SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Software is a workflow management system designed to capture documents and route for compliance and peer reviews. Software does not in itself contain any technical information but is used as a business system to manage and release information from a research organization.
Purpose: To develop a fast secondary MU calculation tool for an integrated MR-Linac, an emerging technology for delivering image-guided on-line adaptive therapy. Methods: A software tool was developed to implement a modified Clarkson integration algorithm for point-dose calculation, considering the dosimtric effect of a magnetic field. A research planning system, considering the presence of a perpendicular 1.5 T magnetic field, was used to extract the beam data (e.g., depth doses and beam profiles) required for the MU calculation in homogeneous phantom of 60×60×40 cm3 for a prototype of MR-Linac integrating a 1.5 T MR scanner with 6MV photon beams. These dosimetric data may be replaced with those to be collected during the commissioning measurement of the MR-Linac. Results: The presence of the magnetic field resulted in noticeable changes including (1) shifts in beam profiles for small fields (< 2×2 cm2), and (2) changes in central-axis doses when one of field edges is < 2 cm away from the central axis. For example, the maximum change in central-axis dose was up to 8% for one of the field borders with a distance away from the central axis of <1 cm. These changes are relevant for the MU calculation particularly for IMRT and VMAT, where most of segments are small. These magnetic field affected beam profiles and depth doses were included in the software tool. The MU calculations from the software tool for selective representative plans were found to agree with those from the research planning system within 10%. Conclusion: The presence of a perpendicular magnetic field results in noticeable changes in beam profiles and depth doses for small fields. These changes are included in the secondary MU calc{sup 33}u{sup 3}lation to be used as a redundancy check for an integrated MR-Linac.
While confidence in photovoltaic (PV) modeling software has always been essential, the rapid pace of new PV plant developments makes accuracy and credibility more critical than ever. Independent assessments, particularly through blind modeling comparisons, are therefore necessary to ensure unbiased benchmarking across PV modeling software. Previous studies have been limited by a narrow range of models compared, anonymized results, or system size. This study presents results from the first-ever onymous blind modeling comparison, evaluated using both lab- and utility-scale fixed-tilt, monofacial, south-facing systems at sub-hourly time intervals. Seven commercially used PV software tools were compared: 3E SynaptiQ, PlantPredict, PVsyst, RatedPower, SAM, SolarFarmer, and Solargis Evaluate. Predictions were submitted directly by software representatives, providing unique insights into each software’s implementation and resulting prediction behavior. Notable features, including plane-of-array (POA) transposition model, module temperature model, shading model, and performance model were analyzed and compared. Four summary tables compile these features of the software, serving as a resource to help users understand the methodological differences and select the most suitable software for their applications. The software tools show deviations from mean error in annual yield up to 2.5 % in the lab-scale system, increasing to 6.0 % for the utility-scale system. These differences arise from a combination of user decisions and the inherent behavior of the software, indicating the need for continuous and rigorous validation of modeling methods using these software tools against complex, real-world systems.
Background: Top-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation. Results: In this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution. Conclusions: L-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization.
SAND2025-11738O Wind Turbine Airfoil Design Tools is a software tool that connects existing open-source airfoil analysis tools with optimization software to produce modern airfoil design for wind turbine applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Orbit Software Suite is used to support a variety of NASA/DM (Dependable Multiprocessor) mission planning and analysis activities on the IPS (Intrusion Prevention System) platform. The suite of Orbit software tools (Orbit Design and Orbit Dynamics) resides on IPS/Linux workstations, and is used to perform mission design and analysis tasks corresponding to trajectory/ launch window, rendezvous, and proximity operations flight segments. A list of tools in Orbit Software Suite represents tool versions established during/after the Equipment Rehost-3 Project.
U.S. Department of Energy (DOE) energy system-analysis software tools have been used for years helping to identify over 170 TBTU/year of energy savings through the Save Energy Now “Energy Savings Assessment” and Better Plants “In-Plant Training” Programs. While most of the fundamental energy savings opportunities do not evolve much with time, computer technology never stops, leaving most of these software tools inoperable on modern computers.This has sparked an initiative to revitalize the DOE energy system analysis tools into a new, open-source, integrated software tool suite - MEASUR. Designed for industrial energy coordinators, engineers, utility-program implementers and individuals who are interested in improving system efficiency, MEASUR can aide users in identifying, assessing, and quantifying energy savings opportunities within pump, fan, process heating, steam, motors, and compressed air systems. In addition, the software contains over 40 equipment and property calculators for simple energy-related calculations and analyses.This paper will present a results comparison for each energy-consuming system using data from past DOE-sponsored energy assessments (to establish the accuracy of the savings algorithms) to validate and verify the accuracy of the energy and cost savings calculated by MEASUR. Additionally, an example highlighting 3rd-party development of the tool’s opensource framework into real-time sensor systems and efficiency-optimization will be presented.
