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Optimization of aquifer thermal energy storage (ATES) performance in a building system is an important topic for maximizing the seasonal offset between energy demand and supply and minimizing the building's primary energy consumption. To evaluate ATES performance with bidirectional operation, this study develops an analytical solution-based model to simulate the spatiotemporal thermal response in an aquifer. The model consists of three temperature response functions, similar to the G functions in borehole thermal energy storage (BTES), to estimate the transient temperature profile in the aquifer during seasonally varying injection and extraction of hot/cold water. Applying machine learning (ML) based data classification and regression techniques to the results of a series of finite element (FE) benchmark simulations of typical ATES configurations, model input parameters are linked to the subsurface thermal, hydrogeological, and ATES operational properties. Compared to the benchmark simulation results, the errors of the proposed model in estimating the annual energy storage and locating the thermally affected area are about 3 % and 1 %, respectively. The model was applied to a previous short-term case study, and the error in the transient production temperature estimation is about 1 %. The long-term heat recovery ratio estimated from the model also compares well to those calculated from the previous study and the validated numerical model. Because of its fast computation, the proposed model can be coupled with the individual building system simulation and used for preliminary ATES design, and this will allow for greater exploration of ATES operational space and, therefore, better choices of ATES operating conditions. The proposed model can also be coupled with the district heating and cooling network simulation for computationally efficient city-scale long-term ATES potential assessment.

Located within the seismically active Pacific Ring of Fire, California's transportation infrastructure, especially in the Bay Area, is susceptible to earthquakes. A review of current research and stakeholder interviews revealed a growing awareness of emergency preparedness among local jurisdictions and transit agencies in recent years. However, many have yet to formalize and publish their recovery plans. This study introduces an agent-based multimodal transportation simulation tool to enhance post-earthquake transportation resilience. Integrating a road network simulator with a metro system simulator, the tool employs an optimized Dijkstra-based algorithm to calculate optimal routes, travel times, and fares. A case study is conducted for the East Bay, using the simulator to gauge the impact of a compromised Bay Area Rapid Transit (BART) system. The results suggested that original BART passengers could face either longer commute times or higher costs during the recovery phase of a major earthquake without appropriate policies. Such outcomes could disproportionately burden low-income riders, affecting their mobility and overall travel time.

Located on the tectonic boundary with multiple active faults, the San Francisco Bay Area is highly vulnerable to earthquakes. The United States Geological Survey (USGS) has estimated a 72% probability of an earthquakewith a magnitude of 6.7 or greater striking the region within the next 30 years. Historical seismic events have demonstrated the profound impact earthquakes can have on transportation systems. During the 1989 Loma Prieta Earthquake, the closure of the San Francisco-Oakland Bay Bridge, a critical transit route for San Francisco commuters, left nearly 400,000 commutersand approximately 245,000 vehicles daily with limitedalternative routes.

Many small, resource-strapped communities located in areas vulnerable to wildfire don’t have resources to conduct dedicated evacuation studies and many do not consider the impact of background traffic (i.e., normal traffic rather than evacuating traffic) on evacuation. In response, we explored the performance of several generalizable evacuation strategies with background traffic for representative communities in Marin County, including the Ross Valley, Woodacre Bowl, Tamalpais Valley, and an area near Highway 101 and Ignacio Boulevard in Novato (hereafter referred to as ‘Novato Neighborhood’). The strategies we explored include vehicle reduction (i.e., evacuees share a vehicle), phased evacuation (i.e., evacuees in different zones have different departure times), and off-street parking (i.e., street parking is prohibited on a high-fire Red Flag Day to increase overall road capacity in the event of an evacuation). We then tested each strategy using a wildfire-traffic simulation framework.

In the event of a wildfire, government agencies need to make quick, well-informed decisions to safely evacuate people. Small communities, such as in Marin County, with a mix of residences and flammable vegetation in Wildland-Urban Interface zones tend to lack resources to conduct evacuation studies. Consequently, this study uses a framework of wildfire and traffic simulations to test the performance of potential evacuation strategies, including reducing the volume of evacuating vehicles through car-pooling, phasing evacuations by staggering evacuation times by zone, and prohibiting street parking in four representative areas of Marin County. Results show that reducing vehicle numbers lowers the average travel time by 20%-70% and average exposure time to wildfire by 27%-60% from the baseline. Phased evacuations with suitable time intervals lower the average travel time by 13.5%-70%, but may expose more vehicles to fire in some situations. Prohibiting street parking yields varying results due to different numbers of exits and evacuees. In some cases, prohibiting street parking reduces the average travel time by over 50%, while in other cases it only reduces the average travel time by 9%, contributing little to evacuation efficiency. Altogether, Marin County may want to consider developing a communication and parking plan to reduce the number of evacuating vehicles in wildfire situations. Phased evacuation is also highly recommended, but the suitable phasing interval depends on the speed of fire spread and number of evacuees. Further, whether to establish street parking prohibition policies for a certain area depends on the number of exits and the number of vehicles on the streets.

