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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-Temperature Gas-Cooled Reactor Research Survey and Overview: Preliminary Data Platform Construction for the Nuclear Energy University Program

Since the U.S. Department of Energy Office of Nuclear Energy initiated the Nuclear Energy University Program (NEUP) in 2009, there are 29 NEUP projects focusing on high-temperature gas-cooled reactor (HTGR) research up to July 2022. The resultant research product, either experimental or computational, were published as final NEUP reports, journal articles and conference proceedings. However, these federally funded products have been scattered and sometimes cannot be easily accessed. To improve access to this valuable HTGR validation data and optimize the return on the significant investment made by the Department of Energy, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects to develop a public-access database specific for HTGRs applications that can be used to retrieve computational fluid dynamics and system code validation data. This effort will help guide future NEUP-funded research, define new state of the ART Phenomena Identification and Ranking Table (PIRT), and promote the usage of this data in the codes validation matrices. This report provides an overview of the NEUP-funded HTGR-related research projects from Fiscal Year (FY) 2009–2021 and identifies validation knowledge gaps still existing in HTGR thermal-fluid research. A preliminary data platform has been developed for the 29 NEUP projects investigating HTGR thermal hydraulics, including their final reports as well as the available scientific publications. As an ultimate goal for this work, the ART-GCR program will create a central database at Idaho National Laboratory to identify, organize, and store these datasets generated by experimental investigations or computational models, experimental facility descriptions, and publicly-available academic products from the HTGR-related NEUP projects and provide future guidance for the storage and transmission of important project documentations for later NEUP projects as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

HybridSystemsSimulations.jl - Solving the Merchant Collocated Facilities with JuMP

The development of new clean-generation technologies also leads to new plant-level architectures that combine several generation and storage assets behind the point of connection. These co-located generation resources (Hybrid Systems) primarily operate as merchant assets that employ automated market bidding models and internal Energy Management Systems (EMS) to comply with the operator's signals. Formulating an optimal bidding model requires embedding the EMS control model into the bidding algorithm, resulting in a bi-level optimization problem. In this presentation, we first showcase using JuMP to formulate and solve this problem effectively for multiple merchant systems and the bidding outcomes considering different model formulations. Second, the bidding outcomes are later integrated into a PowerSimulations.jl (also built with JuMP) simulation to study the system-level effects of the various merchant bidding and the interactions between market-clearing models and the embedded EMS model. We will showcase simulations conducted in the RTS system considering different levels of merchant hybrid systems participation. The presentation provides the following specific insights on JuMP usage: 1) the Formulation of specialized bi-level problems with custom cuts to solve the merchant hybrid system bidding problem; 2) the integration of a modular model within a complex simulation workflow supported by JuMP in PowerSimulations.jl; 3) Accelerating the solution of power systems operations simulation that employ agent optimization problems using JuMP.

energy markets↗
DOE OSTI2023

A detailed genome-scale metabolic model of Clostridium thermocellum investigates sources of pyrophosphate for driving glycolysis

Lignocellulosic biomass is an abundant and renewable source of carbon for chemical manufacturing, yet it is cumbersome in conventional processes. A promising, and increasingly studied, candidate for lignocellulose bioprocessing is the thermophilic anaerobe Clostridium thermocellum given its potential to produce ethanol, organic acids, and hydrogen gas from lignocellulosic biomass under high substrate loading. Possessing an atypical glycolytic pathway which substitutes GTP or pyrophosphate (PP i ) for ATP in some steps, including in the energy-investment phase, identification, and manipulation of PP i sources are key to engineering its metabolism. Previous efforts to identify the primary pyrophosphate have been unsuccessful. Here, we explore pyrophosphate metabolism through reconstructing, updating, and analyzing a new genome-scale stoichiometric model for C. thermocellum, iCTH669. Hundreds of changes to the former GEM, iCBI655, including correcting cofactor usages, addressing charge and elemental balance, standardizing biomass composition, and incorporating the latest experimental evidence led to a MEMOTE score improvement to 94%. We found agreement of iCTH669 model predictions across all available fermentation and biomass yield datasets. The feasibility of hundreds of PP i synthesis routes, newly identified and previously proposed, were assessed through the lens of the iCTH669 model including biomass synthesis, tRNA synthesis, newly identified sources, and previously proposed PP i -generating cycles. In all cases, the metabolic cost of PP i synthesis is at best equivalent to investment of one ATP suggesting no direct energetic advantage for the cofactor substitution in C. thermocellum. Even though no unique source of PP i could be gleaned by the model, by combining with gene expression data two most likely scenarios emerge. First, previously investigated PP i sources likely account for most PP i production in wild-type strains. Second, alternate metabolic routes as encoded by iCTH669 can collectively maintain PP i levels even when previously investigated synthesis cycles are disrupted. Model iCTH669 is available at github.com/maranasgroup/iCTH669.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Lookup Table Based Low Resource Usage Waveform Data Compression Block in FPGA

