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At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Analyzing the Multiscale Impacts of Implementing Energy-Efficient HVAC Improvements Through Energy Audits and Economic Input–Output Analysis

Abstract Heating, ventilation, and air-conditioning (HVAC) systems are usually an industry’s highest consumer of energy, most of which goes toward space cooling in buildings. Industrial energy-efficiency audits not only benefit manufacturers but also generate significant economic and environmental benefits to localities, states, and the nation. This article analyzes the micro- and macro scale impacts of implementing energy-efficient HVAC systems by integrating the industrial building energy data with the macroeconomic regional economic flow model. Micro-scale data include 10 years of historical energy, cost, and carbon dioxide savings achieved from energy-efficient HVAC implementation offered to manufacturers through industrial energy audits. The data were integrated into the macroeconomic modeling framework to illuminate the cascading regional economic impacts of implementing energy-efficient HVAC recommendations in manufacturing facilities. Results show that if recommendations had been implemented throughout all manufacturers in the region, $656 M energy costs would have been directly saved, 7.8 million metric tons of carbon dioxide emissions would have been avoided, and 4387 jobs could have been created, resulting in a total annual economic impact of $899 M stemming from direct, indirect, and induced impacts. The results offer insight into how industrial energy systems can be designed and provide models for how communities can accomplish a net-zero society.

Energy & Fuels↗

DOE OSTI2023

Modeling and Optimal Design of a Desalination System Integrated Between a Glass-Covered Solar Collection Water Chamber and a Heat Dissipating Chimney

Abstract Solar thermal energy is trapped in a glass-covered water chamber/basin to provide the heat for evaporation of brackish water in a novel desalination system. To harvest clean water, a chimney is integrated with the water basin, which draws air into the chamber to be humidified by the vapor and then being ventilated via buoyance force. Uniquely, thermal conductive metal sheet is recommended to make the chimney, which allows the vapor in the humid air condenses easily when flowing up. The condensate at the inner wall of the chimney flows down to be collected as clean water. Mathematical modeling and numerical computation have been carried out to delineate the coupling of the buoyance-force-driven flow with the heat and mass transfer of air and water in the solar collection chamber and the condensation of vapor in the heat-dissipating chimney. The objective of the simulation and optimization of the system is to find the best match of the dimensions of the water chamber with a chimney to maximize the production of clean water and energy efficiency. The model has been used to simulation several cases (of water from 40 °C to 50 °C) with available experimental data from the authors’ previous work, and the agreement was satisfactory. The optimization studies found that there is a maximum air flowrate corresponding to a critical chimney height due to the requirement that the chimney is designated to dissipate heat as much as possible to condense water vapor. With the chimney height greater than the critical height, the airflow rate will have a slight decrease. Nevertheless, higher than a critical height is still needed for a chimney to condense more moisture. Optimized chamber diameters at different chimney heights are provided for reference of optimal system designs.

Engineering↗

DOE OSTI2026

Energy Impact of Radiative Cooling Paints in Warehouses Under Various United States Climates

Although radiative cooling research is widely found in the literature, no comprehensive study has yet been conducted on the impact of novel radiant cooling (>0.91 reflectance) on the energy efficiency of warehouses. Here, in this work, we develop three building models based on a Department of Energy prototype warehouse model using trnsys, representing a typical warehouse with a black roof, a typical warehouse with a white roof, and a warehouse with novel radiative cooling (RC) paint on its roof. These models are run for 15 different cities, each representative of a different ASHRAE climate zone, to better understand the impact of RC in many different climates. It was found that an RC-coated roof in a warehouse could reduce the building's annual heating, ventilation, and air conditioning (HVAC) loads by up to 14.11 kWh/m 2 of the roof area compared to a black roof, resulting in a maximum reduction in energy costs of 0.55 $\$$/m 2 or $\$$2646/year for a large 4835 m 2 warehouse. Similarly, replacing the typical white roof coating with an RC coating could reduce the warehouse's energy consumption by up to 8.17 kWh/ m 2 of roof area, thus reducing energy costs by as much as 0.29 $\$$/m 2 or $\$$1386/year for a 4835 m 2 warehouse. In addition, applying RC paint to an unconditioned warehouse could reduce the building's ASHRAE Standard 55 indoor temperature exceedance by up to 1330 h/year compared to a black roof and up to 532 h/year compared to a white roof.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

