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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Electrolyte and Cutoff Potential Effects on Cycle Life of Li4Ti5O12/LiNi0.9Mn0.1O2 Batteries for Behind-the-Meter Storage Applications

Behind-the-Meter Storage (BTMS) is a stationary battery energy storage system that is connected to the electrical distribution system on the customer's side of the utility's service meter. BTMS systems are used to store electrical energy from the grid as well as inconstant, renewable energy, such as local solar and wind generation. A successful BTMS system will allow the customer to pair their energy generation and storage to optimize electrical consumption from the grid, improving reliability and minimizing cost. For BTMS applications, batteries must be designed and optimized with different set of criteria from other leading segments of the Li-ion battery market, like transportation, due the system being stationary and proximal to the residential or commercial building it's benefitting. BTMS applications prioritize safety, cost (low/no-critical materials), reliability (20-year calendar life), and durability (10,000 cycle life), while having the ability to (minimally) compromise energy density and rate capability. Lithium titanate (Li4Ti5O12-, LTO) is a promising anode candidate for BTMS applications due to its high safety and capacity retention, while maintaining a reasonable 160 mAhg-1 reversable capacity and composition of relatively abundant materials. (1) Specifically, LTO has a high working voltage which helps to prevent Li dendrite formation, improving safety. Furthermore, LTO also has negligible lithiation-based volume change, leading to less mechanical pulverization, or loss of active material, upon cycling. For the cathode, materials with little or no Co are of high interest due to the high cost and low abundance of Co. LiMn2O4 (LMO) has been paired with LTO for BTMS applications in the past due to its safety, low cost (abundancy), and reasonably high operating voltage. (2-4) However, the low capacity of LMO limits energy density and specific energy. While not the highest priority for BTMS applications, increasing energy density will enable deployment in space constrained BTMS applications and decrease total cost. LiNi0.9Mn0.1O2 (LN-MO) is a recently developed material with promise due to its high operating voltage and relatively low price. (5) However, Ni-rich layered oxides, including LNMO, tend to struggle with capacity retention during high-voltage cycling due to mechanical pulverization, irreversible phase transitions, and unstable solid-electrolyte interphase. The study presented here focuses on building an understanding of how electrolyte solvent and varied cutoff potentials will impact the cycle life of LTO/LN-MO cells. Specifically, a comparison is provided between ethylene carbonate (EC), ethyl methyl carbonate (EMC), fluoroethylene carbonate (FEC), and Gen2 electrolyte solvents with 1M Lithium hexafluorophosphate (LiPF6) salt, cycling to two upper termination potentials, 2.6V and 2.7V. Electrochemical testing and diagnostics (e.g., differential capacity analysis, area specific impedance, constant voltage hold, and rate capability) and post-mortem characterization will be used to understand the aging behavior and failure mechanisms of the 8 cell combinations (four electrolytes and two voltage cutoffs). Cells with FEC electrolyte showed a lower initial capacity compared to cells with Gen2, EMC, and EC cycling at both voltages; however, the cells with FEC showed consistent trends in capacity retention with 2.6V and 2.7V termination potentials, while the cells with the other electrolytes showed much higher rates of capacity loss when cycling to the higher voltage. These results indicate that FEC may play a role in improving durability of high-voltage, Ni-rich electrode systems for use in high-cycle applications, such as BTMS.

electrolyte↗
NASA NTRS1982

NECAP 4.1: NASA's Energy-Cost Analysis Program fast input manual and example

NASA's Energy-Cost Analysis Program (NECAP) is a powerful computerized method to determine and to minimize building energy consumption. The program calculates hourly heat gain or losses taking into account the building thermal resistance and mass, using hourly weather and a response factor method. Internal temperatures are allowed to vary in accordance with thermostat settings and equipment capacity. NECAP 4.1 has a simplified input procedure and numerous other technical improvements. A very short input method is provided. It is limited to a single zone building. The user must still describe the building's outside geometry and select the type of system to be used.

