Dr Monika Johanna Kreitmair

The presence of anthropogenic infrastructure within the ground under cities can act to increase the temperature of the subsurface in a phenomenon known as the subsurface urban heat island effect. Large city-scale numerical subsurface temperature models accounting for both the heat fluxes stemming from human-made structures as well as hydrogeological variation are often computationally prohibitive due to the vastly different scales of modelling required. We present a novel and scalable methodology for the determination of the subsurface thermal climate beneath urban centres at a holistic scale, grouping volumes of ground into archetypes according to common thermal behaviours, identified using supervised machine learning algorithms. We apply this methodology to two boroughs in the centre of London, UK, with the results showing good agreement with a higher-resolution model for one of the boroughs. We further demonstrate the scalability of the approach by modelling the boroughs sequentially, where the second borough was modelled at little to no additional computation cost at a loss of accuracy in the local depth -averaged temperature between 0.10 and 0.23 degrees Celsius. This methodology is an important step towards determining large-scale subsurface climate beneath cities, providing insights for various applications, such as an improved understanding of geothermal energy potential.

Introduction Ground source heat pump (GSHP) or shallow geothermal energy systems are gaining attention for helping combat global warming and the negative effects of urbanisation caused by human activities. In recent decades, energy geo-structure techniques have developed by using subsurface infrastructures to exchange heat with the ground. These techniques can provide space heating and cooling while still preserving the primary structural function. As a result, they have become a valuable part of geothermal energy systems. Researchers have developed an understanding of incorporating ground heat exchangers into foundation piles, retaining walls, and tunnel linings with modest additional cost [1-4]. Pavements are also structures in contact with the ground that have the potential to be used as energy geo-structures (i.e., geothermal pavements), yet, their use has not been extensively studied [5-8]. Soil thermal conductivity is an important factor influencing the efficiency of geothermal pavements since heat transfer in soils occurs primarily by conduction [9]. Microbially induced calcium carbonate (CaCO3) precipitation (MICP) is an innovative technique for strengthening sandy soils by coating and binding soil grains with calcium carbonate crystals. The distribution and arrangement of the CaCO3 within the pore spaces play a crucial role in determining the resulting strength of the treated sand. In addition, these crystals can act as thermal bridges to enhance the soil's thermal conductivity [10]. Combining geothermal pavements with MICP sand is still nascent, and the limited number of studies that exist mainly focus on the associated thermal property changes [11]. However, since the principal function of pavements is transmitting (dynamic) loads to the subbase and the underlying soil, the thermal conductivity of the MICP-treated pavement may vary as a result of the applied mechanical loads (e.g., due to the partial or total loss of thermal bridges and/or particle rearrangement). This research thus investigates the changes in the thermal conductivity of MICP-treated sands as they are subjected to quasi-static triaxial compression. The experimental results collected can deepen our understanding of the thermo-mechanical behaviour of MICP-treated sands and provide practical insights for using MICP to reinforce the subbase or underlying soil of geothermal pavements. Methodology This research performed a series of quasi-static triaxial tests on MICP-treated Houston sand, fine-grained, high-purity silica sand. Sporosarcina pasteurii (strain designation DSM 33) was used for the MICP treatment of the soil specimen. To study the effect of CaCO3 content on the thermo-mechanical performance of MICP-treated sand, three cementation solution treatment cycles were applied, yielding theoretical CaCO3 contents of 0.6%, 1.6% and 2.7% by weight, respectively. Details on the MICP treatment of the samples can be found in [12]. Samples for triaxial testing were treated in cylindrical tubes of 50mm inner diameter and 100mm height. To investigate the influence of the CaCO3 content on the soil thermal conductivity, MICP-treated specimens were air-dried prior to triaxial testing Triaxial tests were subsequently conducted in dry conditions to isolate the effect of the MICP and avoid the influence of water content on soil thermal conductivity (λ). Furthermore, a new miniaturised transient sensor was embedded in the triaxial samples to monitor the λ changes during the sharing phase [12]. Results An example of the evolution of the deviatoric stress with axial strain under 50kPa confining stress (σ3) in the triaxial cell is shown in Figure 1a. Compared to the untreated sand, MICP-treated samples lead to higher peak strength and stiffness. Importantly, λ changes during triaxial testing for different CaCO3 contents are summarised in Figure 1b. Results indicate that the increase in CaCO3 content can significantly improve λ, and that λ rapidly decreases post-peak strength due to dilation and CaCO3 bond breakage. Once the samples reach its ultimate state, λ remains unchanged.

