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At least 163 records · Page 9

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DOE OSTI2022

Dynamic optimization with flexible heat integration of a solar parabolic trough collector plant with thermal energy storage used for industrial process heat

There is an increasing need to reduce fossil fuel consumption used for industrial process heat to slow the effects of climate change. Using solar thermal heat is a viable way to replace fossil fuel use, but solar industrial process heat plants have limited implementation due to large upfront costs and inefficiencies from the inherent variability from solar energy. Having more flexible and optimized control over these plants can enable them to be more efficient. Here in this work, a solar industrial process heat plant with thermal energy storage that can flexibly collect and deliver heat to two industrial processes (flexible heat integration) is dynamically modeled with control setpoints found by a dynamic optimization method. The optimized case can increase the solar efficiency of the plant by 7.5% on average relative to a base case. The results show that it is best to collect heat at lower temperatures for all but ideal solar conditions, only medium to high quality heat should be stored in large quantities, and exchanging heat with a lower temperature heat sink is generally more efficient. The optimized case is able to reduce the levelized cost of heat of the plant to $31.83/MWh th compared to a base case value of $34.10/MWh th . The optimized case can reduce emissions by 22.2% compared to a plant which uses only natural gas. This work shows that dynamic optimization with flexible heat integration can be a cost-effective way to increase efficiency so that more of these types of plants can be implemented.

14 SOLAR ENERGY↗

DOE OSTI2024

Optimal environmental and economic performance trade-offs for fifth generation district heating and cooling network topologies with waste heat recovery

Network topology greatly influences both the economic and environmental performance of fifth generation district heating and cooling (5GDHC) systems. In this study the optimal trade-offs between the environmental and economic performance of 5GDHC network topologies for a five-building district with waste heat recovery were explored. A life cycle assessment method was used to calculate the total life cycle CO 2 emissions (LCCO2) associated with the installation and operation of various network topologies. Twelve months of empirical data from a data center cooling system were analyzed to assess its suitability for integration into a 5GDHC system. The most suitable method for utilizing this waste heat was selected based on the ambient loop warm pipe setpoint, waste heat temperature, and district energy system configuration. A multi-objective optimization algorithm was used to select the 5GDHC network topology that provided the optimal trade-off between LCCO2 and life cycle cost (LCC). A trade-off parameter was employed to weigh the importance of each objective in the selection process. The results showed waste heat from the data center was suitable for integration into the 5GDHC system due to its availability and consistent temperature profile. When return temperatures of 25 °C or higher were available from the liquid-cooled system, direct pre-heating of the ambient loop warm pipe was found to be the most effective waste heat integration method. The selection of the network topology that provided the optimal trade-off between LCCO2 and LCC (optimal trade-off topology) was highly dependent on factors such as fuel prices, CO 2 prices, electricity CO 2 emissions factors, availability of waste heat, embodied CO 2 emissions associated with network installation and network infrastructure costs. Optimal trade-off topologies produced substantial LCCO2 reductions relative to corresponding LCC increases. LCCO2 reduction to LCC increase ratios from 5.78 to 117.79 were identified with CO 2 offset costs ranging from 4.77 to 60.08 ($/tCO 2 e).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Industrial battery operation and utilization in the presence of electrical load uncertainty using Bayesian decision theory

Behind the meter battery storage is becoming increasing popular in all sectors, though enthusiasm has recently lagged in the industrial sector. Even though there may be many factors contributing to this including lack of innovation, prohibitive costs, and undesirable rate structures, a difficulty arises in accounting for uncertainty of electrical load in industrial facilities while still attempting to utilize battery storage as much as possible all while trying to achieve fiscal profitability. Here this study utilizes Gaussian process regression and Bayesian decision theory to organize load data and quantify electrical load uncertainty to properly and effectively discharge industrial battery storage. The study employs a simulation model to set battery load setpoints for the span of the utility billing period according to the degree of risk aversion. This combination of economic analysis according to utility billing period and utilization of degree of risk aversion to make decisions on the uncertainty of the data has not before been applied to battery storage. The method resulted in an annual average reduction of peak demand by 3.8 % at the lowest amount of savings and lowest risk aversion. The highest risk aversion resulted in an annual average reduction of peak demand of 7.5 %. The maximum reduction of peak load in any month was 13.8 % in the month of December with a relatively high risk aversion. With a the highest amount risk aversion tested, the model reduced demand ten of the twelve months of the year.