PHASE CHANGE MATERIAL FOR THERMAL ENERGY STORAGE IN BUILDINGS BASED ON SODIUM SULFATE DECAHYDRATE AND DISODIUM HYDROGEN PHOSPHATE DODECAHYDRATE

The worldwide increasing energy demand and 2050 net zero carbon target urge the globe to solve the energy challenge. Thermal Energy Storage (TES) has received significant attention in recent years as TES can be integrated into heating, ventilation, and air-conditioning systems where the energy would be stored during low-demand times and dispatched during high-demand times, resulting in controlling the peak load and improving energy savings. Material development is an integral part of TES. Salt hydrates are appealing due to cost-effectiveness, low- to no toxicity, and their high melting enthalpy, where energy is stored as latent heat. However, most salt hydrates are prone to incongruent melting (i.e., phase separation upon melting), which results in poor stability and large supercooling. In this study, we produced a highly stable novel energy storage material at a composition of 32 wt% sodium sulfate decahydrate, 52 wt% sodium phosphate dibasic dodecahydrate, 12 wt% milled expanded graphite, and 4 wt% borax. The material has a melting temperature of 28°C and an energy storage capacity of 167 kJ/kg with a supercooling of less than 3°C. The system showed no loss in energy storage performance after 150 cycles. The findings suggest that the novel energy storage material developed in this might be utilized in building heating and cooling applications.

Turnaoglu, Tugba↗

DOE OSTI2025

UrbanScaping: Community Spatial Data Visualization & Analytics

Evaluating the electrification potential of buildings through retrofitting is crucial for reducing carbon emissions and the carbon footprint of built environments. This study leverages the Automatic Building Energy Modeling (AutoBEM) software, integrating the Model America database to create an urban context-based spatial analysis platform for community engagement and development. We selected Camp Hill Borough, PA, as a case study to analyze building-specific energy performance and evaluate the electrification potential of each building by switching to different Heating, Ventilation, and Air Conditioning (HVAC) systems and measurement components. The simulation results generated by the workflow provide retrofitting suggestions to help mitigate the carbon footprint as well as energy saving statistics of buildings. Additionally, the developed web-based interface serves as a community engagement platform, allowing residents to provide feedback and further develop interactive communication protocols. The outcomes of this project offer a baseline for community electrification planning and contribute to the design of low-carbon communities.

Chowdhury, Shovan [ORNL]↗

DOE OSTI2022

EXPERIMENTAL AND MODELLING ANALYSIS OF A LARGE-SCALE TWO-PHASE LOOP THERMOSYPHON

Liquid pumping requires large quantities of electrical energy, including about 7% of the energy of building heating, ventilation, and air conditioning (HVAC) systems. To replace pumped condenser-cooling tower water loops with a passive alternative system, we implemented a commercial-scale two-phase loop thermosyphon (TPLT). The unit consists of a 13 m riser integrated with a commercially available cooling tower and circulation water heater that simulates heat loads up to 25 kW. In addition to providing passive cooling capabilities, the cooling tower unit is also maintenance free, reliable, and can operate in both dry and wet modes. This study characterizes the performance (temperature difference between the evaporator and condenser) and the two-phase flow behavior of the loop under various refrigerant charges. Sight glasses installed throughout the loop are used to identify the operating flow regimes in the riser and downcomer. Over the range of operating conditions, we identified that there is an optimal refrigerant charge range for a specific heat load at which near-isothermal operation can be achieved. We further developed a model to predict the operating flow rate and gravitational height in the downcomer and compared it with the experimental data. The results show that the model agrees with the experimental data, in particular the threshold gravitational head height at which there will be subcooled liquid at the condenser exit, indicating that it can be used as a predictive tool for identifying the optimal loop charge for a given heat load.

