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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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Through an Expert Match technical assistance project, Ford County requested the National Laboratory of the Rockies evaluate solar PV, wind, and battery storage options at the Western State Bank Expo Center, assessing cost savings, on-site generation, and outage resilience. This slide deck presents the findings of this analysis.
U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.
The AO 138-5 experiment has been designed, via the French Cooperative Payload (FRECOPA) experiment with the aim to study the optical behavior of different diffraction gratings submitted to space vacuum long exposure and solar radiation. Samples were rules and holographic gratings, masters or replica, and some additional control mirrors with various coatings. The experiment was located on the B3, trailing edge of the Long Duration Exposure Facility (LDEF) and has been protected against atomic oxygen flux. The experienced thermal cycling has been evaluated from -23 C to 66 C during the flight, 34,000 orbits. The analysis has been focused on the triple point characterization including light efficiency, wavefront flatness quality and stray light level. Tests were conducted on control mirrors and gratings loaded but not exposed to cosmic dust or solar irradiations. They did not show any significant variations. Solar exposure has damaged the coating reflectivity in the ultraviolet region, the degradation is higher with the gratings, in terms of efficiency. However, wavefront flatness quality and stray light level tests revealed no additional changes.
With the growing interest in space exploration, cryogenic technologies involving two-phase flow and heat transfer are in high demand to successfully procure advanced space applications such as fuel depots and nuclear thermal propulsion (NTP) systems for deep space missions. However, the unique and extreme thermal properties of cryogenic fluids introduce distinct flow boiling fluid physics and energy transport phenomena, which differ significantly from those observed with conventional fluids. Understanding the unique two-phase physics in cryogenic flow boiling remains an ongoing challenge. Furthermore, the lack of readily available microgravity cryogenic steady-state heat transfer data hinders the assessment of gravitational effects on cryogenic flow boiling. This study aims to elucidate the gravitational effects on two-phase fluid physics and heat transfer by conducting the first-ever experimental measurement of cryogenic flow boiling performance using a steady-state heated method in a reduced gravity environment. Parabolic flight experiments were performed to acquire both heat transfer measurements and high-speed video of interfacial behaviors, under varying gravity levels (microgravity, hypergravity, Lunar gravity, and Martian gravity). The experiments involved flow boiling of liquid nitrogen (LN 2 ) with a near-saturated inlet along a circular heated tube of dimensions 8.5-mm inner diameter and 680-mm heated length. The operating parameters varied are mass velocity of 398.3 - 1342.8 kg/m2s, inlet quality of -0.08 to -0.01, and inlet pressure of 413.68 - 689.48 kPa. Captured microgravity flow patterns range from bubbly to annular, all having vapor structures that are larger than those under higher gravity levels. Under microgravity, absence of buoyancy yields symmetrical vapor structures without flow stratification, laying a physical foundation for the distinct two-phase heat transfer trends during LN 2 flow boiling in microgravity. Transient data collected during the flight parabolas exhibited decreasing heated wall temperature as the aircraft transitioned from hypergravity to microgravity phases. The temperature variation indicated an enhancement in flow boiling heat transfer with decreasing gravity levels and a reduction with increasing gravity levels. The effect of reduced gravity on cryogenic flow boiling heat transfer coefficient (HTC) is discussed based on steady state heat transfer analysis. Seminal HTC correlations are evaluated against the measured microgravity HTC data, of which one is identified for superior accuracy in predicting microgravity data. Finally, a new HTC correlation is proposed to improve accuracy of microgravity predictions, yet there still exists room for further improvement with future terrestrial flow boiling experiments at different flow orientations relative to Earth gravity.
Computer program calculates spacecraft battery energy at specific times dictated by input sequence of recharge, discharge, and no activity phases. Recharge phases are assumed during times not specified, unless batteries are fully charged. Warnings are printed when energy falls below specified level. Program assumes two identical batteries.
This represents the initial regional tier of the electric vehicle (EV) battery recycling agent base model. Through this model, we can ascertain the number of EV purchases at both the state and regional levels. We employ census data to develop a diverse household profile to inform decisions regarding the acquisition of new or used EVs. The number of EV purchases at the state level will affect the future demand for recycling, reuse, and repurposing of end-of-life EV batteries. Additionally, tax credits, EV rebate programs, and the financial capacity of households will influence the number of EV purchases, thereby further impacting the demand for EV battery recycling.