aA lookup table based lossless data compression scheme suitable for FPGA implementation, with very low silicon resource usage, is developed. Different algorithms can be implemented by simply changing the contents of the lookup tables in the functional block while keeping the logic circuits unchanged. It exhibits performance comparable to the zip compression software in Linux distributions. The compression block can fit into almost any existing FPGA used in the online data acquisition systems of high energy physics experiments. The decompression process can be performed in either software or in FPGA firmware, allowing users to process compressed data flexibly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Uncertainties associated with GAN-generated datasets in high energy physics

Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out that data generated by a GAN cannot statistically be better than the data it was trained on, and critically examine the applicability of GANs in various situations, including a) for replacing the entire Monte Carlo pipeline or parts of it, and b) to produce datasets for usage in highly sensitive analyses or sub-optimal ones. We present our arguments using information theoretic demonstrations, a toy example, as well as in the form of a formal statement, and identify some potential valid uses of GANs in collider simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Cost-efficient copper-nickel alloy for active cooling applications

It is recently proposed that active (Peltier) and passive (conductive) cooling could be combined to design highly-efficient thermal conductors as active heat sinks. Here we study the potential of low-cost copper-nickel alloys for active cooling applications. We show that for this application, copper-nickel alloys prepared using industrial-grade powders to lower the manufacturing cost, are as good as copper-nickel alloys made out of high purity metals. Furthermore, directed energy deposition, a type of additive manufacturing, can produce copper-nickel alloys which have about 10 percent lower thermoelectric power factor compared to mechanical-alloying methods but have larger thermal conductivity values, hence, possessing similar effective thermal conductivity values, reaching 403 W m -1 K -1 at 390K. Much larger values are expected at higher temperatures. The usage of additive manufacturing enables complex geometries and large-scale production.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Discerning structure sensitivity of Ni-core Pd-shell nanoparticles for enhanced nitrite reduction

Denitrification using Pd-based catalysts converts N-oxyanions into harmless products. However, the metal cost and scarcity are barriers to application. In this work, we synthesize water-suspended, structure-controlled Ni-core/Pd-shell nanoparticles (Ni@Pd NPs) that show Pd-catalyzed nitrite (NO 2 − ) reduction catalysis promoted by Ni. The NPs immobilized on Al 2 O 3 (0.47 wt % Pd and 0.74 wt % Ni) have higher NH 4 + selectivity and higher catalytic activity than wet-impregnated Pd/Al 2 O 3 (1.0 wt % Pd), i.e., 315.4 vs. 132.1 Lg surface Pd −1 min −1 . X-ray photoelectron spectroscopy (XPS) and CO-diffuse reflectance infrared Fourier transform spectroscopy (CO-DRIFTS) analyses show that Ni increases Pd electron density, which is proposed to lower the bond dissociation energy barrier for nitric oxide surface intermediate. Low-coordinated Pd atoms correlate to high activity and low NH 4 + selectivity. Using CatCost software, we estimate Ni@Pd/Al 2 O 3 to have a ∼40% lower manufacturing cost compared to wet-impregnated Pd/Al 2 O 3 . This rationally designed catalyst illustrates how nickel promotes Pd catalysis and how Pd usage can be lowered for denitrification and other hydrogenation reactions.

CO-DRIFTS↗
DOE OSTI2021

Stiff neural ordinary differential equations

Neural Ordinary Differential Equations (ODEs) are a promising approach to learn dynamical models from time-series data in science and engineering applications. This work aims at learning neural ODEs for stiff systems, which are usually raised from chemical kinetic modeling in chemical and biological systems. We first show the challenges of learning neural ODEs in the classical stiff ODE systems of Robertson’s problem and propose techniques to mitigate the challenges associated with scale separations in stiff systems. We then present successful demonstrations in stiff systems of Robertson’s problem and an air pollution problem. The demonstrations show that the usage of deep networks with rectified activations, proper scaling of the network outputs as well as loss functions, and stabilized gradient calculations are the key techniques enabling the learning of stiff neural ODEs. The success of learning stiff neural ODEs opens up possibilities of using neural ODEs in applications with widely varying time-scales, such as chemical dynamics in energy conversion, environmental engineering, and life sciences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An open-source framework for simulation-based testing of buildings control strategies

