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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2021

Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes

Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction

42 ENGINEERING↗
DOE OSTI2021

Graph Convolutional Network-Based Topology Embedded Deep Reinforcement Learning for Voltage Stability Control

Topological variations in power system is a common phenomenon and can impose significant challenges to traditional controllers of power system. Recent study revealed the strength of deep reinforcement learning (DRL) based approaches in power system preventive and corrective control. But topological variations are difficult to capture using classical fully connected neural network (FCN) model and has not been explicitly modeled in previous work. Hence, we develop a Graph Convolutional Network (GCN) based DRL framework to tackle topology changes in control design of power system. The GCN model exploits the graph structure of the power network and helps the DRL agent to embed the topology information during learning process. Our GCN based approach is evaluated using the IEEE-39 bus system and it outperforms the FCN-based DRL scheme in terms of training convergence and control performance considering grid topology changes.

Hossain, Ramij Raja↗
DOE OSTI2013

Research and Development for Novel Thermal Energy Storage Systems (TES) for Concentrating Solar Power (CSP)

The overall objective was to develop innovative heat transfer devices and methodologies for novel thermal energy storage systems for concentrating solar power generation involving phase change materials (PCMs). Specific objectives included embedding thermosyphons and/or heat pipes (TS/HPs) within appropriate phase change materials to significantly reduce thermal resistances within the thermal energy storage system of a large-scale concentrating solar power plant and, in turn, improve performance of the plant. Experimental, system level and detailed comprehensive modeling approaches were taken to investigate the effect of adding TS/HPs on the performance of latent heat thermal energy storage (LHTES) systems.

14 SOLAR ENERGY↗
DOE OSTI2015

Embedded wireless sensors for turbomachine component defect monitoring

Various embodiments include detection systems adapted to monitor at least one physical property of a component in a turbomachine. In some embodiments a detection system includes at least one sensor configured to be affixed to a component of a turbomachine, the at least one sensor for sensing information regarding at least one physical property of the turbomachine component during operation of the turbomachine, a signal converter communicatively coupled to the at least one sensor and at least one RF communication device configured to be affixed to a stationary component of the turbomachine, the radio frequency communication device configured to communicate with the at least one signal converter via an RF antenna coupled to the signal converter.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

SU-E-T-453: A Novel Daily QA System for Robotic Image Guided Radiosurgery with Variable Aperture Collimator

Purpose: A novel end-to-end system using a CCD camera and a scintillator based phantom that is capable of measuring the beam-by-beam delivery accuracy of Robotic Radiosurgery has been developed and reported in our previous work. This work investigates its application to end-to-end type daily QA for Robotic Radiosurgery (Cyberknife) with Variable Aperture Collimator (Iris). Methods: The phantom was first scanned with a CT scanner at 0.625 slice thickness and exported to the Cyberknife Muliplan (v4.6) treatment planning system. An isocentric treatment plan was created consisting of ten beams of 25 Monitor Units each using Iris apertures of 7.5, 10, 15, 20, and 25 mm. The plan was delivered six times in two days on the Cyberknife G4 system with fiducial tracking on the four metal fiducials embedded in phantom with re-positioning between the measurements. The beam vectors (X, Y, Z) are measured and compared with the plan from the machine delivery file (XML file). The Iris apertures (FWHM) were measured from the beam flux map and compared with the commissioning data. Results: The average beam positioning accuracies of the six deliveries are 0.71 ± 0.40 mm, 0.72 ± 0.44 mm, 0.74 ± 0.42 mm, 0.70 ± 0.40 mm, 0.79 ± 0.44 mm and 0.69 ± 0.41 mm respectively. Radiation beam width (FWHM) variations are within ±0.05 mm, and they agree with the commissioning data within 0.22 mm. The delivery time for the plan is about 7 minutes and the results are given instantly. Conclusion: The experimental results agree with stated sub-millimeter delivery accuracy of Cyberknife system. Beam FWHM variations comply with the 0.2 mm accuracy of the Iris collimator at SAD. The XRV-100 system has proven to be a powerful tool in performing end-to-end type tests for Robotic Image Guided Radiosurgery Daily QA.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Development of Advanced Numerical Tools for the Nonlinear Seismic Analysis of Nuclear Systems

