Load Estimation Research Articles

Multi-task Gaussian Processes based transient aerodynamic load reconstruction for maglev vehicle using acceleration response

The accurate estimation of aerodynamic loads is crucial for developing high-speed maglev trains, which can reach speeds of up to 600 km/h. Traditionally, this estimation has been achieved through computational aerodynamic simulation or direct pressure measurement. In this paper, we propose a novel framework for reconstructing transient aerodynamic loads on maglev vehicles using on-board acceleration measurements. In the framework, an inverse mathematical model that correlates the measured acceleration and external aerodynamic loads is derived from a well-calibrated maglev vehicle model. To avoid the ill-posed problem when solving the inverse mathematical model, a multi-task Gaussian Processes method is proposed, in which all reconstructed transient aerodynamic loads are treated as Gaussian Processes and the closed-form posterior distributions of these aerodynamic loads could be calculated. To validate the proposed framework, a set of transient vibration data collected from an operational maglev train passing through a double-track tunnel is utilized for load reconstruction. The results demonstrate that the framework offers a cost-effective and efficient means to obtain aerodynamic loads, highlighting its practical relevance for aerodynamic field testing in the context of evolving high-speed maglev technologies.

Computer-aided engineering: Quantification of the heating non-uniformity and distribution of the thermal load occurring during continuous ohmic and conventional thermal food sterilization

The characterization of continuous thermal processing (CTP) is a crucial aspect in the design and selection of technologies for the production of safe products with optimal quality retention after the thermal stress. Conventional methods for CTP characterization are constrained in their capacity to comprehensively capture the complex dynamics of fluid flow through pipelines and the different heating principles that contribute to local temperature variations. These methods rely on isolated local temperature measurements, which fail to account for the intricate interactions between temperature, heat transfer phenomena and fluid dynamics. In view of the aforementioned limitations, this study presents the implementation of a computational fluid dynamic digital model as a toolbox for the characterization and comparison of a conventional heating (CH) ultra-high temperature (UHT) sterilization process and ohmic heating (OH) UHT sterilization. This serves as a tool for an extensive and accurate comparison of the two processes. The model enabled the estimation of each technology's thermal load through the F0 value, thereby providing a more comprehensive assessment than local temperature measurements alone. Furthermore, this approach accounted for the flow behavior throughout the sterilization process. This strategy demonstrated that 2.5 % of the food product is exposed to an up to 75.4 times the average thermal load, whereas for OH treatments only 0.4 % of the product was exposed up to 5.1 times the average thermal load. This is due to the volumetric heating feature of OH, which leads to a 54.7 % reduction in the cooking grade of the product. Furthermore, the computer-aided comparison revealed no statistically significant difference (p-value of 0.6) between the two technologies in their capacity to inactivate Geobacillus stearothermophilus spores in terms of thermal load. This study highlights the importance of computer-aided engineering methodologies for the technological assessment of food sterilization processes prior to industrial transfer.

High Frequency Monitoring and Nitrate Sourcing Reveals Baseflow and Stormflow Controls on Total Dissolved Nitrogen and Carbon Export Along a Rural‐Urban Gradient

AbstractEfforts to reduce nitrogen and carbon loading from developed watersheds typically target specific flows or sources, but across gradients in development intensity there is no consensus on the contribution of different flows to total loading or sources of nitrogen export. This information is vital to optimize management strategies leveraging source reductions, stormwater controls, and restorations. We investigate how solute loading and sources vary across flows and land‐use using high frequency monitoring and stable nitrate isotope analysis from five catchments with different sanitary infrastructure, along a gradient in development intensity. High frequency monitoring allowed estimation of annual loading and attribution to storm versus baseflows. Nitrate loads were 16 kg/km2/yr. from the forested catchment and ranged from 68 to 119 kg/km2/yr., across developed catchments, highest for the septic served site. Across developed catchments, baseflow contributions ranged from 40% of N loading to 75% from the septic served catchment, and the contribution from high stormflows increased with development intensity. Stormflows mobilized and mixed many surface and subsurface nitrate sources while baseflow nitrate was dominated by fewer sources which varied by catchment (soil, wastewater, or fertilizer). To help inform future sampling designs, we demonstrate that grab sampling and targeted storm sampling would likely fail to accurately predict annual loadings within the study period. The dominant baseflow loads and subsurface stormflows are not treated by surface water management practices primarily targeted to surface stormflows. Using a balance of green and gray infrastructure and stream/riparian restoration may target specific flow paths and improve management.

