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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accelerating the design of lattice structures using machine learning

Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R 2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Highly accelerated life testing (HALT): A review from a statistical perspective

Despite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated-stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). Here, this review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high-stress test-analyze-fix-test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Three-dimensional ballistocardiography and respiratory motion in sustained microgravity

BACKGROUND: We measured the three-dimensional ballistocardiogram (BCG) in a free-floating subject in sustained microgravity during spaceflight to test the usefulness of such measurements for future non-invasive monitoring of cardiac function, and to examine the effects of respiratory movement on the BCG in three axes. METHODS: Acceleration was measured using a three-axis accelerometer fastened to the lumbar region of the subject while simultaneous recordings of ECG, and respiratory motion via impedance plethysmography were also made. Data were recorded during a 146-s period of inactivity on the part of the subject during which time there was no contact with the spacecraft. RESULTS: Total body motion due to respiratory activity was consistent with that calculated from the known action of the diaphragm and conservation of momentum. The accelerations due to cardiac activity, ensemble averaged over the R-R interval, were greatest along the head-to-foot axis. Maximum amplitude of the HIJK complex of the BCG generated by ventricular ejection was greatest in the head to foot axis (approximately 70 x 10(-3) m x s(-2)), but there were also substantial accelerations along the dorsoventral axis of up to 43 10(-3) m x s(-2), that are not measured interrestrial two-dimensional studies. The amplitude of the BCG was strongly affected by lung volume, with accelerations being reduced 50 to 70% between end-inspiration and end-expiration. CONCLUSIONS: These data suggest a greatly reduced transmission of the cardiac motion to the body at end-expiration (FRC) than at higher lung volumes. The BCG might be further developed as a non-invasive means of monitoring parameters such as stroke volume in microgravity.

Flight Experiment↗
DOE OSTI2025

Using a Large Language Model as a Building Block to Generate Usable Validation and Verification Suite for OpenMP

In the HPC area, both hardware and software move quickly. Often new hardware is developed and deployed, the corresponding software stack, including compilers and other tools, are under active development while leading edge software developers are working to port and tune their applications, all at the same time. While the software ecosystem is in flux, one of the key challenges for users is obtaining insight into the state of implementation of key features in the programming languages and models their applications are using – whether they have been implemented, and whether the implementation conforms to the specification, especially for newly implemented features (less tested by widespread use). OpenMP is one of the most prominent shared memory programming models used for on-node programming in HPC. With the shift towards accelerators (such as GPUs and FPGAs) and heterogeneous programming OpenMP features are getting more complex. It is natural to ask whether generative AI approaches, and large language models (LLMs) in particular, can help in producing validation and verification test suites to allow users better and faster insights into the availability and correctness of OpenMP features of interest. In this work, we explore the use of ChatGPT-4 to generate a suite of tests for OpenMP features. We have chosen a set of directives and clauses, a total of 78 combinations, which first appeared in OpenMP 3.0 (released in May 2008) but are also relevant for accelerators. We prompted ChatGPT to generate tests in the C and Fortran languages, for both host (CPU) and device (accelerator). On the Summit super-computer using the GNU implementation, we found that, of the 78 generated tests 67 C tests and 43 Fortran tests compiled successfully and fewer than those executed to completion. On further analysis we show that not all generated tests are valid. We document the process, results, and provide detailed analysis regarding the quality of tests generated. With the aim of providing input to a production quality validation and verification suite, we manually implement the corrections required to make the tests valid according to the current OpenMP specification. We quantify this effort as small, medium, or large, and record the lines of code changed to correct the invalid tests. With the corrected tests we validate recent implementations from HPE, AMD, and GNU on the Frontier supercomputer. Our experiment and subsequent analysis show that although LLMs are capable of producing HPC specific codes, they are limited by their understanding of the deeper semantics and restrictions of programming models such as OpenMP. Unsurprisingly more commonly used features have better support, while some OpenMP 3.0 directives such as sections and tasking are not universally supported on accelerators. We demonstrate that successful compilation and execution to completion are inadequate metrics for evaluating generated code and that, at this time, commodity LLMs require expert intervention for code verification. This points to gaps in the training data that is currently available for HPC. We demonstrate that with "small" effort 37% of generated invalid C tests and 63% of generated invalid Fortran tests could be corrected. This improves productivity of test generation as we circumvent writing from scratch and the common programming errors associated with it.

