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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Life Cycle Analysis Framework for Point Source Capture Systems

NETL studies the costs and benefits of PSC for electricity, industry, and mobile applications. Mobile point source capture (MPSC) and storage applied to freight modes captures emissions directly from exhaust. This poster presents a framework for conducting LCA of PSC systems applied to heavy-duty trucks, freight trains, and marine vessels. The framework defines a wheels-to-storage (gate-to-grave) boundary, including energy demands (electricity, heat, and cooling requirements), solvent use and cycling, onboard system components, carbon storage in a saline aquifer, and upstream manufacturing impacts for equipment, with a suggested functional unit of 1 tonne-km. Potential data sources for analysis include material, energy, and operational data from Oak Ridge National Laboratory, GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model, and scientific literature. The suggested analytical approach includes comparison to publicly available business-as-usual systems without capture across all modes of transportation, sensitivity to composition of the capture solvent, and sensitivity to capture rate variation, all of which would support a wholistic PSC business case analysis. For future consideration, analysis can be augmented with consideration of different sources of electricity (e.g., nuclear), fuel substitution, deploying supportive infrastructure such as pipeline offloading points, and downstream applications like enhanced oil recovery (EOR).

life cycle analysis (LCA)↗
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
DOE OSTI2022

An MILP-Based Distributed Energy Management for Coordination of Networked Microgrids

An MILP-based distributed energy management for the coordination of networked microgrids is proposed in this paper. Multiple microgrids and the utility grid are coordinated through iteratively adjusted price signals. Based on the price signals received, the microgrid controllers (MCs) and distribution management system (DMS) update their schedules separately. Then, the price signals are updated according to the generation–load mismatch and distributed to MCs and DMS for the next iteration. The iteration continues until the generation–load mismatch is small enough, i.e., the generation and load are balanced under agreed price signals. Through the proposed distributed energy management, various microgrids and the utility grid with different economic, resilient, emission and socio-economic objectives are coordinated with generation–load balance guaranteed and the microgrid customers’ privacy preserved. In particular, a piecewise linearization technique is employed to approximate the augmented Lagrange term in the alternating direction method of multipliers (ADMM) algorithm. Thus, the subproblems are transformed into mixed integer linear programming (MILP) problems and efficiently solved by open-source MILP solvers, which would accelerate the adoption and deployment of microgrids and promote clean energy. The proposed MILP-based distributed energy management is demonstrated through various case studies on a networked microgrids test system with three microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Working principles of doping-well structures for high-mobility two-dimensional electron systems

Suppressing electron scattering is essential to achieve high-mobility two-dimensional electron systems (2DESs) that are clean enough to probe exotic interaction-driven phenomena. In heterostructures it is common practice to utilize modulation doping, where the ionized dopants are physically separated from the 2DES channel. The doping-well structure augments modulation doping by providing additional screening for all types of charged impurities in the vicinity of the 2DES, which is necessary to achieve record-breaking samples. Despite its prevalence in the design of ultrahigh-mobility 2DESs, the working principles of the doping-well structure have not been reported. As such, here we elaborate on the mechanics of electron transfer from doping wells to the 2DES, focusing on GaAs/AlGaAs samples grown by molecular beam epitaxy. Based on this understanding we demonstrate how structural parameters in the doping well can be varied to tune the properties of the 2DES.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Remote collaboration methods and systems

Apparatus and associated methods relate to immersive collaboration based on configuring a real scene VRE operable from a real scene and a remote VRE operable remote from the real scene with an MR scene model of the real scene, creating an MR scene in each of the real scene VRE and remote VRE based on augmenting the MR scene model with an object model, calibrating the remote MR scene to correspond in three-dimensional space with the real scene MR scene model, and automatically providing immersive collaboration based on the MR scene in the remote VRE and updating the real scene VRE with changes to the remote VRE. In an illustrative example, the MR scene model of the real scene may be determined as a function of sensor data scanned from the real scene. In some embodiments, the MR scene model may be augmented with an object model identified from the real scene. The object model identified from the real scene may be, for example, selected from a known object set based on matching sensor data scanned from the real scene with an object from a known object set. In some embodiments, the remote MR scene may be calibrated based on applying a three-dimensional transform calculated as a function of the real MR scene and remote MR scene geometries. Some designs may recreate a subset of the real scene in the remote VRE and update the real scene VRE with changes to the remote VRE. Various embodiments may advantageously provide seamless multimedia collaboration based on updates to the remote VRE in response to physical changes to the real scene, and updating the real scene VRE in response to changes in the remote VRE.

