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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SYMBIOSYS: A Methodology for Performance Analysis of Composable HPC Data Services

Microservices are a powerful new way of building, customizing, and deploying distributed services owing to their flexibility and maintainability. Several large-scale distributed platforms have emerged to serve the growing needs of data-centric workloads and services in commercial computing. Concurrently, high-performance computing (HPC) systems and software are rapidly evolving to meet the demands of diversified applications and heterogeneity. The interplay of hardware factors, software configuration parameters, and the flexibility offered with a microservice architecture makes it nontrivial to estimate the optimal service instantiation for a given application workload. Further, this problem is exacerbated when considering that these services operate in a dynamic and heterogeneous HPC environment. An optimally integrated service can be vastly more performant than a haphazardly integrated one. Existing performance tools for HPC either fail to understand the request-response model of communication inherent to microservices or they operate within a narrow scope, limiting the insight that can be gleaned from employing them in isolation. We propose a methodology for integrated performance analysis of HPC microservices frameworks and applications called SYMBIOSYS. We describe its design and implementation within the context of the Mochi framework. This integration is achieved by combining distributed callpath profiling and tracing with a performance data exchange strategy that collects fine-grained, low-level metrics from the RPC communication library and network layers. The result is a portable, low-overhead performance analysis setup that provides a holistic profile of the dependencies among microservices and how they interact with the Mochi RPC software stack. Using HEPnOS, a production-quality Mochi data service, we demonstrate the low-overhead operation of SYMBIOSYS at scale and use it to identify the root causes of poorly performing service configurations.

microservices↗
DOE OSTI2021

The Demand-Side Opportunity: The Roles of Distributed Solar and Building Energy Systems in a Decarbonized Grid

Building energy systems represent a significant and largely untapped demand-side grid resource. Buildings use about 75% of electricity in the United States, including around 80% of peak demand, meaning that changes in building energy use have significant implications for grid operations. Building energy technologies-including energy-efficient devices, flexible loads, and energy storage-can be coordinated and aggregated to perform similar functions as centralized grid assets. Recent advances in information and communication (ICT) technologies have significantly improved building energy technology automation, coordination, and aggregation capabilities. With declining costs and technological advances, building energy systems can increasingly compete with conventional grid assets such as natural gas plants. Building energy technologies enable the deployment of distributed solar photovoltaics (DPV). Energy-efficient buildings allow DPV to meet greater shares of building load while flexible loads and energy storage can reshape building load profiles to optimize the on-site use of DPV. Optimized on-site use of DPV increases the value proposition for DPV adoption and can help utilities cost-effectively integrate higher levels of DPV penetration. Together, DPV and building energy technologies could help grids achieve deep decarbonization more quickly and cost-effectively.

14 SOLAR ENERGY↗
NASA NTRS2008

Integrated Performance of Next Generation High Data Rate Receiver and AR4JA LDPC Codec for Space Communications

Low-density parity-check (LDPC) codes are the state-of-the-art in forward error correction (FEC) technology that exhibits capacity approaching performance. The Jet Propulsion Laboratory (JPL) has designed a family of LDPC codes that are similar in structure and therefore, leads to a single decoder implementation. The Accumulate-Repeat-by-4-Jagged- Accumulate (AR4JA) code design offers a family of codes with rates 1/2, 2/3, 4/5 and lengths 1024, 4096, 16384 information bits. Performance is less than one dB from capacity for all combinations.Integrating a stand-alone LDPC decoder with a commercial-off-the-shelf (COTS) receiver faces additional challenges than building a single receiver-decoder unit from scratch. In this work, we outline the issues and show that these additional challenges can be over-come by simple solutions. To demonstrate that an LDPC decoder can be made to work seamlessly with a COTS receiver, we interface an AR4JA LDPC decoder developed on a field-programmable gate array (FPGA) with a modern high data rate receiver and mea- sure the combined receiver-decoder performance. Through optimizations that include an improved frame synchronizer and different soft-symbol scaling algorithms, we show that a combined implementation loss of less than one dB is possible and therefore, most of the coding gain evidence in theory can also be obtained in practice. Our techniques can benefit any modem that utilizes an advanced FEC code.

