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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.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Efficient wind farm layout optimization with the FLOWERS AEP model and analytic gradients

Wind farm layout optimization (WFLO) studies often aim to maximize the annual energy production (AEP) of a wind farm by choosing an arrangement of turbines that minimizes wake interactions. One way to reduce the cost of WFLO studies is by using more computationally efficient AEP models. The cost of standard AEP modeling approaches, based on the numerical integration of low-fidelity engineering wake models, scales poorly with the number of simulated discrete wind conditions. A second way to reduce cost when using a gradient-based algorithm is to supply exact gradient information instead of finite-difference estimates. However, analytical functions for the derivatives of AEP with respect to turbine positions are not always available in the conventional modeling approach. FLOWERS is a computationally inexpensive, analytical model for wind farm AEP that is specifically developed for WFLO applications. In this paper, we analyze the performance of the FLOWERS AEP model with analytic gradients in a layout optimization study compared with a reference optimization framework across three wind farm case studies. We find that the FLOWERS-based approach reduces computation time by a factor of 50–4000 and improves optimal AEP by about 0.3% with less than half of the variability in AEP across instances with randomized initial conditions. We also find the optimal layouts to be insensitive to model parameter tuning, making FLOWERS-based layout optimization a streamlined, user-friendly approach.

17 WIND ENERGY↗
DOE OSTI2023

Quasi-Steady Simulation of Glaze Ice Accretion and Heat Transfer in the Supercooled Large Droplet Regime in Atmospheric Turbulent Air Flow

Abstract To avoid the use of a computationally intensive full unsteady simulation while providing an accurate solution, a quasi-steady simulation has been performed to study glaze and mixed ice accretion in the supercooled large droplet (SLD) regime in turbulent flow of atmospheric air. We have attempted to find the minimum time-step necessary to adequately simulate the icing process on an airfoil surface. Based on node displacement, a mesh morphing scheme has been adopted in the computations to account for the moving boundaries that are caused by the continuous ice buildup. We have modeled ice accretion on an airfoil surface for a time period of 232 s using several time steps. At each time-step we solved the steady-state conservation equations for the air and droplet phases and then used the results as initial conditions for the next time position. The magnitude of the time-step ranged from a least accurate two-shot simulation (where the time-step is 116 s) to a most accurate 2320-shot simulation (where the time-step is 0.1 s). In-between these two extreme time steps, we have performed a three-shot simulation (where the time-step is 77.33 s), a four-shot simulation (where the time-step is 58 s), a six-shot simulation (where the time-step is 38.67 s), a 46-shot simulation (where the time-step is 5 s), and a 232-shot simulation (where the time-step is 1 s). We have done so to find out the degree of accuracy (or inaccuracy) of the multishot simulation approach and to find out the appropriate time-step needed for a successful and valid quasi-steady simulation. A valid quasi-steady simulation needs to use a time-step that is small enough to reproduce the full time-dependent solution within a very small error band. We have found that both the 1 s and the 0.1 s time steps produce virtually identical results. This is the primary litmus test that proves the validity of the quasi-steady-state assumption. The results adopted in this paper are thus all based on the more conservative 0.1 s time-step. In the process of performing the simulations, remeshing was required in order to maintain the grid density in zones of high curvature to be able to capture the full physics in those zones. After successfully modeling glaze ice accretion over the airfoil surface using the 0.1 s quasi-steady simulation approach, the effects of supercooled large droplets (SLDs) impacting the surface have been examined and presented in terms of the variation of the local collection efficiency, the water film thickness, and the heat transfer rate. Examination of the variation of the angle that the ice horn makes with the airfoil chord line demonstrated a 20% improvement in angle prediction when the time-step is reduced from 116 s to 0.1 s. The analysis also reveals a 12% or 8.5% increase in the maximum collection efficiency, βmax, depending on whether the value of the liquid water content (LWC) has been doubled from 0.5 g/m3 to 1 g/m3, or the value of the freestream velocity has been doubled from 75 m/s to 150 m/s, respectively. Because of the need to monitor the local collection efficiency and convective heat fluxes at each shot (regardless of the number of shots employed), the approach adopted here was found to be effective in successively and successfully reproducing the curvature of the glaze ice horn.