Jensen, R. N.↗
DOE OSTI2023

Optimization of Energy Storage System Economics and Controls by Incorporating Battery Degradation Costs in REopt

The use of stationary electrochemical energy storage systems utilizing lithium-ion batteries has increased rapidly as the production scale and price for lithium-ion batteries has decreased. These energy storage systems are crucial for maintaining grid resiliency, especially for grids operating with high penetration of renewable energy generation assets or for with a variety of distributed energy generation and storage systems. One challenging factor for the development of battery energy storage systems is estimating the proper sizing, in terms of both power and energy, that minimizes total costs over the lifetime of the systems; this calculation is difficult in simple cases, where a battery is costed independently, but is extremely challenging when building loads and electrical generation by photovoltaic resources are also considered. REopt is a techoeconomic optimization tool developed by NREL to address these challenges. Previously, battery degradation has been priced by simply assuming a 10-year replacement schedule for battery systems. However, this does not account for varying degradation trends observed across real-world batteries, or allow for batteries to be operated in a degradation-aware manner that optimizes battery dispatch based on operating costs. This work incorporates a battery life model into REopt. This battery life model is simple, so that it may be solvable within the constrains of a mixed-integer linear optimization problem, but is fit to accelerated aging data recorded in the lab. To achieve the best possible accuracy for lifetime estimates given these constraints, parameters for the battery life model in REopt are estimated by fitting 20-year simulations of battery life after identifying state-space battery degradation model from accelerated aging data. Comparisons of battery life predicted in REopt and from the state-space battery degradation model to ensure validity of lifetime estimates made by REopt. Battery life and cost is optimized by controlling three decision to minimize system life cost: battery sizing, daily state-of-charge, and daily energy-throughput. The cost of battery degradation as a function of these control variables is then estimated assuming two possible maintenance strategies: replacement, where the entire battery system is replaced if cell reach an end-of-life capacity threshold; and augmentation, which establishes a fund to pay for continual purchase of new batteries to maintain the initial energy capacity of the system. These two strategies offer conservative (for replacement) and optimistic (for augmentation) bounds for total system cost. The degradation cost incurred by these strategies is then used to control battery dispatch decisions, operating the battery in a degradation-aware manner that maximizes battery lifetime while also providing energy when favorable. Because the mixed-integer linear program has perfect foresight of future energy needs, batteries with degradation costs are always operated using 'just-in-time' charging, which is unrealistic, as no energy is left in the storage system to perform other energy services or to serve as emergency back-up power. To combat this, an inequality constraint on the average annual state-of-charge is imposed, and the sensitivity of system cost to average stored energy, e.g., the cost of system resiliency, can be quantified. Analysis of results has several conclusions, for instance, oversizing of battery storage systems is not a cost burden when battery storage is an optimal solution, as any additional battery capacity can simply be utilized to avoid costs of purchasing energy from a utility.

battery↗
DOE OSTI2022

Tuning Phonon Energies in Lanthanide‐doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion

Abstract Optical applications of lanthanide‐doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size‐controlled synthesis of low‐phonon‐energy KPb 2 X 5 (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm −1 . KPb 2 Cl 5 nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. The low phonon energies of KPb 2 X 5 nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche‐like emission from KPb 2 Cl 5 : Nd 3+ nanoparticles. The realization of nanoparticles with tunable, ultra‐low phonon energies facilitates the discovery of nanomaterials with phonon‐dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion.

Zhang, Zhuolei↗
DOE OSTI2022

Tuning Phonon Energies in Lanthanide-doped Potassium Lead Halide Nanocrystals for Enhanced Nonlinearity and Upconversion

Optical applications of lanthanide-doped nanoparticles require materials with low phonon energies to minimize nonradiative relaxation and promote nonlinear processes like upconversion. Heavy halide hosts offer low phonon energies but are challenging to synthesize as nanocrystals. Here, we demonstrate the size-controlled synthesis of low-phonon-energy KPb 2 X 5 (X=Cl, Br) nanoparticles and the ability to tune nanocrystal phonon energies as low as 128 cm -1 . KPb 2 Cl 5 nanoparticles are moisture resistant and can be efficiently doped with lighter lanthanides. Further, the low phonon energies of KPb 2 X 5 nanoparticles promote upconversion luminescence from higher lanthanide excited states and enable highly nonlinear, avalanche-like emission from KPb 2 Cl 5 : Nd 3+ nanoparticles. The realization of nanoparticles with tunable, ultra-low phonon energies facilitates the discovery of nanomaterials with phonon-dependent properties, precisely engineered for applications in nanoscale imaging, sensing, luminescence thermometry and energy conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Thermodynamic stabilization of nanocrystalline aluminum