In the framework of Probabilistic Risk Assessment (PRA) of nuclear facilities, a rigorous dynamic characterization of all structural and non-structural components is of paramount concern. The common features of nuclear facilities lies in the combination of three characteristics: 1. low aspect ratio; 2. large mass; and 3. significant embedment in soil. These characteristics make the interaction between the structural system and the surrounding soil a key aspect to consider for seismic risk evaluations. The methodologies and numerical tools currently in use for simulating the seismic response of a nuclear facility with significant Soil Structure Interaction (SSI) date back to the 1970's and rely on a number of simplifying idealizations, mainly related to the characterization of the incident seismic waveforms, the assumption of linear-elastic behavior of the superstructures, and idealization of equivalent linear behavior of the supporting soils. Given this background and taking advantage of the substantial progress in computational mechanics in the last decades, the ongoing research aims at developing advanced numerical tools for the nonlinear, time history simulation of nuclear facilities. This translates into the availability of a robust nonlinear time-domain simulation tool that can accurately model the nonlinearities of coupled soil-structure systems, and whose development includes robust verification and validation. The work described herein is supported by the U.S. Department of Energy (DOE) and focuses on delivering the next-generation, high performance simulation tool in support of DOE needs and mission. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Computational Study of Variable Fuel-Air Ratio and Hydrogen Doping in a Rotating Detonation Engine

Rotating detonation engines (RDEs) hold great promise as a technology for powering aviation engines. The pressure gain that results from continuously traversing detonation waves enables high theoretical performance compared to deflagration-based combustion. However, the harsh operating conditions inside an RDE restrict the availability of suitable experimental diagnostics to study these systems in detail and the range of spatial and temporal scales render numerical simulations of these systems difficult. In this study, we have combined adaptive mesh refinement and a robust shock-capturing approach to study RDE systems computationally using the compressible, reactive solver PeleC. Multi-species transport along with compressible Navier-Stokes equations are solved in the model along with finite-rate chemistry. Complex geometries are represented using an embedded boundary method with second-order spatial accuracy and the system is evolved in time using a second-order Runge-Kutta method. We present studies of a methane-air RDE system that is doped with varying levels of hydrogen. Our analysis focusses on how fuel mixture and doping levels effect the detonation flame structure and the generation of multiple wave modes.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers (Final Technical Report)

This report summarizes the carbon capture research and development conducted by The State University of New York at Buffalo (UB) and GTI Energy (GTI) for award “DE-FE0031969: Direct Air Capture Using Trapped Small Amines in Hierarchical Nanoporous Capsules on Porous Electrospun Fibers” sponsored by the U.S. Department of Energy (DOE). The objective of this project is to develop an innovative sorbent structure of trapped small amines in HNC embedded in PEF for DAC. This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An innovative sorbent structure of trapped small amines in hierarchical nanoporous capsules (HNC) embedded in porous electrospun fibers (PEF) was developed for direct air capture (DAC). This involves tailoring both sorbent and PEF materials to achieve a compact system for DAC with high capacity for CO 2 at concentrations typically available in air and at near ambient conditions. An interfacial polymerization process was developed, which utilized loaded amines inside mesoporous silica and trimesoyl chloride (TMC) dissolved in organic solvents as the precursors, to generate a polyamide (PA) coating layer on mesoporous silica and thus trap amines. Reaction conditions, including TMC concentration, organic solvents, reaction time, etc., for interfacial polymerization were optimized to effectively trap loaded amines, and cyclic heating-cooling operation was conducted to evaluate the coating quality. Larger pore volume mesoporous silica was also synthesized to increase amine loading and thus increase CO 2 capacity. The optimized sorbent material exhibited CO 2 capacity as high as 4.88 mmol/g under humid DAC conditions and negligible loss (<1%) during 10 cyclic heating-cooling operations. The optimized PA-coated sorbent also showed fast adsorption and desorption kinetics, with <20% t1/2 increase compared to uncoated sorbent. PEF fabrication conditions, including organic solvents for dissolving core and shell polymers, voltage, distance from the nozzle to the collection panel, etc. were adjusted to better incorporate HNC. After incorporating the optimized sorbent material into PEF, the structured sorbent had a CO 2 capacity of approximately 4.0 mmol/g under humid DAC conditions, with capacity loss of 0.17% per cycle and t1/2 increase less than 10%. A techno-economic analysis (TEA) for the process design for a DAC system based on our developed sorbent structure of trapped small amines in HNC embedded in PEF was conducted. The process design included process description and major equipment sizing and energy and mass balances in addition to scale-up research results and estimated capture cost. Aspen Adsorption Simulator was used to fit the experimentally measured breakthrough curves and extract equilibrium and kinetic data of the optimized sorbent. Our results indicated that for a DAC plant with CO 2 productivity of 3,000 tonne/year, the levelized cost of CO 2 capture was $\$$612/tonne, with the largest contribution of 44.33% from the fixed operation cost. Increasing CO 2 productivity, while maintaining similar fixed operation cost, is expected to significantly reduce the CO 2 capture cost. A sensitivity study was also conducted to understand the influence of total plant cost, sorbent cost, CO 2 concentration in the feed, sorbent mat lifetime, sorbent regeneration electricity, and adsorption blower pressure drop on the levelized cost of CO 2 capture, revealing a capture cost range of $\$$520-870/tonne.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enabling Interoperable SCADA Communications for PV Inverters through Embedded Controllers

The percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.

14 SOLAR ENERGY↗
DOE OSTI2012

On affine extension of splint root systems

Splint of root system of simple Lie algebra appears naturally in the study of (regular) embeddings of reductive subalgebras. It can be used to derive branching rules. Application of splint properties drastically simplifies calculations of branching coefficients. We study affine extension of splint root system of simple Lie algebra and obtain relations on theta and branching functions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Majorana spin in magnetic atomic chain systems

In this study, we establish that Majorana zero modes emerging from a topological band structure of a chain of magnetic atoms embedded in a superconductor can be distinguished from trivial localized zero energy states that may accidentally form in this system using spin-resolved measurements. To demonstrate this key Majorana diagnostics, we study the spin composition of magnetic impurity induced in-gap Shiba states in a superconductor using a hybrid model. By examining the spin and spectral densities in the context of the Bogoliubov-de Gennes (BdG) particle-hole symmetry, we derive a sum rule that relates the spin densities of localized Shiba states with those in the normal state without superconductivity. Extending our investigations to a ferromagnetic chain of magnetic impurities, we identify key features of the spin properties of the extended Shiba state bands, as well as those associated with a localized Majorana end mode when the effect of spin-orbit interaction is included. We then formulate a phenomenological theory for the measurement of the local spin densities with spin-polarized scanning tunneling microscopy (STM) techniques. By combining the calculated spin densities and the measurement theory, we show that spin-polarized STM measurements can reveal a sharp contrast in spin polarization between an accidental-zero-energy trivial Shiba state and a Majorana zero mode in a topological superconducting phase in atomic chains. We further confirm our results with numerical simulations that address generic parameter settings.

Majorana fermions↗
DOE OSTI2012

INL High Level Language Microcontroller Implementa

The overall goal of this project is to improve the security of embedded devices (microcontrollers) used in monitoring and control of energy-sector infrastructure. As these devices are essentially scaled-down personal computers (PCs), they are vulnerable to common low-level cyber-attacks such as buffer overflows and insecure authentication. The implementation of a modern high-level programming language on these microcontrollers would disallow such low-level attacks as the languages would have inherent safeguards against these types of cyber exploits. Idaho National Laboratory (INL) has partnered with Siemens Corporate Research (SCR) to develop and test a 4 th -generation language DNP3 implementation on real-world embedded hardware. These tests were carried out on control-system device models currently deployed in real-world applications. Testing was performed with existing C-based DNP implementation as well as the Python version developed by INL.

4GL↗
DOE OSTI2016

Subcritical Copper-Reflected-α-phase Plutonium (SCRαP) Integral Experiment Design

