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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.

Community detection, graph algorithms↗
DOE OSTI2024

Self-Assembling Cell-Free Systems for Scalable Bioconversion

This project focused on developing cell-free systems to directly express multi-enzyme catalysts and perform CO2 bioconversions for industrial chemical production. The use of cell-free expression (CFE) systems derived from bacterial lysates is emerging as a promising approach for biomanufacturing. CFEs are genetically programmable, permit the expression of toxic enzymes, and allow for rapid prototyping of metabolic pathways. Research Contributions: 1. Understanding the Area Investigated: This research advances the understanding of cell-free systems by demonstrating their capability to perform complex multi-enzyme reactions. By directly expressing multi-gene systems, CFEs avoid the high costs and inefficiencies associated with producing and purifying enzymes for multi-step pathways. 2. Technical Effectiveness and Economic Feasibility: The project successfully engineered a CFE-based multienzyme biocatalyst for the de novo synthesis of serine and glycine from CO2 equivalents (formate and bicarbonate) and ammonia. This method achieved a 30% conversion rate of formate into these industrially important amino acids. Additionally, an 8-gene CFE biocatalyst was developed to produce malate, conserving 43% of carbon that would otherwise be lost as CO2. This approach has the potential to reach higher carbon efficiency than microbial production. 3. Public Benefit: The cell-free production of chemicals like serine, glycine, and malate using electrochemically generated formate could significantly reduce CO2 emissions. For example, satisfying the global malate market with this method could avoid approximately 400,000 tons of CO2 emissions annually. This work demonstrates the potential of CFE systems to produce platform chemicals, contributing to environmental sustainability and reducing reliance on petrochemicals. Future Prospects: The CFE-based biocatalyst process could be extended to produce a variety of chemicals, including other industrial di-acids, aromatics, terpenes, alcohols, and polymers. This project showcases the capabilities of cell-free expression systems for prototyping carbon-conserving pathways and sustainably bioproducing platform chemicals, marking a significant step towards economically-viable industrial processes.

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

High Performing Inverted Flexible Perovskite Solar Cells via Solution Phase Deposition of Yttrium-Doped SnO 2 Directly on Perovskite

Solution processing of flexible perovskite solar cells (f-PSCs) provides an avenue for scalable, high-throughput printing of lightweight, scalable, and cost-effective flexible solar cells. However, the deposition of fully solution-processed metal oxide charge transport layers on perovskites has been limited by solvent incompatibilities and high processing temperatures for metal oxide nanoparticles. In this study, we present high-performance, inverted f-PSCs from the direct deposition of yttrium doped SnO 2 nanoparticles functionalized with acetate on top of perovskite as an ink in anhydrous ethanol via blade coating. Yttrium doping improved device performance by improving the charge extraction leading with a decreased series resistance leading to improvements in the open-circuit voltage and fill factor. Furthermore, the champion power conversion efficiency for 0.1 cm 2 devices increased from 14.3% for undoped SnO 2 to 18.2% with 2% Y:SnO 2 doping, which is unprecedented for f-PSCs on ITO-PET substrate employing SnO 2 as an ETL.

14 SOLAR ENERGY↗
DOE OSTI2021

Developing a Low Cost, High Volume and Scalable Manufacturing Technology for Undoped and Heavily P-Type Doped CdTe Feedstock Materials (Final Report)

The goal of this project was to establish a cost-effective and scalable production approach of feedstock (CdTe and Cd-Se-Te) for the solar industry, with the material properties necessary to improve performance and reduce costs. The primary focus was to develop a CdTe synthesis and growth process that pushed the p-type doping of CdTe to previously unattainable levels, while creating a path to scalable production. In our approach, the first step was bulk CdTe growth doped with Group V dopants (P, As, and Sb) using the Modified Vertical Bridgman (MVB) and high Pressure Bridgman (HPB) techniques, and the second step was examination of films deposited by Close-Spaced Sublimation (CSS) and Vapor Transport Deposition (VTD) using this specialized feedstock.