Engineering↗
DOE OSTI2020

Real-time cure monitoring of fiber-reinforced polymer composites using infrared thermography and recursive Bayesian filtering

Despite the desirable characteristics of fiber-reinforced polymer (FRP) composites, their utilization in high volume production industries is limited by a lack of efficient manufacturing techniques. Monitoring the curing process of these composites can help with improving the quality and efficiency of the manufacturing process. This article discusses a method that uses Kalman-filter-based fusion of the information obtained from infrared thermography (surface temperature measurements) and a heat conduction model to estimate degrees of cure and internal temperatures of curing FRP composite parts in real time. The effectiveness of the methodology is demonstrated by successfully monitoring the curing of an FRP composite part in a laboratory experiment. Finally, the proposed methodology is a crucial step towards identifying anomalies in the curing process that negatively impact the quality of FRP composite parts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

From Sim to Real: A Pipeline for Training and Deploying Traffic Smoothing Cruise Controllers

Designing and validating controllers for connected and automated vehicles to enhance traffic flow presents significant challenges, from the complexity of replicating real-world stop-and-go traffic dynamics in simulation, to the intricacies involved in transitioning from simulation to actual deployment. In this work, we present a full pipeline from data collection to controller deployment. Specifically, we collect 772 km of driving data from the I-24 in Tennessee, and use it to build a one-lane simulator, placing simulated vehicles behind real-world trajectories. Using policy-gradient methods with an asymmetric critic, we improve fuel efficiency by over 10% when simulating congested scenarios. Our comprehensive approach includes reinforcement learning for controller training, software verification, hardware validation and setup, and navigating various sim-to-real challenges. Furthermore, we analyze the controller's behavior and wave-smoothing properties, and deploy it on four Toyota Rav4’s in a real-world validation experiment on the I-24. Lastly, we release the driving dataset, the simulator and the trained controller, to enable future benchmarking and controller design.

42 ENGINEERING↗
DOE OSTI2021

Binder Jet Printing of H13 Tool Steel

ORNL and Carpenter worked on evaluating H13 powders with different powder size distributions for deposition via binder jet additive manufacturing (AM). Binder jet AM has the potential to be a costeffective technique for fabrication of near net shaped tools with complex geometries and internal features such as cooling channels that can improve the production efficiency of injection/compression molding tools. However, a critical challenge in binder jet AM is the densification of the said tool for it to be usable. Powder size distribution can affect the process settings required to deposit the material and the subsequent sintering behavior. In this collaborative effort ORNL and Carpenter demonstrated that the process variables depend on the powder size distribution while the carbon pickup during sintering depends on the binder burnout atmosphere and temperature. On the other hand, there was no noticeable difference in the microstructural evolution of H13 deposited using the different powder size distributions.

42 ENGINEERING↗
DOE OSTI2024

Precision Structure Engineering of High-Entropy Oxides under Ambient Conditions

High-entropy oxides (HEOs) have unveiled a unique frontier in the realm of heterogeneous catalysis, taking advantage of the entropic effect and increased complexities to deliver ultrahigh stability and large tuning capability. However, current HEO synthesis mainly relies on high-temperature annealing approaches affording HEOs possessing no or low surface area, inferior active site exposure efficiency, and low controllability over the structure tuning. The grand challenge lies in producing high-quality HEO catalysts with high active site utilization efficiency, which relies on precision structure engineering, preferably under mild conditions. In this work, an in situ lattice engineering approach was developed to afford a supported HEO catalyst under ambient conditions. The HEO compositions (CuCoFeNiMnO x ) were uniformly integrated into the lattice of CeO 2 driven by cavitation-induced nucleation being generated via ultrasonication. The as-afforded catalysts were featured by high surface area, atomically dispersed HEO compositions, active redox properties, abundant oxygen vacancies (O V ), antiagglomeration, and high phase stability under harsh conditions. Compared with the ex situ introduction of HEO on the surface, the in situ method provides dual benefits to maintain the dispersity of HEO via entropic and lattice confinement effects. Engineering the complex HEO within the lattice of fluorite-structured CeO 2 also yields abundant defects (e.g., O V ) and active metal sites with strong reducing properties (e.g., Ce 3+ and Cu + ), which greatly improves the activity of the lattice oxygen and tunability of the adsorption behavior of the guest molecules, especially in the presence of impurities (e.g., water and propane). The catalytic performance of the supported HEO catalyst in oxidative procedures surpasses the pure dense phase HEO as well as the ex situ-generated catalysts. Further, the synthesis approach being developed in this work, together with the fundamental understanding in structure evolution and reaction mechanism, showcases a facile pathway under ambient conditions to generate stable catalysts capable of maintaining structural robustness in high-temperature conditions while delivering enhanced catalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Workshop on Sustainable Aviation Fuel-Use Research Opportunities