Nanocrystalline metals are generally unstable due to a large volume fraction of high-energy grain boundaries associated with a small grain size. Preferential dopant segregation to the high-energy grain boundaries is observed to enhance the stability of the material’s microstructure by minimizing its energy. Nanocrystalline aluminum-dopant systems were evaluated for thermodynamic stability against grain growth and phase precipitation via the mechanism of grain boundary segregation according to a modified regular nanocrystalline solution model. Fifty-one potential dopant elements have been evaluated for their efficacy in stabilizing nanostructures with three potential candidates, magnesium, lanthanum, and silicon, identified possessing the characteristics to promote grain boundary segregation and a state of thermodynamic stability in aluminum’s nanocrystalline regime. Here, the minimum dopant content required to achieve nanocrystalline microstructure stability is identified for each of the three candidate elements. Beyond this minimum content, further addition of the dopant elements decreased the final microstructure’s stability with no effects on the existence of a stable nanocrystalline state.

36 MATERIALS SCIENCE↗
NASA NTRS1983

NECAP 4.1: NASA's energy-cost analysis program user's manual

The Enery Cost Analysis Program (NECAP) is a powerful computerized method to determine and to minimize building energy consumption. The program calculates hourly heat gain or losses taking into account the building thermal resistance and mass, using hourly weather and a "response factor' method. Internal temperatures are allowed to vary in accordance with thermostat settings and equipment capacity. A simplified input procedure and numerous other technical improvements are presented. This Users Manual describes the program and provides examples.

Jensen, R. N.↗
DOE OSTI2026

Optimal Operation of Residential High Performance Water Heater for Reduction of Electricity Cost and Peak Demand Through Field Validation

Water heating accounts for about 18% of a typical US home’s energy use. Modern water heaters have enabled control options through APIs, offering customers the opportunity to reduce their energy cost and peak demand by dynamically adjusting settings. A water heater’s capacity to store energy using its storage tank makes it an asset for peak demand reduction and energy cost savings. For this reason, a mixed-integer linear programming model is proposed to minimize the energy cost of a high-performance water heater while also reducing the peak demand of the residential household under a time-of-use utility rate by dynamically changing the water heater’s running mode. Specifically, a multi-objective optimization model is formulated to determine the mode settings of the water heater considering hot water use, time-of-use rate, and peak demand limit of the residential household. The mode settings are associated with different dead bands of water temperature for triggering on/off action of the heat pump and heating element. A 66-gal hybrid electric high performance water heater was used for numerical simulation and practical experiments. The simulation results were well aligned with measurements of practical experiments, validating the soundness of the thermodynamic model. In addition, reductions of energy cost, enabling affordability, and reducing peak demand are demonstrated. The research team also developed a software framework with dashboards to automatically and continuously monitor and manage devices.

Liu, Guodong [ORNL] (ORCID:0000000213498608)↗
DOE OSTI2024

Optimal thermodynamic conditions to minimize kinetic by-products in aqueous materials synthesis

Abstract Phase diagrams offer substantial predictive power for materials synthesis by identifying the stability regions of target phases. However, thermodynamic phase diagrams do not offer explicit information regarding the kinetic competitiveness of undesired by-product phases. Here we propose a quantitative and computable thermodynamic metric to identify synthesis conditions under which the propensity to form kinetically competing by-products is minimized. We hypothesize that thermodynamic competition is minimized when the difference in free energy between a target phase and the minimal energy of all other competing phases is maximized. We validate this hypothesis for aqueous materials synthesis through two empirical approaches: first, by analysing 331 aqueous synthesis recipes text-mined from the literature; and second, by systematic experimental synthesis of LiIn(IO 3 ) 4 and LiFePO 4 across a wide range of aqueous electrochemical conditions. Our results show that even for synthesis conditions that are within the stability region of a thermodynamic Pourbaix diagram, phase-pure synthesis occurs only when thermodynamic competition with undesired phases is minimized.

Wang, Zheren (ORCID:0000000227429451)↗
DOE OSTI2025

A kinetic-based regularization method for data science applications

We propose a physics-based regularization technique for function learning, inspired by statistical mechanics. By drawing an analogy between optimizing the parameters of an interpolator and minimizing the energy of a system, we introduce corrections that impose constraints on the lower-order moments of the data distribution. This minimizes the discrepancy between the discrete and continuum representations of the data, in turn allowing to access more favorable energy landscapes, thus improving the accuracy of the interpolator. Our approach improves performance in both interpolation and regression tasks, even in high-dimensional spaces. Unlike traditional methods, it does not require empirical parameter tuning, making it particularly effective for handling noisy data. We also show that thanks to its local nature, the method offers computational and memory efficiency advantages over Radial Basis Function interpolators, especially for large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Optimization-based algorithms for nonlinear mechanics and frictional contact