14 SOLAR ENERGY↗
NASA NTRSDate not supplied

Attitude Control System for the Solar Cruiser and Future Solar Sail Missions

NASA’s Marshall Space Flight Center (MSFC) is developing the Solar Cruiser solar sail mission to continue to mature solar sail propulsion. Solar Cruiser is a 95kg spacecraft capable of deploying a 1654 square meter solar sail. Solar Cruiser will demonstrate science observation capabilities and navigation in sub Lagrange Point (L1) halo-orbits. Solar Cruiser sail technology can be scaled to larger solar sail missions, including attitude control system and sail membrane and boom technology. Larger solar sails could be used to image the solar poles, enabling Solar Polar Imager (SPI) missions currently prohibitively expensive for propellant-based propulsion methods. Similarly, solar sails can be used to create artificial equilibria and indefinite station-keeping at locations sunward of Lagrange Point one, L1, along the Sun-Earth line (SEL), which can provide space-weather monitor and prediction and reveal discoveries about our Sun and solar system. To achieve the high characteristic acceleration required for larger solar sail missions such as SPI, a solar sail area of approximately 7000 square-meters would be needed. In comparison, the largest solar sail flown to date is the JAXA’s IKAROS mission with a 196 square-meters sail. Attitude control an SPI-size sail poses challenges, including station keeping under solar sail induced torques and thrust vector pointing. In this study, the scalability of the Solar Cruiser sail attitude control architecture to larger sails is studied, such as pointing control performance, mass and power requirements. Solar Cruiser attitude control actuators include propellant-based, such as ion engines, and propellantless options which create differential solar pressures to generate spacecraft torques. Propellantless attitude control includes active translation of the center of mass, Reflectivity Control Devices (RCDs), and tip vanes. The study summarizes the scalability characteristics of the different attitude control architectures for larger solar sail missions.

Solar Sail Propulsion↗
DOE OSTI2025

Computational Performance Bounds Prediction in Quantum Computing With Unstable Noise

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical computing. Yet, noise in quantum devices poses significant barriers to realizing this supremacy. Understanding noise’s impact is crucial for reproducibility and application reuse; moreover, the next-generation quantum-centric supercomputing essentially requires efficient and accurate noise characterization to support system management (e.g., job scheduling), where ensuring correct functional performance (i.e., fidelity) of jobs on available quantum devices can even be higher-priority than traditional objectives. However, noise fluctuates over time, even on the same quantum device, which makes predicting the computational bounds for on-the-fly noise is vital. Noisy quantum simulation can offer insights but faces efficiency and scalability issues. Here, in this work, we propose a data-driven workflow, namely QuBound, to predict computational performance bounds. It decomposes historical performance traces to isolate noise sources and devises a novel encoder to embed circuit and noise information processed by a Long Short-Term Memory (LSTM) network. For evaluation, we compare QuBound with a state-of-the-art learning-based predictor, which only generates a single performance value instead of a bound. Experimental results show that the result of the existing approach falls outside of performance bounds, while all predictions from our QuBound with the assistance of performance decomposition better fit the bounds. Moreover, QuBound can efficiently produce practical bounds for various circuits with over 106 speedup over simulation; in addition, the range from QuBound is over 10× narrower than the state-of-the-art analytical approach.

Li, Jinyang [George Mason Univ., Fairfax, VA (Unit↗
DOE OSTI2025

A robust fourth-order finite-difference discretization for the strongly anisotropic transport equation in magnetized plasmas