Pophale, Swaroop [ORNL] (ORCID:0000000185446367)↗
DOE OSTI2023

Multi-scale models of whole cells: progress and challenges

Whole-cell modeling is “the ultimate goal” of computational systems biology and “a grand challenge for 21st century”. These complex, highly detailed models account for the activity of every molecule in a cell and serve as comprehensive knowledgebases for the modeled system. Their scope and utility far surpass those of other systems models. In fact, whole-cell models (WCMs) are an amalgam of several types of “system” models. The models are simulated using a hybrid modeling method where the appropriate mathematical methods for each biological process are used to simulate their behavior. Given the complexity of the models, the process of developing and curating these models is labor-intensive and to date only a handful of these models have been developed. While whole-cell models provide valuable and novel biological insights, and to date have identified some novel biological phenomena, their most important contribution has been to highlight the discrepancy between available data and observations that are used for the parametrization and validation of complex biological models. Another realization has been that current whole-cell modeling simulators are slow and to run models that mimic more complex (e.g., multi-cellular) biosystems, those need to be executed in an accelerated fashion on high-performance computing platforms. In this manuscript, we review the progress of whole-cell modeling to date and discuss some of the ways that they can be improved.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Elevating SolTrace's Capabilities for the Next Generation of Concentrating Solar Analysis

SolTrace is an open-source Monte Carlo ray tracing software developed at NREL. SolTrace can characterize concentrating solar thermal (CST) collector optical performance and is CST technology agnostic. Shown in Fig. 1, SolTrace is a foundational tool in NREL's CST system and component modeling suite. SolTrace's generic surface elements can flexibly model novel collector and receiver designs to predict spatial and temporal flux distributions - critical to understand for CST component design, performance prediction, and system integration. Since its initial development, SolTrace has over 1,650 references on Google Scholar, over 9,800 downloads since 2017, and has served the CST research and development community as a benchmark of 3rd party verification. SolTrace provides users with many options for defining surface shape and boundaries. However, SolTrace provides limited documentation which can result in a steep learning curve for new users. Additionally, SolTrace lacks the computational performance required to evaluate optical performance of a CST system over the course of a year and/or iteratively over design parameters in a timely manner. To address this, we are working towards a new release of SolTrace that enables increased computational throughput by implementing ray tracing acceleration structures and enabling GPU parallelization. Additionally, we are working to improve SolTrace's usability, accessibility, and maintainability by (1) automating solar position time-dependent simulation processes, (2) creating general CST collector templates of grouped elements, (3) updating the user interface to better visualize model inputs and outputs, and (4) creating a user support network through forums, "how to" videos, and documentation.

14 SOLAR ENERGY↗
NASA NTRS1982

Reliability calculation using randomization for Markovian fault-tolerant computing systems

The randomization technique for computing transient probabilities of Markov processes is presented. The technique is applied to a Markov process model of a simplified fault tolerant computer system for illustrative purposes. It is applicable to much larger and more complex models. Transient state probabilities are computed, from which reliabilities are derived. An accelerated version of the randomization algorithm is developed which exploits ''stiffness' of the models to gain increased efficiency. A great advantage of the randomization approach is that it easily allows probabilities and reliabilities to be computed to any predetermined accuracy.

Miller, D. R.↗
NASA NTRS2004

Numerical Investigation of Dual-Mode Scramjet Combustor with Large Upstream Interaction

Dual-mode scramjet combustor configuration with significant upstream interaction is investigated numerically, The possibility of scaling the domain to accelerate the convergence and reduce the computational time is explored. The supersonic combustor configuration was selected to provide an understanding of key features of upstream interaction and to identify physical and numerical issues relating to modeling of dual-mode configurations. The numerical analysis was performed with vitiated air at freestream Math number of 2.5 using hydrogen as the sonic injectant. Results are presented for two-dimensional models and a three-dimensional jet-to-jet symmetric geometry. Comparisons are made with experimental results. Two-dimensional and three-dimensional results show substantial oblique shock train reaching upstream of the fuel injectors. Flow characteristics slow numerical convergence, while the upstream interaction slowly increases with further iterations. As the flow field develops, the symmetric assumption breaks down. A large separation zone develops and extends further upstream of the step. This asymmetric flow structure is not seen in the experimental data. Results obtained using a sub-scale domain (both two-dimensional and three-dimensional) qualitatively recover the flow physics obtained from full-scale simulations. All results show that numerical modeling using a scaled geometry provides good agreement with full-scale numerical results and experimental results for this configuration. This study supports the argument that numerical scaling is useful in simulating dual-mode scramjet combustor flowfields and could provide an excellent convergence acceleration technique for dual-mode simulations.