Neeter, Eduardo J.↗
DOE OSTI2025

Projection-based multifidelity linear regression for data-scarce applications

Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifidelity methods for multiple-input multiple-output linear regression targeting data-limited applications with high-dimensional outputs. Multifidelity methods integrate many inexpensive low-fidelity model evaluations with limited, costly high-fidelity evaluations. We introduce two projection-based multifidelity linear regression approaches with linear and nonlinear features that leverage principal component basis vectors for dimensionality reduction and combine multifidelity data through: (i) a direct data augmentation using low-fidelity data, and (ii) a data augmentation incorporating explicit linear corrections between low-fidelity and high-fidelity data. The data augmentation approaches combine high-fidelity and low-fidelity data into a unified training set and train the linear regression model through weighted least squares with fidelity-specific weights. We introduce a proximity-based weighting scheme with automatic weight selection strategy through cross-validation. Here, the proposed multifidelity linear regression methods are demonstrated on approximating the surface pressure field of a hypersonic vehicle in flight and the temperature field on an aircraft disc braking system. In an ultra low-data regime of no more than twelve high-fidelity samples, multifidelity linear regression achieves approximately 2% – 12% improvement in median accuracy and a higher R 2 score relative to single-fidelity methods at comparable computational cost.

data augmentation↗
DOE OSTI2022

High-fidelity photoneutron detection via neutron activation analysis

To interdict illicit radioactive sources and prevent nuclear terror, governments have deployed portal monitor systems at ports of entry. Such portal monitors rely upon the detection of spontaneously-emitted radiation signatures, however, detection scenarios become more challenging for highly enriched uranium, which emits relatively few spontaneous radiation signatures. Photon active interrogation techniques can augment current cargo inspection scenarios and improve detection capabilities for illicit nuclear material. A high-energy photon interrogation source can induce photofission in nuclear material, producing strong radiation signatures for detection. Prompt fission signals are of specific interest because prompt neutrons are greater than 99% of the fission signal. When using detectors capable of spectroscopy, e.g., pulse shape discrimination-capable organic scintillators, the intense interrogating source often will cause pulse pile-up, degrading particle classification. In this work, we demonstrate photoneutron detection via neutron activation of aluminum and iron. The experiments rely upon nuclear reactions that can only be induced by neutrons, and are robust against pile-up, thereby providing high-confidence neutron rates. In this work, we apply neutron activation analysis for the detection of high-energy neutrons produced by high-energy photon irradiation; the depleted uranium sample was irradiated with a 9-MeV bremsstrahlung beam. We find that the irradiated depleted uranium produced an aluminum activation flux of 1.77 × 10 5 n cm –2 s –1 and an iron-56 activation flux of 1.05 × 10 5 n cm –2 s –1 . Furthermore, the ability to detect radioactivity induced by neutrons created in (, n) and photofission reactions demonstrates a new approach for photon active interrogation to identify nuclear materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Prediction of the Thermal Runaway Limit and Optimal Operation of Heat Transfer-Limited, Fixed-Bed Reactor Systems

Here, we derive a new prediction for thermal runaway starting from the alpha model for fixed-bed reactor systems. This method accounts for thermal resistance internal to the reactor tube and the radial temperature gradients that result. To showcase our method, we compare its predictions to other common criteria for thermal runaway using o-xylene oxidation as the example chemistry. Even in systems where internal heat transfer is negligible, the empirical practical design criterion for thermal runaway is inaccurate. For cases where internal heat transfer is relevant, our runaway limit is more stringent than limits derived from simpler 1-D models. To augment our work, we optimize the product yield with the thermal runaway constraint using orthogonal collocation. Using the alpha model, the results illustrate that the thermal runaway limit can be accurately determined using either numerical or analytical methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Concordant Mode Approach (CMA): Vibrational Analysis of New and Upgraded Intermolecular Benchmarks for Noncovalent Bonding