An optimization-based strategy for solving nonlinear mechanics problems is proposed. In contrast to typical nonlinear equation solver algorithms that aim to find zeros in the residual force function, we minimize an energy (or energy-like) function to encourage solutions which are locally stable equilibria. These smooth and potentially non-convex objective functions are minimized using a preconditioned conjugate-gradient trust-region algorithm. Contact is formulated as an inequality constrained minimization problem, and is solved with an augmented Lagrangian algorithm. Friction is included in the approach via a regularized quasi-potential energy, and other dissipative behavior is included through the use of variational constitutive updates. Finally, to accelerate convergence rates for the Lagrange multipliers, we propose a novel multiplier update algorithm utilizing the Fischer-Burmeister function, and demonstrate super-linear solver convergence for some applications.

42 ENGINEERING↗
DOE OSTI2020

Energy efficiency and renewable energy utilization in desalination systems

Desalination technologies and industry have advanced significantly in the past two decades to meet the growing freshwater demands stimulated by the compounding issues of both water quality and quantity in many regions of the world. As desalination processes are energy demanding, there have been many efforts dedicated to improve energy-efficiency of the process units, enhance energy conservation and recovery, and increase renewable energy integration in desalination plants. This review article highlights recent key advances and discusses possible venues for further development in desalination energy portfolio to reduce specific energy consumption and, via integration with solar energy, to minimize the environmental footprint associated with freshwater production. First, an overview of current desalination technologies and their energy requirements are presented followed by a discussion on opportunities for improving energy efficiency and energy recovery in both membrane and thermal desalination technologies. Then, various combinations of renewable energy driven desalination plants are discussed with some recent highlights in solar energy driven membrane, thermal and hybrid desalination processes. Technological readiness levels for novel desalination processes, their perceived impact and expected near-future developments in renewable energy integrated desalination technologies are presented. Lastly, the potential for solar driven desalination as a cost-competitive freshwater supply alternative is discussed

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1989

Optimal trajectories for hypervelocity flight

Optimal trajectories for hypervelocity flight of interest in aeroassisted orbital transfer are discussed. Both coplanar and noncoplanar transfer are studied. More precisely, the geosynchronous-earth-orbit-, high-earth orbit- and low-earth-orbit-to-low earth-orbit transfers are considered in connection with a spacecraft that is controlled during the atmospheric pass by the angle of attack (coplanar case) or by the angle of attack and the angle of bank (noncoplanar case). Within the framework of classical optimal control, the following problems are studied: minimize the energy required for orbital transfer; maximize the time of flight during the atmospheric portion of the trajectory; and minimize the time integral of the square of the path inclination. Within the framework of minimax optimal control, the problem studied is to minimize the peak rate. Numerical solutions for the problems are obtained by means of the sequential gradient-restoration algorithm. The engineering implications of the results are discussed.

Miele, A.↗
DOE OSTI2020

Constraining Alfvénic turbulence with helicity invariants

ABSTRACT In this paper, we study the constraints imposed by the invariants (generalized helicities and energy) of extended magnetohydrodynamics on some global characteristics of turbulence. We show that the global turbulent kinetic and magnetic energies will approach equipartition only under certain circumstances that depend on the ratio of the generalized helicities. In systems with minimal thermal energy, we demonstrate that the three invariants collectively determine the characteristic length-scale associated with Alfvénic turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Standard model Higgs inflation supplemented by minimal dark matter

Renormalization group analysis with the present measurements of the top quark mass m t = 172.69 ± 0.30 GeV [1] indicates that the Standard Model (SM) Higgs potential becomes unstable at energy scales ∼ 10 10 GeV . This may be interpreted as hinting at new particles at high energy. The minimal extension of the SM that can avoid this instability while leaving the SM Higgs as the sole scalar particle of the theory is obtained by adding suitable fermions to the SM. These fermions are good dark matter candidates and the model is known as the minimal dark matter model. We revisit the inflationary scenario based on the minimal dark matter model, taking into account updated parameter constraints and recent understanding of reheating dynamics. We explore the model with different values of the right-handed neutrino mass and find that the cosmological prediction is insensitive to such details. We obtained a spectral index of the cosmic microwave background n s = 0.9672 and a tensor-to-scalar ratio r = 0.0031 as a robust prediction of this scenario. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2022