25 ENERGY STORAGE↗

DOE OSTI2018

Enhancing the thermosiphon-driven discharge of a latent heat thermal storage system used in a personal cooling device

Personal cooling devices reduce energy loads by allowing buildings to operate with elevated setpoint temperatures, without compromising on the occupant comfort. One such novel technology called the Roving Comforter (RoCo) uses a compact R134a based vapor compression system for cooling. Following its cooling operation, during which waste heat from the condensing refrigerant is stored in a phase change material (PCM), a two-phase loop thermosiphon is used to discharge (solidify) the PCM to enable its next operation. The transient operation of this thermosiphon is the focus of the present paper. Use of a PCM as the storage medium provides high energy density due to the ability to store thermal energy as latent heat during the phase transition; however, the discharge rate is limited by the low thermal conductivity of the PCM. Insertion of a graphite foam within the PCM can increase the rate of discharge and decrease the downtime of the cooling device. Since graphite enhancement involves a tradeoff between improving the discharge time at the expense of PCM volumetric latent heat, the impact of graphite foam density on the PCM discharge rate is investigated by using a Modelica-based transient model of the thermosiphon. The semi-empirical model, which uses relevant heat transfer coefficient and pressure drop correlations for both refrigerant and airside heat transfer, captures the complex phenomena involving simultaneous phase change of the refrigerant and the PCM. Finally, the graphite enhanced PCM selected from this analysis results in a 51% reduction in the discharge time with addition of only 5% to the thermal storage weight, without compromising the required cooling time.

42 ENGINEERING↗

DOE OSTI2020

A Performance analysis of the Claridge-Culp-Liu dehumidification process: A novel approach for drying moist air based on membrane separation, vacuum compression and sub-atmospheric condensation

This article covers a basic model for analyzing the performance of the Claridge-Culp-Liu dehumidification process. The fundamental process efficiency limit for dehumidification is close to COP Carnot , but for the eight dehumidification cases examined, the limiting or ideal energy use required is 26% to 56% that of a Carnot condensing system as shown in an earlier paper. The model presented in this paper is used to show the membrane system performance reduction caused by finite membrane area, finite water vapor permeance, non-zero air permeance, non-zero system air pressure drop, non-ideal compressors, vacuum pumps, and condensers. The performance of a “conservative” membrane system based on the use of existing components is computed for eight specific conditions along with that of a “target” system that assumes expected component performance after additional future component development. The “conservative” membrane system would use 36% to 66% as much energy as a system with a COP=7 chiller to produce the same dehumidification for the eight cases examined while the “target” system would use 15% to 40% the energy of a system with a COP=7 chiller. In addition to the significant energy reduction over conventional technology, the membrane system offers the advantages of: 1) no HFC refrigerant use; 2) direct isothermal control over humidity ratio setpoint; 3) maximum capacity occurs at design conditions; and 4) system generates pure water extracted from air as a by-product.

42 ENGINEERING↗

DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗

DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Online task-space motion control for positioner-coordinated multi-robot manufacturing systems

Incorporating multiple robotic manipulators into large-scale manufacturing systems enhances production efficiency and expands manufacturing capabilities beyond those of single-robot systems. Workpiece positioners in robotic manufacturing have demonstrated significant benefits for process optimization, but coordination strategies for multi-robot systems with shared positioners have received limited attention. This work presents a task-space coordinated trajectory-tracking control framework for multi-robot manufacturing systems, in which robots coordinate their motions within a shared, dynamic workpiece positioning frame. A workpiece positioner actively adjusts the pose of the manufactured component to enable greater operational concurrency and improve overall production efficiency. The proposed motion-coordination scheme employs a distributed and scalable architecture, supporting coordination across heterogeneous multi-robot systems. Two optimization methodologies are introduced to manage kinematic redundancies and maintain continuous, near-optimal operation throughout the manufacturing process. The first strategy exploits a task-space dimensionality reduction to achieve locally optimal configurations by leveraging symmetry-axis rotations of the tool. The second strategy utilizes the workpiece positioner to drive the coordinated robots toward stable and kinematically favorable configurations. For both optimization strategies, multiple objectives are defined to improve key performance metrics, including manipulability, configuration consistency, proximity to mechanical limits, and motion efficiency. Addressing a key limitation of existing coordination approaches, the framework is designed around online setpoint modification, allowing coordinated robots to respond effectively to in-situ process feedback. The proposed control framework is validated using the Robot Operating System (ROS) middleware on a combination of physical and simulated multi-robot system hardware.