Loop thermosyphon, passive two phase↗

DOE OSTI2020

Surface reservoirs dominate dynamic gas-surface partitioning of many indoor air constituents

Human health is affected by indoor air quality. One distinctive aspect of the indoor environment is its very large surface area that acts as a poorly characterized sink and source of gas-phase chemicals. In this work, air-surface interactions of 19 common indoor air contaminants with diverse properties and sources were monitored in a house using fast-response, on-line mass spectrometric and spectroscopic methods. Enhanced-ventilation experiments demonstrate that most of the contaminants reside in the surface reservoirs and not, as expected, in the gas phase. They participate in rapid air-surface partitioning that is much faster than air exchange. Phase distribution calculations are consistent with the observations when assuming simultaneous equilibria between air and large weakly polar and polar absorptive surface reservoirs, with acid-base dissociation in the polar reservoir. Chemical exposure assessments must account for the finding that contaminants that are fully volatile under outdoor air conditions instead behave as semivolatile compounds indoors.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2022

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Electricity Pricing aware Deep Reinforcement Learning based Intelligent HVAC Control

Recently, deep reinforcement learning (DRL) based intelligent control of Heating, Ventilation, and Air Conditioning (HVAC) has gained a lot of attention due to DRL's ability to optimally control HVAC for minimizing operational cost while maintaining resident's comfort. The success of such DRL-based techniques largely depends on the articulation of the problem in terms of states, actions, and reward function. Inclusion of the electricity pricing information in the problem formulation can play an important role in saving the cost of HVAC operation. However, less attention has been given in the literature on formulating well-crafted state features based on electricity pricing. In this work, we propose an approach for training the DRL model with a specific focus on feature engineering based on electricity pricing. During training, we generate random but sufficiently realistic electricity price signals so that the pre-trained DRL model is robust and adaptive to the dynamic and variable electricity prices. The validation results are encouraging and show the potential of ≈12%-15% savings in the one day cost of HVAC operation, proving the usefulness of including electricity pricing related features as state features.

Kurte, Kuldeep↗

DOE OSTI2021

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.

Xu, Shichao↗

DOE OSTI2021

Comparative analysis of model-free and model-based HVAC control for residential demand response

In this paper, we present a comparative analysis of model-free reinforcement learning (RL) and model predictive control (MPC) approaches for intelligent control of heating, ventilation, and air-conditioning (HVAC). Deep-Q-network (DQN) is used as a candidate for model-free RL algorithm. The two control strategies were developed for residential demand-response (DR) HVAC system. We considered MPC as our golden standard to compare DQN's performance. The question we tried to answer through this work was, What % of MPC's performance can be achieved by model-free RL approach for intelligent HVAC control?. Based on our test result, RL achieved an average of ≈ 62% daily cost saving of MPC. Considering the pure optimization and model-based nature of MPC methods, the RL showed very promising performance. We believe that the interpretations derived from this comparative analysis provide useful insights to choose from various DR approaches and further enhance the performance of the RL-based methods for building energy managements.

Kurte, Kuldeep↗

DOE OSTI2023

Data-Driven Co-optimization of Energy Efficiency and Indoor Environmental Quality in Commercial Buildings

In this paper, we use publicly available data of a highly instrumented building to estimate how zonal temperature and carbon dioxide (CO2) concentration are related to some key operational and environmental measurements. Subsequently, we have developed, simulated, and evaluated an optimization framework for minimizing the energy consumption of the central heating, ventilation and air conditioning (HVAC) unit while meeting zonal temperature and indoor air quality (IAQ) standards. Finally, we have evaluated the achievable energy savings for our proposed approach as compared to a baseline approach and reported significant savings potential.

Naqvi, Syed Ahsan Raza↗

DOE OSTI2020

MBMS1.0: An Open-Source Code for Modeling and Simulation of Membrane-Based Dehumidification and Energy Recovery

Membrane-based dehumidification is currently being considered as a promising solution for the building application due to its low cost and very limited energy consumption. Developing a simple and efficient open-source code simulation tool is important for boosting the optimization and evaluation of such device in HVAC community. This paper reports a first-order physics based model which accounts for the fundamental heat and mass transfer of humid-air vapor at feed side to flow stream at permeate side. The current model comprises two membrane mass transfer submodels (i.e. microstructure model and performance map model); and it adopts a segment-by-segment methodology for discretizing heat and mass transfer governing equations. The model is capable of simulating both dehumidifiers and energy recovery ventilators with parallel-flow cross-flow, and counter-flow configurations. The model was validated with the measurements at appropriate device. The practices in dehumidification and energy recovery exchangers are also discussed. The model and open-source codes are expected to become a solid fundament for developing a more comprehensive and accurate membrane-based dehumidification in the future.