Mohieldin, T. O.↗
DOE OSTI2024

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scaling Wind Power Innovation Assessment for Rapid Energy Transition with Artificial Intelligence

Planning for energy system decarbonization requires new insights into the potential of renewable technologies, deployed at unprecedented scale, to meet urgent sustainability goals. However, limited scalability of current wind energy research tools restricts characterization of innovation impacts to isolated reference sites, challenging investment and decision making under rapid growth. We demonstrate the transformative potential of artificial intelligence (AI) to inform future technology advancement and energy systems design by leveraging a state-of-the-art surrogate model to conduct a series of fleet-wide wind plant layout optimizations for greater than 6,800 projected U.S. onshore buildout locations. We show how innovative wake steering technology can address an array of barriers to large-scale deployment and integration of wind power. Specifically, wake steering reduces required plant area by an average of 18% and could preserve upwards of 13,000 km2 for future greenfield deployment, potentially easing siting challenges associated with wind energy infrastructure. Further, by enabling reduced turbine spacing and increased energy production, flexible operations of wake steering improve levelized cost of energy, particularly for large plants and in land-constrained settings. Finally, optimizations that consider dynamic energy prices can deliver increased power production and revenue capture during high-value (often low-wind) periods, further bolstering plant economics. Our computationally efficient approach offers a pathway to accelerate nationwide geographic evaluation of innovative technologies.

graph neural networks↗
NASA NTRS1999

Work on Planetary Atmospheres and Planetary Atmosphere Probes

A summary final report of work accomplished is presented. Work was performed in the following areas: (1) Galileo Probe science analysis, (2) Galileo probe Atmosphere Structure Instrument, (3) Mars Pathfinder Atmosphere Structure/Meteorology instrument, (4) Mars Pathfinder data analysis, (5) Science Definition for future Mars missions, (6) Viking Lander data analysis, (7) winds in Mars atmosphere Venus atmospheric dynamics, (8) Pioneer Venus Probe data analysis, (9) Pioneer Venus anomaly analysis, (10) Discovery Venus Probe Titan probe instrument design, and (11) laboratory studies of Titan probe impact phenomena. The work has resulted in more than 10 articles published in archive journals, 2 encyclopedia articles, and many working papers. This final report is organized around the four planets on which there was activity, Jupiter, Mars, Venus, and Titan, with a closing section on Miscellaneous Activities. A major objective was to complete the fabrication, test, and evaluation of the atmosphere structure experiment on the Galileo probe, and to receive, analyze and interpret data received from the spacecraft. The instrument was launched on April 14, 1989. Calibration data were taken for all experiment sensors. The data were analyzed, fitted with algorithms, and summarized in a calibration report for use in analyzing and interpreting data returned from Jupiter's atmosphere. The sensors included were the primary science pressure, temperature and acceleration sensors, and the supporting engineering temperature sensors. Computer programs were written to decode the Experiment Data Record and convert the digital numbers to physical quantities, i.e., temperatures, pressures, and accelerations. The project office agreed to obtain telemetry of checkout data from the probe. Work to extend programs written for use on the Pioneer Venus project included: (1) massive heat shield ablation leading to important mass loss during entry; and (2) rapid planet rotation, which introduced terms of motion not needed on Venus. When the Galileo Probe encountered Jupiter, analysis and interpretation of data commenced. The early contributions of the experiment were to define (1) the basic structure of the deep atmosphere, (2) the stability of the atmosphere, (3) the upper atmospheric profiles of density, pressure, and temperature. The next major task in the Galileo Probe project was to refine, verify and extend the analysis of the data. It was the verified, and corrected data, which indicated a dry abiabatic atmosphere within measurement accuracy. Temperature in the thermosphere was measured at 900 K. Participation in the Mars atmospheric research included: (1) work as a team member of the Mars Atmosphere Working Group, (2) contribution to the Mars Exobiology Instrument workshop, (3) asssistance in planning the Mars global network and (4) assitance in planning the Soviet-French Mars mission in 1994. This included a return to the Viking Lander parachute data to refine and improve the definition of winds between 1.5 and 4 kilometer altitude at the two entry sites. The variability of the structure of Mars atmosphere was addressed, which is known to vary with season, latitude, hemisphere and dust loading of the atmosphere. This led to work on the Pathfinder project. The probe had a deployable meteorology mast that had three temperature sensors, and a wind sensor at the tip of the mast. Work on the Titan atmospheric probe was also accomplished. This included developing an experiment proposal to the European Space Agency (ESA), which was not selected. However, as an advisor in the design and preparation of the selected experiment the researcher interacted with scientist on the Huygens Probe Atmosphere Structure Experiment. The researcher also participated in the planning for the Venus Chemical Probe. The science objectives of the probe were to resolve unanswered questions concerning the minor species chemistry of Venus' atmosphere that control cloud formation, greenhouse effectiveness, and the thermal structure. The researcher also reviewed problems with the Pioneer Venus Probe, that caused anomalies which occurred on the Probes at and below 12.5 km level of the Venus' atmosphere. He convened and participated in a workshop that concluded the most likely hardware cause was insulation failure in the electrical harness outside the Probes' pressure vessels. It was discovered that the shrink tubing material failed at 600K. This failure could explain the anomalies experienced by the probes. The descent data of the Pioneer probes, and the Soviet Vega Lander was analyzed to evaluate the presence of small scale gravity waves in and below the Venus cloud layer.