Cheng, Michael K.↗
DOE OSTI2023

Analysis of Building Model Forecasts using Autonomous HVAC Optimization System for Residential Neighborhood

Heating, ventilation, and air conditioning (HVAC) systems account for the highest share of home energy consumption in the United States. Optimized HVAC control can provide thermal improved comfort to the occupants, improve energy efficiency, reduce energy cost, and support grid services. In this paper, we discuss a multi-agent and cloud-based software framework that has been deployed in occupied residential neighborhood. This system enables automatic data collection, learning, optimization, and dispatches signals to neighborhood devices. HVAC optimization is based on model predictive control (MPC). Since the operational performance of MPC depends on model forecasting accuracy, it is crucial to evaluate the model continuously and modify or retrain it as necessary. In this research, we developed an automated workflow to evaluate the performance of temperature and power forecasts based on measured data in the real world. This will provide researchers with a deeper understanding of the model and how it can be improved.

Lebakula, Viswadeep↗
DOE OSTI2022

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Distributionally Robust Partially Observable Markov Decision Process with Moment-Based Ambiguity

In this paper, we consider a distributionally robust partially observable Markov decision process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint distribution using bounded moments via conic constraints and seek an optimal policy to maximize the worst-case (minimum) reward for any distribution in the set. We show that the value function of DR-POMDP is piecewise linear convex with respect to the belief state and propose a heuristic search value iteration method for obtaining lower and upper bounds of the value function. We conduct numerical studies and demonstrate the computational performance of our approach via testing instances of a dynamic epidemic control problem. Our results show that DR-POMDP can produce more robust policies under misspecified distributions of transition-observation probabilities as compared to POMDP but has less costly solutions than robust POMDP. The DR-POMDP policies are also insensitive to varying parameter in the ambiguity set and to noise added to the true transition-observation probability values obtained at the end of each decision period.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Density Matrix Quantum Circuit Simulation via the BSP Machine on Modern GPU Clusters

As quantum computers evolve, simulations of quantum programs on classical computers will be essential in validating quantum algorithms, understanding the effect of system noise, and design applications for future quantum computers. In this paper, we propose a novel multi-GPU programming model called MG-BSP that constructs a virtual BSP machine on top of modern multi-GPU platforms, and tweak the programming model to build a multi-GPU density matrix quantum simulator. We propose and evaluated a new formulation minimizing communication and prove that the transformation conserves original semantics when noise is introduced. We build the tool-chain to support quantum assembly open standard, synthesize testing quantum circuit, and enable ultra-deep quantum simulation. We evaluated our design on four state-of-the-art multi-GPU platforms including the latest DGX-1 and DGX-2 systems. We demonstrate simulation of 1 million gates in 94 minutes, far deeper circuits than has been demonstrated in prior work. A roofline-model analysis show that we have reached near-optimal performance under memory bound.

Li, Ang↗
DOE OSTI2022

Work Smarter, Not Harder: Improving Energy Efficiency and Safety through Smarter Ventilation: Preprint