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. The design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

Climate change leads to extreme climate events that result in frequent wildfires that cause numerous adverse societal impacts. Public Safety Power Shutoffs, adopted by utilities to minimize the risk of wildfires, pose many challenges to electricity consumers. Microgrids, have been proposed to improve the resilience of energy infrastructure during wildfire events for vulnerable communities. However, a comprehensive techno-economic and environmental assessment of the potential of such energy systems have not been performed. To address this research gap, the present study introduces a modeling framework, consisting of (1) clustering algorithms that identify the communities based on building footprint data, fire hazard severity, and renewable energy potential; (2) a building simulation model to quantify the energy demand; and (3) an energy system optimization model to assist the Microgrid design. A novel optimization tool was introduced to model Microgrids in wildland-urban interface, and subsequently, a comprehensive assessment was performed, focusing on seven localities from California, United States, with different climatic conditions. The study reveals that Microgrids can keep the average levelized energy cost and annual Public Safety Power Shutoffs below $0.3/kilowatt-hour (kWh) and 2%–3% (of the annual energy demand), respectively. Furthermore, renewable energy penetration levels can be maintained above 60% of the annual energy demand. Therefore, Microgrid may become an attractive solution to reduce the adverse impacts of wildfires and enhance the resilience of energy infrastructure. However, the study reveals that Microgrid cannot completely eliminate the Public Safety Power Shutoffs. The levelized cost and renewable energy generation curtailments (waste of renewable energy) become notably high when attempting to eliminate Public Safety Power Shutoffs completely. A notable reduction in energy storage cost is essential to achieve zero Public Safety Power Shutoffs, and this is expected with the evolution of energy storage technologies. The present study recommends Microgrids for communities affected by wildfires to enhance the resilience of energy infrastructure and protect the health and safety of residents. The modeling framework and optimization tool developed in this study can be used by stakeholders and their consultants to inform design and optimization of Microgrids for investment decision making.

Quantitative dynamic strain measurements of the ground would be useful for engineering scale problems such as monitoring for natural hazards, soil-structure interaction studies, and non-invasive site investigation using full waveform inversion (FWI). Distributed acoustic sensing (DAS), a promising technology for these purposes, needs to be better understood in terms of its directional sensitivity, spatial position, and amplitude for application to engineering-scale problems. This study investigates whether the physical measurements made using DAS are consistent with the theoretical transfer function, reception patterns, and experimental measurements of ground strain made by geophones. Results show that DAS and geophone measurements are consistent in both phase and amplitude for broadband (10 s of Hz), high amplitude (10 s of microstrain), and complex wavefields originating from different positions around the array when: (1) the DAS channels and geophone locations are properly aligned, (2) the DAS cable provides good deformation coupling to the internal optical fiber, (3) the cable is coupled to the ground through direct burial and compaction, and (4) laser frequency drift is mitigated in the DAS measurements. The transfer function of DAS arrays is presented considering the gauge length, pulse shape, and cable design. The theoretical relationship between DAS-measured and pointwise strain for vertical and horizontal active sources is introduced using 3D elastic finite-difference simulations. The implications of using DAS strain measurements are discussed including directionality and magnitude differences between the actual and DAS-measured strain fields. Estimating measurement quality based on the wavelength-to-gauge length ratio for field data is demonstrated. A method for spatially aligning the DAS channels with the geophone locations at tolerances less than the spatial resolution of a DAS system is proposed.

Well integrity is critical to the safety and success of subsurface energy exploration and management, as leakage of fluids from subsurface reservoirs is often induced by compromised wells. This study investigates bending deformation of a mockup of an oil/gas well that is subjected to loads expected in deviated wells under reservoir compaction and assesses the feasibility of utilizing distributed fiber optic strain sensing to monitor the deformation. A well mockup, which consists of outer and inner steel pipes with a cemented annulus, is tested under three-point bending loading and its strain and curvature development is monitored by Brillouin optical time domain reflectometry/analysis (BOTDR/A) as well as optical frequency domain reflectometry (OFDR). The primary objective of this research is to assess the strain sensing performance of newly fabricated fiber optic cables and to identify key cable characteristics which could improve the quality of distributed strain measurements with BOTDR/A. Results show that the tight-buffered cable is best suited for strain sensing as its maximum errors in the strain measurement were −36% and −24% against conventional sensors at the maximum elastic and plastic bending loads, respectively, whereas those of the non-tight-buffered cable were −45% and −71%, respectively. Similar trends were obtained in the bending curvature measurement. The detailed design of the tight-buffered cable is presented to elucidate key characteristics of such a cable, which will facilitate accurate distributed strain sensing in oil and gas wells.