Real-Time Aerodynamic Load Estimation for Hypersonics via Strain-Based Inverse Maps

This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynamic loads acting on the vehicle for ground and flight testing, as well as guidance, navigation, and control applications. Specifically, the approach targets hypersonic flight conditions where direct measurement of the surface pressures is challenging due to the harsh aerothermal environment. For problems employing a linear elastic structural model, the inference problem can be posed as a least-squares problem with a linear constraint arising from a finite element discretization of the governing elasticity partial differential equation. Due to the linearity of the problem, an explicit solution is given by the normal equations. Precomputation of the resulting inverse map enables rapid evaluation of the surface pressure and corresponding integrated quantities, such as the force and moment coefficients. The inverse approach additionally allows for uncertainty quantification, providing insights for theoretical recoverability and robustness to sensor noise. Numerical studies demonstrate the estimator performance for reconstructing the surface pressure field, as well as the force and moment coefficients, for the Initial Concept 3.X (IC3X) conceptual hypersonic vehicle.

Evaluation of mode-I delamination toughness of composites for automotive applications

Abstract With the increasing need for energy efficiency, fiber-reinforced composites are being widely used in the automotive industry. This study investigates the inter-laminar fracture behavior of fiber-reinforced composite double cantilever beam specimens considering a non-zero slope at the root. Experiments were conducted on unidirectional and cross-ply glass fiber reinforced epoxy composite specimens and the critical delamination toughness G I C has been evaluated by the proposed weighted residual equation. The proposed mathematical model requires crack length and the load versus displacement data to evaluate delamination fracture energy. This method accounts the measurement scatter and the rotation at the crack tip. For a particular specimen, the proposed model gives a unique value of critical delamination toughness value. The critical interface failure load estimates utilizing the evaluated delamination toughness agrees well with literature and in-house test results of double cantilever beam specimens.

A Simulation Study of the Effects of Additive, Multiplicative, Correlated, and Uncorrelated Errors on Principal Component Analysis

ABSTRACTMeasurement errors are ubiquitous in all experimental sciences. Depending on the particular experimental platform used to acquire data, different types of errors are introduced, amounting to an admixture of additive and multiplicative error components that can be uncorrelated or correlated. In this paper, we investigate the effect of different types of experimental error on the recovery of the subspace with principal component analysis (PCA) using numerical simulations. Specifically, we assessed how different error characteristics (variance, correlation, and correlation structure), loading structures, and data distributions influence the accuracy to estimate an error‐free (true) subspace from sampled data with PCA. Quality was assessed in terms of the mean squared reconstruction error and the congruence to the error‐free loadings, using the pseudorank and adjusting for rotational ambiguity. Analysis of variance reveals that the error variance, error correlation structure, and their interaction with the loading structure are the factors mostly affecting quality of loading estimation from sampled data. We advocate for the need to characterize and assess the nature of measurement error and the need to adapt formulations of PCA that can explicitly take into account error structures in the model fitting.

On modeling added mass and circulation terms with fast potential-flow methods for oscillating-foil propulsors

The design of cycloidal thrusters and other oscillating foil propulsors requires fast tools that provide accurate estimates of propulsor hydrodynamic performance and mechanical loads. Potential-flow theory coupled with empirical formulae that account for viscous effects is the best choice for fast multi-parameter optimization. Within this context, oscillating foils are usually approximated as thin wings with flow solutions derived from small-disturbance theory. Added mass (non-circulatory) effects may be relevant even under such assumptions. They can be easily included in the flow equations assuming that the foil is a flat plate heaving and pitching. However, for blade motions involving complex trajectories like those in Voith Schneider propellers, the foils describe cycloidal loops, and the assumption of small pitch angles is no longer valid. Therefore, a more accurate modeling of added mass and circulatory terms is required.This paper presents extensions to a previous model, which allows for a more precise representation of the added mass terms in the potential flow equations, combining low computational cost and accurate modeling. The results show clear improvement in the evolution foil forces in time when the new approach is used. The extensions are especially needed for the simulations of foils describing complex paths with large variations of pitch angle. Applications to transverse-axis cycloidal propulsors are presented as well as to unconventional counter-flapping propulsors that have recently appeared in the scientific literature. Validation of the method with RANS computations is provided.