A Subcritical Copper-Reflected α-phase Plutonium (SCRαP) integral benchmark experiment is proposed where multiplication will be approximated using correlated neutron data from a detector system consisting of {sup 3}He tubes inside polyethylene. Measurements will be performed on various subcritical experimental configurations consisting of a weapons-grade plutonium sphere surrounded by different Cu thicknesses. In addition to the proposed base experimental configurations with Cu, additional configurations are planned with the plutonium ball nested in various thicknesses of interleaved polyethylene spherical shells mixed in with the Cu shells. The polyethylene is intended to provide fast neutron moderation and reflection, resulting in additional measurements with differing multiplication, spectra, and nuclear data sensitivity. The proposed experiments will performed at the National Criticality Experiments Research Center (NCERC). NCERC is home to a 4.5-kg α-phase stainless steel clad plutonium sphere that is referred to as the BeRP (Beryllium-Reflected Plutonium) ball due to its historical use in a beryllium-reflected critical experiment. In 2014, similar subcritical measurements were performed with the BeRP ball surrounded by nickel and tungsten. The nickel-reflected measurements were documented as a benchmark evaluation which was accepted by the International Criticality Safety Benchmark Evaluation Project (ICSBEP). Similar to past measurements, the proposed work will help both identify deficiencies and quantify uncertainties in nuclear data and validate computational methods related to neutron multiplication inference for subcritical benchmark evaluations. Monte Carlo simulations were performed for the preliminary design of a subcritical Copper-Reflected - phase Plutonium (SCRP) integral benchmark experiment. Specifically, MCNP{sup R} version 6 was used to determine the k{sub eff} and associated nuclear data sensitivities for different preliminary configurations (reflector thickness, material type). The final experimental design process will include continuous-energy nuclear data sensitivity analysis. This will help assess the potential impact of this integral measurement as it relates to international efforts to continuously improve current libraries (ENDF, JEFF, JENDL etc) that still tend to over/under-estimate the measured results of integral experiments, sometimes significantly. Deficiencies in underlying nuclear data quantities such as Nubar have been shown to have an effect on inferred values from subcritical measurements as well. The proposed experiments and subsequent computational validation will help identify such deficiencies. It will also help validate new nuclear data evaluations, including one for Cu that was recently performed in the resolved region up to 100 MeV for {sup 65}Cu and {sup 63}Cu which is expected to improve Cu-related benchmark simulation performance. The final experimental design phase will also include simulation results that will incorporate the detectors that will be used for the subcritical measurements. For the benchmark portion of the measurements, the main detectors that are being considered are portable neutron multiplicity counters made up of {sup 3}He tubes embedded in polyethylene and lined with cadmium. These detectors employ a data acquisition system that produces list-mode data (correlated information where each event is listed sequentially) that can be analyzed using the Feynman variance-to-mean method. Other detector systems including SNAP (single {sup 3}He tube embedded in polyethylene), and a high-purity germanium (HPGe) detector are being considered for supplementary measurements only and are not intended to be used for the final benchmark measurements. Decoupling the supplementary detectors from the system eliminates any contribution to the total system multiplication, and greatly simplifies the final computational benchmark model. Therefore, an optimization of the portable neutron multiplicity counter placement in the benchmark measurements will be performed to ensure that there is minimal contribution to the total system multiplication. In addition, new temperature measurement techniques and hardware will be explored. (authors)

42 ENGINEERING↗
DOE OSTI2019

Split-pane electrochromic window control based on an embedded photometric device with real-time daylighting computing

Well-designed electrochromic (EC) glazing control can improve the energy performance of buildings and visual comfort of occupants in highly glazed buildings. Here in this paper, we designed and demonstrated a compact integrated EC glazing automation system to control tint states of a split-pane EC window according to variations of sky conditions. The control is based on monitoring the luminance distribution of the sky and real-time lighting computation for a building interior, using an embedded photometric device (EPD). It optimizes tint states of EC glazing to offer sufficient daylight provision and temper discomfort glare for occupants, which potentially mitigates excessive solar heat gain. ’In-situ’ experiments were conducted in a full-scale testbed to demonstrate the daylighting performance under various sky conditions. Finally, experimental results showed 83% of the working time for work-plane illuminance (WPI) and 95% of the time for daylight glare probability (DGP) were constrained in comfort range (WPIϵ[500, 2000] lux, DGP ≤ 0.35) by the automated EC glazing (controlled by EPD) under clear skies; 68% of the time for WPI and 94% of the time for DGP in confined range under clear skies with thin clouds; 62% of the time for WPI and 85% of the time for DGP in confined range under partly cloudy skies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Mineralized sclerites in the gorgonian coral Leptogorgia chilensis as a natural jamming system