Increasing urban development is leading to a growing demand for subsurface utilisation. As more infrastructure is built into the subsurface, heat from tunnels, sewers, and basements, among others, alter the thermal state of the ground, acting as sources and sinks of heat, leading to a net-increase of underground temperatures, a phenomenon known as the Subsurface Urban Heat Island (SUHI) [7-8]. This additional heat can have impacts on, for example, health and maintenance of underground structures, increased ventilation costs for underground spaces, and quality and quantity of groundwater flow [1,2]. However, this additional heat in the subsurface can also be harvested by ground-source heat pump (GSHP) systems to provide heating to buildings [3-6], operating more efficiently due to the higher ground temperatures and reducing these temperatures through operation, thus mitigating the risks and impacts of SUHI. This work demonstrates how heated basements can contribute to the operation of GSHP systems to provide heating. The area of Downing College, located in central Cambridge, UK, is used as a case study site, investigating how much of the college’s heating demand a number of geothermal boreholes could provide, when the heat from the building basements is taken into account and when it is not. Measured gas consumption data from the college are used to estimate the heat demand. The geology of the site is obtained by importing historical borehole records for the wider Cambridge area into the British Geological Survey (BGS) Groundhog® Desktop Geoscientific Information System and constraining the domain using BGS generated superficial deposit and bedrock geology maps, thus producing a 3D lithological profile for the region*, while the hydrological conditions were obtained using measured water level time-series data, from water wells in the area, curtesy of the Environment Agency. A total of 88 boreholes are considered, placed symmetrically in the college courtyard, between the main buildings. Typical single U-loop borehole configurations are used with pipes of 32 mm outer diameter. Acknowledging that the effect of heated basements is greater in shallower regions of the subsurface, two typical borehole length values are considered: 50 m, providing about 40% of the required heating load, and 100 m, providing 100%. The operation is simulated over 50 years, using a full 3D numerical model coupling heat transfer, groundwater flow, and pipe flow governing equations, created in COMSOL Multiphysics®. The temperature of the heated basements is assumed to be maintained at 18 °C throughout the simulation. During the first 10 years, the GHEs are not operating, to allow heat accumulation in the ground to occur from the heated basements, following which, 40 years of operating GHEs are simulated. The results of the simulations suggest that an increase in performance occurs when heated basements are present. For all scenarios, the fluid temperatures keep decreasing over time, as the ground temperature around the boreholes keeps decreasing, making it more difficult to extract heat. Over the 50 years of simulation, the fluid temperature reaches a minimum of -3.20 °C and -2.13 °C for the 50 m GHEs, and 4.52 °C and 3.88 °C for 100 m GHEs, in both cases the first value being the simulation with heated basements and the second without. The COP values over time are presented in Figure 1-right, showing the difference in COP between the cases with and without heated basements increasing over time for both 50 m and 100 m GHEs, for the former at a higher rate. This increase corresponds to the increase in the average fluid temperature between the cases with and without heated basements, also shown in the figure, and reaches a maximum difference of about 0.08 for the 50 m GHEs and 0.05 for the 100 m GHEs, corresponding to a fluid temperature difference of about 1.10 °C and 0.65 °C, respectively. These figures correspond to a reduction in operating costs (assuming an electricity rate of £0.34 per kWh) of £17,352 for 50 m GHEs and £18,197 for 100 m GHEs. While for this case these values are relatively small considering 40 years of operation, the fluid temperature difference suggests that more energy can be extracted by the GHE borheoles when heated basements are present, especially for shorter boreholes, and thus potential savings could be obtained by reducing the number of GHEs.