Lester, Peter↗
DOE OSTI2025

AI Applications to Physics Experiments at Jefferson Lab

We survey how AI/ML is being deployed across Jefferson Lab's experimental and accelerator programs. In EPSCI, Hydra applies computer vision to automate real-time data-quality monitoring across all four experimental halls, replacing manual inspection of hundreds to thousands of histograms per shift. AIEC (AI Experiment Controls) uses ML to stabilize drift chamber gains and is now part of standard CEBAF production running, while AI Optimized Polarization (AIOP) targets autonomous control of polarized targets and photon beam angular alignment. In CASA, cavity fault classification models identify faulted cavities and trip types from waveform data with ~85% and ~78% agreement to labeled data, respectively, and are deployed in production; a separate effort applies LLMs and hybrid search to make the CEBAF operations logbook AI-ready. QCD-focused work includes transformer- and GAN-based generative models for particle-level event simulation, with distributed GAN training scaling studies on Polaris. Additional efforts span ML-on-FPGA for the EIC and a new Data Science Department coordinating anomaly detection, uncertainty quantification, and HPC-scalable ML lab-wide. Collectively, these projects illustrate AI's growing role in improving efficiency across JLab's nuclear physics mission.

Mei, Xinxin [Thomas Jefferson National Accelerator↗
DOE OSTI2021

Prime factorization using quantum variational imaginary time evolution

The road to computing on quantum devices has been accelerated by the promises that come from using Shor’s algorithm to reduce the complexity of prime factorization. However, this promise hast not yet been realized due to noisy qubits and lack of robust error correction schemes. Here we explore a promising, alternative method for prime factorization that uses well-established techniques from variational imaginary time evolution. We create a Hamiltonian whose ground state encodes the solution to the problem and use variational techniques to evolve a state iteratively towards these prime factors. We show that the number of circuits evaluated in each iteration scales as \(O(n^{5}d)\) , where n is the bit-length of the number to be factorized and d is the depth of the circuit. We use a single layer of entangling gates to factorize 36 numbers represented using 7, 8, and 9-qubit Hamiltonians. We also verify the method’s performance by implementing it on the IBMQ Lima hardware to factorize 55, 65, 77 and 91 which are greater than the largest number (21) to have been factorized on IBMQ hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Impact Report: Quantum Systems Accelerator

The Quantum Systems Accelerator (QSA) is a U.S. National Quantum Information Science Research Center established in August 2020 and funded by the Department of Energy (DOE) Office of Science. QSA is composed of 15 partner institutions— universities and national laboratories—bringing together pioneers of many of today’s unique quantum information science (QIS) and engineering capabilities. Led by Lawrence Berkeley National Laboratory (Berkeley Lab), with Sandia National Laboratories (Sandia Labs) as the lead partner, 250+ QSA researchers are catalyzing U.S. leadership in a fast-growing field that seeks solutions to the Nation’s and the world’s most pressing problems by harnessing the laws of quantum mechanics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Effect of Strain on the Resistivity and Thermal Conductivity of High Purity Niobium