Ventilation is a key component to maintaining healthy, safe indoor air quality. Especially important in laboratories, ventilation is the first line of defense against airborne hazards produced during research activities. Though a vital component, laboratory ventilation systems are often victim to ineffective operation, posing a risk to the most important asset - the researchers. Furthermore, system inefficiencies can lead to up to 50% wasted energy. To improve both energy efficiency and safety in laboratories, we present the Smart Labs Toolkit - a resource developed by the U.S. Department of Energy Federal Energy Management Program and the International Institute for Sustainable Laboratories that guides laboratory stakeholders through a straight-forward, holistic approach to achieve dynamic, high-performance laboratories. Smart Labs enable safe and efficient world class science to occur in laboratories through high-performance methods. A Smart Labs program employs a combination of physical, administrative, and management techniques to assess, optimize, and manage high performance laboratories. We will focus on a central component of the Smart Labs approach - the Laboratory Ventilation Risk Assessment, a systematic process for identifying risk due to airborne hazards to inform the operation of dynamic ventilation that optimizes safety and efficiency. Case studies of organizations who have successfully implemented Smart Labs ventilation management programs will also be shared. In learning ventilation strategies successful in critical laboratory environments, learn the tools and resources needed to successfully manage energy in any building through smarter, safer ventilation.

buildings↗
DOE OSTI2020

Real-Time MPC for Residential Building Water Heater Systems to Support the Electric Grid

The world of IoT (internet of things) is spawning new control options and decision making that has not been available before. This is providing a wealth of opportunities for transactive type controls and systems that can negotiate for a common goal. This paper discusses a transactive residential neighborhood with optimization at the home level utilizing a system of agents. The real-time optimization utilizes information and modeling to optimize heat pump water heater operation in occupied homes. Data is presented showing the performance of the optimization and conclusions are drawn on next steps for development.

Starke, Michael↗
DOE OSTI2026

Higher Efficiency, Demand Flexible Refrigerator with On-Demand Micro-Vibrational De-icing Technology

Refrigerator technology has advanced significantly over the last couple of decades. Today’s refrigerators use only about 25% of the energy that was required to power models built in 1975. Even as they continually improve efficiency to meet standards, refrigerators have increased in size by almost 20%, added energy-consuming features such as through-the-door ice, and provide more benefits than ever before. However, a few challenges and technology gaps are preventing further improvement of the demand responsiveness and efficiency of the refrigerators. One of the major technology gaps in existing refrigerators is their outdated de-icing process. When the evaporator generates frost, an old-fashioned resistive heating element melts the ice. Most refrigerators have a timed defrost cycle, rather than an active system that could monitor the state of the frost. In these systems, not only is the precious electricity used at its least efficient form of conversion (direct conversion of electricity to heat), but also all the latent heat associated with the ice is wasted during the melting process. On top of that, the refrigerator needs to work harder to pull the temperature down after defrosting, and, last but not least, the food quality is severely impacted by the temperature swings during the defrost cycle. According to a study, the EU alone wastes 89 million tons of food in the supply chain every year. Any temperature swing during defrosting (about 6F according to Emerson for low-temperature cases) can negatively impact the shelf life of meat and other products for multiple days. All these issues can happen during the peak demand time of the electric grid. Unlike the conventional systems, the proposed novel advanced micro-vibrational deicing process uses no heat for defrosting. Instead, it uses the micro vibrations generated by a piezoelectric or vibration-generating module to mechanically break ice from the heat exchanger almost instantaneously. The project titled “Higher Efficiency, Demand Flexible Refrigerator with On-Demand Micro-Vibrational De-icing Technology, performed by Ultrasonic Technology Solutions, LLC (UTS) of Knoxville, TN, in collaboration with Emerson (now Copeland), represents the final phase of a multi-year effort funded under the U.S. Department of Energy’s Building Technologies Office (BTO) BENEFIT FOA 2020. Initiated on October 1, 2021, and completed after a nine-month no-cost extension ending September 30, 2025, this project aimed to develop and validate a novel micro-vibrational mechanical defrosting system, achieving more than 25% improvement in defrosting energy efficiency over conventional baseline defrosting technologies. Over sixteen quarters, the project advanced from fundamental ice-mechanical characterization and prototype development to full-scale system integration and validation. Initial efforts established project management infrastructure and characterized ice adhesion properties, followed by the design and fabrication of early aluminum-based prototypes for resonance frequency testing. Subsequent quarters saw rapid technical progression, including the identification of optimal piezoelectric and motor-based vibration mechanisms, the demonstration of effective de-icing over 6x6-inch aluminum surfaces. The team achieved its Go/No-Go milestone by exceeding the 25% energy-efficiency improvement target—reaching up to 3,340% under optimized conditions—and later confirmed that motor-driven systems offered superior performance and energy efficiency compared to piezoelectric alternatives. Continued refinement led to the development of amplifier systems on printed circuit boards, improved control and instrumentation hardware, and integration into full-scale heat exchanger (HX) prototypes at both UTS and Copeland facilities. Multiple vibration-mounting studies and frost-growth experiments guided mechanical optimization and noise-mitigation strategies, achieving a 17.5 dB reduction in sound pressure level and verifying robust mechanical performance. Advanced analyses, including modal and harmonic simulations, established a quantitative understanding of vibrational behavior and de-icing efficiency across >1000 cm² systems. The final project phase successfully demonstrated scalable integration within reach-in and chest freezer prototypes, confirmed >25% efficiency improvements in large-area systems, and completed a comprehensive business model and scale-up strategy identifying electric defrost systems as the primary beachhead market. The culmination of this DOE-supported effort establishes micro-vibrational defrosting as a viable, high-efficiency, low-noise, and demand-flexible de-icing technology, paving the way for commercial deployment and broader application in next-generation refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Using Reinforcement Learning to Optimize Quantum Circuits in thePresence of Noise

