NAFEMS - Data Driven Benchmark - Plate with Hole

Inspired by strategic discussions within the NAFEMS ASSESS Initiative, this curated finite element analysis dataset builds upon NAFEMS' 40-year legacy of establishing trusted industry benchmarks for physics-based simulation and looks to the emerging use of AI in engineering simulation.

The collection provides baseline data on principal stress distributions within plate geometries. A deliberately simple problem was selected to maximise the dataset's educational merit. Because the peak principal stress for this geometry can be validated against a well-established analytical solution (Heywood's equations), the dataset provides a transparent baseline. This simplicity allows engineers and researchers to study fundamental behaviour of the data driven models, such as demonstrating how the quantity and distribution of training data impacts the model predictions. As artificial intelligence models increasingly integrate into commercial simulation tools, this dataset is intended to serve as a useful resource for engineers looking for a vendor neutral dataset.

To maintain NAFEMS' commitment to vendor neutrality, the results are provided in the open standard VMAP format, alongside training and test data in HyperMesh (*.h3d) and Nastran (*.op2) formats. For those who wish to recreate the results on their own systems solver input decks have been provided in optistruct (*.fem) and Nastran (*.bdf) format.

Download the problem description

NAFEMS would like to thank Altair and the Fraunhofer Institute for their support with this project.

Problem Variables

Variable	Range Lower Bound	Range Upper Bound
Hole diameter	30mm	240mm
Plate width	300mm	1000mm
Plate length	300mm	1000mm
Plate thickness	1mm	20mm
Applied load	5000N	100000N
Offset width direction	0mm	0.4 x (Plate Width - Hole diameter)
Offset length direction	0mm	0.4 x (Plate Length - Hole diameter)

The Datasets

6 datasets have been provided. The first 5 datasets are relatively simple and vary between one and three parameters.

The 6th dataset represents a significantly more complicated problem where 7 parameters are varied.The variables in the 6th dataset have been selected using a latin hypercube sampling approach. Users are encouraged to parse the dataset for outliers.

Dataset	# training sets	# test sets	Variables	Dataset location
1	19	3	Hole diameter	Link
2	19	6	Hole diameter, plate thickness	Link
3	19	3	Hole Diameter, plate width, plate length	Link
4	19	3	Hole diameter, applied load	Link
5	19	3	Hole diameter, hole location	Link
6	97	-	Hole diameter, plate thickness, applied load, plate width, plate length, hole location	NAFEMS Member Download button