High purity niobium (Nb) is a technologically important material for large-scale accelerator and nano-scale quantum computing applications in microwave frequency range. The high thermal conductivity and low resistivity of Nb are critical to the high performance at temperature range of 0.01–4.0 K. The presence of interstitials such as O, N, H, and C act as scattering centers and alter the mean free path, reducing resistivity and thermal conductivity, and contributing significantly to Nb's thermal performance for temperatures of 2.0 K and above. The residual resistivity ratio (RRR), defined as the ratio of the normal state resistivity at 300 K to that at 4.2 K (Nb, T c = 9.2 K), is an accepted direct estimate of the impurity content of fully recrystallized Nb. Complete re-crystallization of Nb is challenging unless very high temperatures are employed, which is often impractical, hence, in practice, dislocation and dislocation structures impact the thermal performance of Nb due to strong phonon scattering contributions. Furthermore, this paper reports on the degradation of thermal conductivity and RRR of high purity Nb large grain, single crystal with fixed impurity, varying strain, and dislocation content levels. Experimental thermal conductivity data fits the Boltzmann transport equation incorporating dislocation density.

36 MATERIALS SCIENCE↗
DOE OSTI2022

MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation

The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tensor contractions. More specifically, it targets general GPU architectures (both NVIDIA and AMD) and optimizes many-body correlation’s memory management by exploiting a set of memory allocation and communication redundancy elimination opportunities: first, GPU memory allocation redundancy: the intermediate output frequently occurs as input in the subsequent calculations; second, CPU-GPU communication redundancy: although all tensors are allocated on both CPU and GPU, many of them are used (and reused) on the GPU side only, and thus, many CPU/GPU communications (like that in existing Unified Memory designs) are unnecessary; third, GPU oversubscription: limited GPU memory size causes oversubscription issues, and existing memory management usually results in near-reuse data eviction, thus incurring extra CPU/GPU memory communications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Effect of altered gravity on temperature regulation in mammals: Investigation of gravity effect on temperature regulation in mammals

Male, Long-Evans hooded rats were instrumented for monitoring core and hypothalamic temperatures as well as shivering and nonshivering thermogenesis in response to decreased ambient temperature in order to characterize the nature of the neural controller of temperature in rats at 1G and evaluate chronic implantation techniques for the monitoring of appropriate parameters at hypergravic fields. The thermoregulatory responses of cold-exposed rats at 2G were compared to those at 1G. A computer model was developed to simulate the thermoregulatory system in the rat. Observations at 1 and 2G were extended to acceleration fields of 1.5, 3.0 and 4.0G and the computer model was modified for application to altered gravity conditions. Changes in the acceleration field resulted in inadequate heat generation rather than increased heat loss. Acceleration appears to impair the ability of the neurocontroller to appropriately integrate input signals for body temperature maintenance.

Horwitz, B. A.↗
DOE OSTI2022

COMPILE: a GWAS computational pipeline for gene discovery in complex genomes

Abstract Background Genome-Wide Association Studies (GWAS) are used to identify genes and alleles that contribute to quantitative traits in large and genetically diverse populations. However, traits with complex genetic architectures create an enormous computational load for discovery of candidate genes with acceptable statistical certainty. We developed a streamlined computational pipeline for GWAS (COMPILE) to accelerate identification and annotation of candidate maize genes associated with a quantitative trait, and then matches maize genes to their closest rice and Arabidopsis homologs by sequence similarity. Results COMPILE executed GWAS using a Mixed Linear Model that incorporated, without compression, recent advancements in population structure control, then linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements contained in any genome. COMPILE was validated using published data to identify QTL associated with the traits of α-tocopherol biosynthesis and flowering time, and identified published candidate genes as well as additional genes and non-coding RNAs. We then applied COMPILE to 274 genotypes of the maize Goodman Association Panel to identify candidate loci contributing to resistance of maize stems to penetration by larvae of the European Corn Borer ( Ostrinia nubilalis ). Candidate genes included those that encode a gene of unknown function, WRKY and MYB-like transcriptional factors, receptor-kinase signaling, riboflavin synthesis, nucleotide-sugar interconversion, and prolyl hydroxylation. Expression of the gene of unknown function has been associated with pathogen stress in maize and in rice homologs closest in sequence identity. Conclusions The relative speed of data analysis using COMPILE allowed comparison of population size and compression. Limitations in population size and diversity are major constraints for a trait and are not overcome by increasing marker density. COMPILE is customizable and is readily adaptable for application to species with robust genomic and proteome databases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