in Figure 6). There is a distinct cold intermediate layer (CIL) extending from the Bosphorus Strait into the Marmara Sea at the halocline depth in the model. Previous observations also reported a cold temperature anomaly around 11–12 ◦C at the same depth in June–July between 1996 and 2000 [13]. We conclude that the model successfully reproduces CIL, which is an important feature for the Marmara Sea water mass structure.

The two-layered water mass structure can also be seen in the Dardanelles Strait (first 100 km of the section shown in Figure 6). The mean salinity and temperature vertical structure are similar to those of Sannino et al. [19] and Aydogdu et al. [21]. In the Bosphorus Strait, salinity in the bottom layer is mixed with the ambient water when the layer flows into the Black Sea at the northern exit (section between 320 km and 400 km shown in Figure 6). Previous observations and numerical studies reported a similar salinity distribution [9,14,19,21,31]. The flow in the Bosphorus Strait has two distinct hydraulic control locations which increase the amount of mixing between two layers. The cold upper layer of the Black Sea water mixes with the relatively warm bottom layer of the Marmara Sea water and merges into the CIL at the southern side of the Bosphorus Strait. Surface layer velocities are higher at the southern exits of the Dardanelles and Bosphorus Straits compared to their northern exits. This is due to increasing exit widths in the straits and also contraction regions. For more details of the hydraulic controls in the Bosphorus and Dardanelles Straits, see Sozer and Ozsoy [14], Sannino et al., [19] and references therein.

**Figure 6.** The mean salinity (**left**) and temperature (**right**) for 2016–2019 along the thalweg section shown as a red line in Figure 1.

#### *3.2. Water Mass Validation*

We used four data sets of in situ salinity and temperature observations which were collected by RV TUBITAK Marmara of the TUBITAK Marmara Research Center in order to validate the simulation. These observation datasets covered all over the Marmara Sea and were collected in different seasons; for the Summer 2017 on 8–16 August, for the Winter 2018 on 15–30 January, for the Spring 2018 on 24 April–3 May, and finally for the Summer 2018 10– 28 August within the framework of the "Integrated Marine Pollution Monitoring 2017–2019 Program" by the Turkish Ministry of Environment and Urbanization and TUBITAK-MRC.

In each cruise, conductivity, temperature, and depth (CTD) measurements were done from the surface up to the sea bottom at the pre-defined same 90 stations at the speed of 16 Hz. The accuracy rates of the sensors were 0.001 ◦C and 0.0003 S/m for temperature and conductivity, respectively. After quality control, the high-resolution data were averaged to 1 m bin size during the post-processing for later use.

Figure 7a shows the spatial distribution of the interpolated sea surface temperature field in August 2017 (summer cruise). The black dots represent the station locations. The interpolation was performed using the Data-Interpolating Variational Analysis (DIVA) method. There is an approximately 3 °C temperature gradient between the east and west of the Marmara basin. Relatively cold Black Sea water ( ≈23 °C) has spread to the exit of the Bosphorus Strait (northeast side of the basin). However, the cold water at the coast of the Bozburun Peninsula (southeastern side of the basin) is probably due to an upwelling event, since the salinity at the same location is much higher than the Black Sea brackish water (Figure 8a). Observations show that the western part of the Marmara Sea is around 26–27 °C. Figure 7b shows the model SST field in August 2017. Cold jet water can be seen at the eastern side of the domain. One of the prominent features of the model is the cold upwelling at the Saroz Bay, north of the Dardanelles. The northern coast of the Marmara Sea is one degree warmer in the model than in the observation.

(**a**) Observed surface temperature field interpolated over the Marmara Sea.

(**b**) Surface model temperature field.

**Figure 7.** Sea surface temperature field for the observations and model for the summer 2017 Marmara cruise.

Vertical salinity/temperature bias and root mean square (rms) error profiles compared to all observations are shown in Figures 9 and 10, respectively. The mean surface salinity bias improves over time. The largest bias in all seasons is around the mixed layer depth (20–30 m). This is probably due to the missing representation of mixed layer dynamics

such as insufficient mixing. The surface temperature mean bias is around 0.1 °C in the top 10 m in August 2018. Similarly to salinity, temperature bias and rms error are highest around the seasonal thermocline depth. We believe that the bias below 40 m is due to the initial conditions.

The sea surface temperature at the interior of the Marmara Sea is close to the observed values. Figure 8a shows the sea surface salinity field from the observations. The surface salinity clearly shows a wide spread between 18 psu in the Black Sea entrance of the Bosphorus and 25 psu at the Dardanelles exit of the Marmara Sea. Once again, there is an east-western gradient in salinity in the basin. The exit of the Bosphorus and its surroundings have less than 21 psu of salinity, while the western side surface salinity is more than 24 psu. The low salinity jet flow can also be seen in the model simulation (Figure 8b). Interior salinity values of the model are similar to those in the observed field. The southern coast of the Marmara Sea is slightly saltier in the model due to the upwelling of the saltier deep waters to the surface. This is also likely due to relatively colder waters in the SST field.