Arbogast, Alex [ORNL] (ORCID:0000000154740723)↗

DOE OSTI2024

An intrinsic ductility parameter derived from anisotropic linear elasticity theory

A new indicator of intrinsic ductility (κ) is introduced based on linear elasticity. In the limit of elastic isotropy this parameter is equal to the Pugh ratio ( B / G ) plus a constant; but unlike the Pugh ratio, κ incorporates anisotropy and crystallography to improve predictive value. We identify a single ductile-to-brittle transition setpoint for κ that predicts crack-tip plasticity in atomistic simulations, experimental elongation to failure of polycrystalline elemental metals pulled in tension, and fracture energy measurements of glasses, suggesting commonality in the relationship between plasticity and fracture across all three of these cases. Statistical analysis supports the superiority of κ over B / G at predicting crack-tip plasticity in atomistic simulations and elongation to failure of polycrystalline elemental metals.

Winter, Ian S.↗

DOE OSTI2022

Setting boundaries on the recipe for a successful RbF post-deposition treatment of CIGS

RbF post-deposition treatments have been explored in the literature for increasing the open-circuit voltage, fill factor, and hence the efficiency of Cu(In,Ga)Se 2 solar cells. However, given the few papers documenting the experimental steps, it was difficult to quickly reproduce the results. This contribution describes some of the optimization steps that led to a successful RbF PDT based on device performance. Here we present results that put boundaries on the temperatures of the RbF cell and the lamp (for sample heating) setpoint. The best recipe for our specific growth process is documented in detail so that others may copy the procedure and possibly arrive at a successful RbF PDT in a reasonable time.

14 SOLAR ENERGY↗

DOE OSTI2024

Coupled management of electric vehicle workplace charging and office building loads

Significant building energy savings are realized through heating, ventilation, and air conditioning (HVAC) setpoint adjustment and daylighting control. Workplace charging (WPC) enables colocation of electric vehicles (EVs) with office building loads. We proposed managing energy use of workplace EV charging and the office building and determined the number of EVs that building energy savings can facilitate charging. We simulated building energy savings in typical medium offices in Chicago IL, Baltimore MD, and Houston TX, spanning three US climate regions. Considering the EV hosting capacity of the saved building energy and travel patterns of roundtrip commuting, we minimized EV charging costs under time-of-use electricity pricing. Managed WPC can reduce charging electricity bills compared to first-come, first-served charging. The ratio of EVs to chargers, the coincident period of commuters’ dwell time and lower electricity prices, and the number of EVs in the office impacted the economic benefits achieved through charging management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Calibrating cryogenic temperature of TEM specimens using EELS

Cryogenic Scanning/Transmission Electron Microscopy has been established as a leading method to image sensitive biological samples and is now becoming a powerful tool to understand materials' behavior at low temperatures. However, achieving precise local temperature calibration at low temperatures remains a challenge, which is especially crucial for studying phase transitions and emergent physical properties in quantum materials. In this study, we employ electron energy loss spectroscopy (EELS) to measure local cryogenic specimen temperatures. Here we use the temperature-dependent characteristics of aluminum's bulk plasmon peak in EEL spectra, which shifts due to changes in electron density caused by thermal expansion and contraction. We successfully demonstrate the versatility of this method by calibrating different liquid nitrogen cooling holders in various microscopes, regardless of whether a monochromated or non-monochromated electron beam is used. Temperature discrepancies between the actual temperature and the setpoint temperatures are identified across a range from room temperature to 100 K. This work demonstrates the importance of temperature calibrations at intermediate temperatures and presents a straightforward, robust method for calibrating local temperatures of cryogenically-cooled specimens in electron microscopes.