On February 15-16, 2022, the U.S. Department of Energy’s (DOE’s) Vehicle Technology Office (VTO) within Energy Efficiency and Renewable Energy (EERE) convened a workshop to assess the end-use research opportunities for Sustainable Aviation Fuels (SAF). This report summarizes the proceedings and findings of the workshop, which the U.S. Department of Energy’s Argonne National Laboratory hosted as a virtual event. During these two half-days, invited members of national laboratories, industry, government, and academia connected virtually to share perspectives on SAF research needs for improving end-use adoption.

09 BIOMASS FUELS↗
DOE OSTI2023

Next Generation Durable, Cost Effective, Energy Efficient Tubular SOFC (Final Scientific/Technical Report)

The overall objective of this project is to develop and optimize a YSZ electrolyte-based solid oxide fuel cell (SOFC) technology for low cost, low temperature (~ 650°C), and high energy efficiency operation. The developed technology will be implemented and demonstrated in a high efficiency 2~3kW SOFC with applicability to sub-MW systems. A summary of significant accomplishments is provided below. Key accomplishments: 1. Improved fundamental cell technology demonstrated at single tube and system scale. Improved power output by 54% while operating at the normal temperature of 750°C. And improved power output by 33% while operating at 650°C, 100°C lower than normal temperature. 2. SPS patented internal recycle arrangement was developed and extended to operate on LPG fuel directly in a compact, high-efficiency (> 40%) system. 3. System testing was conducted to prove the long-term durability of cell improvements. Demonstrated over 8000 hours of operation at 0.21 %/1000 hrs degradation. 4. A large-scale, 2.5kW net power system demonstration was completed, which demonstrated 40% net efficiency over 1000 hours. 5. System design and cost analysis of a 1MW system utilizing a 2.5kW bundle was completed. Cost optimization of the bundle showed a reduction of nearly 80% is possible from $\$$5,790/kW to $\$$1250/kW. This lower cost is considered viable for SPS commercialization.

03 NATURAL GAS↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2022

A direct numerical simulation study of the dilution tolerance of propane combustion under spark-ignition engine conditions

Modern spark ignition internal combustion (IC) engines rely on highly diluted fuel-air mixtures to achieve high brake thermal efficiencies. To support this, new engine designs have introduced high stroke-to-bore ratios and cylinder head designs that promote high tumble flow and turbulence intensities. However, mixture dilution through exhaust gas recirculation (EGR) is limited by combustion instabilities manifested in the form of cycle-to-cycle variability. Propane has been observed to have superior EGR dilution tolerance than gasoline, which makes it a very competitive low-carbon fuel for the new IC engines without sacrificing efficiency. Two-dimensional direct numerical simulations (DNS) are performed with detailed chemistry to study and contrast the effect of turbulence intensity and dilution on propane and iso-octane premixed flames at high pressure conditions similar to those in-cylinder. A new reduced mechanism for propane consisting of 53 transported species and 17 quasi-steady state species is developed based on a previously published mechanism and used in these simulations. Three levels of turbulence intensity and two levels of exhaust gas dilution are chosen based on conditions relevant to IC engine operation. The DNS results are analyzed based on the evolution of the flame surface area and the statistics of its driving terms, which are found to be similar for both fuels when there is no dilution but considerably different under high dilution. The analysis of the DNS data provides fundamental insights into the underlying mechanisms for improved stability under dilution.