Here this paper presents a simulation framework for evaluating building control strategies (BCSs), developed with VOLTTRON, an opensource platform that integrates data, devices, and systems for sensing and control applications, and is based on co-simulation interfaces. It realizes an integrated environment for both testing and deploying BCSs, thereby eliminating the need for having a dedicated implementation of BCS for testing. For the first time, this framework provides critical functionalities, including scalable communication management and time-drive simulation advance, i.e., advancing the simulation based on clock time. We applied this framework to evaluating two BCSs from ASHRAE Guideline 36-2008 and ASHRAE Standard 90.1-2019. The evaluation results show that those BCSs primarily benefit the heating operation by reducing gas usage but yield insignificant savings in electricity consumption. The results also emphasize the importance of tuning the parameters of the BCSs to achieve better performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Calendar Year 2020 Post-Closure Monitoring Report for Corrective Action Unit 99: Rainier Mesa/Shoshone Mountain, Underground Test Area, Nevada National Security Site, Nevada (Rev. 1)

This report presents the results of monitoring conducted for water quality, water levels, and institutional controls in calendar year (CY) 2020 by the U.S. Department of Energy (DOE), Environmental Management (EM) Nevada Program’s Underground Test Area (UGTA) Activity at Corrective Action Unit (CAU) 99, Rainier Mesa/Shoshone Mountain (RM/SM), at the Nevada National Security Site (NNSS), Nevada. This report also presents analytical laboratory results (including results of quality assurance/quality control samples, such as field duplicates [FDs]), and verification of use restrictions (URs), institutional controls, and water usage. Groundwater samples were collected; water levels were measured; and well site surveillance was conducted in support of the Underground Test Area Closure Report (CR) for Corrective Action Unit 99: Rainier Mesa/Shoshone Mountain, Nevada National Security Site, Nevada (DOE/EMNV, 2020). Rainier Mesa (RM) was the site of 61 underground nuclear tests, and Shoshone Mountain (SM) was the site of 6 underground nuclear tests. As a result of these activities, which took place from 1957 to 1992, radionuclides (RNs) were released in the subsurface in the vicinity of the detonations. These 67 underground nuclear tests are associated with 66 specific corrective action site (CAS) numbers, as listed in the Federal Facility Agreement and Consent Order (FFACO) (1996, as amended). Two tests, HURON LANDING and DIAMOND ACE (conducted simultaneously), are included within one CAS. The CR (DOE/EMNV, 2020) establishes the regulatory boundaries and regulatory boundary objectives, monitoring program, UR boundaries and URs, and other institutional controls agreed to by EM Nevada Program and the Nevada Division of Environmental Protection (NDEP) for closure of CAU 99. Regulatory boundaries for the RM/SM CAU were established to protect receptors of groundwater from RN contamination within the three downgradient groundwater basins that receive recharge from RM. The SM regulatory boundary was established to verify that RN contamination does not reach the lower carbonate aquifer below SM. The UR boundaries were established based on contaminant simulation forecasts from the flow and transport simulations with consideration of site-specific topographical controls. The URs associated with the UR boundaries were identified to protect onsite workers and the public from inadvertent exposure to contaminated groundwater as forecasted to occur assuming current conditions. The UR is intended to restrict activities that might expose workers to contaminated groundwater within the UR area. In addition to the URs, other institutional controls are established to monitor and limit access to groundwater. These include federal ownership and management in perpetuity, controlled access of the NNSS and surrounding areas, and reporting water use on the NNSS and surrounding hydrographic basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Transactive Energy Network Template Metamodel