Numerical evaluation of vertical and horizontal distances for nitrogen attenuation from Onsite Sewage treatment and disposal systems (OSTDS)

Onsite Sewage Treatment and Disposal Systems (OSTDS) used for domestic wastewater treatment are a source of nitrogen (ammonium and nitrate) contamination to groundwater and surface water. One way to manage the contamination is to establish setback distances for nitrogen attenuation through nitrification and denitrification reactions within the setback distances. Determining the setback distances requires investigating nitrogen attenuation as a function of distance from OSTDS. While field investigations have been conducted, there have been few modeling investigations. Using an ArcGIS-based Nitrogen Load Estimation Toolkit, this study simulates nitrogen attenuation for over 20,000 OSTDS at five study areas across Florida, where a third of households are served by OSTDS. Different from conventional studies that only consider the horizontal distances (between an OSTDS and a surface water body that receives OSTDS effluents), this study also considers the vertical distances (between an OSTDS drainfield and water table beneath the drainfield) to account for the nitrogen reactive transport processes in the vadose zones and groundwater aquifers, making the assessment of nitrogen attenuation more comprehensive. Modeling results yield the following three empirical but quantitative relations: (1) vertical attenuation rate as a function of vertical distance and saturated hydraulic conductivity (Ks) available in the Soil Survey Geographic Database, (2) horizontal attenuation rate as a function of horizontal distance and Ks, (3) total attenuation rate as a multiplication of the vertical and horizontal attenuation rates. These relations can be used to determine nitrogen attenuation within vertical and horizontal distances to OSTDS for practical management of OSTDS nitrogen contamination.

Enhancing building energy efficiency: An integrated approach to predicting heating and cooling loads using machine learning and optimization algorithms

Predicting cooling and heating loads is essential for efficient building energy management in order to maintain indoor comfort. This study employs machine learning methods containing Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and a Dempster-Shafer Theory-based ensemble model using a dataset of 768 samples. 5-fold cross-validation selects 80 % of the dataset for training, with optimization algorithms like Coot, Dingo, and Sea-Horse Optimizers fine-tuning model applicability. Evaluation metrics such as R2, RMSE, n10-index, MARE, SI, WAPE, and NSE assess model performance. XGB outperforms SVR in heating and cooling load prediction. The XGSH model, combining XGB with the Sea-Horse Optimizer, obtains the highest performance, with an RMSE of 0.9729 and R2 of 99.17 % for heating load (HL) estimation. Similarly, for cooling load (CL) estimation, the XGSH model shows the highest R2 (99.36 %) and lowest RMSE (0.7723) during training. The results suggest practical usability for energy management and indoor comfort maintenance, benefiting engineers and decision-makers alike.

PV Based Microgrid for Remote Area Electrification in Nigeria: A Systematic Review of Concepts and Extant Strategies

Centralized power generation, transmission and distribution system operations in Nigeria can no longer deliver affordable and reliable electricity to remote households, both on and off the national grid. Therefore, a balanced approach, through renewable energy generation is the best option that would potentially lead accelerated journey to full electrification in the country. Renewable energy has become a panacea to energy problems worldwide because it is clean, environmentally friendly and ultimately cheaper. Due to the massive capacity of Nigeria to generate electricity from its numerous green energy resources, it has become crystal clear that access to sustainable and reliable energy in Nigeria is required for proper economic and social development. This work is based on an extensive review of recent experiences of more than 120 papers covering mostly the off-grid PV sector in the past 15years. The review included: scientific journals, conference papers, (PhD) theses, books and scientific reports from governments/NGOs and publications from energy institutions. A comprehensive review of relevant concepts and current microgrids optimization strategies applied to sizing, siting, load estimation and energy management has been conducted. Future research directions and potential of PV based microgrids for rural electrification have equally been specified.