Purpose: It has been reported recently that the change of CT number (CTN) during and after radiation therapy (RT) may be used to assess RT response. The purpose of this work is to develop a tool to automatically segment the regions with differentiating CTN and/or with change of CTN in a series of CTs. Methods: A software tool was developed to identify regions with differentiating CTN using K-mean Cluster of CT numbers and to automatically delineate these regions using convex hull enclosing method. Pre- and Post-RT CT, PET, or MRI images acquired for sample lung and pancreatic cancer cases were used to test the software tool. K-mean cluster of CT numbers within the gross tumor volumes (GTVs) delineated based on PET SUV (standard uptake value of fludeoxyglucose) and/or MRI ADC (apparent diffusion coefficient) map was analyzed. The cluster centers with higher value were considered as active tumor volumes (ATV). The convex hull contours enclosing preset clusters were used to delineate these ATVs with color washed displays. The CTN defined ATVs were compared with the SUV- or ADC-defined ATVs. Results: CTN stability of the CT scanner used to acquire the CTs in this work is less than 1.5 Hounsfield Unit (HU) variation annually. K-mean cluster centers in the GTV have difference of ∼20 HU, much larger than variation due to CTN stability, for the lung cancer cases studied. The dice coefficient between the ATVs delineated based on convex hull enclosure of high CTN centers and the PET defined GTVs based on SUV cutoff value of 2.5 was 90(±5)%. Conclusion: A software tool was developed using K-mean cluster and convex hull contour to automatically segment high CTN regions which may not be identifiable using a simple threshold method. These CTN regions were reasonably overlapped with the PET or MRI defined GTVs.
Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks
The Klonos is a compiler-based tool that can help users for scientific application porting. The tool is based on the similarity analysis with the help of the OpenUH compiler (a branch of Open64 compiler). This tool combines syntactic and cost-model-provided metrics clusters, which aggregate similar subroutines that can be ported similarity. The generated porting plan, which allows programmers and compilers to reuse porting experience as much as possible during the porting process.
Key results are documented from attending the 4th workshop on verification, validation, and testing. The most interesting part of the workshop was when representatives from the U.S., Japan, and Europe presented surveys of VV&T within their respective regions. Another interesting part focused on current efforts to define industry standards for artificial intelligence and how that might affect approaches to VV&T of expert systems. The next part of the workshop focused on VV&T methods of applying mathematical techniques to verification of rule bases and techniques for capturing information relating to the process of developing software. The final part focused on software tools. A summary is also presented of the EPRI conference on 'Methodologies, Tools, and Standards for Cost Effective Reliable Software Verification and Validation. The conference was divided into discussion sessions on the following issues: development process, automated tools, software reliability, methods, standards, and cost/benefit considerations.
Application Binary Interface (ABI) compatibility is essential for system or software updates to ensure that libraries continue to function. Tools that can assess a binary or library ABI can thus be used to make predictions about compatibility, and predict downstream bugs by informing developers and users about issues. In this work, we are interested in describing a set of well-known tools for assessing ABI, and testing them in a controlled set experiments to assess tool agreement. We run 7660 smaller experiments across tools (N=30,640 total results) to evaluate not only predictions, but also each tool’s ability to provide detail about underlying issues. In this paper, along with highlighting the problem of assessing ABI compatibility and critiquing the pros and cons of currently available tools, we provide guidance to developers interested to test ABI based on our empirical results and suggestions for future work.
Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.
The experience gained in the design and development of Shuttle flight and ground support embedded software systems along with projections of increasing role and size of software in the proposed Space Station and other future NASA projects provides the basis for forecasting substantial changes in the tools and methodologies by which embedded software systems are developed and acquired. Similar changes in software architectures and operator interfaces will lead to substantial changes in the approach and techniques involved in software test and system integration. Increasing commonality among different flight systems and between flight and supporting ground systems is projected, along with a more distributed approach to software acquisition in highly complex projects such as Space Station.