42 ENGINEERING↗
DOE OSTI2021

Supercritical Carbon Dioxide Primary Power Large-Scale Pilot Plant

The United States electrical power generation fleet encompasses a wide range of technologies, ranging from traditional combustion-based power generation, to nuclear power, to renewable energy. While the distribution of these assets continues to shift due to economic and regulatory influences, coal combustion continues to be a key part of the US energy portfolio. Despite the relative maturity of coal combustion technology, improvements in overall fuel-to-power efficiency and generation flexibility are still possible and will improve both the economic and environmental factors of coal combustion. Echogen Power Systems proposes to lead a world-class team including the University of Missouri, Electric Power Research Institute and Louis Perry Associates in the design, construction and operation of a 10MWe coal-fired supercritical carbon dioxide (sCO 2 ) large-scale pilot. This transformational technology uses sCO 2 as a working fluid instead of water to achieve high thermodynamic efficiencies that can significantly exceed advanced steam-Rankine cycles. Further, the compact nature of sCO 2 turbomachinery offers capital cost and footprint advantages, and the low maintenance of a water-free power cycle can significantly reduce operation and maintenance (O&M) costs over conventional steam-Rankine systems. Recent integration studies of sCO 2 with coal combustion power plants highlight the significant improvements in plant efficiency that sCO 2 can offer relative to even advanced steam Rankine cycles. At commercial scales, coal-sCO 2 plant net efficiency is predicted to be 39-44.0% (HHV), or 10-20% higher output than conventional steam-Rankine systems, which will significantly improve the competitiveness of coal-fired generation. This proposal builds upon projects previously funded by the Department of Energy, including DE-FE0025959 (High-Efficiency Thermal Integration of Closed Supercritical CO 2 Brayton Power Cycles with Oxy-Fired Heaters) and DE-NE0008470 (Conceptual Design for sCO 2 Power Cycle Test Facility). An appropriately-scaled and properly designed and operated pilot project is essential to overcome the natural risk-aversion of the power generation industry and project financing community. The 10 MWe coal-fired sCO 2 pilot power plant proposed herein will reduce the technical and economic risk of this transformational technology, enabling commercial deployment at the conclusion of the project. For the second phase of this project, Echogen lead a team that completed and refined the pilot system conceptual system and key component designs resulting in the completion of a front-end-engineering-design (FEED) study, completed the NEPA review process, completed the permitting process for construction and operation, refined the techno-economic analysis of the proposed system at commercial scale and received commitments for Phase III cost share. The end result of the program will be to demonstrate the technical and economic superiority of the sCO 2 power cycle for coal-fired operation. Major risk elements will have been retired with sufficient operation at high power to enable the power generation industry to move forward with the first commercial deployment of this transformational system.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

AIMD‐Based Protocols for Modeling Exciplex Fluorescence Spectra and Inter‐System Crossing in Photocatalytic Chromophores

ABSTRACT This study introduces a computational protocol for modeling the emission spectra of exciplexes using excited‐state ab initio molecular dynamics (AIMD) simulations. The protocol is applied to a model exciplex formed by oligo‐p‐phenylenes (OPPs) and triethylamine (TEA), which is of interest in the context of photocatalytic reduction of . AIMD facilitates efficient sampling of the conformational space of OPP3 and OPP4 exciplexes with TEA, offering a dynamic alternative to previously employed static methods. The AIMD‐based protocol successfully reproduces experimental emission spectra for OPP‐TEA exciplexes, agreeing with previous computational and experimental findings. The results show that AIMD simulations provide an efficient means of sampling the conformational space of these exciplexes, requiring less user input and, in some instances, fewer computational resources than multiple excited‐state optimizations initiated from user‐specified initial structures. The study also evaluates the yield of intersystem crossing (ISC) using AIMD and Landau‐Zener probability. The results suggest that ISC is a minor decay channel for OPP3 and OPP4. This work provides new insights into the structural flexibility and emission characteristics of OPP‐TEA photoredox catalyst systems, potentially contributing to improved design strategies for organic chromophores in reduction applications.