Suspended Load Estimation in Data Scarce Rivers

AbstractSediment rating curves (SRCs) are tools of satisfactory reliability in the attempt to describe the sediment regime in catchments with limited or poor-quality records. The study valorised the most suitable SRC development method for the estimation of the coarse suspended sediment load at the outlet of nine Mediterranean sub-watersheds. Four established grouping techniques were assessed, to minimize the uncertainty of the results, namely simple rating curve, different ratings for the dry and wet season of the year, hydrographic classification, and broken line interpolation, at three major Greek rivers (Aliakmon, Acheloos – upper route, Arachthos). The methods’ performance was benchmarked against sediment discharge field records, utilizing statistical measures and graphical analyses. The necessary observations were conducted by the Greek Public Power Corporation. The results were site/station dependent, and no methodology emerged as universally accepted. The analysis designated that the simple rating curve performs best at the cross-sections Moni Ilarion, Moni Prodromou, and Arta bridge, the different ratings for the dry and wet season of the year at Grevena bridge and Gogo bridge, the hydrographic classification at Velventos and Plaka bridge, and the broken line interpolation at Avlaki dam and Tsimovo bridge. In this regard, the study advocates the use of multiple SRC methods. Despite its limitations, the method merits a rather simple and cost-effective generation of a (continuous, detailed, sufficiently accurate) synthetic suspended sediment discharge timeseries, with high interpolating, extrapolating and reproducibility potential. The success of the application could benefit, among others, water quality restoration and dam management operations.

Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning

Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning

Impact of upstream buildings on Wind-Driven Rain Loading: Refining Obstruction Factor in ISO semi-empirical model based on CFD

CFD is a valuable tool for assessing Wind-Driven Rain (WDR) loading, one of the most important environmental loads for façade design. The majority of previous studies on this topic have primarily concentrated on simple building configurations, i.e., stand-alone buildings. Hence, prior findings may not be applicable to consider the impact of upstream buildings in urban areas, which significantly alter wind flow field, consequently, change WDR loadings on downstream building facades compared to the stand-alone building. Part A: four different steady-state RANS models (i.e., standard k−ω, realizable k−ε, RNG k−ε, and standard k−ε) coupled with the Eulerian Multiphase (EM) technique (RANS-EM) are compared and implemented using OpenFOAM-7. These models are validated and verified based on wind-tunnel and field measurement data obtained from the literature for a six-story mid-rise residential building located in an urban area in Vancouver, Canada. The study considers 13 distinct rainfall events, for the test building with/without overhangs. All four RANS models are deemed suitable for modeling WDR in urban areas, while the steady-state standard k-ω RANS-EM approach without incorporating turbulent dispersion showing slightly better performance, thus utilized for the reminder of the study. Part B: a sensitivity analysis is presented on how the upstream buildings influence the WDR loading on a downstream building, denoted as Obstruction Factor. A comparison between the CFD and ISO semi-empirical model shows significant discrepancies, potentially reaching up to factors of 5. Thus, updated Obstruction Factors are suggested to enhance the ISO model for more accurate estimation of WDR loads.

Estimating fuel load for wildfire risk assessment at regional scales using earth observation data: A case study in Southwestern Australia

The risk of wildfires is increasing globally and models are critical to reducing this risk. Such models require information on fuel load, a crucial factor of fire behaviour, which is generally determined using a combination of fuel age and fuel accumulation models. Traditionally, estimating fuel load relies on manually compiled fire history data (MCFH). In this paper, we introduce an approach to estimate fuel load using readily available earth observation (EO) data, MODIS MCD64A1. The approach is applied to a wildfire-prone region in Southwestern Australia from 2001 to 2021. Results suggest that MODIS produces more accurate and reliable estimates of fuel load compared with MCFH. It is effective in maintaining spatially and temporally complete records of fires, as it reports 11,019 more hectares of burned areas associated with wildfires over the study period. MODIS performs better in capturing wildfires than prescribed burns, as the spatial overlapping ratio is higher for wildfires (0.63) than prescribed burns (0.42). The high agreement between the two datasets for fuel load estimation (weighted kappa of 0.91) results from grassland covering the majority of the landscape. However, the agreement is reduced for other vegetation types — 0.24 for pine, 0.36 for mallee heath, 0.39 for shrubland, and 0.58 for forest. MODIS has lower effectiveness in detecting small and under-canopy fires such as prescribed burns, suggesting the value in combining EO and manually compiled data to obtain improved estimates of fuel load. Due to the scope of objectives, the integration of EO and MCFH has not been fully explored in this study, which will be included in our future research. This study highlights the potential of earth observation data in assessing wildfire risk as the data are easily accessible and reliable, as well as efficient and cost-effective, and they provide the opportunity to develop mitigation strategies at regional scales.