Many quantum computing frameworks currently use noise aware algorithms for implementing quantum circuits which do not scale efficiently as the size of the hardware architecture increases. As we move towards devices which utilize more qubits, it becomes increasing more important to map quantum circuits in a way that uses resources efficiently as well as maximizes the reliability of the results of that circuit. However, as the hardware increases to the point where Quantum supremacy is attainable, it will infeasible for a brute-force algorithm to find the most optimal circuit layout for circuits of medium to large depth sizes. To this end, we will to rely on reinforcement learning (RL) as a method of building quantum circuits based on observations of the noise characteristics in its environment. In this work, we create a working reinforcement learning environment in which an agent is able to make action which will build the class of circuits which creates the GHZ state. In addition to this, we also get preliminary results of the performance of a Deep Q Neural Network, which initially does not perform as well as we believe it can. In the future, we want to improve the performance of the agent and potentially generalize this environment to more classes of circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

SNF Canister Coatings for Corrosion Prevention and Mitigation (FY21 Status Report)

This report summarizes the current actives in FY21 related to the effort by Sandia National Laboratories to identify and test coating materials for the prevention, mitigation, and repair of spent nuclear fuel dry storage canisters against potential chloride-induced stress corrosion cracking. This work follows up on the details provided in Sandia National Laboratories FY20 report on the same topic, which provided a detailed description of the specific coating properties desired for application and implementation on spent nuclear fuel canisters, as well as provided detail into several different coatings and their applicability to coat spent nuclear fuel canisters. In FY21, Sandia National Laboratories has engaged with private industry to create a Memorandum of Understanding and established a collaborative R&D program building off the analytical and laboratory capabilities at Sandia National Laboratories and the material design and synthesis capabilities of private industry. The resulting Memorandum of Understanding included four companies to date (Oxford Performance Materials, White Horse R&D, Luna Innovations, and Flora Coating) proposing six different coating technologies (polyetherketoneketone, modified polyimide/polyurea, modified phenolic resin, silane-based polyurethane hybrid with and without a Znrich primer, and a quasi-ceramic sol-gel polyurethane hybrid) to be tested, evaluated, and optimized for their potential use for this application. This report provides a detailed description of each of the coating systems proposed by the participating industry partners. It also provides a description of the planned experimental actives to be performed by Sandia National Laboratories including physical tests, electrochemical tests, and characterization methods. These analyses will be used to identify specific ways to further improve coating technologies toward their application and implementation on spent nuclear fuel canisters. In FY21, Sandia National Laboratories began baseline testing of the base metal material in according with activities of the Memorandum of Understanding. In FY22, Sandia National Laboratories will receive coated coupons from each of the participating industry partners and begin characterization, physical, and electrochemical testing following the test plan described herein.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Preparing an Incompressible-Flow Fluid Dynamics Code for Exascale-Class Wind Energy Simulations: Preprint

