Search NASA⌕ Search

SEARCH · Search NASA

Results for “Retrofitting”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Extending cost–benefit analysis for the sustainability impact of inter-urban Intelligent Transport Systems

The paper reports research involving three cost–benefit analyses performed on different ITS schemes (Active Traffic Management, Intelligent Speed Adaptation and the Automated Highway System) on one of the UK's busiest highways — the M42. The environmental scope of the assets involved is widened to take into account the possibility of new technology linked by ICT and located within multiple spatial regions. The areas focused on in the study were data centre energy emissions, the embedded emissions of the road-side infrastructure, vehicle tailpipe emissions, additional hardware required by the vehicles (if applicable) and safety, and all aspects of sustainability. Dual discounting is applied which aims to provide a separate discount rate for environmental elements. For ATM, despite the energy costs of the data centre, the initial implementation costs and mitigation costs of its embedded emissions, a high cost–benefit ratio of 5.89 is achieved, although the scheme becomes less effective later on its lifecycle due to rising costs of energy. ISA and AHS generate a negative result, mainly due to the cost of getting the vehicle on the road. In order to negate these costs, the pricing of the vehicle should be scaled depending upon the technology that is outfitted. Retrofitting on vehicles without the technology should be paid for by the driver. ATM will offset greenhouse gas emissions by 99 kt of CO{sub 2} equivalency over a 25 year lifespan. This reduction has taken into account the expected improvement in vehicle technology. AHS is anticipated to save 280 kt of CO{sub 2} equivalency over 15 years of operational usage. However, this offset is largely dependent on assumptions such as the level of market penetration. - Highlights: • Three cost–benefit analyses are applied to inter-urban intelligent transport. • For ATM, a high cost–benefit ratio of 5.89 is achieved. • ATM offsets greenhouse gas emissions by 99 kt of CO{sub 2} equivalency over 25 years. • ISA and AHS generate a negative result due to vehicle implementation costs. • AHS is anticipated to save 280 kt of CO{sub 2} equivalency over 15 years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Advancing practice relating to SEA alternatives

Developing and assessing alternatives is a key and central stage to Strategic Environmental Assessment (SEA). However, research has repeatedly reported this stage as one of the most poorly undertaken aspects of the SEA process. Current practice limitations include belated consideration of reasonable alternatives, narrow scope of alternatives that often include unrealistic or retrofitted options, limited stakeholder and public involvement in their identification, assessment and selection, lack of systematic approaches to their assessment and comparison, and inadequate reporting of the ‘storyline’ on how they were identified, what the potential impacts are and why the preferred alternative was selected. These issues have resulted in objections and judicial reviews. On the positive side, a number of good practice case studies enable extraction of key lessons and formulation of a set of general recommendations to advance practice in SEA alternatives. In this paper, practical guidance on the identification and development of alternatives, their assessment and comparison, selection of the preferred option, and documentation of the process and the reasons for selection is provided and discussed to frame good practice approaches. - Highlights: • Alternatives are one of the most poorly completed aspects of Strategic Environmental Assessment. • Current practice limitations need to be addressed to enhance SEA effectiveness. • A set of recommendations are extracted from good practice case studies. • These recommendations can be applied across jurisdictions and sectors and tailored as necessary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Building rehabilitation versus demolition and new construction: Economic and environmental assessment

Since the end of the twentieth century, discussion on dwelling rehabilitation versus its demolition and new construction has been steadily increasing in intensity, which is especially due to the necessity for the regeneration of urban centres. However, rehabilitation is not always considered the most economical solution, and demolition and new construction may constitute a better option. In the present work, a multi-family building in Seville, Spain, is used as a case study. After having suffered damage from a construction failure, it is assessed for its complete rehabilitation. Defective maintenance has worsened the bad condition of the building. A model is proposed, from the project budget perspective, that allows the environmental (Ecological Footprint indicator) and the economic (project's bill of quantities) assessment of the recovery of the dwelling. In the case study, the rehabilitation Ecological Footprint and the project cost are 0.06 gha/m{sup 2} of floor area (457.22 EUR/m{sup 2}) and 0.14 gha/m{sup 2} (576.33 EUR/m{sup 2}) for a new building on the same plot, respectively. It can be deduced that, even with a severely damaged building, the repair and retrofit work incurs a lower economic and environmental impact than that of the total replacement with a new construction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Healthcare Building Sustainability Assessment tool - Sustainable Effective Design criteria in the Portuguese context