The Concordant Mode Approach (CMA) is a novel method that offers tremendous potential for increasing the system size and the level of theory attainable in quantum chemical computations of molecular vibrational frequencies. To investigate the extension of CMA to intermolecular vibrations, computations with coupled cluster singles and doubles with perturbative triples theory [CCSD(T)] using two augmented correlation-consistent polarized-valence triple-ζ basis sets (aug-cc-pVTZ or h-aug-ccpVTZ) were performed on 17 prototypical loosely bound complexes of hydrogen-bonded, dispersion, and mixed character. These Level A results provide new and upgraded benchmarks for noncovalent bonding and a severe test for CMA vibrational analyses. The Level A target frequencies were recovered remarkably well using second-order Møller−Plesset perturbation theory (MP2) with h-aug-cc-pVTZ for generating the underlying (Level B) normal modes of the CMA scheme. Employing this Level B within the lowest-rung CMA-0A method reproduces the 435 benchmark frequencies with a mean absolute error (MAE) of 0.23 cm −1 and a corresponding standard deviation (σ) of 0.84 cm −1 ; strikingly, the corresponding subset of 106 interfragment frequencies exhibits MAE = 0.34 cm −1 and σ = 0.90 cm −1 . Subsequent application of the higher-rung CMA-2A scheme eliminates all outliers and reduces the overall MAE to a minuscule 0.08 cm −1 with the inclusion of only 3.0% of the off-diagonal couplings not accounted for by CMA-0A. Accordingly, the highly efficient CMA methodology proves to be robust even for vibrations on flat potential energy surfaces.

Aromatic compounds↗
DOE OSTI2022

Coupled Heat Power Operation of Smart Buildings via Modular Pumped Hydro Storage

In the United States, building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings’ energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low-grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high-efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground level. This unique design eliminates the geographical limitation associated with the existing state-of-the-art energy storage technologies. It is easy to be scaled for building level, community level, and grid level applications. By using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g., from thermal storage, combined cooling, heat and power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Here, several groups of comparison experiments have been conducted, and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Monitoring Plan for the Idaho National Laboratory Remote Handled Low Level Waste Disposal Facility

This monitoring plan for Idaho National Laboratory’s Remote-Handled Low Level Waste Disposal Facility was developed to meet the requirements for monitoring low-level waste disposal facilities according to the U.S. Department of Energy (DOE) Order 435.1, “Radioactive Waste Management,” and the guidance provided in the associated technical standard “Disposal Authorization Statement and Tank Closure Documentation” (DOE-STD-5002-2017). The purpose of this monitoring plan is to document a monitoring strategy that includes (1) compliance monitoring activities to demonstrate compliance with regulatory standards/limits and (2) performance monitoring to build confidence the facility is performing as demonstrated in the facility performance assessment (PA) (DOE-ID 2018a), composite analysis (CA) (DOE ID 2012), and CA addendum (DOE-ID 2018b). The de minimus impact to the aquifer predicted by the PA suggests that aquifer compliance monitoring should be augmented with performance monitoring of the drainage course materials and sedimentary interbeds in the vadose zone beneath the facility to provide a more effective means of identifying performance deviations. The monitoring approach delineated in this document was informed by the systems evaluation of natural and engineered facility features presented in the PA, an assessment of aquifer baseline conditions (INL 2017d), the dose analysis conducted in support of the PA and CA, and monitoring data collected during the first four years of facility operations (baseline monitoring phase) (INL 2023b). This plan provides monitoring locations, sampling frequencies, and sampling methods; recommendations for data evaluation; and a description of the monitoring plan implementation. Collected data will be used to demonstrate facility compliance and to identify conditions that are not consistent with the key assumptions made by the PA and CA.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2021

Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation

In the U.S., building sector is responsible for around 40% of total energy consumption and contributes about 40% of carbon emissions since 2012. Within the past several years, various optimization models and control strategies have been studied to improve buildings energy efficiency and reduce operational expenses under the constraints of satisfying occupants’ comfort requirements. However, the majority of these studies consider building electricity demand and thermal load being satisfied by unidirectional electricity flow from the power grid or on-site renewable energy generation to electrical and thermal home appliances. Opportunities for leveraging low grade heat for electricity have largely been overlooked due to impracticality at small scale. In 2016, a modular pumped hydro storage technology was invented in Oak Ridge National Laboratory, named Ground Level Integrated Diverse Energy Storage (GLIDES). In GLIDES, employing high efficiency hydraulic machinery instead of gas compressor/turbine, liquid is pumped to compress gas inside high-pressure vessel creating head on ground-level. This unique design eliminates the geographical limitation associated with existing state of the art energy storage technologies. It is easy to be scaled for building level, community level and grid level applications. Using this novel hydro-pneumatic storage technology, opportunities for leveraging low-grade heat in building can be economical. In this research, the potential of utilizing low-grade thermal energy to augment electricity generation of GLIDES is investigated. Since GLIDES relies on gas expansion in the discharge process and the gas temperature drops during this non-isothermal process, available thermal energy, e.g. from thermal storage, Combined Cooling, Heat and Power system (CCHP), can be utilized by GLIDES to counter the cooling effect of the expansion process and elevate the gas temperature and pressure and boost the roundtrip efficiency. Several groups of comparison experiments have been conducted and the experimental results show that a maximum 12.9% cost saving could be achieved with unlimited heat source for GLIDES, and a moderate 3.8% cost improvement can be expected when operated coordinately with CCHP and thermal energy storage in a smart building.

Chen, Yang↗
DOE OSTI2023

Towards constrained grid-forming control

Grid-forming converters are commonly envisioned to replace conventional synchronous generators as the cornerstone of future power systems. However, compared to synchronous generators, power converters and their power sources (e.g., renewable generation, energy storage) are subject to significant physical constraints (e.g., current limits, modulation limits). Grid-forming controls are commonly designed neglecting these constraints and subsequently augmented with limiters for a subset of constraints. In this work, we propose a systematic approach to constrained grid-forming control of two-level voltage source converters interfacing photovoltaics. We first formulate grid-forming control objectives and constraints of voltage source converters and photovoltaics in a one-step optimal grid-forming control problem. Next, we leverage primal-dual dynamics to obtain a dynamic feedback controller that approximates the solution of the one-step optimal grid-forming control problem. Finally, electromagnetic transient simulations are used to illustrate the results.

Gross, Dominic↗
DOE OSTI2020

CP2K: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling electronic structure methods and state-of-the-art ab-initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post-Hartree-Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension. TDK has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 716142). VRR has been supported by the Swiss National Science Foundation in the form of Ambizione grant No. PZ00P2 174227 and RZK by the Natural Sciences and Engineering Research Council of Canada (NSERC) through Discovery Grants (RGPIN-2016-0505). GKS and CJM are supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences. UK based work was funded under the embedded CSE programme of the ARCHER UK National Supercomputing Service (http://www.archer.ac.uk), grants eCSE03-011, eCSE06-6, eCSE08-9, eCSE13-17 and the EPSRC (EP/P022235/1) grant “Surface and Interface Toolkit for the Materials Chemistry Community". Computational resources were provided by the Swiss National Supercomputing Centre (CSCS) and Compute Canada. The generous allocation of computing time on the FPGA-based supercomputer “Noctua" at PC2 is kindly acknowledged.