The use of data collection to support decision making through the reduction of uncertainty is ubiquitous in the management, operation, and design of building energy systems. However, no existing studies in the building energy systems literature have quantified the economic benefits of data collection strategies to determine whether they are worth their cost. This work demonstrates that Value of Information analysis (VoI), a Bayesian Decision Analysis framework, provides a suitable methodology for quantifying the benefits of data collection. Three example decision problems in building energy systems are studied: air-source heat pump maintenance scheduling, ventilation scheduling for indoor air quality, and ground-source heat pump system design. Smart meters, occupancy monitoring systems, and ground thermal tests are shown to be economically beneficial for supporting these decisions respectively. It is proposed that further study of VoI in building energy systems would allow expenditure on data collection to be economized and prioritised, avoiding wastage.

Shallow geothermal energy pile systems have emerged as cost-effective and low-carbon alternatives for heating and cooling buildings, compared to traditional air-conditioning systems. Geothermal applications have been researched extensively in recent years under the assumption of ground homogeneity, and the effect of ground stratification remains mostly unexplored. To investigate this, a 3D finite element numerical model is developed and validated against laboratory-scale experimental data, to study the transient diffusion-convection heat transfer linked with Darcy groundwater flow around energy piles in multi-layered lithology. The model is used to undertake long-term assessments under balanced and unbalanced thermal loads to evaluate the thermal effects of soil layering and discrepancies against commonly assumed equivalent homogeneous stratum, for soil profiles with different thermal conductivity distributions. The groundwater flow effect at various depths and seepage velocities on the thermal performance of the energy pile is investigated as well. Results demonstrate the need to account for the spatial variability in thermal properties, particularly for unbalanced thermal loading scenarios. The thermal yield can be underestimated by up to 19.6 % due to an inaccuracy of 48.2 % in the accumulated temperature after 25-year of operation with respect to an equivalent homogeneous ground with depth-weighted average thermal conductivity. This discrepancy grows as the contrast between layers increases. An empirical derived formula is presented and tested, presenting a correction to the effective thermal conductivity of the layered systems in this study that considers the thermal contribution of the ground beneath the pile. Groundwater seepage is shown to have a positive impact on the heat exchanger efficiency, and in the layered geology the efficiency under-estimation becomes more critical at low to moderate Darcy velocities, if neglected or inaccurately measured. These findings contribute to a broader understanding of energy piles and can assist engineers and practitioners in optimising energy geo-structure design, boosting the technology’s viability.

Rapid rates of urbanisation are placing growing demands on cities for accommodation and transportation, with increasing numbers of basements and tunnel networks being built to meet these rising demands. Such subsurface structures constitute continuous heat sources and sinks, particularly if maintained at comfortable temperatures. At the city-scale, there is limited understanding of the effect of heat exchange of underground infrastructures with their environments, in part due to limited availability of long-term underground temperature data. The effects of underground temperature changes due anthropogenic heat fluxes can be significant, impacting ventilation and cooling costs of underground spaces, efficiency of geo-energy systems, quality and quantity of groundwater flow, and the health and maintenance of underground structures. In this paper we explore the impact of anthropogenic subsurface structures on the thermal climate of the shallow subsurface by developing a heat transfer model of the city of Cardiff, UK, utilising a recently developed semi-3D modelling approach.

