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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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The requirement of general covariance imparts to metric theories of gravity, such as general relavity, important structural features. A precise mathematical form results, ensuring that computation of observable physical effects in the theory gives the same answers independently of the chosen system of coordinates.
The requirement of general covariance imparts to metric theories of gravity, such as general relativity, important structural features. A precise mathematical form results, ensuring that computation of observable physical effects in the theory gives the same answers independently of the chosen system of coordinates. This coordinate independence property, in turn, can lead to an equivalence of apparently different physical effects.
A selection criteria for metrics is given related to customer.
Presented here in are the latest metric evaluations for the effectiveness of the tests involved in one of JPL's recent space flight programs.
In this paper we describe an orbit design aide tool, called Telecom Orbit Analysis and Simulation Tool(TOAST). Although it can be used for studying and selecting orbits for any planet, we solely concentrate on its use for Mars. By specifying the six orbital elements for an orbit, a time frame of interest, a horizon mask angle, and some telecom parameters such as the transmitting power, frequency, antenna gains, antenna losses, link margin, received threshold powers for the rates, etc. this tool enables the user to view the animation of the orbit in two and three-dimensional different telecom metrics at any point on the Mars, namely the global planetary map.
This document describes the window and one-sigma depth functions relevant to the Transiting Planet Search (TPS) algorithm in the Kepler pipeline (Jenkins 2002; Jenkins et al. 2017). The window function specifies the fraction of unique orbital ephemeris epochs over which three transits are observable as a function of orbital period. In this context, the epoch and orbital period, together, comprise the ephemeris of an orbiting companion, and ephemerides with the same period are considered equivalent if their epochs differ by an integer multiple of the period. The one-sigma depth function specifies the depth of a signal (in ppm) for a given light curve that results in a one-sigma detection of a transit signature as a function of orbital period when averaged over all unique orbital ephemerides. These planet detection metrics quantify the ability of TPS to detect a transiting planet signature on a star-by-star basis. They are uniquely applicable to a specific Kepler data release, since they are dependent on the details of the light curves searched and the functionality of the TPS algorithm used to perform the search. This document describes the window and one-sigma depth functions relevant to Kepler Data Release 25 (DR25), where the data were processed (Thompson et al. 2016) and searched (Twicken et al. 2016) with the SOC 9.3 pipeline. In Section 4, we describe significant differences from those reported in Kepler Data Release 24 (Burke Seader 2016) and document our verification method.
This paper reviews the work completed on the compilation of a database containing viable penetrating and sampling devices, the performance system level trade study comparing selected devices to a set of prescribed parameters and the employment of a metric for the evaluation and ranking of the traded penetration and sampling devices, with respect to possible useage on the 03 and 05 sample return missions.
We develop a new metric, Relative Critical Value Deviation (RCVD), for classifying and predicting software quality.
The National Aeronautics and Space Administration (NASA) plans to collect X-59 aircraft dose-response data to inform international regulators in their efforts to establish acceptable noise standards to replace the current ban on overland supersonic commercial flight. For this upcoming community test campaign, the sound level from a supersonic overflight of the X-59 constitutes a single-event dose, and an end-of-day survey after multiple flight events constitutes a cumulative response. This study considers how X-59 community tests could be designed to explore the relationship between multiple single events and cumulative annoyance response. Presently, a day-night level summation of single-event doses formulates the cumulative dose; however, a generalization method may improve the understanding of the linkage between multiple single events and cumulative response. This work utilizes an analysis method that employs a parameter to vary the cumulative dose metric such that it can represent the loudest single event, remain as the day-night level summation, or be more responsive to the number of events. The analysis is demonstrated on simulated dose-response datasets derived from previous NASA field studies and from expectations of X-59 community tests. Results demonstrate that certain dose designs are more efficient than others in determining the analysis parameter.
The National Aeronautics and Space Administration (NASA) plans to collect X-59 aircraft dose-response data to inform international regulators in their efforts to establish acceptable noise standards to replace the current ban on overland supersonic commercial flight. For this upcoming community test campaign, the sound level from a supersonic overflight of the X-59 constitutes a single-event dose, and an end-of-day survey after multiple flight events constitutes a cumulative response. This study considers how X-59 community tests could be designed to explore the relationship between multiple single events and cumulative annoyance response. Presently, a day-night level summation of single-event doses formulates the cumulative dose; however, a generalization method may improve the understanding of the linkage between multiple single events and cumulative response. This work utilizes an analysis method that employs a parameter to vary the cumulative dose metric such that it can represent the loudest single event, remain as the day-night level summation, or be more responsive to the number of events. The analysis is demonstrated on simulated dose-response datasets derived from previous NASA field studies and from expectations of X-59 community tests. Results demonstrate that certain dose designs are more efficient than others in determining the analysis parameter.
Presentation giving general information about the SDRD program and some recent metrics. This presentation will be shared during various meetings of SDRD team and management.
