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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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DOE OSTI2016

Strain induced novel quantum magnetotransport properties of topological insulators

Recent theoretical and experimental researches have revealed that the strained bulk HgTe can be regarded as a three-dimensional topological insulator (TI). Motivated by this, we explore the strain effects on the transport properties of the HgTe surface states, which are modulated by a weak 1D in-plane electrostatic periodic potential in the presence of a perpendicular magnetic field. We analytically derive the zero frequency (dc) diffusion conductivity for the case of quasielastic scattering in the Kubo formalism, and find that, in strong magnetic field regime, the Shubnikov–de Haas oscillations are superimposed on top of the Weiss oscillations due to the electric modulation for null and finite strain. Furthermore, the strain is shown to remove the degeneracy in inversion symmetric Dirac cones on the top and bottom surfaces. This accordingly gives rise to the splitting and mixture of Landau levels, and the asymmetric spectrum of the dc conductivity. These phenomena, not known in a conventional 2D electron gas and even in a strainless TI and graphene, are a consequence of the anomalous spectrum of surface states in a fully stained TI. These results should be valuable for electronic and spintronic applications of TIs, and thus we fully expect to see them in the further experiment. - Highlights: • The strain removes the degeneracy in inversion symmetric Dirac cones. • The strain gives rise to the splitting and mixture of the Landau levels. • The strain leads to the asymmetric spectrum of the dc conductivity. • Shubnikov de Haas oscillations are shown to be superimposed on Weiss oscillations. • Interplay between strain and electric field causes different occupancy of TI states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Quantum theory of half-integer spin free particles from the perspective of the Majorana equation

In this study, the Majorana equation for particles with arbitrary spin is solved for a half-integer spin free particle. The solution for the fundamental state, corresponding to the reference frame in which the particle is at rest, is compared with that obtained using the Dirac equation, especially as regards the approximation in the relativistic limit, in which the speed of the particle is close to that of light. Furthermore, the solutions that Majorana defines unphysical, proving that their occupation probability increases with the particle velocity, are taken into consideration. The anomalous behavior exhibited by these states suggests that for high-energy particles with small mass, transitions from a bradyonic state to a tachyonic state could become possible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Self-consistent determination of the many-body state of ultracold bosonic atoms in a one-dimensional harmonic trap

We study zero-temperature quantum fluctuations in harmonically trapped one-dimensional interacting Bose gases,using the self-consistent multiconfigurational time-dependent Hartree method. We define phase fluctuations from the full single-particle density matrix by the spatial decay exponent of off-diagonal long-range order. In a regime of mesoscopic particle numbers and moderate contact couplings, we derive the spatial dependence of the amplitude of phase fluctuations, determined from the self-consistently derived shape of the field operator orbitals and Fock space orbital occupation amplitudes. It is shown that the phase fluctuations display a peak, which in turn corresponds to a dip of the first-order correlations in position space, akin to what has previously been obtained in the Tonks–Girardeau limit of very large interactions and low densities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Benchmarking the multiconfigurational Hartree method by the exact wavefunction of two harmonically trapped bosons with contact interaction

Highlights: • Comparison of exact and numerical single-particle density matrices. • Benchmark for multiconfigurational Hartree. • Convergence for large number of orbitals. We consider two bosons in a one-dimensional harmonic trap, interacting by a contact potential, and compare the exact solution of this problem to a self-consistent numerical solution by using the multiconfigurational time-dependent Hartree (MCTDH) method. We thereby benchmark the predictions of the MCTDH method with a few-body problem that has an analytical solution for the most commonly experimentally realized interaction potential in ultracold quantum gases. It is found that exact ground state energy and first order correlations are accurately reproduced by MCTDH up to the intermediate dimensionless coupling strengths corresponding to typical background scattering lengths of magnetically trapped ultracold dilute Bose gases. For larger couplings, established for example by (a combination of) Feshbach resonances and optical trapping, the MCTDH approach overestimates the depth of the trap-induced correlation dip of first order correlations in position space, as well as overestimates the fragmentation, defined as the average relative occupation of orbitals other than the energetically lowest one. We anticipate that qualitatively similar features in the correlation function may arise for larger particle numbers, paving the way for a quantitative assessment of the accuracy of MCTDH by experiments with ultracold atoms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Quantum dynamics of a nucleon in the Fermi accelerator