Prediction of wind pressures on supertall buildings based on proper orthogonal decomposition and machine learning

AbstractDetailed wind pressure information plays a critical role in the accurate estimation of wind loads on high‐rise buildings, especially for complex‐shaped supertall buildings. However, owing to the limited internal space of a scaled building model and the capacity of data‐acquisition devices, it is often difficult to acquire the wind pressures at all positions of interest on the entire model in wind tunnel testing. To this end, a novel approach that combines the proper orthogonal decomposition (POD) and machine learning (ML) is presented in this paper for the prediction of wind pressure time series (WPTS) on supertall building models in wind tunnel testing. In this approach, the prediction of WPTS is converted into the estimation of several main eigenmodes and mean wind pressures by combining the POD with ML. This strategy can effectively reduce the computational effort compared to the direct prediction of WPTS. A combined ML model consisting of the Gaussian process regression (GPR), decision tree regression (DTR), and random forest (RF) (i.e., POD‐GPR‐DTR‐RF model) is utilized for the prediction of eigenmodes and mean wind pressures. Wind pressure records from a wind tunnel experiment of a 600‐m‐high building are employed to verify the accuracy and effectiveness of the presented approach. The results show that the combined ML model (i.e., POD‐GPR‐DTR‐RF model) developed based on the proposed approach performs satisfactorily in the prediction of WPTS and outperforms the conventional method that combines POD with backpropagation neural network model (i.e., POD‐BPNN model), demonstrating that the proposed approach is an effective tool for prediction of WPTS on supertall buildings.

SARS-CoV-2 viral load is linked to remdesivir efficacy in severe Covid-19 admitted to intensive care

Remdesivir therapy has been declared as efficient in the early stages of Covid-19. Of the 339 patients (males 55.8%, age 71(59;77) years) with a detectable viral load, 140 were treated with remdesivir (of those 103 in the ICU and 57 immunosuppressed) and retrospectively compared with 199 patients (of those 82 in the ICU and 28 immunosuppressed) who were denied therapy due to advanced Covid-19. The viral load was estimated by detecting nucleocapsid antigen in serum (n = 155, median 217(28;1524)pg/ml), antigen in sputum (n = 18, COI 18(4.6;32)), nasopharyngeal antigen (n = 44, COI 17(8;35)) and the real-time PCR (n = 122, Ct 21(18;27)). After adjustment for confounders, patients on remdesivir had better 12-month survival (HR 0.66 (0.44;0.98), p = 0.039), particularly when admitted to the ICU (HR 0.49 (0.29;0.81), p = 0.006). For the immunocompromised patients, the difference did not reach statistical significance (HR 0.55 (0.18;1.69), p = 0.3). The other most significant confounders were age, ICU admission, mechanical ventilation, leukocyte/lymphocyte ratio, admission creatinine and immunosuppression. The impact of monoclonal antibodies or previous vaccinations was not significant. Despite frequent immune suppression including haemato-oncology diseases, lymphopenia, and higher inflammatory markers in the remdesivir group, the results support remdesivir administration with respect to widely available estimates of viral load in patients with high illness severity.

A landscape limnology approach to assessing controls on soluble reactive phosphorus in sediment porewater and internal loading risk

Sediment nutrients can be mobilized to overlying water via internal loading, which can be important to aquatic productivity. Using data from 143 Canadian lakes, we show high (~2400-fold) variation of soluble reactive phosphorus (SRP) concentrations in surficial sediment porewater, with results suggesting internal phosphorus loading (IPL) is also likely to vary widely. Consistent with past work at smaller scales, we show that lake depth, pH, trophic status, and bulk sediment Al:P and Fe:P influence porewater SRP, and IPL. Median porewater SRP concentration in lakes with high Al:P (molar ratios >10) were 4.8-fold smaller than in lakes with lower Al:P. In lakes where bulk sedimentary Fe:P molar ratios were >10 porewater SRP was 3.9-fold lower than in lakes with lower Fe:P. High pH (>7.8), along with hyper-eutrophic lakes were associated with higher porewater SRP. Finally, shallow lakes (<4 m depth) had median porewater SRP concentration 6-fold higher than deep lakes (>9 m depth). Important regional differences emerged, linked to regional variation in pH, soils, lake depth and trophic status, and paralleling areas of poor water quality. For example, median porewater SRP in lakes from the Boreal Plains and Prairies ecozones (dominated by Chernozems/Mollisols) was 64-fold and 44-fold higher than in the Boreal Shield (dominated by Podzols/Spodosols) (respectively), although we note that IPL risk is likely important across many ecozones. Using national data, we found in-lake measurements (particularly pH, and salinity) showed strong capacity in predicting porewater SRP (explaining 60–72 % of the variance in the data). Importantly, watershed predictors showed good predictive power, explaining ~50 % of variance in porewater SRP using variables including soil types, and % agriculture. Porewater SRP can be predicted with reasonable accuracy using easily measured variables, as can estimates of internal phosphorus loading, suggesting that landscape limnology holds strong potential in helping to inform lake management by informing understanding of in-lake nutrient sources.