While transactive energy, which is defined as an allocation of electricity based on dynamically discovered values or prices, has been extensively studied, its uptake and use has been slow. This report describes a tool, the transactive network template, which should hasten the creation and uptake of transactive energy networks. Some basic principles of transactive energy are familiar from existing wholesale electricity markets. Locational prices are calculated today for zones within bulk electric transmission systems. Locational prices differ while accounting for the locational costs of electricity generation and the losses and constraints incurred when electricity is transmitted from generators and distributed to consumers. A transactive energy network might include these transmission zones. However, current research strives to apply transactive energy also in electricity distribution circuits, buildings, and even for individual generating and consuming devices. At the same time, researchers explore how to apply transactive energy in real time during increasingly shorter time intervals. Automated computational agents become necessary as transactive energy becomes applied to smaller circuit zones and at faster dynamic timescales. A transactive energy network is an example of a multi-agent system. Each zone in the network is represented by its transactive agent, which makes decisions for and acts on behalf of a business entity that is responsible for and manages one of the circuit regions. A transactive energy network is also an example of a decentralized, distributed control system. Control decisions and responsibilities are distributed among the network’s transactive agents. The transactive agents are independent; that is, there typically is no centralized authority or oversight function. Instead, transactive agents exchange transactive signals and thereby negotiate the prices and quantities of electricity that they will exchange. Initially, the circuit regions and responsibilities of transactive agents appear to be very dissimilar. Each circuit region may comprise transmission, distribution, or building-level circuits. Each has a unique position and electrical connectivity within the transactive energy network. Each possesses unique assets that either generate or consume electricity, and these (e.g., renewable energy generator, diesel generator, aggregate utility load, building load, space conditioning, refrigerator, etc.) may further differ in their price flexibility and in their strategies for responding to dynamic electricity prices. Given such diversity, an implementer’s first inclination might be to start from scratch to define all these devices and to engineer their seemingly unique interactions. Given that each implementer’s perspective may be narrow within a transactive energy network, it is unlikely that uniquely engineered systems would interact well. This is where the transactive network template is applicable. The transactive network template is a metamodel that has been developed to guide implementers as they configure their own transactive agent within a network of such agents. The object-oriented design of the transactive network template provides basic code object types that may be used and extended by implementers to represent each of the assets in their circuit region. These objects further facilitate the transactive agent’s necessary computations, which are divided among responsibilities to schedule power usage, balance electric supply and demand, and coordinate the exchange of electricity with the other transactive agents. This report addresses the conceptual transactive network template design. Implementers are directed to more formal design documents and reference implementations. A Python™-based1 reference implementation of the transactive network template has been coded, and three implementations have been configured to represent a national laboratory and two university campuses. Version 2 of the transactive node template generalizes the market class and its methods to facilitate multiple, and more diverse market coordination mechanisms than were facilitated by and demonstrated using Version 1. Version 3 includes new Appendix B, which addresses the designs of methods that would make dynamic prices track approved electricity rates. In the future, the author wishes to make the transactive network template more generally applicable to networks that require more accurate power flow. Development of the transactive network template is jointly funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy and the DOE Office of Electricity. In late 2015, one of the first projects to be funded by the DOE Grid Laboratory Modernization Laboratory Consortium was the Clean Energy and Transactive Campus project, led by Pacific Northwest National Laboratory. DOE funds were matched by an investment by the Washington Department of Commerce through its Clean Energy Fund. The transactive network template was developed to guide the implementation of transactive energy networks within this project’s scope.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Deep Learning for Subsurface Flow: A Comparative Study of U‐Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage

Subsurface flow research is essential for the sustainable management of natural resources and the environment. Deep learning (DL) has significantly advanced this field by developing efficient and accurate surrogate models to replace computationally expensive physics‐based simulations. These surrogate models are commonly used to predict the spatiotemporal evolution of state variables, such as gas saturation and reservoir pressure, in heterogeneous geological formations. Despite the various DL models applied to this task, there is a lack of studies systematically comparing their performance. This absence of comparative analysis leads to somewhat arbitrary DL model selection in subsurface flow research, resulting in suboptimal performance and potentially inaccurate predictions. To bridge this gap, we conduct a systematic comparison study of three popular DL architectures—U‐Net, Fourier Neural Operators (FNO), and Segmentation Transformer (SETR)—in surrogate modeling of underground hydrogen storage (UHS). We focus on UHS due to its promise of enhancing clean energy resilience and its cyclic operational conditions that represent common scenarios in various subsurface applications. We evaluate the models based on accuracy, training cost, and inference speed. The comparison shows that U‐Net achieves the highest accuracy, followed by SETR and FNO. Despite its lower accuracy, FNO has the highest inference speed. SETR offers competitive accuracy with the least training memory usage, demonstrating the potential of transformers in learning subsurface flow. Our results provide guidance for selecting DL models for surrogate modeling in a wide range of subsurface flow problems.