A rechargeable lithium molybdenum disulfide battery which has a number of superior performance characteristics which includes a high energy density, a high power density, and a long charge retention time was developed. The first cell sizes developed included a C size cell and an AA size cell. Over the last two years, a project to demonstrate the feasibility of the scale up to this technology to a BC size cell with 65 Ah capacity was undertaken. The objective was to develop, build, and test a .6 kWh storage battery consisting of 6 BC cells in series.
Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.
DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.
Under a Phase II SBIR project funded by NASA Johnson Space Center (Contract No. 80NSSC25C0088), Analytical Scientific Products LLC (ASP) has been developing an actively controlled louver to protect spacecraft radiators from degradation due to exposure to various types of environmental conditions. Of particular interest are ionizing radiation during spacecraft transit through the Van Allen belts, dust during spacecraft landing and surface operations on the moon, and excessive heat loss during the long lunar night especially near the poles where the local ambient temperatures can drop below -200°C. Exposure to ionizing radiation and dust can degrade the optical properties of the radiator coating that in turn reduces its ability to reject excess heat from the spacecraft into the environment. Exposure to the extremely low temperature conditions during the lunar night near the poles can freeze the radiator fluids that can compromise the integrity of the radiator. Passive louvers constructed using thick aluminum vanes are used currently to protect spacecraft radiators from some of the above effects, but they have several drawbacks: (i) their high aerial density makes it impractical to scale them to protect the much larger spacecraft radiators needed for future manned missions to the moon and beyond, (ii) the bimetallic actuators used to open and close the vanes in passive louvers rely on external temperature alone and so the louver cannot offer protection from dust and ionizing radiation when the ambient temperatures are high, and (iii) the bimetallic actuators need time scales of the order of hours to open and close. We have addressed the above limitations of passive louvers through the development of a low aerial density and rapidly actuating actively controlled louver. It is constructed from a custom alloy that offers much higher levels of protection against the ionizing radiation, dust and excessive heat loss at a fraction of weight compared to passive louvers while providing opening and closing time scales of the order of a second. Our modular design makes it easy to scale the system up or down depending on the application. We have recently constructed a 31 inch × 31 inch module of this louver and tested its functionality and effectiveness. These tests have shown that the louver can be opened and closed in less than 2 s. It reduces the transmission of ionizing radiation by 78% to 100% (depending on the radiation source), dust transmission by more than 93%, and heat loss by more than 97%. We are currently preparing to evaluate this device at the Johnson Space Center’s cryogenic vacuum chamber where it can be subjected to simulated lunar surface conditions. This paper provides a detailed discussion of the test designs as well as the data from tests that demonstrate the effectiveness in reducing the transmission of ionizing radiation, dust, and heat under laboratory conditions.
Under a Phase II SBIR project funded by NASA Johnson Space Center (Contract No. 80NSSC25C0088), Analytical Scientific Products LLC (ASP) has been developing an actively controlled louver to protect spacecraft radiators from degradation due to exposure to various types of environmental conditions. Of particular interest are ionizing radiation during spacecraft transit through the Van Allen belts, dust during spacecraft landing and surface operations on the moon, and excessive heat loss during the long lunar night especially near the poles where the local ambient temperatures can drop below -200°C. Exposure to ionizing radiation and dust can degrade the optical properties of the radiator coating that in turn reduces its ability to reject excess heat from the spacecraft into the environment. Exposure to the extremely low temperature conditions during the lunar night near the poles can freeze the radiator fluids that can compromise the integrity of the radiator. Passive louvers constructed using thick aluminum vanes are used currently to protect spacecraft radiators from some of the above effects, but they have several drawbacks: (i) their high aerial density makes it impractical to scale them to protect the much larger spacecraft radiators needed for future manned missions to the moon and beyond, (ii) the bimetallic actuators used to open and close the vanes in passive louvers rely on external temperature alone and so the louver cannot offer protection from dust and ionizing radiation when the ambient temperatures are high, and (iii) the bimetallic actuators need time scales of the order of hours to open and close. We have addressed the above limitations of passive louvers through the development of a low aerial density and rapidly actuating actively controlled louver. It is constructed from a custom alloy that offers much higher levels of protection against the ionizing radiation, dust and excessive heat loss at a fraction of weight compared to passive louvers while providing opening and closing time scales of the order of a second. Our modular design makes it easy to scale the system up or down depending on the application. We have recently constructed a 31 inch × 31 inch module of this louver and tested its functionality and effectiveness. These tests have shown that the louver can be opened and closed in less than 2 s. It reduces the transmission of ionizing radiation by 78% to 100% (depending on the radiation source), dust transmission by more than 93%, and heat loss by more than 97%. We are currently preparing to evaluate this device at the Johnson Space Center’s cryogenic vacuum chamber where it can be subjected to simulated lunar surface conditions. This paper provides a detailed discussion of the test designs as well as the data from tests that demonstrate the effectiveness in reducing the transmission of ionizing radiation, dust, and heat under laboratory conditions.