Immuno-pathogenesis study of local infectious bronchitis virus G1-1 lineage variant showed altered tissue tropism in experimental broiler chickens.

Infectious bronchitis (IB) is an acute contagious disease of poultry caused by infectious bronchitis virus (IBV). This study investigated the immunopathogenesis and tissue tropism of an Indian IBV field isolate (IBV/Chicken/India/IVRI/Rajasthan/01/2023) in experimental broiler chickens. This isolate belongs to the G1-1 lineage and is closely associated with the Mass genotype. 106.23 EID50/0.2 mL of the virus was administered intranasally and intraocularly to the IBV-challenge group, whereas uninoculated allantoic fluid was administered to the control group. Clinical signs, gross and histopathological lesions, immunohistochemistry (IHC), viral load, humoral responses, and the relative expression of immune response genes were evaluated at seven observation points. The infected group showed a significant reduction in weight gain from 3 dpi onwards, with clinical signs of varying severity from 3 to - 11 dpi. Gross lesions and microscopic changes were observed in the nasal turbinates, trachea, lungs, and kidneys, mainly representing epithelial degeneration and necrosis with mononuclear infiltrates. The caecal tonsils also showed microscopic lesions at 7-9 dpi. Absolute viral load estimation in the organs corroborated the lesion severity scores and IHC results. The expression of innate immune responses broadly demonstrated higher expression in the trachea and lungs of the IBV-infected group during the early phase of infection, whereas similar responses were observed in the kidneys and caecal tonsils during the later phases of infection. This study suggests that the given IBV isolate may cause significant production losses in broilers and exhibit tissue tropism for both respiratory and non-respiratory tissues, triggering varying innate and adaptive immune responses.

A decoupling estimation method for baseline load of photovoltaic user clusters based on multidimensional abnormal data restoration

Abstract With the widespread installation of distributed photovoltaics, their fluctuation characteristics with weather changes have posed significant challenges to baseline load estimation. In addition, due to measurement and collection anomalies, it is easy to cause continuous missing and abnormal data collection. To address the issue of low baseline estimation accuracy caused by distributed photovoltaic fluctuation characteristics and abnormal data collection, this paper proposes a photovoltaic user cluster baseline load decoupling estimation method based on multi-dimensional abnormal data repair. Firstly, a multidimensional anomaly data repair system is established based on extreme learning machines and multi-step prediction. Secondly, using the repaired data for baseline estimation, photovoltaic users and non-photovoltaic users are decoupled and separated. Long short-term memory neural network estimation models are used for non-photovoltaic users, and baseline load estimation methods based on weather type analysis are used for photovoltaic users. Finally, the baseline results are accumulated. The simulation results show that compared with the direct estimation method of baseline load, the accuracy of decoupling estimation has improved by 33.7%.

Sustainable cooling solutions for building environments: A comprehensive study of earth-air cooling systems

This research paper demonstrates a case study of a community-centric Earth-Air Cooling system on the Budhanilkantha. The research comprises critical attempts, encompassing heat exchanger design employing the Log Mean Temperature Difference (LMTD) technique, load estimations accomplished utilizing TRNSYS, and temperature abatement assessments executed through ANSYS simulations. The cooling system employed Geothermal energy for summer cooling. The optimization of heat exchanger dimensions required the development of pressure drop-surface area graphs utilizing spreadsheet tools. This resulted in an optimal design characterized by a tube velocity of 4.5m/s, a tube length of 46m, and a total of 12 tubes. This design led to a 33% temperature reduction, noticeably beyond the projected threshold of 23%. The Earth-Air Cooling system displayed a large heat dissipation capacity of 53.2kW, exceeding the peak cooling load requisites. This method provides a cooling mechanism for Budhanilkantha and indicates a more comprehensive application in analogous environmental contexts. To find the optimal sizing of the heat exchanger, a methodology was designed to develop an equation applicable to the domain of Nepal and all around the world that comprises similar climatic conditions.