Wildfires have become a perpetual crisis for communities across California. For life-threatening wildfires, mass evacuation often becomes the only viable option to protect lives. Yet, looking back at recent events, including the devastating 2018 Camp Fire in Northern California, there are significant challenges associated with the evacuation process, such as multi-agency coordination, agency-resident communication, and management of extraordinarily high amounts of traffic within a short period of time. Currently, emergency planners use evacuation models that are typically based on existing traffic simulation models; however, it is increasingly clear that other factors need to be considered, such as fire progression and communication systems. To address this gap, UC Berkeley researchers constructed a framework and set of models that include the combined impacts of three dynamic processes on evacuations – fire progression, communication systems, and traffic flow. The framework and models were applied to two case studies in California: the town of Paradise and the unincorporated community of Bolinas. In the Paradise case, the scenarios were based on the 2018 Camp Fire event. For the Bolinas case, the scenarios were based on hypothetical wildfire events.

In recent years, “smart city” technologies have emerged that allow cities, counties, and other agencies to manage their infrastructure assets more effectively, make their services more accessible to the public, and allow citizens to interface with new web-and mobile-based alternative service providers. This project developed an innovative user-friendly web interface for local and state policymakers that tracks and displays information on the adoption of such technologies in California across the policing, transportation, and water and wastewater sectors for a comprehensive set of local service providers: connectedgov.berkeley.edu. Contrary to conventional smart city indices, our platform allows users to view rates of adoption in maps that attribute adoption to the local public agencies or service providers actually procuring or regulating the technologies in question. Users can construct indices or view technologies one by one. Users can also explore the relationship between technology adoption and local service area conditions and demographics, or download the raw data and scripts used to collect it. This report illustrates the utility of the data we have collected, and the analytics one can perform using our web interface through an analysis of the rollout of three technologies in the transportation sector: electric vehicle (EV) chargers, transportation network company (TNC) service areas, and micromobility services across California.

As demonstrated by the Camp Fire evacuation, communications (city-to-city, city-to-residents) play important roles in coordinating traffic operations and safeguarding region-wide evacuation processes in wildfire events. This collaborative report across multiple domains (fire, communication and traffic), documents a series of simulations and findings of the wildfire evacuation process for resource-strapped towns in Northern California. It consists of: (1) meteorological and vegetation-status dependent fire spread simulation (cellular automata model); (2) agency-level and agency-to-residents communication simulation (system dynamics model); and (3) dynamic traffic assignment (spatial-queue model). Two case studies are conducted: one for the town of Paradise (and the surrounding areas) and another for the community of Bolinas. The data and models are based on site visits and interviews with local agencies and residents. The integrated simulation framework is used to assess the interdependencies among the natural environment, the evacuation traffic and the communication networks from an interdisciplinary point of view, to determine the performance requirements to ensure viable evacuation strategies under urgent, dynamic wildfire conditions. The case study simulations identify both potential traffic and communication bottlenecks. This research supports integrating fire, communication and traffic simulation into evacuation performance assessments.

Sand production encountered in the 2013 offshore field gas production tests at the Nankai Trough, Japan, could be attributed to well failure during reservoir compaction. In this study, well integrity under various reservoir compaction patterns for the Nankai Trough case is examined using a well-formation finite element model. The modelling details include the inclusion of a cement sheath as well as the modelling of construction processes (such as cement shrinkage). Well elongation in the overburden layer becomes significant when the reservoir subsidence is localized near the wellbore under large depressurization. Results show that the maximum plastic deviatoric strain level in the cement could reach 0.7% when the maximum reservoir subsidence reaches 0.85 m and cement shrinkage is limited. When cement shrinkage rises to 0.75%, the maximum plastic deviatoric strain increases to 2.4% as the cement accumulates additional plastic strain during shrinkage due to its deformation being constrained by the casing. In order to prevent the cement from failure, it might be effective to hold the pressure drawdown at a low level (e.g., several MPa) until the hydrate dissociation front advances to a certain radius from the well (e.g., a couple of tens of metres).

Brillouin scattering based fiber optic strain sensor is used to measure distributed strain profile of a structure for structural health monitoring. A distributed strain profile is derived from the Brillouin spectrum peaks but error is induced when a local strain change occurs in a distance scale smaller than the spatial resolution. In this study, a new algorithm is proposed to reconstruct a Brillouin spectrum by eliminating low qualified data from the raw spectrum data. This is achieved by evaluating the strain gradient profile and statistically decomposing the power, width, and peak frequency of the raw spectrum data. This algorithm is versatile because it can be applied to various strain shapes and magnitudes. The reliability of the algorithm is verified experimentally by showing that the error of the strain profile obtained using the new method becomes 25% of that using the conventional peak detection method.