The soft corals (Cnidaria, Octocorallia), a diverse group of colonial marine invertebrates, can reversibly tune their body stiffness in response to external stimuli. This capability is attributed to their dynamic skeletal systems, which consist of thousands of mineralized skeletal elements, called sclerites, embedded within a gel-like matrix that swells/deswells and unjams/jams the sclerites, thus modulating skeletal stiffness. While sclerite morphology is widely used for species identification, its role in the mechanical performance of a soft coral’s skeletal system is largely unknown. Here, we investigated structure-jamming relationships in sclerite-based skeletal architectures using the red gorgonian octocoral Leptogorgia chilensis as a model system. The sclerites of L. chilensis exhibit a shaft-like geometry with two axial branches and two sets of triradiate side branches, which are aligned with the crystallographic symmetry of the constituent magnesium-containing calcite. By combining multiscale three-dimensional (3D) structural characterization, parametric geometrical modeling, 3D printing, mechanical testing, and discrete element simulations, we demonstrate how sclerite geometry achieves a balanced jamming performance in terms of stiffness, weight, strength, and fracture resistance in comparison to alternative geometries parametrically modified from the native sclerites (e.g., changes in the length and number of side branches). Here, we also found that these performance metrics are achieved through the effective interlocking among side and axial branches, which is further enhanced by the fractal-like microscopic spikes on the branch tips. The findings in this natural jamming system offer insights for designing synthetic mechanotunable material architectures for a wide range of applications, from soft robotics to mechanical dampeners.

36 MATERIALS SCIENCE↗
DOE OSTI2014

High Pressure Sensing and Dynamics Using High Speed Fiber Bragg Grating Interrogation Systems

Fiber Bragg gratings (FBGs) are developing into useful sensing tools for measuring high pressure dynamics in extreme environments under shock loading conditions. Approaches using traditional diode array coupled FBG interrogation systems are often limited to readout speeds in the sub-MHz range. For shock wave physics, required detection speeds approaching 100 MHz are desired. We explore the use of two types of FBG sensing systems that are aimed at applying this technology as embedded high pressure probes for transient shock events. Both approaches measure time resolved spectral shifts in the return light from short (few mm long) uniform FBGs at 1550 nm. In the first approach, we use a fiber coupled spectrometer to demultiplex spectral channels into an array (up to 12) of single element InGaAs photoreceivers. By monitoring the detectors during a shock impact event with high speed recording, we are able to track the pressure induced spectral shifting in FBG down to a time resolution of 20 ns. In the second approach, developed at the Special Technologies Lab, a coherent mode-locked fiber laser is used to illuminate the FBG sensor. After the sensor, wavelength-to-time mapping is accomplished with a chromatic dispersive element, and entire spectra are sampled using a single detector at the modelocked laser repetition rate of 50 MHz. By sampling with a 12 GHz InGaAs detector, direct wavelength mapping in time is recorded, and the pressure induced FBG spectral shift is sampled at 50 MHz. Here, the sensing systems are used to monitor the spectral shifts of FBGs that are immersed into liquid water and shock compressed using explosives. In this configuration, the gratings survive to pressures approaching 50 kbar. We describe both approaches and present the measured spectral shifts from the shock experiments.

fiber Bragg grating sensors, shockwave diagnostics↗
DOE OSTI2021

Performance evaluation of a dynamic wall integrated with active insulation and thermal energy storage systems

Thermal energy storage systems in buildings can store cooling/heating energy during non-peak load hours or when renewable energy sources are available for later use when demanded. Using the building envelope as a thermal battery can help to shave the peak load, reduce the burden in electric grids, and enhance the occupant's thermal comfort. Sensible energy storage on wall systems such as thermally activated building systems can provide an active thermal storage strategy. However, most of the stored energy is used through passive means directed by the thermal lag, which can impede the on-demand release of the stored energy. Integrating active insulation systems with building thermal storage systems can increase the flexibility of charging and discharging time and duration. In this study, a wall system equipped with an active insulation system and thermally activated storage system was designed, and its performance on active cooling energy contribution was studied. The performances of energy storage (charging), release (discharging) of the thermal energy storage energy, and the active insulation system were studied separately and together as an integrated system. Results showed that the thermal properties of the thermal energy storage core material and the pipe spacing of both embedded pipes in the thermal energy storage and hydronic pipes used in the active insulation system affected the wall performance the most out of all the tested wall system parameters. During discharging, the heat flux into the wall was as high as 81.92 W/m 2 . This active insulation system's dynamic R-value varied between less than 1ft 2 •°F•h/BTU (0.18 m 2 •K/W) to 98% of the equally thick foam insulation's R-value.

25 ENERGY STORAGE↗