Traditional building energy audits are both expensive, in the range of USD $1.29/m 2-$5.37/m 2, and inconsistent in their prediction of potential energy savings. Automation to reduce costs of evaluating the energy effectiveness of buildings is strongly needed. A key element of such automation is a means to estimate the building envelope energy effectiveness. We present a method that addresses this need by using infrared thermography to characterize building wall envelope effectiveness. To date, thermal imaging approaches for estimating wall R-Values, based upon thermal-physical models of walls, require additional manual measurements and analysis which prohibit low-cost, large-scale implementation. To overcome this implementation challenge, a machine learning approach is used to predict wall R-Values for a set of residences with known thermal resistance by utilizing the measured wall imaging temperature, prior weather conditions, historical energy consumption data, and available building geometrical data. The developed model is shown to predict wall R-Values with a maximum test-set root mean squared error of 7% using as few as nine training houses. This result has significant implications for low-cost large-scale envelope energy effectiveness characterization.

Cooling accounts for 12-38% of total energy consumption in schools in the US, depending on the region. In this study, stacking learning is utilized to predict chiller running capacity for four school buildings (regression) and to predict the chiller status for four another schools (classification) using a collection of interval chiller data and building demand. Singular and multiple measurement periods within one or more seasons are considered. A generalized methodology for modeling building energy systems is posited that informs selection of features, data balancing to attain the best model possible, ensemble-based stacked learning in order to prevent over-fitting, and final model development based upon the results from the stacked learning. The results show that ensemble-based stacked learning improves the model performance substantially; providing the most accurate results for both regression and classification. for both classification and regression. For, classification, the balanced accuracy is 99.79% while Kappa is 99.39%. For regression, the R-squared value, the mean absolute error (MAE) error, and the root mean squared error (RMSE) are 1.78 kW, 2.77 kW, and 0.983 respectively.

Predicting cooling load is essential for many applications such as diagnosing the health of existing chillers, providing better control functionality, and minimizing peak loads. In this study, short-term chiller and total building demand are acquired for five different commercial buildings in the Midwest USA. Four different machine learning models are then used to predict the chiller demand using the total building demand, outdoor weather data, and day/time information. Two data collection scenarios are considered. The first relies upon use of multiple weeks of data collection that includes very warm periods and season transitional periods where the outdoor temperature ranged from very warm to cool conditions in order to envelope all cooling season weather conditions. The second scenario employs use of contiguous data for a several weeks during only the warmest period of the year. The results show that using two or more separate time periods to envelope most of the weather data yields a much more accurate model in comparison to use of data for only one time period. These research findings have importance to energy service companies which often do short term audits (measurements) in order to estimate potential savings from chiller system upgrades (controls or otherwise).

Energy savings based upon use of smart WiFi thermostats ranging from 10 to 15% have been documented, as new features such as geofencing have been added. Here, a new benefit of smart WiFi thermostats is identified and investigated; namely, as a tool to improve the estimation accuracy of residential energy consumption and, as a result, estimation of energy savings from energy system upgrades, when only monthly energy consumption is metered. This is made possible from the higher sampling frequency of smart WiFi thermostats. In this study, collected smart WiFi data are combined with outdoor temperature data and known residential geometrical and energy characteristics. Most importantly, unique power spectra are developed for over 100 individual residences from the measured thermostat indoor temperature in each and used as a predictor in the training of a singular machine learning models to predict consumption in any residence. The best model yielded a percentage mean absolute error (MAE) for monthly gas consumption ±8.6%. Applied to two residences to which attic insulation was added, the resolvable energy savings percentage is shown to be approximately 5% for any residence, representing an improvement in the ASHRAE recommended approach for estimating savings from whole-building energy consumption that is deemed incapable at best of resolving savings less than 10% of total consumption. The approach posited thus offers value to utility-wide energy savings measurement and verification.