Urban expansion and extensive anthropogenic utilisation of the subsurface can lead to thermal changes in the ground, as structures such as basements, sewage systems, and tunnels reject or absorb heat to/from the ground. This phenomenon, known as Subsurface Urban Heat Island, has been widely documented and studied in recent years [1,7]. Investigations have shown that significant soil and groundwater temperature anomalies can be caused, with local hotspots and temperature differences up to 20 °C [6]. These ground temperature anomalies can affect, for example, ground- and drinking water quality, ecosystem biodiversity, and geothermal energy utilisation, with the latter being the focus of this work. The city of Cambridge, UK, shown in Figure 1-left, is adopted as a case study site, and a novel scalable large-scale subsurface modelling methodology [4] is used to obtain an understanding of the ground thermal state, accounting for natural and anthropogenic influences. The geology for the region is obtained by importing historical borehole records for the wider Cambridge area into the British Geological Survey (BGS) Groundhog® Desktop Geoscientific Information System and constraining the lithologies using BGS generated superficial deposit and bedrock geology maps, producing a 3D lithological profile. Water table readings from borehole wells supplied by the Environment Agency are used to create hydraulic head and water table maps for the region. Hydraulic and thermal properties for the materials in the domain were obtained from available literature* [2]. The main anthropogenic features are basements, assumed to be heated at 18 °C, and sewers, assumed linked with building density and at 15 °C. Following the methodology, the domain is separated into 1096 blocks, each 200m by 200 m laterally and 100 m in depth, clustered into 10 archetypes. Each archetype comprises a set of features resulting in a ground thermal state common across all blocks within an archetype [4]. Having thus obtained the spatially varying ground temperature, the performance of typical shallow geothermal systems throughout the domain is assessed, initially investigating the theoretical geothermal potential. Figure 1-middle, shows the amount of heating power a typical 100 m double U-loop borehole can supply, providing a constant ground load from 1st of October to 31st May over a 50-year operation period. The power is computed using the Finite Line Source model and g-functions [5], setting a lower limit of   -2 °C for the ground loop circulating fluid temperature. The results show that hydro-geological features and anthropogenic thermal influences in the region can result in spatial variation of geothermal potential of up to 0.3 kW, or about 1746 kWh per year. A sensitivity analysis indicates that no single feature dominates in the contribution to the magnitude of geothermal potential, suggesting that both natural and anthropogenic sources are important influences on how much energy the ground can provide. Extending the analysis by incorporating estimated residential heating demand data [3], Figure 1-right shows the percentage of residential demand that can be fulfilled using geothermal boreholes, assuming these are drilled in suitable parking and non-major road areas for each block, at a minimum spacing of 6 m to avoid thermal interference. The calculations use g-functions to compute how much of the estimated heating demand a single borehole can supply, using half-hour demand distributions for 50 years (repeated annually), and multiplied by the estimated number of boreholes in each block to determine the total geothermal energy that can be supplied. For a large portion of the modelled domain, the entirety of the residential heat demand is expected to be feasibly fulfilled using shallow geothermal energy. Certain areas, mostly agricultural and green spaces with no to low demand, contain no suitable borehole drilling locations, i.e., parking or road areas (a conservative assumption adopted in this study), resulting in no energy being supplied geothermally (light gray). Average demand supplied within the remaining region is 91%, with a standard deviation of 21%. As the world urgently seeks to transition to a more sustainable energy infrastructure, utilising different clean energy technologies in a more extensive and organised way becomes increasingly necessary. Geothermal energy technologies can be particularly suited for coordinated large-scale utilisation, due to the significant capital costs and the continuous nature of the ground, acting as a shared resource for large communities. This work briefly demonstrates the capacity that geothermal technologies have to fulfil a significant portion of the residential heat demand at large scales, using the city of Cambridge as an example, and that organisations or governments can take advantage of the potential that exists in finding common ground.

The settling velocity of sediment flocs is central to the study of the transportation process of contaminants in aqueous ecosystems. To describe the irregular shape of flocs, fractal theory based on the image analysis method is commonly used. However, this method usually leads to non-unique results as it requires the selection of a threshold intensity. Therefore, the main objective of this study is to develop a method to determine the settling velocity of both flocs and particles without using the fractal dimension. To achieve this goal, porosity was introduced as a substitute for the fractal dimension, and a simple method with three variables, floc diameter, mass concentration, and volume concentration of flocs, was developed. A density function method was used to obtain the floc porosity from a laser particle sizer which could obtain the volume concentration of sediment and an optical backscatter point sensor (OBS). Laboratory tests on two sediments from two different lakes were conducted. Results indicate that this method has a higher accuracy than traditional methods such as the Stokes equation and the Rubey equation. The variable density function performed better than the uniform density function and was, therefore, recommended for calculating the settling velocities for both micro and macro flocs. Using the developed method, the drag coefficient for the flocs was calculated and its accuracy analyzed. The method presented in this paper, which is simpler in determining in-situ settling velocities than traditional methods, also allows for direct inter-comparison between results derived from various studies.