Tools and methods to improve current practices and quality in the healthcare building sector are necessary to support decision-making at different building life cycle phases. Furthermore, Healthcare Building Sustainability Assessment (HBSA) Methods are based on criteria organised into different levels, such as categories and indicators. These criteria highlight aspects of significant importance when designing and operating a sustainable healthcare building. To bring more objectivity to the sustainability assessments, the standardisation bodies (CEN and ISO) proposed core indicators that should be used in the evaluation of the environmental, societal and economic performances of buildings. Nevertheless, relying on state of the art analysis, it is possible to conclude that there are aspects of major importance for the operation of healthcare buildings that are not considered in the HBSA methods. Thus, the aim of this paper is to discuss the context of sustainability assessment methods in the field of healthcare buildings and to present a proposal for the incorporation of Sustainable-Effective Design (SED) criteria in a new HBSA method. The used research method is innovative since in the development of the list of sustainability criteria it considers the opinion of main healthcare buildings' stakeholders, the existing healthcare assessment methods and the ISO and CEN standardisation works in the field of the methods to assess the sustainability of construction works. As a result, the proposed method is composed of fifty-two sustainability indicators that cover the different dimensions of the sustainability concept to support decision making during the design of a new or retrofitted healthcare building in urban areas. - Highlights: •A new system to assess the sustainability of healthcare buildings is presented. •We propose a method to develop the list of sustainability indicators for hospitals. •We propose a new concept – Sustainable-Effective Design (SED). •The system is adapted to the Portuguese building sector sustainability contexts. •The approach can be used to develop similar systems in other countries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗
DOE OSTI2017

Demonstration of reduced-order urban scale building energy models

The aim of this study is to demonstrate a developed framework to rapidly create urban scale reduced-order building energy models using a systematic summary of the simplifications required for the representation of building exterior and thermal zones. These urban scale reduced-order models rely on the contribution of influential variables to the internal, external, and system thermal loads. OpenStudio Application Programming Interface (API) serves as a tool to automate the process of model creation and demonstrate the developed framework. The results of this study show that the accuracy of the developed reduced-order building energy models varies only up to 10% with the selection of different thermal zones. In addition, to assess complexity of the developed reduced-order building energy models, this study develops a novel framework to quantify complexity of the building energy models. Consequently, this study empowers the building energy modelers to quantify their building energy model systematically in order to report the model complexity alongside the building energy model accuracy. An exhaustive analysis on four university campuses suggests that the urban neighborhood buildings lend themselves to simplified typical shapes. Specifically, building energy modelers can utilize the developed typical shapes to represent more than 80% of the U.S. buildings documented in the CBECS database. One main benefits of this developed framework is the opportunity for different models including airflow and solar radiation models to share the same exterior representation, allowing a unifying exchange data. Altogether, the results of this study have implications for a large-scale modeling of buildings in support of urban energy consumption analyses or assessment of a large number of alternative solutions in support of retrofit decision-making in the building industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A novel Variable Refrigerant Flow (VRF) heat recovery system model: Development and validation

As one of the latest emerging HVAC technologies, the Variable Refrigerant Flow (VRF) system with heat recovery (HR) configurations has obtained extensive attention from both the academia and industry. Compared with the conventional VRF systems with heat pump (HP) configurations, VRF-HR is capable of recovering heat from cooling zones to heating zones and providing simultaneous cooling and heating operations. Moreover, this could lead to substantial energy saving potential and more flexible zonal control. In this paper, a novel model is developed to simulate the energy performance of VRF-HR systems. It adheres to a more physics-based development with the ability to simulate the refrigerant loop performance and consider the dynamics of more operational parameters, which is crucial for representing more advanced control logics. Another key feature of the model is the introduction of component-level curves for indoor units and outdoor units instead of overall performance curves for the entire system, and thus it requires much fewer user-specified performance curves as model inputs. The validation study reflects good agreements between the simulated energy use from the new VRF-HR model and the laboratory measurement data across all operational modes at sub-hourly time steps. The model has been adopted in the official release of the EnergyPlus simulation program since Version 8.6, which enables more accurate and robust assessments of VRF-HR systems to support their applications in energy retrofit of existing buildings or design of zero-net-energy buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

A novel occupancy detection solution using low-power IR-FPA based wireless occupancy sensor