Design parameters are presented for Tajoura reactor core utilizing the new fuel assemblies with low enriched uranium (LEU, using IRT-4M fuel assemblies) in the steady state safety operational parameters and Loss of Flow transient mathematical models (NATIRT - computer program. The calculated results of the model are presented in the cases of forced convection steady state, transient during emergency tank filling and natural convection after emergency tank filling modes at different reactor core thermal power level. The results of NATIRT for all cases of flow were in good agreement with the PARET and PLTEMP computer programs.

In certain extremely low probability, severe accident scenarios which have been postulated for liquid metal cooled fast reactors,large bubble cavities containing fuel vapor and fission products transit a layer of coolant and release this material to the cover gas thereby presenting a contribution to an accident-specific source term [5].Mie model in radiation heat transfer has been investigated to analysis and interpret the experiments that conducted during 1980's for oxide UO 2 fueled reactors in Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory (ORNL).These analyses are applied to estimate the bubble collapse of Liquid Metal reactors (LMR's) during a hypothetical core disruptive accident (HCDA).InMie scattering model the particle size was 0.07 µm [6]. The scattering coefficient of UO 2 particles (σ = 1.24 m-1), was calculated by using Mie theory,at the same number of stable nuclei's N (2.9 E15 nuclei/m 3) that resulted from theabsorbed coefficientk = 0.082 m-1 [7].P 1 approximation method was used to solve the radiative heat transfer equation (RTE) in spherical coordinates of participating medium confined between the two concentric spheres.The surfaces of the spheres are assumed to be gray, diffusely emitting and diffusely reflecting boundaries, and an isothermal boundary conditions were assumed at these surfaces.Marsak's boundary condition was to computed, the net radiative heat flux q(τ), and the incident radiation G(τ), to analyze and interpret the CVD experiments data that were conducted in the FAST facility at ORNL [8] and Fast Flux Test Facility reactor (FFTF) in Argonne National Laboratory ANL.The conclude that extracted from this study is greater margin of safety when the bubble rising time is greater than the bubble collapse time since the bubble collapses (UO 2 condenses) before it can reach the top of the vessel therefore there is less chance of release of aerosol as in Oak Ridge National Laboratory (ORNL) FAST experiments and Argonne National Laboratory (FFTF) reactor.

In certain extremely low probability, severe accident scenarios which have been postulated for liquid metal cooled fast reactors, large bubble cavities containing fuel vapor and fission products transit a layer of coolant and release this material to the cover gas thereby presenting a contribution to an accident-specific source term [5]. Rayleigh model in radiation heat transfer has been investigated to analysis and interpret the experiments that conducted during 1980's for oxide UO 2 fueled reactors in Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory (ORNL).These analyses are applied to estimate the bubble collapse of Liquid Metal reactors (LMR's) during a hypothetical core disruptive accident (HCDA). In Rayleigh non-scattering model the particle size was 0.01 µm [6],and according to Mie theory principle, the absorption coefficient for small particle-size distribution was estimated (k = 10 m-1 was used) from reference [7] at complex refractive index of UO 2 at λ = 600 µm and x = 0.0785.A MATLAB code was used to solvethe radiative heat equation (RTE) in spherical coordinates. The mixture is in local thermodynamic equilibrium inside the bubble which has a black body surface boundary.The mixture in the cavity contains three components: the non-condensable gas Xenon, Uranium dioxide vapor, and fog.To simulate fuel bubble's geometry as realistically as possible, according to experimental observation, the energy equation in a spherical coordinate system has been solved with the radiative flux heat transfer equation (RTE) to obtain the effect of fuel bubble's geometry on the transient radiative heat flux and to predict the transient temperature distribution in the participating medium during a hypothetical core disruptive accident (HCDA) for liquid metal fast breeding reactor (LMFBR) for FAST. The transient temperature distribution in fog region was utilized to predict the amount of condensable UO 2 vapor = − ! " ! #. The conclusion that can be drawn from the present study, is that the Fuel Aerosol Simulant Test (FAST) facility at Oak Ridge National Laboratory has a larger margin of safety since the bubble rising time is greater than the bubble collapse time.