We propose a second-order temporally implicit, fourth-order-accurate spatial discretization scheme for the strongly anisotropic heat transport equation characteristic of hot, fusion-grade plasmas. Following Du Toit et al. (2018), the scheme transforms mixed-derivative diffusion fluxes (which are responsible for the lack of a discrete maximum principle) into nonlinear advective fluxes, amenable to nonlinear-solver-friendly monotonicity-preserving limiters. The scheme enables accurate multi-dimensional heat transport simulations with up to seven orders of magnitude of heat-transport-coefficient anisotropies with low cross-field numerical error pollution and excellent algorithmic performance, with the number of linear iterations scaling very weakly with grid resolution and grid anisotropy, and scaling with the square-root of the implicit timestep. We propose a multigrid preconditioning strategy based on a lower-order approximation that renders the scheme efficient and scalable under grid refinement. Several numerical tests are presented that display the expected spatial convergence rates and strong algorithmic performance, including fully nonlinear magnetohydrodynamics simulations of kink instabilities in a Bennett pinch in 2D helical geometry and of ITER in 3D toroidal geometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1995

High Performance Parallel Computational Nanotechnology

At a recent press conference, NASA Administrator Dan Goldin encouraged NASA Ames Research Center to take a lead role in promoting research and development of advanced, high-performance computer technology, including nanotechnology. Manufacturers of leading-edge microprocessors currently perform large-scale simulations in the design and verification of semiconductor devices and microprocessors. Recently, the need for this intensive simulation and modeling analysis has greatly increased, due in part to the ever-increasing complexity of these devices, as well as the lessons of experiences such as the Pentium fiasco. Simulation, modeling, testing, and validation will be even more important for designing molecular computers because of the complex specification of millions of atoms, thousands of assembly steps, as well as the simulation and modeling needed to ensure reliable, robust and efficient fabrication of the molecular devices. The software for this capacity does not exist today, but it can be extrapolated from the software currently used in molecular modeling for other applications: semi-empirical methods, ab initio methods, self-consistent field methods, Hartree-Fock methods, molecular mechanics; and simulation methods for diamondoid structures. In as much as it seems clear that the application of such methods in nanotechnology will require powerful, highly powerful systems, this talk will discuss techniques and issues for performing these types of computations on parallel systems. We will describe system design issues (memory, I/O, mass storage, operating system requirements, special user interface issues, interconnects, bandwidths, and programming languages) involved in parallel methods for scalable classical, semiclassical, quantum, molecular mechanics, and continuum models; molecular nanotechnology computer-aided designs (NanoCAD) techniques; visualization using virtual reality techniques of structural models and assembly sequences; software required to control mini robotic manipulators for positional control; scalable numerical algorithms for reliability, verifications and testability. There appears no fundamental obstacle to simulating molecular compilers and molecular computers on high performance parallel computers, just as the Boeing 777 was simulated on a computer before manufacturing it.

Saini, Subhash↗
NASA NTRS1996

Parallel Climate Data Assimilation PSAS Package

We have designed and implemented a set of highly efficient and highly scalable algorithms for an unstructured computational package, the PSAS data assimilation package, as demonstrated by detailed performance analysis of systematic runs on up to 512node Intel Paragon. The equation solver achieves a sustained 18 Gflops performance. As the results, we achieved an unprecedented 100-fold solution time reduction on the Intel Paragon parallel platform over the Cray C90. This not only meets and exceeds the DAO time requirements, but also significantly enlarges the window of exploration in climate data assimilations.

PSAS data scalable algorithms Intel Paragon 512nod↗
DOE OSTI2020

Scalable Electrolytic Systems for Renewable Hydrogen Production (Cooperative Research and Development Final Report, CRADA Number CRD-18-747)

The project focuses on offshore wind energy which is an emergent renewable energy source in the United States. Onsite hydrogen production for this sector is not available. The overall goal of GTA's work is development of scalable multi-megawatt water electrolysis systems that exclude platinum group metals, are constructed from readily available commodity materials and are mass produced using advanced manufacturing and robotics. They fabricated and successfully tested a scalable TRL 4 laboratory prototype that demonstrated promising results for achieving the goal. They further performed calculations and prepared scale drawings for an envisioned onsite TRL 8 electrolytic hydrogen production system at an offshore 8 MW wind turbine. The work plan of the CRADA proposal between NREL and GTA focuses on validation of the current TRL 4 prototype with potential development and refinement for advancement to a TRL 5 system. GTA, Inc. proposes joining the H2@Scale consortium to leverage DOE funds, benefit from and contribute to the common knowledge-base and work with NREL as a CRADA partner.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