Giudetti, Goran [Department of Chemistry Universit↗
DOE OSTI2021

Modeling Spectrally-Selective Reflection for Thermal Management in Monofacial and Bifacial Modules

Parasitic absorption in photovoltaic modules is a major source of waste heat, which drives operating temperatures 20-30K above ambient. Spectrally-selective sub-bandgap reflection can reduce parasitic absorption, thereby improving module efficiency and power output. Here, we investigate the performance of 1-D spectrally-selective mirrors in monofacial Al BSF and PERC modules, and bifacial PERC modules. In monofacial modules, these mirrors offer >1.2% increase in energy yield compared to single-layer anti-reflection coatings, while cooling by over 1K on average. Mirrors reduced bifacial module parasitic absorption by up to 34 W/m2 out of 1240 W/m2 incident.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

Modern advanced Phasor Measurement Units (PMUs) are developed with ultra-high reporting rates to meet the demand for monitoring the power systems dynamics in detail. Due to the large volume of data, the communication and storage systems are seriously challenged with the presence of Ultra-High-Density (UHD) synchrophasor and Point on Wave (POW) data. Therefore, it is an urgent task to compress the UHD data for more efficient communication and data storage. This paper proposes several methods to compress the synchrophasor and POW data in a lossless manner. First, an Improved-Time-Series-Special Compression (ITSSC) method is proposed to compress the UHD frequency data. Second, a Delta-difference Huffman method is combined with the TSSC algorithm to compress the UHD phase angle data. Finally, a cyclical high-order delta modulation method is proposed to compress the UHD POW data. The proposed models are extensively tested and compared with different existing lossless compression algorithms using the field-collected synchrophasor and POW data at different reporting rates. The results indicate that the proposed algorithms are efficient in performing lossless compression for the UHD synchrophasor and POW data in real time.

42 ENGINEERING↗
DOE OSTI2021

Machine learning for deep elastic strain engineering of semiconductor electronic band structure and effective mass

Abstract The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials. With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond, employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit, through the design of an optimal straining pathway. Such simulations, however, call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties. Motivated by this challenge, we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k -space. These calculations enable us to identify ways in which the physical properties can be altered through “deep” elastic strain engineering up to a large fraction of the ideal strain. Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure. By training a surrogate model with ab initio computational data, our method can identify the most efficient strain energy pathway to realize physical property changes. The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass. We illustrate the applications of the method with specific results for diamonds, although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials.

Tsymbalov, Evgenii↗
DOE OSTI2020

A Guide to Energy Master Planning of High-Performance Districts and Communities

This guide was developed with partners throughout the United States to demonstrate how implementing district-scale high-performance strategies can be successful and scalable approaches to achieving deep energy savings that increase affordability, improve resilience, reduce emissions, and foster economic development. This document serves as a framework for districts, campuses, and communities, illustrating an iterative process of building support for, planning, and implementing high-performance districts by engaging stakeholders, setting aggressive energy goals, completing technical and financial planning, and implementing a high-performance energy master plan. The information in this guide is based on a 3-year U.S. Department of Energy Zero Energy District Accelerator and a range of real-world examples of emerging high-performance districts. It is particularly useful for architects, planners, engineers, local government agencies, and real estate developers in the early phases of planning a district with high-performance or other deep energy goals. For the purposes of this guide, a high-performance district is a multibuilding development that achieves aggressive energy and related goals such as zero energy, carbon neutrality, sustainability, ultra-efficiency, etc. High-performance districts optimize energy efficiency to reduce energy loads and use renewable energy resources to meet the remaining loads whenever possible. The Energy Independence and Security Act of 2007 defines a high-performance building as “a building that integrates and optimizes on a life cycle basis all major high-performance attributes, including energy conservation, environment, safety, security, durability, accessibility, cost-benefit, productivity, sustainability, functionality, and operational considerations.” High-performance districts are collections of such buildings that take advantage of the synergies available when energy consumption and production are considered at a district level rather than one building at a time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