This report summarizes the achievements and final results of this program. The objective of this program is to develop a general model-based sensor network design methodology and tools to address key issues in the design of an optimal sensor network configuration: the type, location and number of sensors used in a network, for online condition monitoring. In particular, the focus in this work is to develop software tools for optimal sensor placement (OSP) and use these tools to design optimal sensor network configuration for online condition monitoring of gasifier refractory wear and radiant syngas cooler (RSC) fouling. The methodology developed will be applicable to sensing system design for online condition monitoring for broad range of applications. The overall approach consists of (i) defining condition monitoring requirement in terms of OSP and mapping these requirements in mathematical terms for OSP algorithm, (ii) analyzing trade-off of alternate OSP algorithms, down selecting the most relevant ones and developing them for IGCC applications (iii) enhancing the gasifier and RSC models as required by OSP algorithms, (iv) applying the developed OSP algorithm to design the optimal sensor network required for the condition monitoring of an IGCC gasifier refractory and RSC fouling. Two key requirements for OSP for condition monitoring are desired precision for the monitoring variables (e.g. refractory wear) and reliability of the proposed sensor network in the presence of expected sensor failures. The OSP problem is naturally posed within a Kalman filtering approach as an integer programming problem where the key requirements of precision and reliability are imposed as constraints. The optimization is performed over the overall network cost. Based on extensive literature survey two formulations were identified as being relevant to OSP for condition monitoring; one based on LMI formulation and the other being standard INLP formulation. Various algorithms to solve these two formulations were developed and validated. For a given OSP problem the computation efficiency largely depends on the “size” of the problem. Initially a simplified 1-D gasifier model assuming axial and azimuthal symmetry was used to test out various OSP algorithms. Finally these algorithms were used to design the optimal sensor network for condition monitoring of IGCC gasifier refractory wear and RSC fouling. The sensors type and locations obtained as solution to the OSP problem were validated using model based sensing approach. The OSP algorithm has been developed in a modular form and has been packaged as a software tool for OSP design where a designer can explore various OSP design algorithm is a user friendly way. The OSP software tool is implemented in Matlab/Simulink© in-house. The tool also uses few optimization routines that are freely available on World Wide Web. In addition a modular Extended Kalman Filter (EKF) block has also been developed in Matlab/Simulink© which can be utilized for model based sensing of important process variables that are not directly measured through combining the online sensors with model based estimation once the hardware sensor and their locations has been finalized. The OSP algorithm details and the results of applying these algorithms to obtain optimal sensor location for condition monitoring of gasifier refractory wear and RSC fouling profile are summarized in this final report.
Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.
Sculptor(R) is a commercially available software tool, based on an Arbitrary Shape Design (ASD), which allows the user to perform shape optimization for computational fluid dynamics (CFD) design. The developed software tool provides important advances in the state-of-the-art of automatic CFD shape deformations and optimization software. CFD is an analysis tool that is used by engineering designers to help gain a greater understanding of the fluid flow phenomena involved in the components being designed. The next step in the engineering design process is to then modify, the design to improve the components' performance. This step has traditionally been performed manually via trial and error. Two major problems that have, in the past, hindered the development of an automated CFD shape optimization are (1) inadequate shape parameterization algorithms, and (2) inadequate algorithms for CFD grid modification. The ASD that has been developed as part of the Sculptor(R) software tool is a major advancement in solving these two issues. First, the ASD allows the CFD designer to freely create his own shape parameters, thereby eliminating the restriction of only being able to use the CAD model parameters. Then, the software performs a smooth volumetric deformation, which eliminates the extremely costly process of having to remesh the grid for every shape change (which is how this process had previously been achieved). Sculptor(R) can be used to optimize shapes for aerodynamic and structural design of spacecraft, aircraft, watercraft, ducts, and other objects that affect and are affected by flows of fluids and heat. Sculptor(R) makes it possible to perform, in real time, a design change that would manually take hours or days if remeshing were needed.
There is a growing interest in including light source spectrum in advanced lighting software tools and simulations. Given that traditional lighting software tools have been used primarily for calculating photometric quantities, their simplifying assumptions may not be suitable for calculating other a-opic quantities. Commonly used simulation tools use three values to represent the three primary colors (red, green, and blue), but others have expanded the number of bands (i.e., the spectral resolution) to 9, 27, or 81 in an attempt to more accurately represent variations in light source spectrum and downstream spectrally-derived quantities. It remains unclear, however, to what extent spectral resolution affects calculated quantities. To address this gap, a numerical analysis was completed using a large spectral power distribution database (n = 1,302 light sources). Calculated illuminance, a-opic irradiance, luminous efficacy of radiation (LER), melanopic efficacy of radiation (MER), and melanopic to photopic (M/P) ratio were compared for spectral resolutions of 3 and 9 bands compared to a baseline of 81 bands. Across all examined lighting quantities, considerable errors—a mean absolute percent error of 18.4%—were found when using 3 band calculations. These were reduced to 4% for 9 band calculations. The errors varied by metric and light source type. The results suggest that the use of 9 bands can more accurately characterize performance of different light source types across a range of metrics compared to using 3 bands.