ChemComp: A Compilation Framework for Computing with Chemical Reaction Networks

The acceleration of scientific computation, data analytics, and artificial intelligence is driving a surge in computational requirements. Yet, state-of-the-art high-performance computing systems are approaching physical limitations that impede further significant improvements in energy efficiency. As we move towards post-exascale computing systems, innovative approaches are necessary to overcome this barrier in power consumption. Novel analog and hybrid digital-analog architectures hold promise for enhancing energy efficiency by several orders of magnitude. Biochemical computation stands out among the various solutions being explored due to its potential to enable new classes of devices with immense computational capabilities. These devices can capitalize on the inherent efficacy of biological cells in solving optimization problems and are scalable through increasing reaction system size or vessel capacity, potentially satisfying scientific computing's high-performance requirements. Nonetheless, several theoretical and practical limitations persist, including problem formulation and mapping to chemical reaction networks (CRNs) and implementation of actual CRN devices. In this paper, we propose a framework for biochemical computation using systems chemistry. We present the initial components of our approach: an abstract chemical reaction dialect implemented as a multi-level intermediate representation (MLIR) compiler extension and a pathway to represent mathematical problems with CRNs. To showcase the potential of this approach, we emulate a simplified chemical reservoir device. This work lays the groundwork for leveraging chemistry's computing potential in creating energy-efficient, high-performance computing systems tailored to contemporary computational needs.

artificial intelligence↗
DOE OSTI2025

Overcoming the Conductance versus Crossover Trade-off in State-of-the-Art Proton Exchange Fuel-Cell Membranes by Incorporating Atomically Thin Chemical Vapor Deposition Graphene

Permeance–selectivity trade-offs are inherent to polymeric membranes. In fuel cells, thinner proton exchange membranes (PEMs) could enable higher proton conductance and increased power density with lower area-specific resistance (ASR), smaller ohmic losses, and lower ionomer cost. However, reducing thickness is accompanied by an increase in undesired species crossover harming performance and long-term efficiency. Here, we show that incorporating atomically thin monolayer graphene synthesized via scalable chemical vapor deposition (CVD) and tunable defect density into PEMs (Nafion, ~5–25 μm thick) can allow for reduced H 2 crossover (~34–78% of Nafion of a similar thickness) while maintaining adequate areal proton conductance for applications (>4 S cm –2 ). In contrast to most prior work using >50 μm symmetric Nafion sandwich structures, we elucidate the interplay of graphene defect density and Nafion proton transport resistance on the performance of Nafion|graphene composite membranes and find high-quality low-defect density CVD graphene (G) supported on Nafion 211 (~25 μm); i.e., N211|G has a high areal proton conductance (~6.1 S cm –2 ) and the lowest H 2 crossover (~0.7 mA cm –2 ). Fully functional centimeter-scale N211|G fuel-cell membranes demonstrate performance comparable to that of state-of-the-art Nafion N211 at room temperature as well as standard operating conditions (~80 °C, ~150–250 kPa-abs) with H 2 /air (power density ~0.57–0.63 W cm –2 ) and H 2 /O 2 feed (power density ~1.4–1.62 W cm –2 ) and markedly reduced H 2 crossover (~53–57%).