(**a**) Observed surface salinity field interpolated over the Marmara Sea.

(**b**) Surface model salinity field.

**Figure 8.** Sea surface salinity field for the observations and model for the summer 2017 Marmara cruise

**Figure 9.** Vertical distributions of salinity (**left**) and temperature (**right**) bias in August 2017, January 2018, April 2018, and August 2018.

**Figure 10.** Vertical distributions of salinity (**left**) and temperature (**right**) RMS error in August 2017, January 2018, April 2018, and August 2018.

#### *3.3. Volume Fluxes through the Straits*

Figure 11 shows the daily and monthly averaged net, upper and lower layers' volume fluxes at different exits of the Bosphorus and Dardanelles Straits. The means of each time series are also shown in Table 1. Positive values indicate flows towards the Black/Marmara Sea for the Bosphorus/Dardanelles sections. Significant temporal variability can be seen in all sections. Daily fluctuations may be three times more than the mean value of the corresponding time series. The upper layer variability is higher than the lower layer along the Bosphorus Strait sections, while layer fluctuations are comparable in the Dardanelles. The net flow at the Bosphorus Strait reverses in the winter because of weakening and/or reversing of the upper layer. The lower layer at the Bosphorus Strait is at its minimum in the summer. Field observations show similar fluctuation differences and a reverse of the upper layer in the Bosphorus Strait [32]. The net volume transports in our model are around 140 km3yr−1 and 170 km3yr−1 at the Bosphorus Strait and the Dardanelles Strait, respectively. The difference may be due to the evaporation–precipitation (E–P) balance in the Sea of Marmara and numerical errors during the post-processing of the unstructured grid. Nevertheless, the net values are approximately half those of the historical estimation which is around 300 km3yr−1 and which is based on the conservation of mass in the Black Sea. However, net transport using direct measurements obtained at the exits of the

Bosphorus Strait is around 110–120 km3yr−1 [32]. We conclude that model transport values are within the uncertainty of the recent observations and other model studies.

The previous studies that obtained the net transport of 300 km3yr−1 assumed that maximum salinity values of the deep layer can be used to compute a two-layer budget analysis with (E–P) balance in the Black Sea [5]. Here, we use mean salinity values of the bottom and top layers at the northern and southern sections of the Bosphorus Strait and show that the net transport cannot be that high. The net transport (*Qnet*) is the difference between the top layer transport (*Q*1) and bottom layer transport (*Q*2) at the northern section

$$Q\_{\text{net}} = Q\_1 - Q\_2.\tag{1}$$

This is also true for the top layer transport (*Q*3) and bottom layer transport (*Q*4) at the southern section (i.e., *Qnet* = *Q*3 − *Q*4). We then follow the two-layer analytical model for the salt transport at each sections,

$$\begin{aligned} S\_1 Q\_1 - S\_2 Q\_2 &= 0 \\ S\_3 Q\_3 - S\_4 Q\_4 &= 0 \\ S\_1 Q\_1 - S\_2 (Q\_1 - Q\_{\text{net}}) &= 0 \\ S\_3 Q\_3 - S\_4 (Q\_3 - Q\_{\text{net}}) &= 0 \end{aligned}$$

where *Si* is the average salinity of the respected layer. The last two equations above are equal to each other, thus we get

$$(S\_1 - S\_2)Q\_1 + S\_2 Q\_{net} = (S\_3 - S\_4)Q\_3 + S\_4 Q\_{net}.\tag{2}$$

Change in transport of upper layer between sections (Δ*Q* = *Q*1 − *Q*3) is around 50 km3yr−1 both in our model and in the analytical solution [5]. In addition, in the model, the averaged salinity differences between the upper and lower layers at a cross section are approximately equal to each other (i.e., Δ*S* = *S*1 − *S*2 ≈ *S*3 − *S*4). Hence,

$$\frac{\Delta S \Delta \mathcal{Q}}{Q\_{\text{net}}} = (\mathcal{S}\_4 - \mathcal{S}\_2). \tag{3}$$

If we choose the maximum salinity values in a layer as used in [5], *S*1 = 17 psu, *S*2 = 35 psu, *S*3 = 20 psu and *S*4 = 37 psu, then Equation (3) satisfies *Qnet* = 300 km3yr−1. However, the averaged values in a layer are significantly different to the maximum ones because of the mixing in the interfacial layer. The results of the model show that Δ*S* is around 8 psu rather than 17 psu. This ensures that *Qnet* should be approximately 150 km3yr−1.