This presentation communicates information about the MIRACL project Resilience Metrics report and Resilience Framework report. It was created for the 2021 MIRACL advisory board meeting. We propose a three-tiered approach for the resilience framework. At the top level, we consider the time horizons on which resilience will be evaluated and executed. At the middle level, we consider the core functions of resilience, which span across the time horizons. At the lower level, we consider the process steps that correspond to implementing practices for resilience in each of the core functions. The framework considers three time horizons in order to enable organizations to assess and improve their system’s resilience throughout its lifecycle. We call these time horizons the planning, operational, and future stages. The planning stage uses organizational needs and current system evaluation to prepare for potential hazards. The operational stage seeks to execute responses to hazards as prudently and efficiently as possible to maintain system resilience. The future stage seeks to improve on current system resilience and feeds back into the planning stage to promote continuous improvement. While all three time horizons are important when considering a specific topic, the planning and evaluation phase (i.e., what is done in advance of the event) is critical in defining a system’s resilience characteristics and in outlining how a system responds to an event. This framework intentionally emphasizes the planning stage to highlight the overarching emphasis of this effort, not to imply that the other two time-related horizons (i.e., operational and change the future) are less important. The core functions in the framework are identify, prepare, detect, adapt, and recover. These five functions stem from a rigorous analysis of definitions used across the industry, and they represent the core capabilities that an organization must have to enable lifecycle resilience. Within each core function, process steps are described that help walk an organization through the information gathering, evaluation, decision-making, and implementation processes they will need to ensure their resilience goals are maintained throughout the system and the system lifecycle. Also highlighted in the figure is the concept that a resilience framework should be cyclical in nature. Because a system’s resilience is based on finite resources and time, it must continually evolve through this framework’s risk management and capital investment steps at an appropriate level of scope and pace.
The Hausdorff metric provides a way to measure the distance between nonempty compact sets in $\mathbb{R}^N$, from which we can build a geometry of sets. This geometry is very different than the standard Euclidean geometry and provides many interesting results. In this paper we focus on line segments in this geometry, where pairs of disjoint sets $A$ and $B$ satisfying certain distance conditions have the property that there are exactly $m$ different sets on the line segment $\overline{AB}$ at every distance from $A$, where $m$ can assume many values different than one. We provide new families of sets that generate previously unrecorded integer sequences via these values of $m$ by connecting the values of $m$ to the number of edge coverings of a graph corresponding to the sets $A$ and $B$.
Off-gas research within the Material Recovery and Waste Form Development (MRWFD) team is dispersed among four national laboratories and multiple universities and small businesses. Ensuring research is conducted consistently and in a manner conducive to direct comparison is a nontrivial task. The team assembled to realign goals and expectations by identifying distinct R&D phases, prioritizing crucial metrics, and outlining standardized tests specifically associated with Kr, Xe, and I capture.
With high-performance computing systems now running at exascale, optimizing power-scaling management and resource utilization has become more critical than ever. This paper explores runtime power-capping optimizations that leverage integrated CPU-GPU power management on architectures like the NVIDIA GH200 superchip. We evaluate energy-performance metrics that account for simultaneous CPU and GPU power-capping effects by using two complementary approaches: speedup-energy-delay and a Euclidean distance-based multi-objective optimization method. By targeting a mostly compute-bound exascale science application, the Locally Self-Consistent Multiple Scattering (LSMS), we explore challenging scenarios to identify potential opportunities for energy savings in exascale applications, and we recognize that even modest reductions in energy consumption can have significant overall impacts. Our results highlight how GPU task-specific dynamic power-cap adjustments combined with integrated CPU-GPU power steering can improve the energy utilization of certain GPU tasks, thereby laying the groundwork for future adaptive optimization strategies.
Abstract Embedding symmetries in the architectures of deep neural networks can improve classification and network convergence in the context of jet substructure. These results hint at the existence of symmetries in jet energy depositions, such as rotational symmetry, arising from the physical features of the underlying processes. We introduce new jet observables, Jet Rotational Metrics (JRMs), which provide insights into the substructure of jets by comparing them to jets with perfect discrete rotational symmetry. We show that JRMs are formidable jet features, achieving good classification scores when used as inputs to deep neural networks. We also show that when used in combination with other jet observables, like N-subjettiness and EFPs, our features increase classification performance. The results suggest that JRMs may capture information not efficiently captured by the other observables, motivating the design of future jet observables for learning the underlying symmetries in the physical processes.
Here, in this work, a long-established but sparsely documented method of obtaining semi-analytic derivatives of thermodynamic properties with respect to equilibrium conditions is briefly reviewed and rigorously derived. This procedure is then leveraged to construct general forms of derivatives of the residual driving force, a metric for measuring phase stability used in CALPHAD model optimization, with respect to overall system and individual phase compositions. Applied examples – calculating heat capacity in the Al-Fe system, thermodynamic factors in the Nb-V-W system, and residual driving force derivatives in the Ni-Ti system – demonstrate the versatility, accuracy, and extensibility of this method. Using the developed method, residual driving force gradients can be applied directly in CALPHAD model optimizers, as well as in materials design frameworks, to identify regions of phase stability with an efficient, gradient-based approach.
The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.