The US Department of Energy has identified Exascale-Class wind farm simulation tools as critical to wind energy scientific discovery. A primary objective of the Exawind project is to build high-performance, predictive Computational Fluid Dynamics tools that satisfy these modeling needs. GPU accelerators will serve as the computational thoroughbreds of next generation, Exascale-Class, platforms. Here, we report on our efforts for preparing the Exawind unstructured mesh solver, Nalu-Wind, for Exascale-Class machines. For computing at this scale, a simple port of the incompressible-flow algorithms to GPUs is not sufficient. One needs novel algorithms that are application aware, memory efficient, and optimized for latest generation GPU devices to get high-performance. The result of our efforts are unstructured mesh simulations of wind turbines that use 1/6 the compute resources of Summit supercomputer at Oak Ridge National Lab. In particular, we demonstrate a first-of-its-kind, simulation using Algebraic Multigrid solvers on over 4000 GPUs.

algebraic multigrid↗
DOE OSTI2021

Electrochemical reduction of acetonitrile to ethylamine

Electrifying chemical manufacturing using renewable energy is an attractive approach to reduce the dependence on fossil energy sources in chemical industries. Primary amines are important organic building blocks; however, the synthesis is often hindered by the poor selectivity because of the formation of secondary and tertiary amine byproducts. Herein, we report an electrocatalytic route to produce ethylamine selectively through an electroreduction of acetonitrile at ambient temperature and pressure. Among all the electrocatalysts, Cu nanoparticles exhibit the highest ethylamine Faradaic efficiency (~96%) at -0.29 V versus reversible hydrogen electrode. Under optimal conditions, we achieve an ethylamine partial current density of 846 mA cm -2 . A 20-hour stable performance is demonstrated on Cu at 100 mA cm -2 with an 86% ethylamine Faradaic efficiency. Moreover, the reaction mechanism is investigated by computational study, which suggests the high ethylamine selectivity on Cu is due to the moderate binding affinity for the reaction intermediates.

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

High-temperature low-humidity proton exchange membrane with “stream-reservoir” ionic channels for high-power-density fuel cells

The perfluorosulfonic acid (PFSA) proton exchange membrane (PEM) is the key component for hydrogen fuel cells (FCs). We used in situ synchrotron scattering to investigate the PEM morphology evolution and found a “stream-reservoir” morphology, which enables efficient proton transport. The short-side-chain (SSC) PFSA PEM is fabricated under the guidance of morphology optimization, which delivered a proton conductivity of 193 milliSiemens per centimeter [95% relativity humidity (RH)] and 40 milliSiemens per centimeter (40% RH) at 80°C. The improved glass transition temperature, water permeability, and mechanical strength enable high-temperature low-humidity FC applications. Performance improvement by 82.3% at 110°C and 25% RH is obtained for SSC-PFSA PEM FCs compared to Nafion polymer PEM devices. The insights in chain conformation, packing mechanism, crystallization, and phase separation of PFSAs build up the structure-property relationship. In addition, SSC-PFSA PEM is ideal for high-temperature low-humidity FCs that are needed urgently for high-power-density and heavy-duty applications.

25 ENERGY STORAGE↗
NASA NTRS1972

An integrated computer system for preliminary design of advanced aircraft.