Geothermal pavements can be used with ground-source heat pump systems to sustainably provide energy for heating and cooling by incorporating ground heat exchanger elements underneath pavement surfaces. This work investigates the suitability of geothermal pavements at scale, adopting the city of Cardiff, UK, as a case-study. A two-scale modelling framework, combining detailed small-scale with holistic large-scale approaches, is presented, incorporating the accuracy of the former with the continuity of the latter. The results show that between 184 kWh and 345 kWh of thermal energy per metre length of pavement can be supplied annually, depending on soil profile. Moreover, geothermal operation can reduce anthropogenic heat flux into the ground from heated basements, and its associated negative impacts, by about 390 MWh/year. A city-scale analysis using population-consistent geographic areas called LSOAs, estimates that geothermal pavements can supply about 23% of the entire city residential heat demand, or up to 75% with heat sharing between LSOAs. The suitability of geothermal pavements for larger LSOAs is highlighted, supplying up to 100% of the annual domestic heat demand. Investigating the carbon emissions of heating and cooling technologies shows potential reductions of up to 75% when replacing gas boilers and resistance heating with geothermal pavement systems. 

Understanding the subsurface is crucial in building a sustainable future, particularly for urban centers. Importantly, the thermal effects that anthropogenic infrastructure, such as buildings, tunnels, and ground heat exchangers, can have on this shared resource need to be well understood to avoid issues, such as overheating the ground, and to identify opportunities, such as extracting and utilizing excess heat. However, obtaining data for the subsurface can be costly, typically requiring the drilling of boreholes. Bayesian statistical methodologies can be used towards overcoming this, by inferring information about the ground by combining field data and numerical modeling, while quantifying associated uncertainties. This work utilizes data obtained in the city of Cardiff, UK, to evaluate the applicability of a Bayesian calibration (using GP surrogates) approach to measured data and associated challenges (previously not tested) and to obtain insights on the subsurface of the area. The importance of the data set size is analyzed, showing that more data are required in realistic (field data), compared to controlled conditions (numerically-generated data), highlighting the importance of identifying data points that contain the most information. Heterogeneity of the ground (i.e., input parameters), which can be particularly prominent in large-scale subsurface domains, is also investigated, showing that the calibration methodology can still yield reasonably accurate results under heterogeneous conditions. Finally, the impact of considering uncertainty in subsurface properties is demonstrated in an existing shallow geothermal system in the area, showing a higher than utilized ground capacity, and the potential for a larger scale system given sufficient demand.

Anthropogenic infrastructures in the shallow subsurface, such as heated basements, tunnels or shallow geothermal systems, are known to increase ground temperatures, particularly in urban areas. Numerical modelling helps inform on the extent of thermal influence of such structures, and its potential uses. Realistic modelling of the subsurface is often computationally costly and requires large amounts of data which is often not readily available, necessitating the use of modelling simplifications. This work presents a case-study on the city centre of Cardiff, UK, for which high resolution data is available, and compares modelling results when three key modelling components (namely ground elevation, hydraulic gradient distribution and basement geometry) are implemented either ‘realistically’, i.e. with high resolution data, or ‘simplified’, utilising commonly accepted modelling assumptions. Results are presented at a point (local) scale and at a domain (aggregate) scale to investigate the impacts such simplifications have on model outputs for different purposes. Comparison to measured data at individual locations shows that the accuracy of temperature outputs from numerical models is largely insensitive to simplification of the hydraulic gradient distribution implemented, while changes in basement geometry affect accuracy of the mean temperature predicted at a point by as much as 3.5 °C. At the domain scale, ground temperatures within the first 20 m show a notable increase (approximately 1 °C volume-averaged and 0.5 °C surface-averaged), while the average heat flux over the domain is about 0.06 W/m2 at 20 m depth. These increased temperatures result in beneficial conditions for shallow geothermal utilisation, producing drilling cost savings of around £1700 per typical household system or about 9% increase in thermal energy potential. Simplifications of basement geometry and (to a lesser degree) the hydraulics can result in an overestimation of these temperatures and therefore over-predict geothermal potential, while the elevation simplification showed little impact. [Display omitted] •Heated basements shown to increase volumetric ground temperature by up to 1.1 °C•Locally, modelling simplifications are more appropriate within groundwater flow.•At global scale, modelling simplifications somewhat overestimate ground temperature.•Simplifying heat source (basement) geometries shows greatest impact on temperature.•In this case study, heated basements increase shallow geothermal potential by 9–11%.