Minimizing Ecological Impacts of Marine Energy Lighting

Marine energy is poised to become an important renewable energy contributor for the U.S., but widespread deployment of the technology hinges on its benefits outweighing the potential ecological impacts. One stressor marine energy installations introduce is light, which is known to cause varying responses among wildlife and has not yet been addressed as an environmental concern. This review discusses requirements and regulations for similar structures and how lighting design choices can be made to meet these requirements while minimizing environmental consequences. More practical guidance on implementing lighting for marine energy is needed, as well as updated guidelines to reflect technological and research advances. Known responses of wildlife to light are introduced in addition to how the responses of individuals may lead to ecosystem-level changes. The impact of light associated with marine energy installations can be reduced by following basic guidance provided herein, such as removing excess lighting, using lights with high directionality, and employing controls to reduce light levels. Continued research on animal responses to light, such as findings on minimum light levels for animal responses, alongside the development of highly-sensitivity spectral characterization capabilities can further inform lighting guidelines for deploying future open ocean marine energy devices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

RL-HEMS: Reinforcement Learning Based Home Energy Management System for HVAC Energy Optimization

Heating, ventilation, and air conditioning (HVAC) is one of the major energy consumers in the residential sector. It is important to be able to monitor and control the energy consumed to provide utility services such as load shaping while satisfying the comfort and economic constraints of the homeowner. The objective of this work is to create the optimal schedule for HVAC operation to reduce the cost while satisfying the home-owner and equipment’s constraints using a model-free Reinforcement Learning (RL)-based optimization. The specific goal is to find the right balance between reducing energy cost, consumption, and customer comfort level. Our research effort addresses this optimization problem using multiple components: the development of initial learning test-bed and implementation of RL techniques on a real home. This will enable the rapid evaluation of the RL techniques and provide an early baseline to train before implementation on site. The RL algorithm is designed to learn the energy use patterns and generate the optimized schedule for HVAC within an acceptable time-interval to satisfy the homeowner’s comfort and minimize the energy usage. Our preliminary results are promising and we have observed a 17% reduction in the total cost and a 15% reduction in the power utilization using our RL-based HVAC model—RL-HEMS.

Kotevska, Olivera↗
NASA NTRS2016

Heat Exchanger Performance Impacts on Optimum Cost Conditions in Thermoelectric Energy Recovery Designs

Cost is just as important as power density or efficiency for the adoption of waste heat recovery thermoelectric generators (TEG). Prior work [1] has shown that the system design that minimizes cost (e.g., the $/W value) can be close to the designs that maximize the system’s efficiency or power density, however, it is important to understand the relationship between those designs to optimize TEG performance-cost compromises. Expanding on recent work [1, 2, 3] the impact of heat exchanger conditions on the optimum TEG fill factors and cost scaling of a waste heat recovery thermoelectric generator with a detailed treatment of the hot side exhaust heat exchanger has been investigated further. The effect of the heat lost to the environment and updated relationships between the hot-side and cold-side conductances [4] that maximize power output are considered. The optimum fill factor to minimize TEG energy recovery system costs is strongly dependent on the heat leakage fraction, σ, the mass flow rate of the exhaust, the hot-side heat exchanger effectiveness, heat exchanger UAh, and heat flux. These relationships are explored and characterized for typical exhaust gas-flow conditions to show the inherent design complexities. The heat exchanger costs often dominate the TEG cost equation and it is critical to fully understand the tradeoff between heat exchanger performance, optimum TEG fill factors, and cost to establish potentially optimum design points within the cost-performance design space. This work will explore the design tradeoffs and relationships within the cost-efficiency-power density design space for a typical thermoelectric energy recovery system application. The interplay between optimum TEG fill factors and heat exchanger design can impact system footprint, volume, and mass in weight-sensitive applications. Less-effective, low-cost heat exchangers may outperform higher cost alternatives from a market adoption perspective. This shift of emphasis acknowledging the interdependence of optimum TEG fill factors and heat exchanger performance has significant implications on thermoelectric waste heat recovery systems designs and their operation. In addition, preferred TEG design regimes exist that accommodate reasonable compromises in TE performance and cost. This effort highlights how the optimum fill factor–heat exchanger performance relations couple to these optimum TEG performance-cost domains based on TEG-system-level analyses and provides a focus for future system research and development efforts.

Energy recovery↗