Pt additions substituted for Ni in NiTi alloys are known to increase the transformation temperature of the alloy but only at fairly high Pt levels. However, until now only ternary compositions with a very specific stoichiometry, Ni 50-x Pt x Ti 50 , have been investigated and then only to very limited extent. In order to learn more about this potential high-temperature shape memory alloy system, a series of over twenty alloys along and on either side of a line of constant stoichiometry between NiTi and TiPt were arc melted, homogenized, and characterized in terms of their microstructure, transformation temperatures, and hardness. The resulting microstructures were examined by scanning electron microscopy and the phase compositions quantified by energy dispersive spectroscopy. "Stoichiometric" compositions along a line of constant stoichiometry between NiTi to TiPt were essentially single phase but any deviations from a stoichiometry of (Ni,Pt) 50 Ti 50 resulted in the presence of at least two different intermetallic phases, depending on the overall composition of the alloy. Essentially all alloys, whether single or two-phase, still under went a martensitic transformation. It was found that the transformation temperatures were depressed with initial Pt additions but at levels greater than 10 at.% the transformation temperature increased linearly with Pt content. Also, the transformation temperatures were relatively insensitive to alloy stoichiometry within the range of alloys examined. Finally, the dependence of hardness on Pt content for a series of Ni 50-x Pt x Ti 50 alloys showed solution softening at low Pt levels, while hardening was observed in ternary alloys containing more than about 10 at.% Pt. On either side of these "stoichiometric" compositions, hardness was also found to increase significantly.
A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.
Empirical formula represents performance of electrical storage batteries. Formula covers many battery types and includes numerous coefficients adjusted to fit peculiarities of each type. Battery and load parameters taken into account include power density in battery, discharge time, and electrolyte temperature. Applications include electric-vehicle "fuel" gages and powerline load leveling.
Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery
The U.S. Department of Energy (DOE) is leading the National effort to manage the back end of the nuclear fuel cycle, encompassing the safe transportation, storage/staging, and/or eventual disposal of used nuclear fuel (UNF) and high-level radioactive waste. The Next Generation System Analysis Model (NGSAM) is DOE’s discrete-event, agent-based simulation tool designed to model the full life cycle of UNF from reactor discharge to final disposal. NGSAM supports the DOE Office of Spent Fuel and High-Level Waste Disposition by enabling a detailed, scenario-based analysis of logistics, infrastructure, and shipping strategies. NGSAM replaces legacy models with a modern, flexible platform built on Repast Simphony and enhanced by the Process Analysis Tool. NGSAM simulates the movement and interaction of individual fuel assemblies with system components such as canisters, casks, railcars, and facilities. The model integrates with the Java Transportation Operations Model to plan and execute transportation scenarios, supporting both constrained and unconstrained resource allocation. Key features include customizable allocation and acceptance algorithms, detailed facility-level operations, and a Quick Edit tool for rapid scenario adjustments. NGSAM supports multimodal transportation modeling (e.g. rail, road, barge) and provides comprehensive cost, schedule, and infrastructure data. NGSAM utilizes data from sources such as DOE’s STANDARDS UNF database and DOE’s Stakeholder Tool for Assessing Radioactive Transportation, while also allowing user-defined inputs for scenario customization. NGSAM enables stakeholders to evaluate complex UNF management strategies, assess system performance under varying assumptions, and inform decision making for future infrastructure investments. Its modular architecture and integration with other Integrated Waste Management System tools make it a critical asset for planning the safe and efficient disposition of the Nation’s growing UNF inventory.
The Parties are working together to utilize a one-step atmospheric microplasma reaction for the elimination of electrolyte degradation products and the relithiation of end-of-life cathode materials. This project aims to streamline the direct recycling process of battery components by reducing the required steps and time.
Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.