Highlights: • In nuclear physics, the Fermi accelerator is a coherent open quantum system. • There is not a true equilibrium for a nucleon‘s mean-energy in the Fermi accelerator. • Occupation of higher energy eigen-states remains even after the mean-energy plateaus. • This work paves the way for work on quantum tunnelling in the Fermi accelerator. The quantum dynamics of a particle in a one-dimensional box with an oscillating wall (the Fermi accelerator) is investigated. The model is applied to the motion of a single nucleon in the mean-field potential of a heavy atomic nucleus whose surface vibrates. By directly solving the time-dependent Schrödinger equation, both the state of the particle and its mean-energy are studied. The effects of the frequency of the wall oscillation on the nucleon’s energy are addressed. Its energy oscillates in phase with the moving wall for all frequencies, showing no chaotic behaviour. There is a large initial peak of the nucleon’s energy as the particle adjusts to the sudden change in the size of the box and a varying relaxation time as it plateaus towards lower energy and a partial equilibrium. Small oscillations in energy continue, since there cannot be a true equilibrium while the wall is moving. The quantum coherence between the different parts of the nucleon’s wave-function in real space is very much preserved. This research lays the foundation for future investigations into quantum tunnelling in the Fermi accelerator.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Low-energy effective quantum field theoretic description of excitations about soliton configurations

Solitons are the classical field configurations connecting two trivial vacua. These are also the solutions of classical field equations of motion with particle-like properties. Moreover, they are localized in space, having finite energy, and are stable against decay into radiation. The coherent state description of kink-solitons is discussed in the present article. Further, the relation between topological solitons and occupation numbers corresponding to low momentum excitations are also discussed coherently. The description of the low energy excitations about solitons in quantum field theory is the main theme of this article. Further, a few physical observables, namely some low order correlation functions, are computed up to certain integral forms. Furthermore, we have shown that it is possible to detect the presence of soliton-like classical configurations in many-particle systems from the nature of the one-point function and non-conservation of momentum feature of one-point, two-point and three-point functions in this low-energy effective field theory of these excitations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

A regression-based approach to estimating retrofit savings using the Building Performance Database

Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Evaluation of “Autotune” calibration against manual calibration of building energy models

Our paper demonstrates the application of Autotune, a methodology aimed at automatically producing calibrated building energy models using measured data, in two case studies. In the first case, a building model is de-tuned by deliberately injecting faults into more than 60 parameters. This model was then calibrated using Autotune and its accuracy with respect to the original model was evaluated in terms of the industry-standard normalized mean bias error and coefficient of variation of root mean squared error metrics set forth in ASHRAE Guideline 14. In addition to whole-building energy consumption, outputs including lighting, plug load profiles, HVAC energy consumption, zone temperatures, and other variables were analyzed. In the second case, Autotune calibration is compared directly to experts’ manual calibration of an emulated-occupancy, full-size residential building with comparable calibration results in much less time. Lastly, our paper concludes with a discussion of the key strengths and weaknesses of auto-calibration approaches.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Foresee: A user-centric home energy management system for energy efficiency and demand response

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Personal thermal management using portable thermoelectrics for potential building energy saving

Heating and cooling of buildings consume approximately 15% of all energy used in the United States. Such a large energy demand is primarily due to heating and cooling of the entire building space to temperature setpoints usually between 21.1 degrees C (70 degrees F) and 23.9 degrees C (75 degrees F). However, even with such a narrow range of temperature setpoints, more than 20% of the occupants do not feel thermally comfortable due to individual differences (e.g. age, gender, clothing, or physiology). The personal thermal management techniques, which create a local thermal envelope around a human body instead of heating or cooling the entire building space, have the potential to greatly reduce the building energy consumption and to enhance thermal comfort of individuals. In this study, a portable thermoelectric energy conversion unit (TECU) that converts electricity into cooling and heating energy is developed. The TECU supplies cool air (in the cooling mode) or warm air (in the heating mode) to regulate the thermal comfort of a human body. The cool or warm air is supplied through a tree-like rubber tube network that is knitted into a thermoregulatory undergarment. To achieve a cooling/heating target that provides satisfactory thermal comfort, the required cooling/heating power supply from the TECU is determined first while a theoretical model is then developed to guide the design of the TECU. To minimize the TECU weight and make it suitable for portable applications, relationships between weight and thermal resistances of commercial off-the-shelf heat sinks are established first, and a method to find the minimal weight of heat sinks for the TECU is then developed. This methodology is also applicable for other applications where heat sink weight needs to be minimized. The thermal manikin tests demonstrate that 24.6 W of personal cooling power and 18.5 W of personal heating power are achieved by using the TECU.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems

Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors’ impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20%25 in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimal electric-distribution-grid planning considering the demand-side flexibility of thermal building systems for a test case in Singapore

The planning of district-scale electric grids, i.e., distribution grids, has traditionally relied on finding the most cost-effective design such that they are able to supply the peak loads in a district. With the advent of electric demand side flexibility (DSF), there is the opportunity to reshape peak loads such that the investment cost of the electric grid decreases in exchange for a minor increase in the operation cost. This paper formulates an optimal planning approach for the electric grid at the district scale, which incorporates the DSF from thermal building systems, e.g., heating ventilation and air-conditioning (HVAC) systems. The problem is formulated as a mixed-integer linear program (MILP) and aims at minimizing the investment cost for the grid along with the operation cost of the flexible loads. This is subjected to the fixed electricity demand and thermal comfort constraints of building occupants. To this end, linear models for the thermal comfort in the buildings and the power flow in electric grid are considered. The approach is tested on a district planning test case based in Singapore, where the results show up to 30.9 % reductions in investment cost and up to 3.7 % reduction in total annualized cost. Urban planning authorities, developers and utility companies can all benefit from the presented approach to make optimized investment decisions. For building operators, the results point to the need of adopting their control systems for DSF.

Troitzsch, Sebastian↗
DOE OSTI2020

Data-driven evaluation of HVAC operation and savings in commercial buildings

Commercial buildings consumed 36% of electricity, or 1.35 trillion kWh, in the United States in 2017, and almost 30% of this energy was wasted. Much of this loss can be attributed to inefficient heating ventilation and air con­ditioning (HVAC) systems. By improving the operational conditions of HVAC, significant savings can be achieved. However, most buildings and building equipment do not use costly sub-meters to monitor and address performance issues, and on-site auditing can be expensive and insufficient. Alternatively in this study, we propose a data-driven method to identify savings opportunities using only whole building meter data and without setting foot in the building. For this purpose, we introduced two algorithms that virtually quantify the value of a thermostat setpoint setback and HVAC rescheduling. Additionally, we developed novel methods for detecting occupancy patterns and quantifying the baseload of the HVAC operation. Using a clustering algorithm, we identified those buildings for which HVAC savings was significant and further categorized the buildings based on their potential for savings. A population study of over 432 commercial buildings demonstrated a median percentage energy savings of 1.6% from a baseload reduction and 2.1% from HVAC rescheduling. Additionally, results indicate that retail buildings have the highest potential for savings among the building types studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies

Electrification and the growth of distributed energy resources (DERs), including flexible loads, are changing the energy landscape of electric distribution systems and creating new challenges and opportunities for electric utilities. Changes in demand profiles require improvements in distribution system load models, which have not historically accounted for device controllability or impacts on customer comfort. Although building modeling research has focused on these features, there is a need to incorporate them into distribution load models that include DERs and can be used to study grid-interactive buildings. In this paper, we present the Object-oriented, Controllable, High-resolution Residential Energy (OCHRE) model. OCHRE is a controllable thermal-electric residential energy model that captures building thermal dynamics, integrates grid-dependent electrical behavior, contains models for common DERs and end-use loads, and simulates at a time resolution down to 1 minute. It includes models for space heaters, air conditioners, water heaters, electric vehicles, photovoltaics, and batteries that are externally controllable and integrated in a co-simulation framework. Using a proposed zero energy ready community in Colorado, we co-simulate a distribution grid and 498 all-electric homes with a diverse set of efficiency levels and equipment properties. We show that controllable devices can reduce peak demand within a neighborhood by up to 73% during a critical peak period without sacrificing occupant comfort. We also demonstrate the importance of modeling load diversity at a high time resolution when quantifying power and voltage fluctuations across a distribution system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Home energy management under realistic and uncertain conditions: A comparison of heuristic, deterministic, and stochastic control methods

We report home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