Well integrity is crucial in enabling sustainable gas production from methane hydrate reservoirs and real-time distributed monitoring techniques can potentially facilitate proper and timely inspection of well integrity during gas production. In this research, the feasibility of distributed fibre optic strain monitoring with Brillouin optical time domain reflectometry/analysis (BOTDR/A) for well monitoring was examined by conducting a laboratory test on a well model subjected to axial tensile deformation, which occurs due to reservoir compaction during gas production. First, the validity of the proposed experimental methodology is assessed by a finite element analysis and theoretical modelling of a well subjected to reservoir compaction. A 3 m long well model is developed from the modelling and is instrumented with different types of fibre optic cables to measure the distributed strain development during tensile loading. Results show that the proposed well model and loading scheme can satisfactorily simulate the axial tensile deformation of the well in the laboratory condition. BOTDR is capable of capturing the tensile strain development of the well model accurately within the limitation of the spatial resolution of the BOTDR measurement. To enable accurate distributed strain monitoring of well deformation with BOTDR/A, the following issues are discussed: tightly buffered coating layers around optical fibre cores through mechanical compression and/or chemical adhesion, and a small number of coating layers.

A real-time dynamic Brillouin optical time domain reflectometry (BOTDR)-based system is introduced by using small gain stimulated Brillouin scattering with fast data managing and saving method. The system achieves 2-cm read-out interval, 2-m spatial resolution, and about 7- \mu \varepsilon accuracy with about 7 Hz or even faster with 23 Hz continuous monitoring. A movement response on a polymer mat made pavement was successfully monitored by the proposed system. For example, the pace distance at each walking step was detected to be 0.62 m. In addition, the minimum strain difference measured after several human loads is about 10~\mu \varepsilon with 7- \mu \varepsilon uncertainty and 3- \mu \varepsilon repeatability, indicating the possibility of real-time strain monitoring and the movement or trajectory detection by distributed fiber optic strain sensing.

The unconsolidated nature of offshore methane hydrate-bearing formation poses challenges to sustainable methane gas production as the weak formation is susceptible to disturbance during wellbore construction. This could contribute to loss of well integrity which could manifest as sand production and error in the interpretation of downhole tests such as mini-frac tests. In this study, a simulation methodology of wellbore construction process is proposed. A finite element model adopting this methodology is developed in order to assess the effect of wellbore construction process on the integrity of the unconsolidated methane hydrate-bearing formation in the Nankai Trough, Japan. The main objectives are (i) to develop a modelling methodology of well construction process for numerical simulations, (ii) to assess the zone and magnitude of well construction-induced stress/strain disturbance in the formation and (iii) to evaluate relative impact of each well construction stage on the integrity of the formation. The results from this study show that the zone of horizontal stress disturbance from the geostatic state due to wellbore construction could extend to more than three times the radius of the wellbore. Following the wellbore construction, the deviator stress is concentrated in the hydrate reservoir sublayers with high hydrate saturation while plastic deviatoric strain has accumulated in the sublayers with low hydrate saturation. The results also show that modelling of cement shrinkage process is crucial in predicting the concentration of deviator stress in the high hydrate saturation layers.

Controlling cement shrinkage in a wellbore is important in maintaining its integrity. Although numerous laboratory experiments on the water absorption and shrinkage behaviour of oil well cement have been reported in the past, such behaviour in the wellbore annulus with consideration of pore water migration from the surrounding formation has seldom been examined. In this study, using a cement shrinkage model calibrated against available experimental data, a coupled hydromechanical finite element analysis of a cement-formation model is conducted to simulate the water migration, absorption and shrinkage behaviour of early-age cement placed in the annulus of a wellbore. The objectives of this study are (i) to identify the threshold permeability value of the formation above which there is no longer a bottleneck for pore water to flow into the cement and (ii) to estimate a reasonable range of cement bulk shrinkage volume in wellbore annulus geometry. Results show that the threshold permeability of the formation would be around 0.1 mD for three different types of cement examined in this study: Class G cement, rapid setting (RS) cement and Schlumberger optimized particle size distribution (OPSD) technology cement. The bulk shrinkage volume varies from 0.01% to 2.4% depending on cement type and formation permeability (1 mD to 0.1 μD). The proposed methodology facilitates the simulation of water migration/absorption and shrinkage behaviour of well cement in different formations.