Uncertainty affects estimates of the power potential of tidal currents, resulting in large ranges in values reported for a given site, such as the Pentland Firth, UK. We examine the role of bottom friction, one of the most important sources of uncertainty. We do so by using perturbation methods to find the leading-order effect of bottom friction uncertainty in theoretical models by Garrett & Cummins (2005 Proc. R. Soc. A 461 , 2563–2572. ( doi:10.1098/rspa.2005.1494 ); 2013 J. Fluid Mech. 714 , 634–643. ( doi:10.1017/jfm.2012.515 )) and Vennell (2010 J. Fluid Mech. 671 , 587–604. ( doi:10.1017/S0022112010006191 )), which consider quasi-steady flow in a channel completely spanned by tidal turbines, a similar channel but retaining the inertial term, and a circular turbine farm in laterally unconfined flow. We find that bottom friction uncertainty acts to increase estimates of expected power in a fully spanned channel, but generally has the reverse effect in laterally unconfined farms. The optimal number of turbines, accounting for bottom friction uncertainty, is lower for a fully spanned channel and higher in laterally unconfined farms. We estimate the typical magnitude of bottom friction uncertainty, which suggests that the effect on estimates of expected power lies in the range −5 to +30%, but is probably small for deep channels such as the Pentland Firth (5–10%). In such a channel, the uncertainty in power estimates due to bottom friction uncertainty remains considerable, and we estimate a relative standard deviation of 30%, increasing to 50% for small channels.

Uncertainty affects estimates of the power potential of tidal currents, resulting in large ranges in values reported for sites such as the Pentland Firth, UK. Kreitmair et al. (2019, R. Soc. open sci. 6, 180941. ()) have examined the effect of uncertainty in bottom friction on tidal power estimates by considering idealized theoretical models. The present paper considers the role of bottom friction uncertainty in a realistic numerical model of the Pentland Firth spanned by different fence configurations. We find that uncertainty in removable power estimates resulting from bed roughness uncertainty depends on the case considered, with relative uncertainty between 2% (for a fully spanned channel with small values of mean roughness and input uncertainty) and 44% (for an asymmetrically confined channel with high values of bed roughness and input uncertainty). Relative uncertainty in power estimates is generally smaller than (input) relative uncertainty in bottom friction by a factor of between 0.2 and 0.7, except for low turbine deployments and very high mean values of friction. This paper makes a start at quantifying uncertainty in tidal stream power estimates, and motivates further work for proper characterization of the resource, accounting for uncertainty inherent in resource modelling.

The use of monitored data to improve the accuracy of building energy models and operation of energy systems is ubiquitous, with topics such as building monitoring and Digital Twinning attracting substantial research attention. However, little attention has been paid to quantifying the value of the data collected against its cost. This paper argues that without a principled method for determining the value of data, its collection cannot be prioritised. It demonstrates the use of Value of Information analysis (VoI), which is a Bayesian Decision Analysis framework, to provide such a methodology for quantifying the value of data collection in the context of building energy modelling and analysis. Three energy decision-making examples are presented: ventilation scheduling, heat pump maintenance scheduling, and ground source heat pump design. These examples illustrate the use of VoI to support decision-making on data collection.