A progress report is given on the first phase of a research project to develop a system of Integrated Programs for Aerospace-Vehicle Design (IPAD) which is intended to automate to the largest extent possible the preliminary and detailed design of advanced aircraft. The approach used is to build a pilot system and simultaneously to carry out two major contractual studies to define a practical IPAD system preparatory to programing. The paper summarizes the specifications and goals of the IPAD system, the progress to date, and any conclusion reached regarding its feasibility and scope. Sample calculations obtained with the pilot system are given for aircraft preliminary designs optimized with respect to discipline parameters, such as weight or L/D, and these results are compared with designs optimized with respect to overall performance parameters, such as range or payload.

Fulton, R. E.↗
NASA NTRS2014

Physiological Observations and Omics to Develop Personalized Sensormotor Adaptability Countermeasures Using Bed Rest and Space Flight Data

Astronauts experience sensorimotor disturbances during the initial exposure to microgravity and during the re-adapation phase following a return to an earth-gravitational environment. These alterations may disrupt the ability to perform mission critical functional tasks requiring ambulation, manual control and gaze stability. Interestingly, astronauts who return from space flight show substantial differences in their abilities to readapt to a gravitational environment. The ability to predict the manner and degree to which individual astronauts would be affected would improve the effectiveness of countermeasure training programs designed to enhance sensorimotor adaptability. For such an approach to succeed, we must develop predictive measures of sensorimotor adaptability that will allow us to foresee, before actual space flight, which crewmembers are likely to experience the greatest challenges to their adaptive capacities. The goals of this project are to identify and characterize this set of predictive measures that include: 1) behavioral tests to assess sensory bias and adaptability quantified using both strategic and plastic-adaptive responses; 2) imaging to determine individual brain morphological and functional features using structural magnetic resonance imaging (MRI), diffusion tensor imaging, resting state functional connectivity MRI, and sensorimotor adaptation task-related functional brain activation; 3) genotype markers for genetic polymorphisms in Catechol-O-Methyl Transferase, Dopamine Receptor D2, Brain-derived neurotrophic factor and genetic polymorphism of alpha2-adrenergic receptor that play a role in the neural pathways underlying sensorimotor adaptation. We anticipate these predictive measures will be significantly correlated with individual differences in sensorimotor adaptability after long-duration space flight and an analog bed rest environment. We will be conducting a retrospective study leveraging data already collected from relevant ongoing/completed bed rest and space flight studies. These data will be combined with predictor metrics that will be collected prospectively - behavioral, brain imaging and genomic measures; from these returning subjects to build models for predicting post-mission (bed rest - non-astronauts or space flight - astronauts) adaptive capability as manifested in their outcome measures. Comparisons of model performance will allow us to better design and implement sensorimotor adaptability training countermeasures that are customized for each crewmember's sensory biases, adaptive capacity, brain structure and functional capacities, and genetic predispositions against decrements in post-mission adaptive capability. This ability will allow more efficient use of crew time during training and will optimize training prescriptions for astronauts to ensure expected outcomes.

Mulavara, A. P.↗
DOE OSTI2022

Fusion Energy Sciences Network Requirements Review. Final Report, April - October 2021

The Energy Sciences Network (ESnet) is the high-performance network user facility for the US Department of Energy (DOE) Office of Science (SC) and delivers highly reliable data transport capabilities optimized for the requirements of data-intensive science. In essence, ESnet is the circulatory system that enables the DOE science mission by connecting all of its laboratories and facilities in the US and abroad. ESnet is funded and stewarded by the Advanced Scientific Computing Research (ASCR) program and managed and operated by the Scientific Networking Division at Lawrence Berkeley National Laboratory (LBNL). ESnet is widely regarded as a global leader in the research and education networking community. Throughout 2021, ESnet and the Office of Fusion Energy Sciences (FES) of the DOE SC organized an ESnet requirements review of FES-supported activities. Preparation for these events included identification of key stakeholders: program and facility management, research groups, and technology providers. Each stakeholder group was asked to prepare formal case study documents about their relationship to the FES program to build a complete understanding of the current, near-term, and long-term status, expectations, and processes that will support the science going forward.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