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

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Here this article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Recent advances show promising results for model-free DRL-based methods in power systems control problems. But in power systems applications, these model-free methods have certain issues related to training time (clock time) and sample efficiency; both are critical for making state-of-the-art DRL algorithms practically applicable. DRL-agent learns an optimal policy via a trial-and-error method while interacting with the real-world environment. It is also desirable to minimize the direct interaction of the DRL agent with the real-world power grid due to its safety-critical nature. Additionally, the state-of-the-art DRL-based policies are mostly trained using a physics-based grid simulator where dynamic simulation is computationally intensive, lowering the training efficiency. We propose a novel model-based DRL framework where a deep neural network (DNN)-based dynamic surrogate model (SM), instead of a real-world power grid or physics-based simulation, is utilized within the policy learning framework, making the process faster and more sample efficient. However, having stable training in model-based DRL is challenging because of the complex system dynamics of large-scale power systems. We addressed these issues by incorporating imitation learning to have a warm start in policy learning, reward-shaping, and multi-step loss in surrogate model training. Finally, we achieved 97.5% reduction in samples and 87.7% reduction in training time for an application to the IEEE 300-bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

GraphAide: Advanced Graph-Assisted Query and Reasoning System

Curating knowledge from multiple siloed sources that contain both structured and unstructured data is a major challenge in many real-world applications. Pattern matching and querying represent fundamental tasks in modern data analytics that leverage this curated knowledge. The development of such applications necessitates overcoming several research challenges, including data extraction, named entity recognition, data modeling, and designing query interfaces. Moreover, the explainability of these functionalities is critical for their broader adoption. The emergence of Large Language Models (LLMs) has accelerated the development lifecycle of new capabilities. Nonetheless, there is an ongoing need for domain-specific tools tailored to user activities. The creation of digital assistants has gained considerable traction in recent years, with LLMs offering a promising avenue to develop such assistants utilizing domain-specific knowledge and assumptions. In this context, we introduce an advanced query and reasoning system, GraphAide, which constructs a knowledge graph (KG) from diverse sources and allows to query and reason over the resulting KG. GraphAide harnesses both the KG and LLMs to rapidly develop domain-specific digital assistants. It integrates design patterns from retrieval augmented generation (RAG) and the semantic web to create an agentic LLM application. GraphAide underscores the potential for streamlined and efficient development of specialized digital assistants, thereby enhancing their applicability across various domains.

Purohit, Sumit [BATTELLE (PACIFIC NW LAB)] (ORCID:↗
DOE OSTI2021

A novel lung-inspired 3D-printed desiccant-coated heat exchanger for high-performance humidity management in buildings

Offering an independent humidity management method for buildings, desiccant-coated heat exchangers (DCHXs) are deemed a promising approach to improve the overall energy efficiency of air conditioning (AC) systems. State-of-the-art DCHXs, however, are bound with conventional HX topologies either providing limited desiccant-air interfacial areas or introducing excessive pressure drop penalties. In this paper a novel 3D-printed DCHX concept inspired by the bronchi arrangement of a human lung is introduced to address the shortcomings inherent in existing DCHX designs. The proposed lung-inspired DCHX utilizes two intertwined bicontinuous flow networks enabling highly efficient heat and mass transfer characteristics for augmented adsorption and regeneration processes at low pressure drop penalties. While the first network evenly distributes an incoming air stream through the entire volume of the lung-inspired DCHX, the second network volumetrically splits a cooling water stream within and through the first network. Effects of various parameters including air flow rate, outdoor air humidity ratio, and regeneration temperature on dehumidification performance and energy efficiency of the proposed lung-inspired DCHX were investigated. Experimental results indicated the proposed lung-inspired 3D-printed DCHX outperforms existing DCHX systems by demonstrating an excellent balance between a high volumetric adsorption rate and a low pressure drop penalty. The volumetric adsorption rate of the proposed lung-inspired DCHX technology is 54.8 g/m 3 -s, a more than two-fold improvement compared with state-of-the-art DCHX systems. Additionally, the lung-inspired DCHX offers high thermal energy efficiency of 56% at a low regeneration temperature of 40 °C. Therefore, the proposed lung-inspired 3D-printed DCHX offers a new solid-desiccant-based air dehumidification pathway for next-generation high-performance AC systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