36 MATERIALS SCIENCE↗

DOE OSTI2021

A Novel Multiagent Resource Sharing Algorithm for Control of Advanced Energy Systems

This paper implements a novel resource sharing control strategy on a fuel cell–gas turbine hybrid power system at the National Energy Technology Laboratory’s Hybrid Performance Facility (Hyper). In a fuel cell–gas turbine hybrid power system, the simultaneous interaction of the gas turbine and the fuel cell creates a tightly coupled environment characterized by conflicting dynamics. In this paper, a model-free control approach is applied to solve the tightly coupled control problem posed by this challenging environment. Specifically, this control problem is presented as a resource sharing problem that can be solved using a resource sharing algorithm that is defined based on the distribution construction concept. This algorithm creates computational agents and solves the problem through the distribution and redistribution of shared resources defined as blocks. Furthermore, two agents were created; the first agent (agent 1) controls the gas turbine speed by adjusting the electric load, and the second agent (agent 2) controls the cathode mass flow through the fuel cell using the cold-air bypass valve. A parametric study was performed over the course of 15 experimental tests for both agents 1 and 2 through an evaluation of the responses based on setpoint changes. The algorithm was shown to have behavior comparable to a previously implemented multi-input multioutput state-space controller, which was designed through a model-based control approach. The resource sharing algorithm was able to find stable performance during run-time operations without any prior system knowledge identification on the power plant and without creating models used in traditional control strategies.

25 ENERGY STORAGE↗

DOE OSTI2023

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Abstract Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris . This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗

DOE OSTI2017

Protecting against damage from refraction of high power microwaves in the DIII-D tokamak

Here, several new protective systems are being installed on the DIII D tokamak to increase the safety margins for plasma operations with injected ECH power at densities approaching cutoff. Inadvertent overdense operation has previously resulted in reflection of an rf beam back into a launcher causing extensive arcing and melt damage on one waveguide line. Damage to microwave diagnostics, which are located on the same side of the tokamak as the ECH launchers, also has occurred. Developing a reliable microwave based interlock to protect the many vulnerable systems in DIII-D has proved to be difficult. Therefore, multiple protective steps have been taken to reduce the risk of damage in the future. Among these is a density interlock generated by the plasma control system, with setpoint determined by the ECH operators based on rf beam trajectories and plasma parameters. Also installed are enhanced video monitoring of the launchers, and an ambient light monitor on each of the waveguide systems, along with a Langmuir probe at the mouth of each launcher. Versatile rf monitors, measuring forward and reflected power in addition to the mode content of the rf beams, have been installed as the last miter bends in each waveguide line. As these systems are characterized, they are being incorporated in the interlock chains, which enable the ECH injection permits. The diagnostics most susceptible to damage from the ECH waves have also been fitted with a variety of protective devices including stripline filters, thin resonant notch filters tuned to the 110 GHz injected microwave frequency, blazed grating filters and shutters. Calculations of rf beam trajectories in the plasmas are performed using the TORAY ray tracing code with input from kinetic profile diagnostics. Using these calculations, strike points for refracted beams on the vacuum vessel are calculated, which allows evaluation of the risk of damage to sensitive diagnostics and hardware.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2014

Frequency-modulated atomic force microscopy operation by imaging at the frequency shift minimum: The dip-df mode

In frequency modulated non-contact atomic force microscopy, the change of the cantilever frequency (Δf) is used as the input signal for the topography feedback loop. Around the Δf(z) minimum, however, stable feedback operation is challenging using a standard proportional-integral-derivative (PID) feedback design due to the change of sign in the slope. When operated under liquid conditions, it is furthermore difficult to address the attractive interaction regime due to its often moderate peakedness. Additionally, the Δf signal level changes severely with time in this environment due to drift of the cantilever frequency f{sub 0} and, thus, requires constant adjustment. Here, we present an approach overcoming these obstacles by using the derivative of Δf with respect to z as the input signal for the topography feedback loop. Rather than regulating the absolute value to a preset setpoint, the slope of the Δf with respect to z is regulated to zero. This new measurement mode not only makes the minimum of the Δf(z) curve directly accessible, but it also benefits from greatly increased operation stability due to its immunity against f{sub 0} drift. We present isosurfaces of the Δf minimum acquired on the calcite CaCO{sub 3}(101{sup ¯}4) surface in liquid environment, demonstrating the capability of our method to image in the attractive tip-sample interaction regime.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