Dataset 6 Details

ID	Test/ Train	Diameter (mm)	Thickness (mm)	Width (mm)	Length (mm)	WidthOffset (mm)	LengthOffset (mm)	Load (N)
Train61	Train	198	1	625	823	80	55	72727
Train62	Train	155	13	583	484	15	84	33636
Train63	Train	125	13	590	604	69	36	42727
Train64	Train	66	6	597	562	91	24	57273
Train65	Train	68	6	951	859	296	51	31818
Train66	Train	92	11	618	335	76	36	35455
Train67	Train	58	14	555	908	193	316	84545
Train68	Train	174	9	661	936	136	162	91818
Train69	Train	166	18	753	307	176	11	83636
Train610	Train	98	14	873	385	108	52	30000
Train611	Train	115	12	470	378	98	43	18182
Train612	Train	232	17	809	399	210	27	48182
Train613	Train	138	7	576	873	35	212	27273
Train614	Train	238	18	498	371	67	10	25455
Train615	Train	191	16	399	922	3	222	86364
Train616	Train	43	4	710	986	131	94	70000
Train617	Train	164	18	837	512	221	66	51818
Train618	Train	119	2	378	951	42	110	100000
Train619	Train	189	1	901	569	97	137	95455
Train620	Train	79	14	547	533	51	47	89091
Train621	Train	53	5	731	463	217	83	60909
Train622	Train	206	11	717	767	98	123	58182
Train623	Train	64	4	880	441	72	53	70909
Train624	Train	38	10	965	717	315	79	98182
Train625	Train	153	12	448	894	12	116	88182
Train626	Train	176	20	456	639	98	106	44545
Train627	Train	132	6	682	618	112	95	54545
Train628	Train	72	15	300	689	5	67	77273
Train629	Train	96	3	993	795	291	257	68182
Train630	Train	204	1	929	710	44	87	37273
Train631	Train	34	20	979	958	249	369	99091
Train632	Train	32	3	519	470	123	103	26364
Train633	Train	41	9	569	844	120	35	82727
Train634	Train	168	17	760	427	137	102	66364
Train635	Train	181	11	958	583	165	84	80000
Train636	Train	47	1	632	434	152	132	56364
Train637	Train	223	17	852	745	234	171	40000
Train638	Train	130	2	724	519	43	154	76364
Train639	Train	227	20	802	788	48	182	90909
Train640	Train	51	3	307	632	12	98	75455
Train641	Train	219	11	972	760	250	61	17273
Train642	Train	162	20	562	661	142	116	50909
Train643	Train	85	18	915	321	239	90	63636
Train644	Train	200	5	675	555	106	98	78182
Train645	Train	183	16	364	349	51	24	62727
Train646	Train	195	13	866	646	118	110	52727
Train647	Train	70	19	703	576	139	180	81818
Train648	Train	45	19	328	753	32	142	61818
Train649	Train	87	10	745	738	163	174	13636
Train650	Train	159	5	844	625	167	164	47273
Train651	Train	202	12	441	597	43	21	53636
Train652	Train	172	7	526	300	139	50	39091
Train653	Train	94	16	1000	392	268	78	73636
Train654	Train	147	9	689	809	15	167	28182
Train655	Train	77	14	406	357	100	102	85455
Train656	Train	234	19	823	915	184	164	43636
Train657	Train	217	16	859	703	36	138	20909
Train658	Train	55	12	788	547	50	146	65455
Train659	Train	89	13	342	781	69	89	69091
Train660	Train	229	14	491	724	31	10	46364
Train661	Train	142	3	434	328	63	70	11818
Train662	Train	81	18	427	979	11	158	71818
Train663	Train	212	9	420	406	72	8	96364
Train664	Train	100	19	943	830	111	181	41818
Train665	Train	157	4	392	668	74	69	38182
Train666	Train	145	12	830	540	5	127	45455
Train667	Train	170	5	696	590	194	25	29091
Train668	Train	121	20	463	880	40	3	79091
Train669	Train	178	15	774	342	229	50	59091
Train670	Train	187	4	512	816	60	23	49091
Train671	Train	134	7	738	837	230	222	40909
Train672	Train	221	10	505	887	30	200	30909
Train673	Train	140	8	413	972	27	276	67273
Train674	Train	185	2	781	611	160	3	97273
Train675	Train	111	10	646	731	49	35	55455
Train676	Train	102	5	533	505	41	74	93636
Train677	Train	225	16	936	901	119	81	12727
Train678	Train	113	17	639	675	209	193	21818
Train679	Train	60	15	321	993	76	142	10909
Train680	Train	151	15	484	364	25	3	16364
Train681	Train	36	15	668	774	96	195	90000
Train682	Train	117	8	922	413	322	66	60000
Train683	Train	136	9	894	654	94	17	22727
Train684	Train	240	8	611	1000	59	164	19091
Train685	Train	75	11	540	929	168	239	32727
Train686	Train	128	2	349	456	46	5	50000
Train687	Train	108	2	357	420	1	97	23636
Train688	Train	49	7	795	491	176	129	15455
Train689	Train	104	13	314	498	42	33	64545
Train690	Train	30	17	986	866	42	281	92727
Train691	Train	123	19	908	965	295	57	24545
Train692	Train	215	8	887	448	161	6	74545
Train693	Train	62	4	767	943	45	307	20000
Train694	Train	149	7	371	682	35	102	14545
Train695	Train	83	3	385	526	39	121	34545
Train696	Train	236	1	477	802	74	16	87273
Train697	Train	208	10	816	477	15	103	36364
Train698	Train	106	8	604	696	26	73	10000
Train699	Train	193	6	654	852	158	61	94545
Train6100	Train	210	6	335	314	2	10	80909

Document Details

Reference	assess-26-06
Author	Symington. I
Language	English
Audiences	Analyst Developer
Type	Knowledge Base
Date	22nd February 2026
Organisation	NAFEMS

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Data Driven Benchmark - Plate with Hole - Dataset 6

Problem Variables

The Datasets

Dataset 6 Details

Document Details

Download

NAFEMS Membership

Data Driven Benchmark - Plate with Hole - Dataset 6

P​roblem Variables

T​he Datasets

Dataset 6 Details

Document Details

Download

NAFEMS Membership

Problem Variables

The Datasets