Significant energy savings can be achieved by operating heating, ventilation, and air conditioning control systems with indoor occupancy measurement information. This paper presents a novel plug-and-play occupancy sensing method which will enable temporal minimization of building energy consumption to meet building usage behavior without privacy concerns. The proposed wireless occupancy sensing platform is based on long-wave infra-red (LWIR) focal-plane arrays (FPAs), or thermal imagers, that detect thermal energy rather than visible light. We developed an advanced sensor package consisting of multiple thermal imagers with low-cost optical enhancements to increase field of view and increase sensitivity to occupant detection (filtering building clutter). These imagers can be coupled with radio frequency and ultrasonic-based radar to enhance data collection at key occupant zone boundaries to improve accuracy. Standard filtering and estimation techniques from the image processing and computer vision communities are introduced to overcome the accuracy issues suffered by traditional PIR based sensing, especially when occupants remain relatively still. Accurate low-level counting of individuals can be achieved with minimal impact on privacy. As a result, the proposed occupancy detection method can work as a retrofit to enable real-time understanding of the building operational state and usage behavior. Sensor data obtained from real test building zones was used to test the efficacy of the method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Resilient cooling strategies – A critical review and qualitative assessment

The global effects of climate change will increase the frequency and intensity of extreme events such as heatwaves and power outages, which have consequences for buildings and their cooling systems. Buildings and their cooling systems should be designed and operated to be resilient under such events to protect occupants from potentially dangerous indoor thermal conditions. This study performed a critical review on the state-of-the-art of cooling strategies, with special attention to their performance under heatwaves and power outages. We proposed a definition of resilient cooling and described four criteria for resilience—absorptive capacity, adaptive capacity, restorative capacity, and recovery speed —and used them to qualitatively evaluate the resilience of each strategy. The literature review and qualitative analyses show that to attain resilient cooling, the four resilience criteria should be considered in the design phase of a building or during the planning of retrofits. The building and relevant cooling system characteristics should be considered simultaneously to withstand extreme events. A combination of strategies with different resilience capacities, such as a passive envelope strategy coupled with a low-energy space-cooling solution, may be needed to obtain resilient cooling. Finally, a further direction for a quantitative assessment approach has been pointed out.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Optimal operation of multi-plant steam district heating systems for enhanced efficiency and sustainability

Despite their crucial role in supplying heat and power to universities, industries, and healthcare facilities, many steam-based district heating systems rely on outdated control methods. Among these, multi-central plant districts are particularly challenging due to the complexities of coordinating multiple plants, optimizing load distributions, and managing system downtime. In response, new operational strategies are developed to enhance the efficiency and sustainability of steam districts while utilizing existing resources. These strategies include reducing plant operational pressure without compromising the reliable supply to buildings and optimizing load allocation across multiple plants. The load allocation considers boiler part-load efficiency, runtime, network losses, and building pressure set points, and is compared with traditional multi-boiler controls. To support this exploration, new dynamic Modelica models are developed. In addition, methods to reduce modeling complexities are incorporated, enhancing their suitability for practical applications. A holistic district-wide analysis using a real university case study demonstrates a 4.7% fuel savings by lowering boiler operational pressure from 900 kPa to 600 kPa, along with a 13.3% reduction in condensation losses across the distribution network. Furthermore, the load allocation approach results in a 13.1% reduction in fuel consumption during peak winter periods and 15.3% during shoulder periods, with corresponding decreases in carbon emissions and fuel costs. This approach can also save maintenance costs by reducing the boiler runtime by 49.6%. In conclusion, this research underscores the benefits of retrofitting aging steam district heating systems, offering immediate operational improvements by enhancing efficiency, meeting regulatory compliance, and extending infrastructure lifespans while delaying costly overhauls.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Prior research has shown that land use patterns and the spatial configurations of cities have a significant impact on residential energy demand. Given the pressing issues surrounding energy security and climate change, there is renewed interest in developing and retrofitting cities to make them more energy efficient. Yet deriving micro-scale residential energy footprints of metropolitan areas is challenging because high resolution data from energy providers is generally unavailable. In this study, a bottom-up model is proposed to estimate residential energy demand using datasets that are commonly available in the United States. The model applies novel machine learning methods to match records in the Residential Energy Consumption Survey with Public Use Microdata samples. This matching and machine learning produce a synthetic household energy distribution at a neighborhood scale. The model was tested and validated with data from the Atlanta metropolitan region to demonstrate its application and promise.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Energy, economic, and environmental analysis of integration of thermal energy storage into district heating systems using waste heat from data centres