Gao, Zhiming↗

DOE OSTI2020

OpenStudio System-level KPI Reporting Measure v1.0

This is a OpenStudio measure that calculates and reports the system-level key performance indicators (KPIs) for evaluating building energy performance at the system level. The systems are defined by the service it provides in commercial buildings, which include (1) lighting, (2) Miscellaneous Electric Loads (MELs), (3) Heating, Ventilation and Air Conditioning (HVAC), and (4) Service Water Heating (SWH).

Li, Han↗

DOE OSTI2021

Functional Mock-up Interface - Machine Learning Center (FMI-MLC) v1

The Functional Mock-up Interface - Machine Learning Center is a tool to interface the Python-Tensorflow library with building simulation models, to develop and train machine learning-based controls. In particular, this framework allows the use of reinforcement learning through the standardized Functional Mock-up Interface, an open industry standard for co-simulation. First application was the development of a controller for dynamic façade and heating ventilation and air conditioning (HVAC) systems.

Gehbauer, Christoph↗

DOE OSTI2021

Budget Constrained Machine Learning for Early Prediction of Adverse Outcomes for COVID-19 Patients

Background: Machine learning (ML) based risk stratification models of Electronic Health records (EHR) data may help to optimize treatment of COVID-19 patients, but are often limited by their lack of clinical interpretability and cost of laboratory tests. We develop a ML based tool for predicting adverse outcomes based on EHR data to optimize clinical utility under a given cost structure. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Healthcare in northwest Ohio and southeastern Michigan. Methods: We tested performance of various ML approaches for predicting either increasing ventilatory support or mortality and the set of model features under a budget constraint was optimized via exhaustive search across all combinations of features. Results: The optimal sets of features for predicting ventilation under any budget constraint included demographics and comorbidities (DCM), basic metabolic panel (BMP), D-dimer, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), CRP, brain natriuretic peptide (BNP), and procalcitonin and for mortality included DCM, BMP, complete blood count, D-dimer, LDH, CRP, BNP, procalcitonin and ferritin. Conclusions: This study presents a quick, accurate and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

Cadena Pico, JoseE.↗

DOE OSTI2022

Haxall-based (Axon) fault auto-correction package for building HVAC system (Haxall-based Fault Correction) v1.0

Haxall-based Fault Correction is a set of fault auto-correction algorithms implemented using the Axon language. These algorithms can automatically fix equipment and control problems as they arise and improve the operation of building Heating, Ventilation, and Air Conditioning (HVAC) systems. The coded auto-correction algorithms include mitigation of rogue zones, ASHRAE Guideline 36 static pressure and supply air reset, resolution of control hunting due to improper settings in a proportional-integral-derivative controller, correction of biased temperature sensors, etc. FDD tools with enhanced auto-correction capability can resolve control problems as they are detected, increasing energy savings and emissions reductions while freeing up operational staff expertise for the hardest facility problems.

Vitti, Raphael↗

DOE OSTI2023

Dynamic Subgrid Turbulence Modeling for Shallow Cumulus Convection Simulations beyond LES Resolutions

A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC Cloud (MONC) model using two averaging flavors, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky–Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-gray-zone and gray-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale- and stability-dependent profile of the subfilter turbulence length scale across the chosen resolution range. At gray-zone resolutions the adaptive length scales can better represent the early precloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result, the initialization and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary layer, which fails to ventilate moisture from the boundary layer, resulting in the delayed spinup of the cloud layer. Moreover, strong downgradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for nonlocal transports, leading to overenergetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behavior.

54 ENVIRONMENTAL SCIENCES↗