The layer transports at the Dardanelles are higher than shown by the model results provided by [21] but still lower than the historical estimates which are around 850/550 km3yr−1 for the upper/lower layer in the northern section and 1200/900 km3yr−1 for the upper/lower layer in the southern section, respectively [1,5,6,33]. This disagreement in layer fluxes may be due to an inaccurate representation of turbulent mixing and/or forcing along the open boundaries.

**Table 1.** Annual mean of net, upper layer, and lower layer volume fluxes for the whole simulation period at different sections of the Turkish Straits.

**Figure 11.** Daily upper layer (blue, UL), lower layer (red, LL), and net (grey, NET) volume transports through northern Bosphorus (**a**), southern Bosphorus (**b**), northern Dardanelles (**c**), and southern Dardanelles (**d**). Monthly averages are overlaid with a darker tone of the same color.

#### **4. Summary and Conclusions**

We have presented a new high resolution unstructured grid model for the Turkish Strait System using realistic atmospheric surface forcing and ocean lateral open boundary conditions. The SHYFEM-TSS model was run for four years and successfully reproduced

vertical water mass structure, horizontal circulation, and volume fluxes through the Bosphorus and Dardanelles Straits.

The model captures the buoyant jet coming from the Bosphorus into the Marmara Sea on the surface. There is an anticyclonic gyre in the center of the Marmara Sea due to this jet and its potential vorticity injection into the semi-enclosed sea. There is also another fast current flowing out of the Dardanelles into the Aegean Sea carrying relatively fresh Black Sea water to the Eastern Mediterranean Sea. Previous models also observed similar circulation patterns [5,17,21]. Both mean and eddy kinetic energy fields are high around the jets. The mean kinetic energy also shows the main branch of the flow pattern in the Marmara Sea. The eddy kinetic energy field is high around the islands of the Marmara Sea, indicating the so-called island effect, eddies generated due to topographic features.

The temperature and salinity profiles were compared with the observations, and bias/rms fields were computed. Our model performs well compared to the observation, and reproduces the two-layer structure in the Marmara Sea. The SHYFEM-Marmara captures the realistic distribution of the water mass structure. The largest bias is around the pycnocline depth which indicates less mixing in the model. However, it is also possible that small changes at the interface depth show larger errors especially in a two-layer system.

The volume transports at the Straits were also computed. However, the net Bosphorus transport is smaller than the historical value of 300 km3/year estimated based on estuary circulation equations using the maximum salinity of the layers [5], and agrees well with recent observation values 120 km3/year [32] measured by multiple bottom-mounted acoustic Doppler current profilers. When we computed the estuary circulation equations using the averaged layer salinity values from the model results, we also get the 150 km3/year which is in line with net transport in the model. The upper and lower layer transports in both Straits display significant variability at different time scales.

We have demonstrated that the new SHYFEM-Marmara model using a high resolution unstructured grid with realistic atmospheric forcing and lateral open boundary conditions is capable of simulating the main circulation and water characteristics of the Marmara Sea. The future of Marmara Sea modeling relies on the data-assimilation for forecasting capability and also coupling with a biological model to study the impact of man-made pollution coming from the river discharge into the Sea of Marmara.

**Author Contributions:** Conceptualization, M.I.; methodology, I.F., I.B., M.I.; validation, H.K., S.M., and M.I.; writing—original draft preparation, M.I.; writing—review and editing, M.I., G.C., N.P.; upstream data: S.A.C., E.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** M.I. is funded through a project named Development of the new Turkish Strait System Model for optimal interface between the Mediterranean and Black Sea models by ITUNOVA A.S., and is also supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under project number 120C137. This research was funded by the Copernicus Marine Environment Monitoring Service for the Black Sea Monitoring and Forecasting Center (Contract No. 72-CMEMS-MFS-BS) and the Mediterranean Sea Monitoring and Forecasting Center (Contract No. 74-CMEMS-MFC-MED). Partial funding through the Strategic project #4 "A multihazard prediction and analysis testbed for the global coastal ocean" of CMCC is gratefully acknowledged.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** this work has been used CMEMS data from the Med-MFC and BS-MFC, in particular analysis and forecasting products for the physical component of the Mediterranean Sea and the Black Sea (https://marine.copernicus.eu/ (accessed on 9 July 2021)).

**Acknowledgments:** The Authors thank the "Integrated Marine Pollution Monitoring 2017–2019 Program" carried out by the Turkish Ministry of Environment and Urbanization/General Directorate of EIA, Permit and Inspection/ Department of Laboratory, Measurement and coordinated by TUBITAK-MRC ECPI for the provisioning of the data used in this study for model validation.

**Conflicts of Interest:** The authors declare no conflict of interest.