Data centres produce waste heat, which can be utilized in district heating systems. However, the mismatch between data centres’ heat supply and district heating systems’ heat demands limits its utilization. Further, high peak loads increase the operation cost of district heating systems. This study aimed to solve these problems by introducing thermal energy storages. A water tank and a borehole thermal energy storage system were selected as the short-term and long-term thermal energy storage, respectively. Energy, economic, and environmental indicators were introduced to evaluate different solutions. The case study was a campus district heating system in Norway. Results showed that the water tank could shave the peak load by 31% and save the annual energy cost by 5%. The payback period was lower than 15 years when the storage efficiency remained higher than 80%. However, it had no obvious benefits in terms of mismatch relieving and CO emissions reduction. In contrast, the borehole thermal energy storage increased the waste heat utilization rate to 96% and reduced the annual CO emissions by 8%. However, the payback period was more than 17 years. These results provide guidelines for the retrofit of district heating systems, where data centres’ waste heat is available.

25 ENERGY STORAGE↗
DOE OSTI2023

Building thermal dynamics modeling with deep transfer learning using a large residential smart thermostat dataset

Understanding thermal dynamics and obtaining the computational model of residential buildings enable its scaled application in energy retrofits, control optimization and decarbonization. In this paper, we present a deep learning approach to model building thermal dynamics with smart thermostat data collected from residential buildings, with the goal to investigate model generalizability. In the first stage, we developed and compared different Deep Learning architectures including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) models and CNN-LSTM to predict indoor air temperature in a multi-step time horizon. In the second stage, we implemented a Transfer Learning (TL) process, which aims to improve the prediction performance on a new set of buildings (targets), exploiting the knowledge of related or similar buildings (sources). Different TL strategies and source model identification methods were investigated. The study showed that the CNN-LSTM performed the best among the architectures compared, with an average Mean Absolute Error (MAE) of 0.26 °C for one-hour-ahead (twelve 5-min future steps) predictions. Furthermore, the results showed that freezing the LSTM layer and fine-tuning the other layers of the CNN-LSTM achieved the best performance among four TL strategies, which further improved the performance with respect to a machine learning approach by 10%, and proving the effectiveness and generalizability of the proposed approach. A comparison of three different source model identification methods showed that randomly selecting source models constrained by similar building characteristics can provide good TL performance while retaining simplicity comparing with other quantitative source identification methods.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Computational analysis of flame initiation, quenching, and re-ignition in a prechamber natural gas engine under varying EGR-dilution levels

The on-road natural-gas (NG) fueled transportation relies on stoichiometric spark-ignition engines for the advantages of simple after-treatment system despite the efficiency penalty relative to lean-burn combustion strategies. Exhaust gas recirculation (EGR) has the potential to reduce this efficiency gap at low to moderate loads without the need for complex lean-exhaust aftertreatment systems. However, EGR dilution leads to reduced combustion stability and increased cycle-to-cycle variability. A promising technology that has the potential to achieve reliable operation under diluted conditions is the prechamber ignition (or turbulent jet ignition) which uses chemically active turbulent jets generated from combustion inside a prechamber to initiate, stabilize and accelerate combustion of the mixture inside the main chamber. The present work focusses on developing a RANS-based CFD approach to accurately reproduce in-cylinder phenomena in a stoichiometric NG prechamber-assisted heavy-duty engine without relying on complex combustion models that account for turbulence-chemistry interactions. This is necessary because reactive prechamber jets at high EGR dilution tend to extinguish while emerging into the main chamber, which is followed by a phase of re-ignition — a phenomenon that conventional G-equation or well-stirred reactor combustion models cannot reproduce. With addition of a damping multiplier to the well-stirred reactor model, the predictions are seen to show good agreement with experimental pressure evolution and combustion images acquired from a single cylinder Cummins N-14 optical diesel engine retrofitted with a prechamber ignition system. Model predictions of local heat release in the flame and temperature evolution inside the flame are used to investigate combustion dynamics in the prechamber and the main chamber. It is seen that the well-stirred reactor model with the inclusion of damping is able to reproduce the temporary reduction in heat release within the flame, which can be considered equivalent to quenching of jets, and the subsequent re-ignition of the flame inside the main chamber. The delay between quenching and re-ignition depends on the amount of dilution, as explained by an illustration of flame evolution in a Borghi diagram.

Prechamber ignition↗