25 ENERGY STORAGE↗
DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

In Situ Surface Reconstruction via Lithium Residue Regulation for Direct Recycling of Ni-Rich Cathodes

Surface stability is crucial for the long cycling performance of Ni-rich cathodes, as it dictates and governs side reactions, preserves crystal integrity, and mitigates capacity degradation during cycling. For spent Ni-rich cathodes targeted for one-step direct recycling, constructing a robust and stable surface is particularly challenging because prior cycling induces severe structural and morphological degradation. Here, in this study, we introduce an in situ surface reconstruction strategy that converts surface lithium residues into a protective layer via a direct liquid-phase coating with ammonium dihydrogen phosphate (ADP). During the process, residual lithium remaining after the hydrothermal relithiation is removed, while a conformal lithium phosphate (Li 3 PO 4 , LPO) layer is formed to act as both a chemical barrier and a structural stabilizer. This dual function enhances cycling stability and rate capability in regenerated cathodes, and the approach is applicable to various Ni-rich compositions including spent LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) and scrap LiNi 0.866 Mn 0.066 Co 0.05 Al 0.018 O 2 (NCMA). Its scalability and compositional versatility make it a promising route for sustainable regeneration of high-performance cathode materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ytopt: Autotuning Scientific Applications for Energy Efficiency at Large Scales

As we enter the exascale computing era, efficiently utilizing power and optimizing the performance of scientific applications under power and energy constraints has become critical and challenging. We propose a low-overhead autotuning framework to autotune performance and energy for various hybrid MPI/OpenMP scientific applications at large scales and to explore the tradeoffs between application runtime and power/energy for energy efficient application execution, then use this framework to autotune four ECP proxy applications—XSBench, AMG, SWFFT, and SW4lite. Our approach uses Bayesian optimization with a Random Forest surrogate model to effectively search parameter spaces with up to 6 million different configurations on two large-scale HPC production systems, Theta at Argonne National Laboratory and Summit at Oak Ridge National Laboratory. The experimental results show that our autotuning framework at large scales has low overhead and achieves good scalability. Using the proposed autotuning framework to identify the best configurations, we achieve up to 91.59% performance improvement, up to 21.2% energy savings, and up to 37.84% EDP (energy delay product) improvement on up to 4096 nodes.

Autotuning↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

A Unifying Framework to Enable Artificial Intelligence in High-Performance Computing Workflows

Current trends point to a future where large-scale scientific applications are tightly coupled high-performance computing/artificial intelligence (HPC/AI) hybrids. Hence, we urgently need to invest in creating a seamless, scalable framework where HPC and AI/machine learning can efficiently work together and adapt to novel hardware and vendor libraries without starting from scratch every few years. Finally, the current ecosystem and sparsely connected community are not sufficient to tackle these challenges, and we require a breakthrough catalyst for science similar to what PyTorch enabled for AI.

high-performance computing↗
DOE OSTI2021

Smart Monitoring and Diagnostic System (SMDS) for Packaged Air Conditioners and Heat Pumps for Small/Medium Commercial Buildings: Preparation for Commercialization: CRADA 478 [Abstract only]

This project will enable Pacific Northwest National Laboratory (PNNL) and industry partner, mCloud Technologies, to work collaboratively to ready the Smart Monitoring and Diagnostic System (SMDS) for commercial deployment and validate its performance under real-world conditions at field sites. This project will specifically focus on 1) implementing the SMDS algorithms in a scalable cloud-based software architecture, 2) designing new, innovative, complimentary commercial services based on the SMDS, 3) enhancing the SMDS energy and cost impact algorithms to reduce uncertainty in estimates, 4) determining the lower limits on SMDS performance degradation detection, 5) validating algorithm performance and default values of adjustable thresholds with existing data from controlled physical testing and from customer packaged air conditioners and heat pumps (commonly referred to as rooftop units or RTUs), 6) field testing to validate the system on multiple customer buildings in diverse environments, and 7) expanding field deployment to a larger set of mCloud’s customer buildings. Project results by validating, enhancing, and quantifying the performance of the SMDS will position mCloud, and potential future licensees, to implement the SMDS in commercial offerings that encourage and enable use of condition-based and predictive maintenance, leading to significant reductions in energy use and greenhouse gas emissions associated with space conditioning by RTUs. Furthermore, these enhancements will increase the value of the SMDS for users and increase the potential market for its use and impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