42 ENGINEERING↗
DOE OSTI2022

Spectral function for 4 He using the Chebyshev expansion in coupled-cluster theory

Here, we compute spectral function for 4 He by combining coupled-cluster theory with an expansion of integral transforms into Chebyshev polynomials. Our method allows us to estimate the uncertainty of spectral reconstruction. The properties of the Chebyshev polynomials make the procedure numerically stable and considerably lower in memory usage than the typically employed Lanczos algorithm. We benchmark our predictions with other calculations in the literature and with electron-scattering data in the quasi-elastic peak. The spectral function formalism allows one to extend ab initio lepton-nucleus cross sections into the relativistic regime. This makes it a promising tool for modeling this process at higher-energy transfers. The results we present open the door for studies of heavier nuclei, important for the neutrino oscillation programs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Data Augmentation for Neutron Spectrum Unfolding with Neural Networks

Neural networks require a large quantity of training spectra and detector responses in order to learn to solve the inverse problem of neutron spectrum unfolding. In addition, due to the under-determined nature of unfolding, non-physical spectra which would not be encountered in usage should not be included in the training set. While physically realistic training spectra are commonly determined experimentally or generated through Monte Carlo simulation, this can become prohibitively expensive when considering the quantity of spectra needed to effectively train an unfolding network. In this paper, we present three algorithms for the generation of large quantities of realistic and physically motivated neutron energy spectra. Using an IAEA compendium of 251 spectra, we compare the unfolding performance of neural networks trained on spectra from these algorithms, when unfolding real-world spectra, to two baselines. We also investigate general methods for evaluating the performance of and optimizing feature engineering algorithms.

McGreivy, James (ORCID:0000000321723411)↗
DOE OSTI2020

Radiation Chemistry Research for Improved Nuclear Fuel Cycles

Nuclear energy is a key component of a global, low-carbon energy future. One of the challenges facing large-scale deployment of nuclear power is the long-term disposition of used nuclear fuel. Due to the political and technical challenges of siting deep geologic storage facilities, reprocessing of used nuclear fuel is desirable to reduce the total volume and decay heat of waste requiring long-term storage, enabling efficient usage of geologic storage repositories. However nuclear fuel reprocessing is complicated by intense multi-component radiation fields that degrade solvent systems that are used for this purpose. Resulting radiolytic degradation can result in reduced performance over time, and generation of additional waste. Thus, an understanding of the radiation-induced chemistry of these reprocessing solvent systems is crucial to for the development of new, radiation-resistant processes for efficiently treating used nuclear fuel. Controlled irradiation experiments combined with high-performance chromatography and mass spectrometry have provided comprehensive knowledge of radiolytic degradation pathways and kinetics, which is needed to predict the effect of radiation-induced chemistry on the performance of the nuclear fuel reprocessing technology. This talk will give an overview of recent progress in research on the radiation chemistry of fuel reprocessing molecules conducted by the Idaho National Laboratory Center for Radiation Chemistry Research (CR2) in conjunction with the Molecular Mass Spectrometry and Chromatography group.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Feasibility of Recovering and Recycling Polymer Composites from End-of-Life Marine Renewable Energy Structures: A Review

Over the last few decades, several marine renewable energy (MRE) technologies, such as wave energy converters (WECs) and current energy converters (CECs), have been developed. As opposed to traditional materials such as metal alloys, the structure of these technologies is made up of polymer and polymer composite materials. Most structures have been made using thermoset polymer composites; however, since thermoset polymer composites are not recyclable and lack sustainability, and with recent innovations in recyclable resins, bio-based resins, and the development of additive manufacturing technologies, thermoplastic polymers are increasingly being used. Nevertheless, the methodologies for identifying end-of-life options and recovering these polymer composites, as well as the recycling and reuse processes for MRE structures, are not well-studied. Specifically, since these MRE structures are subjected to salinity, moisture, varying temperature, biofouling, and corrosion effects depending on their usage, the recyclability after seawater aging and degradation needs to be explored. Hence, this review provides an in-depth review of polymer composites used in marine applications, the hygrothermal aging studies conducted so far to understand the degradation of these materials, and the reuse and recycling methodologies for end-of-life MRE structures, with a particular emphasis on sustainability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Performance of discontinuity-free components produced by additive turning computer aided manufacturing strategy

Computer aided manufacturing (CAM) techniques for directed energy deposition (DED) affect the material properties of the manufactured component based on the scan strategy used. In this study, we investigate the material characteristics of turning-style toolpath strategies to generate axisymmetric components with additive manufacturing (AM), referred to in this research as additive turning. This novel approach leverages existing CAM technology for turning, where the component rotates around a stationary cutting tool, to generate toolpath trajectories for DED with varying wall-thicknesses and controlled deposition angles. This strategy allows for entire components to be deposited in one continuous deposition, resulting in reduced cycle-time and improved material usage efficiency compared to conventional AM strategies where the beam is switched off at the end of every layer. Results from this study show that the use of additive turning can produce over 99 % dense components with less variation and anisotropy in texture and hardness, as well as a lower variation in elongation to failure when compared to conventional strategies. This research highlights that various CAM strategies could be deployed for AM to improve process efficiency or enable localized control over part performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